Coupled CFD-DEM simulation of interfacial fluid–particle interaction during binder jet 3D printing

바인더 제트 3D 프린팅 중 계면 유체-입자 상호 작용에 대한 CFD-DEM 결합 시뮬레이션

Joshua J. Wagner, C. Fred Higgs III

https://doi.org/10.1016/j.cma.2024.116747

Abstract

The coupled dynamics of interfacial fluid phases and unconstrained solid particles during the binder jet 3D printing process govern the final quality and performance of the resulting components. The present work proposes a computational fluid dynamics (CFD) and discrete element method (DEM) framework capable of simulating the complex interfacial fluid–particle interaction that occurs when binder microdroplets are deposited into a powder bed. The CFD solver uses a volume-of-fluid (VOF) method for capturing liquid–gas multifluid flows and relies on block-structured adaptive mesh refinement (AMR) to localize grid refinement around evolving fluid–fluid interfaces. The DEM module resolves six degrees of freedom particle motion and accounts for particle contact, cohesion, and rolling resistance. Fully-resolved CFD-DEM coupling is achieved through a fictitious domain immersed boundary (IB) approach. An improved method for enforcing three-phase contact lines with a VOF-IB extension technique is introduced. We present several simulations of binder jet primitive formation using realistic process parameters and material properties. The DEM particle systems are experimentally calibrated to reproduce the cohesion behavior of physical nickel alloy powder feedstocks. We demonstrate the proposed model’s ability to resolve the interdependent fluid and particle dynamics underlying the process by directly comparing simulated primitive granules with one-to-one experimental counterparts obtained from an in-house validation apparatus. This computational framework provides unprecedented insight into the fundamental mechanisms of binder jet 3D printing and presents a versatile new approach for process parameter optimization and defect mitigation that avoids the inherent challenges of experiments.

바인더 젯 3D 프린팅 공정 중 계면 유체 상과 구속되지 않은 고체 입자의 결합 역학이 결과 구성 요소의 최종 품질과 성능을 좌우합니다. 본 연구는 바인더 미세액적이 분말층에 증착될 때 발생하는 복잡한 계면 유체-입자 상호작용을 시뮬레이션할 수 있는 전산유체역학(CFD) 및 이산요소법(DEM) 프레임워크를 제안합니다.

CFD 솔버는 액체-가스 다중유체 흐름을 포착하기 위해 VOF(유체량) 방법을 사용하고 블록 구조 적응형 메쉬 세분화(AMR)를 사용하여 진화하는 유체-유체 인터페이스 주위의 그리드 세분화를 국지화합니다. DEM 모듈은 6개의 자유도 입자 운동을 해결하고 입자 접촉, 응집력 및 구름 저항을 설명합니다.

완전 분해된 CFD-DEM 결합은 가상 도메인 침지 경계(IB) 접근 방식을 통해 달성됩니다. VOF-IB 확장 기술을 사용하여 3상 접촉 라인을 강화하는 향상된 방법이 도입되었습니다. 현실적인 공정 매개변수와 재료 특성을 사용하여 바인더 제트 기본 형성에 대한 여러 시뮬레이션을 제시합니다.

DEM 입자 시스템은 물리적 니켈 합금 분말 공급원료의 응집 거동을 재현하기 위해 실험적으로 보정되었습니다. 우리는 시뮬레이션된 기본 과립과 내부 검증 장치에서 얻은 일대일 실험 대응물을 직접 비교하여 프로세스의 기본이 되는 상호 의존적인 유체 및 입자 역학을 해결하는 제안된 모델의 능력을 보여줍니다.

이 계산 프레임워크는 바인더 제트 3D 프린팅의 기본 메커니즘에 대한 전례 없는 통찰력을 제공하고 실험에 내재된 문제를 피하는 공정 매개변수 최적화 및 결함 완화를 위한 다용도의 새로운 접근 방식을 제시합니다.

Introduction

Binder jet 3D printing (BJ3DP) is a powder bed additive manufacturing (AM) technology capable of fabricating geometrically complex components from advanced engineering materials, such as metallic superalloys and ultra-high temperature ceramics [1], [2]. As illustrated in Fig. 1(a), the process is comprised of many repetitive print cycles, each contributing a new cross-sectional layer on top of a preceding one to form a 3D CAD-specified geometry. The feedstock material is first delivered from a hopper to a build plate and then spread into a thin layer by a counter-rotating roller. After powder spreading, a print head containing many individual inkjet nozzles traverses over the powder bed while precisely jetting binder microdroplets onto select regions of the spread layer. Following binder deposition, the build plate lowers by a specified layer thickness, leaving a thin void space at the top of the job box that the subsequent powder layer will occupy. This cycle repeats until the full geometries are formed layer by layer. Powder bed fusion (PBF) methods follow a similar procedure, except they instead use a laser or electron beam to selectively melt and fuse the powder material. Compared to PBF, binder jetting offers several distinct advantages, including faster build rates, enhanced scalability for large production volumes, reduced machine and operational costs, and a wider selection of suitable feedstock materials [2]. However, binder jetted parts generally possess inferior mechanical properties and reduced dimensional accuracy [3]. As a result, widescale adoption of BJ3DP to fabricate high-performance, mission-critical components, such as those common to the aerospace and defense sectors, is contingent on novel process improvements and innovations [4].

A major obstacle hindering the advancement of BJ3DP is our limited understanding of how various printing parameters and material properties collectively influence the underlying physical mechanisms of the process and their effect on the resulting components. To date, the vast majority of research efforts to uncover these relationships have relied mainly on experimental approaches [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], which are often expensive and time-consuming and have inherent physical restrictions on what can be measured and observed. For these reasons, there is a rapidly growing interest in using computational models to circumvent the challenges of experimental investigations and facilitate a deeper understanding of the process’s fundamental phenomena. While significant progress has been made in developing and deploying numerical frameworks aimed at powder spreading [20], [21], [22], [23], [24], [25], [26], [27] and sintering [28], [29], [30], [31], [32], simulating the interfacial fluid–particle interaction (IFPI) in the binder deposition stage is still in its infancy. In their exhaustive review, Mostafaei et al. [2] point out the lack of computational models capable of resolving the coupled fluid and particle dynamics associated with binder jetting and suggest that the development of such tools is critical to further improving the process and enhancing the quality of its end-use components.

We define IFPI as a multiphase flow regime characterized by immiscible fluid phases separated by dynamic interfaces that intersect the surfaces of moving solid particles. As illustrated in Fig. 1(b), an elaborate IFPI occurs when a binder droplet impacts the powder bed in BJ3DP. The momentum transferred from the impacting droplet may cause powder compaction, cratering, and particle ejection. These ballistic disturbances can have deleterious effects on surface texture and lead to the formation of large void spaces inside the part [5], [13]. After impact, the droplet spreads laterally on the bed surface and vertically into the pore network, driven initially by inertial impact forces and then solely by capillary action [33]. Attractive capillary forces exerted on mutually wetted particles tend to draw them inward towards each other, forming a packed cluster of bound particles referred to as a primitive [34]. A single-drop primitive is the most fundamental building element of a BJ3DP part, and the interaction leading to its formation has important implications on the final part characteristics, such as its mechanical properties, resolution, and dimensional accuracy. Generally, binder droplets are deposited successively as the print head traverses over the powder bed. The traversal speed and jetting frequency are set such that consecutive droplets coalesce in the bed, creating a multi-drop primitive line instead of a single-drop primitive granule. The binder must be jetted with sufficient velocity to penetrate the powder bed deep enough to provide adequate interlayer binding; however, a higher impact velocity leads to more pronounced ballistic effects.

A computational framework equipped to simulate the interdependent fluid and particle dynamics in BJ3DP would allow for unprecedented observational and measurement capability at temporal and spatial resolutions not currently achievable by state-of-the-art imaging technology, namely synchrotron X-ray imaging [13], [14], [18], [19]. Unfortunately, BJ3DP presents significant numerical challenges that have slowed the development of suitable modeling frameworks; the most significant of which are as follows:

1.Incorporating dynamic fluid–fluid interfaces with complex topological features remains a nontrivial task for standard mesh-based CFD codes. There are two broad categories encompassing the methods used to handle interfacial flows: interface tracking and interface capturing [35]. Interface capturing techniques, such as the popular volume-of-fluid (VOF) [36] and level-set methods [37], [38], are better suited for problems with interfaces that become heavily distorted or when coalescence and fragmentation occur frequently; however, they are less accurate in resolving surface tension and boundary layer effects compared to interface tracking methods like front-tracking [39], arbitrary Lagrangian–Eulerian [40], and space–time finite element formulations [41]. Since interfacial forces become increasingly dominant at decreasing length scales, inaccurate surface tension calculations can significantly deteriorate the fidelity of IFPI simulations involving <100 μm droplets and particles.
2.Dynamic powder systems are often modeled using the discrete element method (DEM) introduced by Cundall and Strack [42]. For IFPI problems, a CFD-DEM coupling scheme is required to exchange information between the fluid and particle solvers. Fully-resolved CFD-DEM coupling suggests that the flow field around individual particle surfaces is resolved on the CFD mesh [43], [44]. In contrast, unresolved coupling volume averages the effect of the dispersed solid phase on the continuous fluid phases [45], [46], [47], [48]. Comparatively, the former is computationally expensive but provides detailed information about the IFPI in question and is more appropriate when contact line dynamics are significant. However, since the pore structure of a powder bed is convoluted and evolves with time, resolving such solid–fluid interfaces on a computational mesh presents similar challenges as fluid–fluid interfaces discussed in the previous point. Although various algorithms have been developed to deform unstructured meshes to accommodate moving solid surfaces (see Bazilevs et al. [49] for an overview of such methods), they can be prohibitively expensive when frequent topology changes require mesh regeneration rather than just modification through nodal displacement. The pore network in a powder bed undergoes many topology changes as particles come in and out of contact with each other, constantly closing and opening new flow channels. Non-body-conforming structured grid approaches that rely on immersed boundary (IB) methods to embed the particles in the flow field can be better suited for such cases [50]. Nevertheless, accurately representing these complex pore geometries on Cartesian grids requires extremely high mesh resolutions, which can impose significant computational costs.
3.Capillary effects depend on the contact angle at solid–liquid–gas intersections. Since mesh nodes do not coincide with a particle surface when using an IB method on structured grids, imposing contact angle boundary conditions at three-phase contact lines is not straightforward.

While these issues also pertain to PBF process modeling, resolving particle motion is generally less crucial for analyzing melt pool dynamics compared to primitive formation in BJ3DP. Therefore, at present, the vast majority of computational process models of PBF assume static powder beds and avoid many of the complications described above, see, e.g., [51], [52], [53], [54], [55], [56], [57], [58], [59]. Li et al. [60] presented the first 2D fully-resolved CFD-DEM simulations of the interaction between the melt pool, powder particles, surrounding gas, and metal vapor in PBF. Following this work, Yu and Zhao [61], [62] published similar melt pool IFPI simulations in 3D; however, contact line dynamics and capillary forces were not considered. Compared to PBF, relatively little work has been published regarding the computational modeling of binder deposition in BJ3DP. Employing the open-source VOF code Gerris [63], Tan [33] first simulated droplet impact on a powder bed with appropriate binder jet parameters, namely droplet size and impact velocity. However, similar to most PBF melt pool simulations described in the current literature, the powder bed was fixed in place and not allowed to respond to the interacting fluid phases. Furthermore, a simple face-centered cubic packing of non-contacting, monosized particles was considered, which does not provide a realistic pore structure for AM powder beds. Building upon this approach, we presented a framework to simulate droplet impact on static powder beds with more practical particle size distributions and packing arrangements [64]. In a study similar to [33], [64], Deng et al. [65] used the VOF capability in Ansys Fluent to examine the lateral and vertical spreading of a binder droplet impacting a fixed bimodal powder bed with body-centered packing. Li et al. [66] also adopted Fluent to conduct 2D simulations of a 100 μm diameter droplet impacting substrates with spherical roughness patterns meant to represent the surface of a simplified powder bed with monosized particles. The commercial VOF-based software FLOW-3D offers an AM module centered on process modeling of various AM technologies, including BJ3DP. However, like the above studies, particle motion is still not considered in this codebase. Ur Rehman et al. [67] employed FLOW-3D to examine microdroplet impact on a fixed stainless steel powder bed. Using OpenFOAM, Erhard et al. [68] presented simulations of different droplet impact spacings and patterns on static sand particles.

Recently, Fuchs et al. [69] introduced an impressive multipurpose smoothed particle hydrodynamics (SPH) framework capable of resolving IFPI in various AM methods, including both PBF and BJ3DP. In contrast to a combined CFD-DEM approach, this model relies entirely on SPH meshfree discretization of both the fluid and solid governing equations. The authors performed several prototype simulations demonstrating an 80 μm diameter droplet impacting an unconstrained powder bed at different speeds. While the powder bed responds to the hydrodynamic forces imparted by the impacting droplet, the particle motion is inconsistent with experimental time-resolved observations of the process [13]. Specifically, the ballistic effects, such as particle ejection and bed deformation, were drastically subdued, even in simulations using a droplet velocity ∼ 5× that of typical jetting conditions. This behavior could be caused by excessive damping in the inter-particle contact force computations within their SPH framework. Moreover, the wetted particles did not appear to be significantly influenced by the strong capillary forces exerted by the binder as no primitive agglomeration occurred. The authors mention that the objective of these simulations was to demonstrate their codebase’s broad capabilities and that some unrealistic process parameters were used to improve computational efficiency and stability, which could explain the deviations from experimental observations.

In the present paper, we develop a novel 3D CFD-DEM numerical framework for simulating fully-resolved IFPI during binder jetting with realistic material properties and process parameters. The CFD module is based on the VOF method for capturing binder–air interfaces. Surface tension effects are realized through the continuum surface force (CSF) method with height function calculations of interface curvature. Central to our fluid solver is a proprietary block-structured AMR library with hierarchical octree grid nesting to focus enhanced grid resolution near fluid–fluid interfaces. The GPU-accelerated DEM module considers six degrees of freedom particle motion and includes models based on Hertz-Mindlin contact, van der Waals cohesion, and viscoelastic rolling resistance. The CFD and DEM modules are coupled to achieve fully-resolved IFPI using an IB approach in which Lagrangian solid particles are mapped to the underlying Eulerian fluid mesh through a solid volume fraction field. An improved VOF-IB extension algorithm is introduced to enforce the contact angle at three-phase intersections. This provides robust capillary flow behavior and accurate computations of the fluid-induced forces and torques acting on individual wetted particles in densely packed powder beds.

We deploy our integrated codebase for direct numerical simulations of single-drop primitive formation with powder beds whose particle size distributions are generated from corresponding laboratory samples. These simulations use jetting parameters similar to those employed in current BJ3DP machines, fluid properties that match commonly used aqueous polymeric binders, and powder properties specific to nickel alloy feedstocks. The cohesion behavior of the DEM powder is calibrated based on the angle of repose of the laboratory powder systems. The resulting primitive granules are compared with those obtained from one-to-one experiments conducted using a dedicated in-house test apparatus. Finally, we demonstrate how the proposed framework can simulate more complex and realistic printing operations involving multi-drop primitive lines.

Section snippets

Mathematical description of interfacial fluid–particle interaction

This section briefly describes the governing equations of fluid and particle dynamics underlying the CFD and DEM solvers. Our unified framework follows an Eulerian–Lagrangian approach, wherein the Navier–Stokes equations of incompressible flow are discretized on an Eulerian grid to describe the motion of the binder liquid and surrounding gas, and the Newton–Euler equations account for the positions and orientations of the Lagrangian powder particles. The mathematical foundation for

CFD solver for incompressible flow with multifluid interfaces

This section details the numerical methodology used in our CFD module to solve the Navier–Stokes equations of incompressible flow. First, we introduce the VOF method for capturing the interfaces between the binder and air phases. This approach allows us to solve the fluid dynamics equations considering only a single continuum field with spatial and temporal variations in fluid properties. Next, we describe the time integration procedure using a fractional-step projection algorithm for

DEM solver for solid particle dynamics

This section covers the numerical procedure for tracking the motion of individual powder particles with DEM. The Newton–Euler equations (Eqs. (10), (11)) are ordinary differential equations (ODEs) for which many established numerical integrators are available. In general, the most challenging aspects of DEM involve processing particle collisions in a computationally efficient manner and dealing with small time step constraints that result from stiff materials, such as metallic AM powders. The

Unified CFD-DEM solver

The preceding sections have introduced the CFD and DEM solution algorithms separately. Here, we discuss the integrated CFD-DEM solution algorithm and related details.

Binder jet process modeling and validation experiments

In this section, we deploy our CFD-DEM framework to simulate the IFPI occurring during the binder droplet deposition stage of the BJ3DP process. The first simulations attempt to reproduce experimental single-drop primitive granules extracted from four nickel alloy powder samples with varying particle size distributions. The experiments are conducted with a dedicated in-house test apparatus that allows for the precision deposition of individual binder microdroplets into a powder bed sample. The

Conclusions

This paper introduces a coupled CFD-DEM framework capable of fully-resolved simulation of the interfacial fluid–particle interaction occurring in the binder jet 3D printing process. The interfacial flow of binder and surrounding air is captured with the VOF method and surface tension effects are incorporated using the CSF technique augmented by height function curvature calculations. Block-structured AMR is employed to provide localized grid refinement around the evolving liquid–gas interface.

CRediT authorship contribution statement

Joshua J. Wagner: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. C. Fred Higgs III: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by a NASA Space Technology Research Fellowship, United States of America, Grant No. 80NSSC19K1171. Partial support was also provided through an AIAA Foundation Orville, USA and Wilbur Wright Graduate Award, USA . The authors would like to gratefully acknowledge Dr. Craig Smith of NASA Glenn Research Center for the valuable input he provided on this project.

References (155)

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Figure 3. The simulated temperature distribution and single-layer multi-track isothermograms of LPBF Hastelloy X, located at the bottom of the powder bed, are presented for various laser energy densities. (a) depicts the single-point temperature distribution at the bottom of the powder bed, followed by the isothermograms corresponding to laser energy densities of (b) 31 J/mm3 , (c) 43 J/mm3 , (d) 53 J/mm3 , (e) 67 J/mm3 , and (f) 91 J/mm3 .

An integrated multiscale simulation guiding the processing optimisation for additively manufactured nickel-based superalloys

적층 가공된 니켈 기반 초합금의 가공 최적화를 안내하는 통합 멀티스케일 시뮬레이션

Xing He, Bing Yang, Decheng Kong, Kunjie Dai, Xiaoqing Ni, Zhanghua Chen
& Chaofang Dong

ABSTRACT

Microstructural defects in laser powder bed fusion (LPBF) metallic materials are correlated with processing parameters. A multi-physics model and a crystal plasticity framework are employed to predict microstructure growth in molten pools and assess the impact of manufacturing defects on plastic damage parameters. Criteria for optimising the LPBF process are identified, addressing microstructural defects and tensile properties of LPBF Hastelloy X at various volumetric energy densities (VED). The results show that higher VED levels foster a specific Goss texture {110} <001>, with irregular lack of fusion defects significantly affecting plastic damage, especially near the material surface. A critical threshold emerges between manufacturing defects and grain sizes in plastic strain accumulation. The optimal processing window for superior Hastelloy X mechanical properties ranges from 43 to 53 J/mm3 . This work accelerates the development of superior strengthductility alloys via LPBF, streamlining the trial-and-error process and reducing associated costs.

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Figure 1. Experimental setup and materials. (a) Schematic of the DED process, where three types of base materials were adopted—B1 (IN718), B2 (IN625), and B3 (SS316L), and two types of powder materials were adopted—P1 (IN718) and P2 (SS316L). (b) In situ high-speed imaging of powder flow and the SEM images of IN718 and SS316L powder particle. (c) Powder size statistics, and (d) element composition of powder IN718 (P1) and SS316L (P2).

Printability disparities in heterogeneous materialcombinations via laser directed energy deposition:a comparative stud

Jinsheng Ning1,6, Lida Zhu1,6,∗, Shuhao Wang2, Zhichao Yang1, Peihua Xu1,Pengsheng Xue3, Hao Lu1, Miao Yu1, Yunhang Zhao1, Jiachen Li4, Susmita Bose5 and Amit Bandyopadhyay5,∗

Abstract

적층 제조는 바이메탈 및 다중 재료 구조의 제작 가능성을 제공합니다. 그러나 재료 호환성과 접착성은 부품의 성형성과 최종 품질에 직접적인 영향을 미칩니다. 적합한 프로세스를 기반으로 다양한 재료 조합의 기본 인쇄 가능성을 이해하는 것이 중요합니다.

여기에서는 두 가지 일반적이고 매력적인 재료 조합(니켈 및 철 기반 합금)의 인쇄 적성 차이가 레이저 지향 에너지 증착(DED)을 통해 거시적 및 미시적 수준에서 평가됩니다.

증착 프로세스는 현장 고속 이미징을 사용하여 캡처되었으며, 용융 풀 특징 및 트랙 형태의 차이점은 특정 프로세스 창 내에서 정량적으로 조사되었습니다. 더욱이, 다양한 재료 쌍으로 처리된 트랙과 블록의 미세 구조 다양성이 비교적 정교해졌고, 유익한 다중 물리 모델링을 통해 이종 재료 쌍 사이에 제시된 기계적 특성(미세 경도)의 불균일성이 합리화되었습니다.

재료 쌍의 서로 다른 열물리적 특성에 의해 유발된 용융 흐름의 차이와 응고 중 결과적인 요소 혼합 및 국부적인 재합금은 재료 조합 간의 인쇄 적성에 나타난 차이점을 지배합니다.

이 작업은 서로 다른 재료의 증착에서 현상학적 차이에 대한 심층적인 이해를 제공하고 바이메탈 부품의 보다 안정적인 DED 성형을 안내하는 것을 목표로 합니다.

Additive manufacturing provides achievability for the fabrication of bimetallic and
multi-material structures; however, the material compatibility and bondability directly affect the
parts’ formability and final quality. It is essential to understand the underlying printability of
different material combinations based on an adapted process. Here, the printability disparities of
two common and attractive material combinations (nickel- and iron-based alloys) are evaluated
at the macro and micro levels via laser directed energy deposition (DED). The deposition
processes were captured using in situ high-speed imaging, and the dissimilarities in melt pool
features and track morphology were quantitatively investigated within specific process
windows. Moreover, the microstructure diversity of the tracks and blocks processed with varied
material pairs was comparatively elaborated and, complemented with the informative
multi-physics modeling, the presented non-uniformity in mechanical properties (microhardness)
among the heterogeneous material pairs was rationalized. The differences in melt flow induced
by the unlike thermophysical properties of the material pairs and the resulting element
intermixing and localized re-alloying during solidification dominate the presented dissimilarity
in printability among the material combinations. This work provides an in-depth understanding
of the phenomenological differences in the deposition of dissimilar materials and aims to guide
more reliable DED forming of bimetallic parts.

Figure 2. Deposition process and the track morphology. (a)–(c) Display the in situ captured tableaux of melt propagation and some physical
features during depositing for P1B1, P1B2, and P1B3, respectively. (d) The profiles of the melt pool at a frame of (t0 + 1) ms, and the flow
streamlines in the molten pool of each case. (e) The outer surface of the formed tracks, in which the colored arrows mark the scanning
direction. (f) Cross-section of the tracks. The parameter set used for in situ imaging was P-1000 W, S-600 mm·min–1, F-18 g·min–1. All the
scale bars are 2 mm. — Figure 2. Deposition process and the track morphology. (a)–(c) Display the in situ captured tableaux of melt propagation and some physical features during depositing for P1B1, P1B2, and P1B3, respectively. (d) The profiles of the melt pool at a frame of (t0 + 1) ms, and the flow streamlines in the molten pool of each case. (e) The outer surface of the formed tracks, in which the colored arrows mark the scanning direction. (f) Cross-section of the tracks. The parameter set used for in situ imaging was P-1000 W, S-600 mm·min–1, F-18 g·min–1. All the scale bars are 2 mm.

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원문 PDF 보기

Fig. 3. (a–c) Snapshots of the CtFD simulation of laser-beam irradiation: (a) Top, (b) longitudinal vertical cross-sectional, and (c) transversal vertical cross-sectional views. (d) z-position of the solid/liquid interface during melting and solidification.

Solute segregation in a rapidly solidified Hastelloy-X Ni-based superalloy during laser powder bed fusion investigated by phase-field simulations and computational thermal-fluid dynamics

Masayuki Okugawa ^ab, Kenji Saito ^a, Haruki Yoshima ^a, Katsuhiko Sawaizumi ^a, Sukeharu Nomoto ^c, Makoto Watanabe ^c, Takayoshi Nakano ^ab, Yuichiro Koizumi ^abShow moreAdd to MendeleyShareCite

https://doi.org/10.1016/j.addma.2024.104079

Get rights and content Under a Creative Commons license open access

Abstract

Solute segregation significantly affects material properties and is a critical issue in the laser powder-bed fusion (LPBF) additive manufacturing (AM) of Ni-based superalloys. To the best of our knowledge, this is the first study to demonstrate a computational thermal-fluid dynamics (CtFD) simulation coupled multi-phase-field (MPF) simulation with a multicomponent-composition model of Ni-based superalloy to predict solute segregation under solidification conditions in LPBF. The MPF simulation of the Hastelloy-X superalloy reproduced the experimentally observed submicron-sized cell structure. Significant solute segregations were formed within interdendritic regions during solidification at high cooling rates of up to 10⁸K s^-1, a characteristic feature of LPBF. Solute segregation caused a decrease in the solidus temperature (T_S), with a reduction of up to 30.4 K, which increases the risk of liquation cracks during LPBF. In addition, the segregation triggers the formation of carbide phases, which increases the susceptibility to ductility dip cracking. Conversely, we found that the decrease in T_S is suppressed at the melt-pool boundary regions, where re-remelting occurs during the stacking of the layer above. Controlling the re-remelting behavior is deemed to be crucial for designing crack-free alloys. Thus, we demonstrated that solute segregation at the various interfacial regions of Ni-based multicomponent alloys can be predicted by the conventional MPF simulation. The design of crack-free Ni-based superalloys can be expedited by MPF simulations of a broad range of element combinations and their concentrations in multicomponent Ni-based superalloys.

Graphical abstract

Keywords

Laser powder-bed fusion, Hastelloy-X Nickel-based superalloy, solute element segregation, computational thermal-fluid dynamics simulation, phase-field method

1. Introduction

Additive manufacturing (AM) technologies have attracted considerable attention as they allow us to easily build three-dimensional (3D) parts with complex geometries. Among the wide range of available AM techniques, laser powder-bed fusion (LPBF) has emerged as a preferred technique for metal AM [1], [2], [3], [4], [5]. In LPBF, metal products are built layer-by-layer by scanning laser, which fuse metal powder particles into bulk solids.

Significant attempts have been made to integrate LPBF techniques within the aerospace industry, with a particular focus on weldable Ni-based superalloys, such as IN718 [6], [7], [8], IN625 [9], [10], and Hastelloy-X (HX) [11], [12], [13], [14]. Non-weldable alloys, such as IN738LC [15], [16] and CMSX-4 [1], [17] are also suitable for their sufficient creep resistance under higher temperature conditions. However, non-weldable alloys are difficult to build using LPBF because of their susceptibility to cracking during the process. In general, a macro solute-segregation during solidification is suppressed by the rapid cooling conditions (up to 10⁸ K s^-1) unique to the LPBF process [18]. However, the solute segregation still occurs in the interdendritic regions that are smaller than the micrometer scale [5], [19], [20], [21]; these regions are suggested to be related to the hot cracks in LPBF-fabricated parts. Therefore, an understanding of solute segregation is essential for the fabrication of reliable LPBF-fabricated parts while avoiding cracks.

The multiphase-field (MPF) method has gained popularity for modeling the microstructure evolution and solute segregation under rapid cooling conditions [5], [20], [21], [22], [23], [24], [25], [26], [27], [28]. Moreover, quantifiable predictions have been achieved by combining the MPF method with temperature distribution analysis methods such as the finite-element method (FEM) [20] and computational thermal-fluid dynamics (CtFD) simulations [28]. These aforementioned studies have used binary-approximated multicomponent systems, such as Ni–Nb binary alloys, to simulate IN718 alloys. While MPF simulations using binary alloy systems can effectively reproduce microstructure formations and segregation behaviors, the binary approximation might be affected by the chemical interactions between the removed solute elements in the target multicomponent alloy. The limit of absolute stability predicted by the Mullins-Sekerka theory [29] is also crucial because the limit velocity is close to the solidification rate in the LPBF process and is different in multicomponent and binary-approximated systems. The difference between the solidus and liquidus temperatures, ΔT₀, directly determines the absolute stability according to the Mullins-Sekerka theory. For example, the ΔT₀ values of IN718 and its binary-approximated Ni–5 wt.%Nb alloy are 134 K [28] and 71 K [30], respectively. The solidification rate compared to the limit of absolute stability, i.e., the relative non-equilibrium of solidification, changes by simplification of the system. It is therefore important to use the composition of the actual multicomponent system in such simulations. However, to the best of our knowledge, there is no MPF simulation using a multicomponent model coupled with a temperature analysis simulation to predict solute segregation in a Ni-based superalloy.

In this study, we demonstrate that the conventional MPF model can reproduce experimentally observed dendritic structures by performing a phase-field simulation using the temperature distribution obtained by a CtFD simulation of a multicomponent Ni-based alloy (conventional solid-solution hardening-type HX). The MPF simulation revealed that the segregation behavior of solute elements largely depends on the regions of the melt pool, such as the cell boundary, the interior of the melt-pool boundary, and heat-affected regions. The sensitivities of the various interfaces to liquation and solidification cracks are compared based on the predicted concentration distributions. Moreover, the feasibility of using the conventional MPF model for LPBF is discussed in terms of the absolute stability limit.

2. Methods

2.1. Laser-beam irradiation experiments

Rolled and recrystallized HX ingots with dimensions of 20 × 50 × 10 mm were used as the specimens for laser-irradiation experiments. The specimens were irradiated with a laser beam scanned along straight lines of 10 mm in length using a laser AM machine (EOS 290 M, EOS) equipped with a 400 W Yb-fiber laser. Irradiation was performed with a beam power of P = 300 W and a scanning speed of V = 600 mm s^-1, which are the conditions generally used in the LPBF fabrication of Ni-based superalloy [8]. The corresponding line energy was 0.5 J mm^-1. The samples were cut perpendicular to the beam-scanning direction for cross-sectional observation using a field-emission scanning electron microscope (FE-SEM, JEOL JSM 6500). Crystal orientation analysis was performed by electron backscatter diffraction (EBSD). The sizes of each crystal grain and their aspect ratios were evaluated by analyzing the EBSD data.

2.2. CtFD simulation

CtFD simulations of the laser-beam irradiation of HX were performed using a 3D thermo-fluid analysis software (Flow Science FLOW-3D® with Flow-3D Weld module). A Gaussian heat source model was used, in which the irradiation intensity distribution of the beam is regarded as a symmetrical Gaussian distribution over the entire beam. The distribution of the beam irradiation intensity is expressed by the following equation.(1)q̇=2ηPπR2exp−2r2R2.

Here, P is the power, R is the effective beam radius, r is the actual beam radius, and η is the beam absorption rate of the substrate. To improve the accuracy of the model, η was calculated by assuming multiple reflections using the Fresnel equation:(2)�=1−121+1−�cos�21+1+�cos�2+�2−2�cos�+2cos2��2+2�cos�+2cos2�.

ε is the Fresnel coefficient and θ is the incident angle of the laser. A local laser melt causes the vaporization of the material and results in a high vapor pressure. This vapor pressure acts as a recoil pressure on the surface, pushing the weld pool down. The recoil pressure is reproduced using the following equation.(3)precoil=Ap0exp∆HLVRTV1−TVT.

Here, p₀ is the atmospheric pressure, ∆H_LV is the latent heat of vaporization, R is the gas constant, and T_V is the boiling point at the saturated vapor pressure. A is a ratio coefficient that is generally assumed to be 0.54, indicating that the recoil pressure due to evaporation is 54% of the vapor pressure at equilibrium on the liquid surface.

Table 1 shows the parameters used in the simulations. Most parameters were evaluated using an alloy physical property calculation software (Sente software JMatPro v11). The values in a previously published study [31] were used for the emissivity and the Stefan–Boltzmann constant, and the values for pure Ni [32] were used for the heat of vaporization and vaporization temperatures. The Fresnel coefficient, which determines the beam absorption efficiency, was used as a fitting parameter to reproduce the morphology of the experimentally observed melt region, and a Fresnel coefficient of 0.12 was used in this study.

Table 1. Parameters used in the CtFD simulations.

Parameter	Symbol	Value	Reference
Density at 298.15 K	ρ	8.24 g cm^-3	[^⁎]
Liquidus temperature	T_L	1628.15 K	[^⁎]
Solidus temperature	T_S	1533.15 K	[^⁎]
Viscosity at T_L	η	6.8 g m^-1 s^-1	[^⁎]
Specific heat at 298.15 K	C_P	0.439 J g^-1 K^-1	[^⁎]
Thermal conductivity at 298.15 K	λ	10.3 W m^-1 K^-1	[^⁎]
Surface tension at T_L	γ_L	1.85 J m^-2	[^⁎]
Temperature coefficient of surface tension	dγ_L/dT	–2.5 × 10⁻⁴ J m^-2 K^-1	[^⁎]
Emissivity	Ε	0.27	[31]
Stefan–Boltzmann constant	σ	5.67 × 10^-8W m^-2 K^-4	[31]
Heat of fusion	ΔH_SL	2.76 × 10² J g^-1	[32]
Heat of vaporization	ΔH_LV	4.29 × 10³J g^-1	[32]
Vaporization temperature	T_V	3110 K	[32]

⁎

Calculated using JMatPro v11.

The dimensions of the computational domain of the numerical model were 4.0 mm in the beam-scanning direction, 0.4 mm in width, and 0.3 mm in height. A uniform mesh size of 10 μm was applied throughout the computational domain. The boundary condition of continuity was applied to all boundaries except for the top surface. The temperature was initially set to 300 K. P and V were set to their experimental values, i.e., 300 W and 600 mm s^-1, respectively. Solidification conditions based on the temperature gradient, G, the solidification rate, R, and the cooling rate were evaluated, and the obtained temperature distribution was used in the MPF simulations.

2.3. MPF simulation

Two-dimensional MPF simulations weakly coupled with the CtFD simulation were performed using the Microstructure Evolution Simulation Software (MICRESS) [33], [34], [35], [36], [37] with the TQ-Interface for Thermo-Calc [38]. A simplified HX alloy composition of Ni-21.4Cr-17.6Fe-0.46Mn-8.80Mo-0.39Si-0.50W-1.10Co-0.08 C (mass %) was used in this study. The Gibbs free energy and diffusion coefficient of the system were calculated using the TCNI9 thermodynamic database [39] and the MOBNi5 mobility database [40]. Τhe equilibrium phase diagram calculated using Thermo-Calc indicates that the face-centered cubic (FCC) and σ phases appear as the equilibrium solid phases [19]. However, according to the time-temperature-transformation (TTT) diagram [41], the phases are formed after the sample is maintained for tens of hours in a temperature range of 1073 to 1173 K. Therefore, only the liquid and FCC phases were assumed to appear in the MPF simulations. The simulation domain was 5 × 100 μm, and the grid size Δx and interface width were set to 0.025 and 0.1 µm, respectively. The interfacial mobility between the solid and liquid phases was set to 1.0 × 10^-8 m⁴ J^-1 s^-1. Initially, one crystalline nucleus with a [100] crystal orientation was placed at the left bottom of the simulation domain, with the liquid phase occupying the remainder of the domain. The model was solidified under the temperature field distribution obtained by the CtFD simulation. The concentration distribution and crystal orientation of the solidified model were examined. The primary dendrite arm space (PDAS) was compared to the experimental PDAS measured by the cross-sectional SEM observation.

In an actual LPBF process, solidified layers are remelted and resolidified during the stacking of the one layer above, thereby greatly affecting solute element distributions in those regions. Therefore, remelting and resolidification simulations were performed to examine the effect of remelting on solute segregation. The solidified model was remelted and resolidified by applying a time-dependent temperature field shifted by 60 μm in the height direction, assuming reheating during the stacking of the upper layer (i.e., the upper 40 μm region of the simulation box was remelted and resolidified). The changes in the composition distribution and formed microstructure were investigated.

3. Results

3.1. Experimental observation of melt pool

Fig. 1 shows a cross-sectional optical microscopy image and corresponding inverse pole figure (IPF) orientation maps obtained from the laser-melted region of HX. The dashed line indicates the fusion line. A deep melted region was formed by keyhole-mode melting due to the vaporization of the metal and resultant recoil pressure. Epitaxial growth from the unmelted region was observed. Columnar crystal grains with an average diameter of 5.46 ± 0.32 μm and an aspect ratio of 3.61 ± 0.13 appeared at the melt regions (Figs. 1b–1d). In addition, crystal grains growing in the z direction could be observed in the lower center.

Fig. 2a shows a cross-sectional backscattering electron image (BEI) obtained from the laser-melted region indicated by the black square in Fig. 1a. The bright particles with a diameter of approximately 2 μm observed outside the melt pool. It is well known that M₆C, M₂₃C₆, σ, and μ precipitate phases are formed in Hastelloy-X [41]. These precipitates mainly consisted of Mo, Cr, Fe, and Ni; The μ and M₆C phases are rich in Mo, while the σ and M₂₃C₆ phases are rich in Cr. The SEM energy dispersive X-ray spectroscopy analysis suggested that the bright particles are the stable precipitates as shown in Fig. S2 and Table S1. Conversely, there are no carbides in the melt pool. This suggests that the cooling rate is extremely high during LPBF, which prevents the formation of a stable carbide during solidification. Figs. 2b–2f show magnified BEI images at different height positions indicated in Fig. 2a. Bright regions are observed between the cells, which become fragmentary at the center of the melt pool, as indicated by the yellow arrow heads in Figs. 2e and 2f.

3.2. CtFD simulation

Figs. 3a–3c show snapshots of the CtFD simulation of HX at 2.72 ms, with the temperature indicated in color. A melt pool with an elongated teardrop shape formed and keyhole-mode melting was observed at the front of the melt region. The cooling rate, temperature gradient (G), and solidification rate (R) were evaluated from the temporal change in the temperature distribution of the CtFD simulation results. The z-position of the solid/liquid interface during the melting and solidification processes is shown in Fig. 3d. The interface goes down rapidly during melting and then rises during solidification. The MPF simulation of the microstructure formation during solidification was performed using the temperature distribution. Moreover, the microstructure formation process during the fabrication of the upper layer was investigated by remelting and resolidifying the solidified layer using the same temperature distribution with a 60 μm upward shift, corresponding to the layer thickness commonly used in the LPBF of Ni-based superalloys.

Figs. 4a–4c show the changes in the cooling rate, temperature gradient, and solidification rate in the center line of the melt pool parallel to the z direction. To output the solidification conditions at the solid/liquid interface in the melt pool, only the data of the mesh where the solid phase ratio was close to 0.5 were plotted. Solidification occurred where the cooling rate was in the range of 2.1 × 10⁵–1.6 × 10⁶K s^-1, G was in the range of 3.6 × 10⁵–1.9 × 10⁷K m^-1, and R was in the range of 8.2 × 10⁻²–6.3 × 10⁻¹ m s^-1. The cooling rate was the highest near the fusion line and decreased as the interface approached the center of the melt region (Fig. 4a). G also exhibited the highest value in the regions near the fusion line and decreased throughout the solid/liquid interface toward the center of the melt pool (Fig. 4b). R had the lowest value near the fusion line and increased as the interface approached the center of the melt region (Fig. 4c).

3.3. MPF simulations coupled with CtFD simulation

MPF simulations of solidification, remelting, and resolidification were performed using the temperature-time distribution obtained by the CtFD simulation. Fig. 5 shows the MPF solidified models colored by phase and Mo concentration. All the computational domains show the FCC phase after the solidification (Fig. 5a). Dendrites grew parallel to the heat flow direction, and solute segregations were observed in the interdendritic regions. At the bottom of the melt pool (Fig. 5d), planar interface growth occurred before the formation of primary dendrites. The bottom of the melt pool is the turning point of the solid/liquid interface from the downward motion in melting to the upward motion in solidification. Thus, the solidification rate at the boundary is zero, and is extremely low immediately above the molt-pool boundary. Here, the lower limit of the solidification rate (R) for dendritic growth can be represented by the constitutional supercooling criterion [29], V_cs = (G × D_L) / ΔT, and planar interface growth occurs at R < V_cs. D_L and ΔT denote the diffusion coefficient in the liquid and the equilibrium freezing range, respectively. The results suggest that planar interface growth occurs at the bottom of the melt pool, resulting in a dark region with a different solute element distribution. Some of the primary dendrites were diminished by competition with other dendrites. In addition, secondary dendrite arms could be seen in the upper regions (Fig. 5c), where solidification occurred at a lower cooling rate. The fragmentation of the solute segregation near the secondary dendrite arms is similar to that observed in the experimental melt pool shown in Figs. 2e and 2f, and the secondary dendrite arms are suggested to have appeared at the center of the melt region. Fig. 6 shows the PDASs measured from the MPF simulation models, compared to the experimental PDASs measured by the cross-sectional SEM observation of the laser-melted regions (Fig. 2). The PDAS obtained by the MPF simulation become larger as the solidification progress. Ghosh et al. [21] evident by the phase-field method that the PDAS decreases as the cooling rate increases under the rapid cooling conditions obtained by the finite element analysis. In this study, the cooling rate was decreased as the interface approached the center of the melt region (Fig. 4a), and the trends in PDAS changes with respect to cooling rate is same as the reported trend [21]. The simulated trends of the PDAS with the position in the melt pool agreed well with the experimental trends. However, all PDASs in the simulation were larger than those observed in the experiment at the same positions. Ode et al. [42] reported that PDAS differences between 2D and 3D MPF simulations can be represented by PDAS_2D = 1.12 × PDAS_3D owing to differences in the effects of the interfacial energy and diffusivity. We also performed 2D and 3D MPF simulations under the solidification conditions of G = 1.94 × 10⁷K m^-1 and R = 0.82 m s^-1 (Fig. S1), and found that the PDAS from the 2D MPF simulation was 1.26 times larger than that from the 3D simulation. Therefore, the cell structure obtained by the CtFD simulation coupled with the 2D MPF simulation agreed well with the experimental results over the entire melt pool region considering the dimensional effects.

Fig. 7b1 and 7c1 show the concentration profiles of the solidified model along the growth direction indicated by dashed lines in Fig. 7a. The differences in concentrations from the alloy composition are also shown in Fig. 7b2 and 7c2. Cr, Mo, C, Mn, and W were segregated to the interdendritic regions, while Si, Fe, and Co were depressed. The solute segregation behavior agrees with the experimentally observation [43] and the prediction by the Scheil-Gulliver simulation [19]. Segregation occurred to the highest degree in Mo, while the ratio of segregation to the alloy composition was remarkable in C. The concentration fluctuations correlated with the position in the melt pool and decreased at the center of the melt pool, which was suggested to correspond to the lower cooling rate in this region. Conversely, droplets that appeared between secondary dendrite arms in the upper regions of the simulation domain exhibited a locally high segregation of solute elements, with the same amount of segregation as that at the bottom of the melt pool.

3.4. Remelting and resolidification simulation

The solidified model was subjected to remelting and resolidification conditions by shifting the temperature profile upward by 60 µm to reveal the effect of reheating on the solute segregation behavior. Figs. 8a and 8b shows the simulation domains of the HX model after resolidification, colored by phase and Mo concentration. The magnified MPF models during the resolidification of the regions indicated by rectangles in Figs. 8a and 8b are also shown as Figs. 8c and 8d. Dendrites grew from the bottom of the remelted region, with the segregation of solute elements occurring in the interdendritic regions. The entire domain become the FCC phase after the resolidification, as shown in Fig. 8a. The bottom of the remelted regions exhibited a different microstructure, and Mo was depressed at the remelted regions, rather than the interdendritic regions. The different solute segregation behavior [44] and the microstructure formation [45] at the melt pool boundary is also observed in LPBF manufactured 316 L stainless steel. We found that this microstructure was formed by further remelting during the resolidification process, which is shown in Fig. 9. Here, the solidified HX model was heated, and the interdendritic regions were preferentially melted while concentration fluctuations were maintained (Fig. 9a1 and 9a2). Subsequently, planer interface growth occurs near the melt pool boundary where the solidification rate is almost zero, and the dendrites outside of the boundary are grown epitaxially (Fig. 9b1 and 9b2). However, these remelted again because of the temperature rise (Fig. 9c1 and 9c2, and the temperature-time profile shown in Fig. 9e). The remelted regions then cooled and solidified with the abnormal solute segregations (Fig. 9d1 and 9d2). Then, dendrite grows from amplified fluctuations under the solidification rate larger than the criterion of constitutional supercooling (Fig. 9d1, 9d2, and Fig. 8d). It has been reported [46], [47] that temperature rising owning to latent heat affects microstructure formation: phase-field simulations of a Ni–Al binary alloy suggest that the release of latent heat during solidification increases the average temperature of the system [46] and strongly influences the solidification conditions [47]. In this study, the release of latent heat during solidification is considered in CtFD simulations for calculating the temperature distribution, and the temperature increase is suggested to have also occurred due to the release of latent heat.

Fig. 10b1 and 10c1 show the solute element concentration line profiles of the resolidified model along the growth direction indicated by dashed lines in Fig. 10a. Fig. 10b2 and 10c2 show the corresponding differences in concentration from the alloy composition. The segregation behavior of solute elements at the interdendritic regions (Fig. 10b1 and 10b2) was the same as that in the solidified model (Figs. 7b1 and 7b2). Here, Cr, Mo, C, Mn, and W were segregated to the interdendritic regions, while Si, Fe, and Co were depressed. However, the concentration fluctuations at the interdendritic regions were larger than those in the solidified model. Moreover, the segregation of the outside of the melt pool, i.e., the heat-affected zone, was remarkable throughout remelting and resolidification. Different segregation behaviors were observed in the re-remelted region: Mo, Si, Mn, and W were segregated, while Ni, Fe, and Co were depressed. These solute segregations caused by remelting are expected to heavily influence the crack behavior.

4. Discussion

4.1. Effect of segregation of solute elements on liquation cracking susceptibility

Strong solute segregation was observed between the interdendritic regions of the solidified alloy (Fig. 7). In addition, the solute segregation behavior was significantly affected by remelting and resolidification and varied across the alloy. Solute segregation can be categorized by the regions shown in Fig. 11a1–11a4, namely the cell boundary (Fig. 11a1), interior of the melt-pool boundary (Fig. 11a2), re-remelted regions (Fig. 11a3), and heat-affected regions (Fig. 11a4). The concentration profiles of these regions are shown in Fig. 11b1–11b4. Solute segregation was the highest in the cell boundary region. The solute segregation in the heat-affected region was almost the same as that in the cell boundary region, but seemed to have been attenuated by reheating during remelting and resolidification. The interior of the melt-pool boundary region also had the same tendency for solute segregation. However, the amount of Cr segregation was smaller than that of Mo. A decrease in the Cr concentration was also mitigated, and the concentration remained the same as that in the alloy composition. Fig. 11c1–11c4 show the chemical potentials of the solute elements for the FCC phase at 1073 K calculated using the compositions of those interfacial regions. All the interfacial regions showed non-constant chemical potentials for each element along the perpendicular direction, but the fluctuations of the chemical potentials differed by the type of interfaces. In particular, the fluctuation of the chemical potential of C at the cell boundary region was the largest, suggesting it can be relaxed easily by heat treatment. On the other hand, the fluctuations of the other elements in all the regions were small. The solute segregations are most likely to remain after the heat treatment and are supposed to affect the cracking susceptibilities.

The solidus temperatures T_S, the difference between the liquidus and solidus temperatures (i.e., the brittle temperature range (BTR)), and the fractions of the equilibrium precipitate phases at 1073 K of the interfacial regions were calculated as the liquation, solidification, and ductility dip cracking susceptibilities, respectively. At the cell boundary (Fig. 12a1), interior of the melt-pool boundary (Fig. 12a1), and heat-affected regions (Fig. 12a1), the internal and interfacial regions exhibited higher and lower T_S compared to that of the alloy composition, respectively. The lowest T_s was obtained with the composition at the cell boundary region, which is the largest solute-segregated region. It has been suggested that strong segregations of solute elements in LPBF lead to liquation cracks [16]. This study also supports this suggestion, and liquation cracks are more likely to occur at the interfacial regions indicated by predicting the solute segregation behavior using the MPF model. Additionally, the BTRs of the cell boundary, interior of the melt-pool boundary, and heat-affected regions were wider at the interdendritic regions, and solidification cracks were also likely to occur in these regions. Moreover, within the solute segregation regions, the fraction of the precipitate phases in these interfacial regions was larger than that calculated using the alloy composition (Fig. 12c1, 12c2, and 12c4). This indicates that ductility dip cracking is also likely to occur at the cell boundary, interior of the melt-pool boundary, and in heat-affected regions. Contrarily, we found that the re-remelted region exhibited a higher T_S and smaller BTR even in the interfacial region (Fig. 12a3 and 12b3), where the solute segregation behavior was different from that of the other regions. In addition, the re-remelting region exhibited less precipitation compared with the other segregated regions (Fig. 12c3). The re-remelting caused by the latent heat can attenuate solute segregation, prevent T_s from decreasing, decrease the BTR, and decrease the amount of precipitate phases. Alloys with a large amount of latent heat are expected to increase the re-remelting region, thereby decreasing the susceptibility to liquation and ductility dip cracks due to solute element segregation. This can be a guide for designing alloys for the LPBF process. As mentioned in Section 3.4, the microstructure [45] and the solute segregation behavior [44] at the melt pool boundary of LPBF-manufactured 316 L stainless steel are observed, and they are different from that of the interdendritic regions. Experimental observations of the solute segregation behavior in the LPBF-fabricated Ni-based alloys are currently underway.

4.2. Applicability of the conventional MPF simulation to microstructure formation under LPBF

As the solidification growth rate increases, segregation coefficients approach 1, and the fluctuation of the solid/liquid interface is suppressed by the interfacial tension. The interface growth occurs in a flat fashion instead of having a cellular morphology at a velocity above the absolute stability limit, R_as, predicted by the Mullins-Sekerka theory [29]: R_as = (ΔT₀ D_L) / (k Γ) where ΔT₀, D_L, k, and Γ are the difference between the liquidus and solidus temperatures, equilibrium segregation coefficient, the diffusivity of liquid, and the Gibbs-Thomson coefficient, respectively.

The R_as of HX was calculated using the equation and the thermodynamic parameters obtained by the TCNI9 thermodynamic database [39]. The calculated R_as of HX was 3.9 m s^-1 and is ten times larger than that of the Ni–Nb alloy (approximately 0.4 m s^-1) [20]. The HX alloy was solidified under R values in the range of 8.2 × 10⁻²–6.3 × 10⁻¹ m s^-1. The theoretically calculated criterion is larger than the evaluated R, and is in agreement with the experiment in which dendritic growth is observed in the melt pool (Fig. 5). In contrast, Karayagiz et al. [20] reported that the R of the Ni–Nb binary alloy under LPBF was as high as approximately 2 m s^-1, and planar interface growth was observed to be predominant under the high-growth-rate conditions. These experimentally observed microstructures agree well with the prediction by the Mullins-Sekerka theory about the relationship between the morphology and solidification rates.

In this study, the solidification microstructure formed by the laser-beam irradiation of an HX multicomponent Ni-based superalloy was reproduced by a conventional MPF simulation, in which the system was assumed to be in a quasi-equilibrium condition. Boussinot et al. [24] also suggested that the conventional phase-field model can be applied to simulate the microstructure of an IN718 multicomponent Ni-based superalloy in LPBF. In contrast, Kagayaski et al. [20] suggested that the conventional MPF simulation cannot be applied to the solidification of the Ni-Nb binary alloy system and that the finite interface dissipation model proposed by Steinbach et al. [48], [49] is necessary to simulate the high solidification rates observed in LPBF. The difference in the applicability of the conventional MPF method to HX and Ni–Nb binary alloys is presumed to arise from the differences in the non-equilibrium degree of these systems under the high solidification rates of LPBF. The results suggest that R_as can be used as a simple index to apply the conventional MPF model for solidification in LPBF. Solidification becomes a non-equilibrium process as the solidification rate approaches the limit of absolute stability, R_as. In this study, the solidification of the HX multicomponent system occurred under a relatively low solidification rate compared to R_as, and the microstructure of the conventional MPF model was successfully reproduced in the physical experiment. However, note that the limit of absolute stability predicted by the Mullins-Sekerka theory was originally proposed for solidification in a binary alloy system, and further investigation is required to consider its applicability to multicomponent alloy systems. Moreover, the fast solidification, such as in the LPBF process, causes segregation coefficient approaching a value of 1 [20], [21], [25] corresponds to a diffusion length that is on the order of the atomic interface thickness. When the segregation coefficient approaches 1, solute undercooling disappears; hence, there is no driving force to amplify fluctuations regardless of whether interfacial tension is present. This phenomenon should be further investigated in future studies.

5. Conclusions

We simulated solute segregation in a multicomponent HX alloy under the LPBF process by an MPF simulation using the temperature distributions obtained by a CtFD simulation. We set the parameters of the CtFD simulation to match the melt pool shape formed in the laser-irradiation experiment and found that solidification occurred under high cooling rates of up to 1.6 × 10⁶K s^-1.

MPF simulations using the temperature distributions from CtFD simulation could reproduce the experimentally observed PDAS and revealed that significant solute segregation occurred at the interdendritic regions. Equilibrium thermodynamic calculations using the alloy compositions of the segregated regions when considering crack sensitivities suggested a decrease in the solidus temperature and an increase in the amount of carbide precipitation, thereby increasing the susceptibility to liquation and ductility dip cracks in these regions. Notably, these changes were suppressed at the melt-pool boundary region, where re-remelting occurred during the stacking of the layer above. This effect can be used to achieve a novel in-process segregation attenuation.

Our study revealed that a conventional MPF simulation weakly coupled with a CtFD simulation can be used to study the solidification of multicomponent alloys in LPBF, contrary to the cases of binary alloys investigated in previous studies. We discussed the applicability of the conventional MPF model to the LPBF process in terms of the limit of absolute stability, R_as, and suggested that alloys with a high limit velocity, i.e., multicomponent alloys, can be simulated using the conventional MPF model even under the high solidification velocity conditions of LPBF.

CRediT authorship contribution statement

Masayuki Okugawa: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Takayoshi Nakano: Writing – review & editing, Validation, Supervision, Funding acquisition. Yuichiro Koizumi: Writing – review & editing, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Sukeharu Nomoto: Writing – review & editing, Validation, Investigation. Makoto Watanabe: Writing – review & editing, Validation, Supervision, Funding acquisition. Katsuhiko Sawaizumi: Validation, Software, Investigation, Formal analysis, Data curation. Kenji Saito: Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. Haruki Yoshima: Visualization, Validation, Software, Investigation, Formal analysis, Data curation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Acknowledgments

This work was partly supported by the Cabinet Office, Government of Japan, Cross-ministerial Strategic Innovation Promotion Program (SIP), “Materials Integration for Revolutionary Design System of Structural Materials,” (funding agency: The Japan Science and Technology Agency), by JSPS KAKENHI Grant Numbers 21H05018 and 21H05193, and by CREST Nanomechanics: Elucidation of macroscale mechanical properties based on understanding nanoscale dynamics for innovative mechanical materials (Grant Number: JPMJCR2194) from the Japan Science and Technology Agency (JST). The authors would like to thank Mr. H. Kawabata and Mr. K. Kimura for their technical support with the sample preparations and laser beam irradiation experiments.

Appendix A. Supplementary material

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Supplementary material.

Data availability

Data will be made available on request.

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Figure 5. Simulation of the molten pool under low-speed scanning (1.06 m/s). (a) Sequential solidification of the molten pool at the end of the melt track for laser powers of 190 and 340 W, respectively. (b) Recoil pressure on the molten pool at the keyhole for laser powers of 190 and 340 W, respectively. (c) The force diagram of the melt at the back of the keyhole at t = 750 μs in case B. (d) Temperature gradient at the solid–liquid interface of the molten pool at the moment the laser is deactivated in case A. (e) Temperature gradient at the solid–liquid interface of the molten pool at the moment the laser is deactivated in case B.

Revealing formation mechanism of end of processdepression in laser powder bed fusion by multiphysics meso-scale simulation

다중물리 메조 규모 시뮬레이션을 통해 레이저 분말층 융합에서 공정 종료의 함몰 형성 메커니즘 공개

Haodong Chen ^a,b, Xin Lin ^a,b,c, Yajing Sund, Shuhao Wang^a,b, Kunpeng Zhu ^a,b,c and Binbin Dan^a,b

To link to this article: https://doi.org/10.1080/17452759.2024.2326599

ABSTRACT

Unintended end-of-process depression (EOPD) commonly occurs in laser powder bed fusion (LPBF), leading to poor surface quality and lower fatigue strength, especially for many implants. In this study, a high-fidelity multi-physics meso-scale simulation model is developed to uncover the forming mechanism of this defect. A defect-process map of the EOPD phenomenon is obtained using this simulation model. It is found that the EOPD formation mechanisms are different under distinct regions of process parameters. At low scanning speeds in keyhole mode, the long-lasting recoil pressure and the large temperature gradient easily induce EOPD. While at high scanning speeds in keyhole mode, the shallow molten pool morphology and the large solidification rate allow the keyhole to evolve into an EOPD quickly. Nevertheless, in the conduction mode, the Marangoni effects along with a faster solidification rate induce EOPD. Finally, a ‘step’ variable power strategy is proposed to optimise the EOPD defects for the case with high volumetric energy density at low scanning speeds. This work provides a profound understanding and valuable insights into the quality control of LPBF fabrication.

의도하지 않은 공정 종료 후 함몰(EOPD)은 LPBF(레이저 분말층 융합)에서 흔히 발생하며, 특히 많은 임플란트의 경우 표면 품질이 떨어지고 피로 강도가 낮아집니다. 본 연구에서는 이 결함의 형성 메커니즘을 밝히기 위해 충실도가 높은 다중 물리학 메조 규모 시뮬레이션 모델을 개발했습니다.

이 시뮬레이션 모델을 사용하여 EOPD 현상의 결함 프로세스 맵을 얻습니다. EOPD 형성 메커니즘은 공정 매개변수의 별개 영역에서 서로 다른 것으로 밝혀졌습니다.

키홀 모드의 낮은 스캔 속도에서는 오래 지속되는 반동 압력과 큰 온도 구배로 인해 EOPD가 쉽게 유발됩니다. 키홀 모드에서 높은 스캐닝 속도를 유지하는 동안 얕은 용융 풀 형태와 큰 응고 속도로 인해 키홀이 EOPD로 빠르게 진화할 수 있습니다.

그럼에도 불구하고 전도 모드에서는 더 빠른 응고 속도와 함께 마랑고니 효과가 EOPD를 유발합니다. 마지막으로, 낮은 스캐닝 속도에서 높은 체적 에너지 밀도를 갖는 경우에 대해 EOPD 결함을 최적화하기 위한 ‘단계’ 가변 전력 전략이 제안되었습니다.

이 작업은 LPBF 제조의 품질 관리에 대한 심오한 이해와 귀중한 통찰력을 제공합니다.

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Schematic diagram of HP-LPBF melting process.

Modeling and numerical studies of high-precision laser powder bed fusion

Yi Wei ;Genyu Chen;Nengru Tao;Wei Zhou
https://doi.org/10.1063/5.0191504

In order to comprehensively reveal the evolutionary dynamics of the molten pool and the state of motion of the fluid during the high-precision laser powder bed fusion (HP-LPBF) process, this study aims to deeply investigate the specific manifestations of the multiphase flow, solidification phenomena, and heat transfer during the process by means of numerical simulation methods. Numerical simulation models of SS316L single-layer HP-LPBF formation with single and double tracks were constructed using the discrete element method and the computational fluid dynamics method. The effects of various factors such as Marangoni convection, surface tension, vapor recoil, gravity, thermal convection, thermal radiation, and evaporative heat dissipation on the heat and mass transfer in the molten pool have been paid attention to during the model construction process. The results show that the molten pool exhibits a “comet” shape, in which the temperature gradient at the front end of the pool is significantly larger than that at the tail end, with the highest temperature gradient up to 1.69 × 10⁸ K/s. It is also found that the depth of the second track is larger than that of the first one, and the process parameter window has been determined preliminarily. In addition, the application of HP-LPBF technology helps to reduce the surface roughness and minimize the forming size.

Topics

Heat transfer, Nonequilibrium thermodynamics, Solidification process, Computer simulation, Discrete element method, Lasers, Mass transfer, Fluid mechanics, Computational fluid dynamics, Multiphase flows

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I. INTRODUCTION

Laser powder bed fusion (LPBF) has become a research hotspot in the field of additive manufacturing of metals due to its advantages of high-dimensional accuracy, good surface quality, high density, and high material utilization.^1,2 With the rapid development of electronics, medical, automotive, biotechnology, energy, communication, and optics, the demand for microfabrication technology is increasing day by day.³ High-precision laser powder bed fusion (HP-LPBF) is one of the key manufacturing technologies for tiny parts in the fields of electronics, medical, automotive, biotechnology, energy, communication, and optics because of its process characteristics such as small focal spot diameter, small powder particle size, and thin powder layup layer thickness.^4–13 Compared with LPBF, HP-LPBF has the significant advantages of smaller focal spot diameter, smaller powder particle size, and thinner layer thickness. These advantages make HP-LPBF perform better in producing micro-fine parts, high surface quality, and parts with excellent mechanical properties.

HP-LPBF is in the exploratory stage, and researchers have already done some exploratory studies on the focal spot diameter, the amount of defocusing, and the powder particle size. In order to explore the influence of changing the laser focal spot diameter on the LPBF process characteristics of the law, Wildman et al.¹⁴ studied five groups of different focal spot diameter LPBF forming 316L stainless steel (SS316L) processing effect, the smallest focal spot diameter of 26 μm, and the results confirm that changing the focal spot diameter can be achieved to achieve the energy control, so as to control the quality of forming. Subsequently, Mclouth et al.¹⁵ proposed the laser out-of-focus amount (focal spot diameter) parameter, which characterizes the distance between the forming plane and the laser focal plane. The laser energy density was controlled by varying the defocusing amount while keeping the laser parameters constant. Sample preparation at different focal positions was investigated, and their microstructures were characterized. The results show that the samples at the focal plane have finer microstructure than those away from the focal plane, which is the effect of higher power density and smaller focal spot diameter. In order to explore the influence of changing the powder particle size on the characteristics of the LPBF process, Qian et al.¹⁶ carried out single-track scanning simulations on powder beds with average powder particle sizes of 70 and 40 μm, respectively, and the results showed that the melt tracks sizes were close to each other under the same process parameters for the two particle-size distributions and that the molten pool of powder beds with small particles was more elongated and the edges of the melt tracks were relatively flat. In order to explore the superiority of HP-LPBF technology, Xu et al.¹⁷ conducted a comparative analysis of HP-LPBF and conventional LPBF of SS316L. The results showed that the average surface roughness of the top surface after forming by HP-LPBF could reach 3.40 μm. Once again, it was verified that HP-LPBF had higher forming quality than conventional LPBF. On this basis, Wei et al.⁶ comparatively analyzed the effects of different laser focal spot diameters on different powder particle sizes formed by LPBF. The results showed that the smaller the laser focal spot diameter, the fewer the defects on the top and side surfaces. The above research results confirm that reducing the laser focal spot diameter can obtain higher energy density and thus better forming quality.

LPBF involves a variety of complex systems and mechanisms, and the final quality of the part is influenced by a large number of process parameters.^18–24 Some research results have shown that there are more than 50 factors affecting the quality of the specimen. The influencing factors are mainly categorized into three main groups: (1) laser parameters, (2) powder parameters, and (3) equipment parameters, which interact with each other to determine the final specimen quality. With the continuous development of technologies such as computational materials science and computational fluid dynamics (CFD), the method of studying the influence of different factors on the forming quality of LPBF forming process has been shifted from time-consuming and laborious experimental characterization to the use of numerical simulation methods. As a result, more and more researchers are adopting this approach for their studies. Currently, numerical simulation studies on LPBF are mainly focused on the exploration of molten pool, temperature distribution, and residual stresses.

Finite element simulation based on continuum mechanics and free surface fluid flow modeling based on fluid dynamics are two common approaches to study the behavior of LPBF molten pool.^25–28 Finite element simulation focuses on the temperature and thermal stress fields, treats the powder bed as a continuum, and determines the molten pool size by plotting the elemental temperature above the melting point. In contrast, fluid dynamics modeling can simulate the 2D or 3D morphology of the metal powder pile and obtain the powder size and distribution by certain algorithms.²⁹ The flow in the molten pool is mainly affected by recoil pressure and the Marangoni effect. By simulating the molten pool formation, it is possible to predict defects, molten pool shape, and flow characteristics, as well as the effect of process parameters on the molten pool geometry.^30–34 In addition, other researchers have been conducted to optimize the laser processing parameters through different simulation methods and experimental data.^35–46 Crystal growth during solidification is studied to further understand the effect of laser parameters on dendritic morphology and solute segregation.^47–54 A multi-scale system has been developed to describe the fused deposition process during 3D printing, which is combined with the conductive heat transfer model and the dendritic solidification model.^55,56
Relevant scholars have adopted various different methods for simulation, such as sequential coupling theory,⁵⁷ Lagrangian and Eulerian thermal models,⁵⁸ birth–death element method,²⁵ and finite element method,⁵⁹ in order to reveal the physical phenomena of the laser melting process and optimize the process parameters. Luo et al.⁶⁰ compared the LPBF temperature field and molten pool under double ellipsoidal and Gaussian heat sources by ANSYS APDL and found that the diffusion of the laser energy in the powder significantly affects the molten pool size and the temperature field.
The thermal stresses obtained from the simulation correlate with the actual cracks,⁶¹ and local preheating can effectively reduce the residual stresses.⁶² A three-dimensional thermodynamic finite element model investigated the temperature and stress variations during laser-assisted fabrication and found that powder-to-solid conversion increases the temperature gradient, stresses, and warpage.⁶³ Other scholars have predicted residual stresses and part deflection for LPBF specimens and investigated the effects of deposition pattern, heat, laser power, and scanning strategy on residual stresses, noting that high-temperature gradients lead to higher residual stresses.^64–67

In short, the process of LPBF forming SS316L is extremely complex and usually involves drastic multi-scale physicochemical changes that will only take place on a very small scale. Existing literature employs DEM-based mesoscopic-scale numerical simulations to investigate the effects of process parameters on the molten pool dynamics of LPBF-formed SS316L. However, a few studies have been reported on the key mechanisms of heating and solidification, spatter, and convective behavior of the molten pool of HP-LPBF-formed SS316L with small laser focal spot diameters. In this paper, the geometrical properties of coarse and fine powder particles under three-dimensional conditions were first calculated using DEM. Then, numerical simulation models for single-track and double-track cases in the single-layer HP-LPBF forming SS316L process were developed at mesoscopic scale using the CFD method. The flow genesis of the melt in the single-track and double-track molten pools is discussed, and their 3D morphology and dimensional characteristics are discussed. In addition, the effects of laser process parameters, powder particle size, and laser focal spot diameter on the temperature field, characterization information, and defects in the molten pool are discussed.

II. MODELING

A. 3D powder bed modeling

HP-LPBF is an advanced processing technique for preparing target parts layer by layer stacking, the process of which involves repetitive spreading and melting of powders. In this process, both the powder spreading and the morphology of the powder bed are closely related to the results of the subsequent melting process, while the melted surface also affects the uniform distribution of the next layer of powder. For this reason, this chapter focuses on the modeling of the physical action during the powder spreading process and the theory of DEM to establish the numerical model of the powder bed, so as to lay a solid foundation for the accuracy of volume of fluid (VOF) and CFD.

1. DEM

DEM is a numerical technique for calculating the interaction of a large number of particles, which calculates the forces and motions of the spheres by considering each powder sphere as an independent unit. The motion of the powder particles follows the laws of classical Newtonian mechanics, including translational and rotational,^38,68–70 which are expressed as follows:��¨=��+∑��ij,

(1)��¨=∑�(�ij×�ij),

(2)

where �� is the mass of unit particle i in kg, ��¨ is the advective acceleration in m/s², And g is the gravitational acceleration in m/s². �ij is the force in contact with the neighboring particle � in N. �� is the rotational inertia of the unit particle � in kg · m². ��¨ is the unit particle � angular acceleration in rad/s². �ij is the vector pointing from unit particle � to the contact point of neighboring particle �⁠.

Equations (1) and (2) can be used to calculate the velocity and angular velocity variations of powder particles to determine their positions and velocities. A three-dimensional powder bed model of SS316L was developed using DEM. The powder particles are assumed to be perfect spheres, and the substrate and walls are assumed to be rigid. To describe the contact between the powder particles and between the particles and the substrate, a non-slip Hertz–Mindlin nonlinear spring-damping model⁷¹ was used with the following expression:�hz=��+��[(��ij−�eff��)−(��+�eff��)],

(3)

where �hz is the force calculated using the Hertzian in M. �� and �� are the radius of unit particles � and � in m, respectively. �� is the overlap size of the two powder particles in m. ��⁠, �� are the elastic constants in the normal and tangential directions, respectively. �ij is the unit vector connecting the centerlines of the two powder particles. �eff is the effective mass of the two powder particles in kg. �� and �� are the viscoelastic damping constants in the normal and tangential directions, respectively. �� and �� are the components of the relative velocities of the two powder particles. �� is the displacement vector between two spherical particles. The schematic diagram of overlapping powder particles is shown in Fig. 1.

FIG. 1.

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Schematic diagram of overlapping powder particles.

Because the particle size of the powder used for HP-LPBF is much smaller than 100 μm, the effect of van der Waals forces must be considered. Therefore, the cohesive force �jkr of the Hertz–Mindlin model was used instead of van der Waals forces,⁷² with the following expression:�jkr=−4��0�*�1.5+4�*3�*�3,

(4)1�*=(1−��2)��+(1−��2)��,

(5)1�*=1��+1��,

(6)

where �* is the equivalent Young’s modulus in GPa; �* is the equivalent particle radius in m; �0 is the surface energy of the powder particles in J/m²; α is the contact radius in m; �� and �� are the Young’s modulus of the unit particles � and �⁠, respectively, in GPa; and �� and �� are the Poisson’s ratio of the unit particles � and �⁠, respectively.

2. Model building

Figure 2 shows a 3D powder bed model generated using DEM with a coarse powder geometry of 1000 × 400 × 30 μm³. The powder layer thickness is 30 μm, and the powder bed porosity is 40%. The average particle size of this spherical powder is 31.7 μm and is normally distributed in the range of 15–53 μm. The geometry of the fine powder was 1000 × 400 × 20 μm³, with a layer thickness of 20 μm, and the powder bed porosity of 40%. The average particle size of this spherical powder is 11.5 μm and is normally distributed in the range of 5–25 μm. After the 3D powder bed model is generated, it needs to be imported into the CFD simulation software for calculation, and the imported geometric model is shown in Fig. 3. This geometric model is mainly composed of three parts: protective gas, powder bed, and substrate. Under the premise of ensuring the accuracy of the calculation, the mesh size is set to 3 μm, and the total number of coarse powder meshes is 1 704 940. The total number of fine powder meshes is 3 982 250.

FIG. 2.

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Three-dimensional powder bed model: (a) coarse powder, (b) fine powder.

FIG. 3.

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Geometric modeling of the powder bed computational domain: (a) coarse powder, (b) fine powder.

B. Modeling of fluid mechanics simulation

In order to solve the flow, melting, and solidification problems involved in HP-LPBF molten pool, the study must follow the three governing equations of conservation of mass, conservation of energy, and conservation of momentum.⁷³ The VOF method, which is the most widely used in fluid dynamics, is used to solve the molten pool dynamics model.

1. VOF

VOF is a method for tracking the free interface between the gas and liquid phases on the molten pool surface. The core idea of the method is to define a volume fraction function F within each grid, indicating the proportion of the grid space occupied by the material, 0 ≤ F ≤ 1 in Fig. 4. Specifically, when F = 0, the grid is empty and belongs to the gas-phase region; when F = 1, the grid is completely filled with material and belongs to the liquid-phase region; and when 0 < F < 1, the grid contains free surfaces and belongs to the mixed region. The direction normal to the free surface is the direction of the fastest change in the volume fraction F (the direction of the gradient of the volume fraction), and the direction of the gradient of the volume fraction can be calculated from the values of the volume fractions in the neighboring grids.⁷⁴ The equations controlling the VOF are expressed as follows:𝛻��+�⋅(��→)=0,

(7)

where t is the time in s and �→ is the liquid velocity in m/s.

FIG. 4.

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Schematic diagram of VOF.

The material parameters of the mixing zone are altered due to the inclusion of both the gas and liquid phases. Therefore, in order to represent the density of the mixing zone, the average density �¯ is used, which is expressed as follows:⁷²�¯=(1−�1)�gas+�1�metal,

(8)

where �1 is the proportion of liquid phase, �gas is the density of protective gas in kg/m³, and �metal is the density of metal in kg/m³.

2. Control equations and boundary conditions

Figure 5 is a schematic diagram of the HP-LPBF melting process. First, the laser light strikes a localized area of the material and rapidly heats up the area. Next, the energy absorbed in the region is diffused through a variety of pathways (heat conduction, heat convection, and surface radiation), and this process triggers complex phase transition phenomena (melting, evaporation, and solidification). In metals undergoing melting, the driving forces include surface tension and the Marangoni effect, recoil due to evaporation, and buoyancy due to gravity and uneven density. The above physical phenomena interact with each other and do not occur independently.

FIG. 5.

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Schematic diagram of HP-LPBF melting process.

Laser heat sourceThe Gaussian surface heat source model is used as the laser heat source model with the following expression:�=2�0��2exp(−2�12��2),(9)where � is the heat flow density in W/m², �0 is the absorption rate of SS316L, �� is the radius of the laser focal spot in m, and �1 is the radial distance from the center of the laser focal spot in m. The laser focal spot can be used for a wide range of applications.
Energy absorptionThe formula for calculating the laser absorption �0 of SS316L is as follows:�0=0.365(�0[1+�0(�−20)]/�)0.5,(10)where �0 is the direct current resistivity of SS316L at 20 °C in Ω m, �0 is the resistance temperature coefficient in ppm/°C, � is the temperature in °C, and � is the laser wavelength in m.
Heat transferThe basic principle of heat transfer is conservation of energy, which is expressed as follows:𝛻𝛻𝛻�(��)��+�·(��→�)=�·(�0��)+��,(11)where � is the density of liquid phase SS316L in kg/m³, �� is the specific heat capacity of SS316L in J/(kg K), 𝛻� is the gradient operator, t is the time in s, T is the temperature in K, 𝛻�� is the temperature gradient, �→ is the velocity vector, �0 is the coefficient of thermal conduction of SS316L in W/(m K), and �� is the thermal energy dissipation term in the molten pool.
Molten pool flowThe following three conditions need to be satisfied for the molten pool to flow:
- Conservation of mass with the following expression:𝛻�·(��→)=0.(12)
- Conservation of momentum (Navier–Stokes equation) with the following expression:𝛻𝛻𝛻𝛻��→��+�(�→·�)�→=�·[−pI+�(��→+(��→)�)]+�,(13)where � is the pressure in Pa exerted on the liquid phase SS316L microelement, � is the unit matrix, � is the fluid viscosity in N s/m², and � is the volumetric force (gravity, atmospheric pressure, surface tension, vapor recoil, and the Marangoni effect).
- Conservation of energy, see Eq. (11)
Surface tension and the Marangoni effectThe effect of temperature on the surface tension coefficient is considered and set as a linear relationship with the following expression:�=�0−��dT(�−��),(14)where � is the surface tension of the molten pool at temperature T in N/m, �� is the melting temperature of SS316L in K, �0 is the surface tension of the molten pool at temperature �� in Pa, and σdσ/ dT is the surface tension temperature coefficient in N/(m K).In general, surface tension decreases with increasing temperature. A temperature gradient causes a gradient in surface tension that drives the liquid to flow, known as the Marangoni effect.
Metal vapor recoilAt higher input energy densities, the maximum temperature of the molten pool surface reaches the evaporation temperature of the material, and a gasification recoil pressure occurs vertically downward toward the molten pool surface, which will be the dominant driving force for the molten pool flow.⁷⁵ The expression is as follows:��=0.54�� exp ��−��0��,(15)where �� is the gasification recoil pressure in Pa, �� is the ambient pressure in kPa, �� is the latent heat of evaporation in J/kg, �0 is the gas constant in J/(mol K), T is the surface temperature of the molten pool in K, and T_e is the evaporation temperature in K.
Solid–liquid–gas phase transitionWhen the laser hits the powder layer, the powder goes through three stages: heating, melting, and solidification. During the solidification phase, mutual transformations between solid, liquid, and gaseous states occur. At this point, the latent heat of phase transition absorbed or released during the phase transition needs to be considered.⁶⁸ The phase transition is represented based on the relationship between energy and temperature with the following expression:�=��,(�<��),�(��)+�−��−��,(��<�<��)�(��)+(�−��)��,(��<�),,(16)where �� and �� are solid and liquid phase density, respectively, of SS316L in kg/m³. �� and �� unit volume of solid and liquid phase-specific heat capacity, respectively, of SS316L in J/(kg K). �� and ��⁠, respectively, are the solidification temperature and melting temperature of SS316L in K. �� is the latent heat of the phase transition of SS316L melting in J/kg.

3. Assumptions

The CFD model was computed using the commercial software package FLOW-3D.⁷⁶ In order to simplify the calculation and solution process while ensuring the accuracy of the results, the model makes the following assumptions:

It is assumed that the effects of thermal stress and material solid-phase thermal expansion on the calculation results are negligible.
The molten pool flow is assumed to be a Newtonian incompressible laminar flow, while the effects of liquid thermal expansion and density on the results are neglected.
It is assumed that the surface tension can be simplified to an equivalent pressure acting on the free surface of the molten pool, and the effect of chemical composition on the results is negligible.
Neglecting the effect of the gas flow field on the molten pool.
The mass loss due to evaporation of the liquid metal is not considered.
The influence of the plasma effect of the molten metal on the calculation results is neglected.

It is worth noting that the formulation of assumptions requires a trade-off between accuracy and computational efficiency. In the above models, some physical phenomena that have a small effect or high difficulty on the calculation results are simplified or ignored. Such simplifications make numerical simulations more efficient and computationally tractable, while still yielding accurate results.

4. Initial conditions

The preheating temperature of the substrate was set to 393 K, at which time all materials were in the solid state and the flow rate was zero.

5. Material parameters

The material used is SS316L and the relevant parameters required for numerical simulations are shown in Table I.^46,77,78

TABLE I.

SS316L-related parameters.

Property	Symbol	Value
Density of solid metal (kg/m³)	�metal	7980
Solid phase line temperature (K)	��	1658
Liquid phase line temperature (K)	��	1723
Vaporization temperature (K)	��	3090
Latent heat of melting (⁠ J/kg⁠)	��	2.60×105
Latent heat of evaporation (⁠ J/kg⁠)	��	7.45×106
Surface tension of liquid phase (N /m⁠)	�	1.60
Liquid metal viscosity (kg/m s)	��	6×10−3
Gaseous metal viscosity (kg/m s)	�gas	1.85×10−5
Temperature coefficient of surface tension (N/m K)	��/�T	0.80×10−3
Molar mass (⁠ kg/mol⁠)	M	0.05 593
Emissivity	�	0.26
Laser absorption	�0	0.35
Ambient pressure (kPa)	��	101 325
Ambient temperature (K)	�0	300
Stefan–Boltzmann constant (W/m² K⁴)	�	5.67×10−8
Thermal conductivity of metals (⁠ W/m K⁠)	�	24.55
Density of protective gas (kg/m³)	�gas	1.25
Coefficient of thermal expansion (/K)	��	16×10−6
Generalized gas constant (⁠ J/mol K⁠)	R	8.314

III. RESULTS AND DISCUSSION

With the objective of studying in depth the evolutionary patterns of single-track and double-track molten pool development, detailed observations were made for certain specific locations in the model, as shown in Fig. 6. In this figure, P1 and P2 represent the longitudinal tangents to the centers of the two melt tracks in the XZ plane, while L1 is the transverse profile in the YZ plane. The scanning direction is positive and negative along the X axis. Points A and B are the locations of the centers of the molten pool of the first and second melt tracks, respectively (x = 1.995 × 10⁻⁴, y = 5 × 10⁻⁷, and z = −4.85 × 10⁻⁵).

FIG. 6.

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Schematic diagram of observation position.

A. Single-track simulation

A series of single-track molten pool simulation experiments were carried out in order to investigate the influence law of laser power as well as scanning speed on the HP-LPBF process. Figure 7 demonstrates the evolution of the 3D morphology and temperature field of the single-track molten pool in the time period of 50–500 μs under a laser power of 100 W and a scanning speed of 800 mm/s. The powder bed is in the natural cooling state. When t = 50 μs, the powder is heated by the laser heat and rapidly melts and settles to form the initial molten pool. This process is accompanied by partial melting of the substrate and solidification together with the melted powder. The molten pool rapidly expands with increasing width, depth, length, and temperature, as shown in Fig. 7(a). When t = 150 μs, the molten pool expands more obviously, and the temperature starts to transfer to the surrounding area, forming a heat-affected zone. At this point, the width of the molten pool tends to stabilize, and the temperature in the center of the molten pool has reached its peak and remains largely stable. However, the phenomenon of molten pool spatter was also observed in this process, as shown in Fig. 7(b). As time advances, when t = 300 μs, solidification begins to occur at the tail of the molten pool, and tiny ripples are produced on the solidified surface. This is due to the fact that the melt flows toward the region with large temperature gradient under the influence of Marangoni convection and solidifies together with the melt at the end of the bath. At this point, the temperature gradient at the front of the bath is significantly larger than at the end. While the width of the molten pool was gradually reduced, the shape of the molten pool was gradually changed to a “comet” shape. In addition, a slight depression was observed at the top of the bath because the peak temperature at the surface of the bath reached the evaporation temperature, which resulted in a recoil pressure perpendicular to the surface of the bath downward, creating a depressed region. As the laser focal spot moves and is paired with the Marangoni convection of the melt, these recessed areas will be filled in as shown in Fig. 7(c). It has been shown that the depressed regions are the result of the coupled effect of Marangoni convection, recoil pressure, and surface tension.⁷⁹ By t = 500 μs, the width and height of the molten pool stabilize and show a “comet” shape in Fig. 7(d).

FIG. 7.

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Single-track molten pool process: (a) t = 50 ��⁠, (b) t = 150 ��⁠, (c) t = 300 ��⁠, (d) t = 500 ��⁠.

Figure 8 depicts the velocity vector diagram of the P1 profile in a single-track molten pool, the length of the arrows represents the magnitude of the velocity, and the maximum velocity is about 2.36 m/s. When t = 50 μs, the molten pool takes shape, and the velocities at the two ends of the pool are the largest. The variation of the velocities at the front end is especially more significant in Fig. 8(a). As the time advances to t = 150 μs, the molten pool expands rapidly, in which the velocity at the tail increases and changes more significantly, while the velocity at the front is relatively small. At this stage, the melt moves backward from the center of the molten pool, which in turn expands the molten pool area. The melt at the back end of the molten pool center flows backward along the edge of the molten pool surface and then converges along the edge of the molten pool to the bottom center, rising to form a closed loop. Similarly, a similar closed loop is formed at the front end of the center of the bath, but with a shorter path. However, a large portion of the melt in the center of the closed loop formed at the front end of the bath is in a nearly stationary state. The main cause of this melt flow phenomenon is the effect of temperature gradient and surface tension (the Marangoni effect), as shown in Figs. 8(b) and 8(e). This dynamic behavior of the melt tends to form an “elliptical” pool. At t = 300 μs, the tendency of the above two melt flows to close the loop is more prominent and faster in Fig. 8(c). When t = 500 μs, the velocity vector of the molten pool shows a stable trend, and the closed loop of melt flow also remains stable. With the gradual laser focal spot movement, the melt is gradually solidified at its tail, and finally, a continuous and stable single track is formed in Fig. 8(d).

FIG. 8.

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Vector plot of single-track molten pool velocity in XZ longitudinal section: (a) t = 50 ��⁠, (b) t = 150 ��⁠, (c) t = 300 ��⁠, (d) t = 500 ��⁠, (e) molten pool flow.

In order to explore in depth the transient evolution of the molten pool, the evolution of the single-track temperature field and the melt flow was monitored in the YZ cross section. Figure 9(a) shows the state of the powder bed at the initial moment. When t = 250 μs, the laser focal spot acts on the powder bed and the powder starts to melt and gradually collects in the molten pool. At this time, the substrate will also start to melt, and the melt flow mainly moves in the downward and outward directions and the velocity is maximum at the edges in Fig. 9(b). When t = 300 μs, the width and depth of the molten pool increase due to the recoil pressure. At this time, the melt flows more slowly at the center, but the direction of motion is still downward in Fig. 9(c). When t = 350 μs, the width and depth of the molten pool further increase, at which time the intensity of the melt flow reaches its peak and the direction of motion remains the same in Fig. 9(d). When t = 400 μs, the melt starts to move upward, and the surrounding powder or molten material gradually fills up, causing the surface of the molten pool to begin to flatten. At this time, the maximum velocity of the melt is at the center of the bath, while the velocity at the edge is close to zero, and the edge of the melt starts to solidify in Fig. 9(e). When t = 450 μs, the melt continues to move upward, forming a convex surface of the melt track. However, the melt movement slows down, as shown in Fig. 9(f). When t = 500 μs, the melt further moves upward and its speed gradually becomes smaller. At the same time, the melt solidifies further, as shown in Fig. 9(g). When t = 550 μs, the melt track is basically formed into a single track with a similar “mountain” shape. At this stage, the velocity is close to zero only at the center of the molten pool, and the flow behavior of the melt is poor in Fig. 9(h). At t = 600 μs, the melt stops moving and solidification is rapidly completed. Up to this point, a single track is formed in Fig. 9(i). During the laser action on the powder bed, the substrate melts and combines with the molten state powder. The powder-to-powder fusion is like the convergence of water droplets, which are rapidly fused by surface tension. However, the fusion between the molten state powder and the substrate occurs driven by surface tension, and the molten powder around the molten pool is pulled toward the substrate (a wetting effect occurs), which ultimately results in the formation of a monolithic whole.^38,80,81

FIG. 9.

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Evolution of single-track molten pool temperature and melt flow in the YZ cross section: (a) t = 0 ��⁠, (b) t = 250 ��⁠, (c) t = 300 ��⁠, (d) t = 350 ��⁠, (e) t = 400 ��⁠, (f) t = 450 ��⁠, (g) t = 500 ��⁠, (h) t = 550 ��⁠, (i) t = 600 ��⁠.

The wetting ability between the liquid metal and the solid substrate in the molten pool directly affects the degree of balling of the melt,^82,83 and the wetting ability can be measured by the contact angle of a single track in Fig. 10. A smaller value of contact angle represents better wettability. The contact angle α can be calculated by�=�1−�22,

(17)

where �1 and �2 are the contact angles of the left and right regions, respectively.

FIG. 10.

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Schematic of contact angle.

Relevant studies have confirmed that the wettability is better at a contact angle α around or below 40°.⁸⁴ After measurement, a single-track contact angle α of about 33° was obtained under this process parameter, which further confirms the good wettability.

B. Double-track simulation

In order to deeply investigate the influence of hatch spacing on the characteristics of the HP-LPBF process, a series of double-track molten pool simulation experiments were systematically carried out. Figure 11 shows in detail the dynamic changes of the 3D morphology and temperature field of the double-track molten pool in the time period of 2050–2500 μs under the conditions of laser power of 100 W, scanning speed of 800 mm/s, and hatch spacing of 0.06 mm. By comparing the study with Fig. 7, it is observed that the basic characteristics of the 3D morphology and temperature field of the second track are similar to those of the first track. However, there are subtle differences between them. The first track exhibits a basically symmetric shape, but the second track morphology shows a slight deviation influenced by the difference in thermal diffusion rate between the solidified metal and the powder. Otherwise, the other characteristic information is almost the same as that of the first track. Figure 12 shows the velocity vector plot of the P2 profile in the double-track molten pool, with a maximum velocity of about 2.63 m/s. The melt dynamics at both ends of the pool are more stable at t = 2050 μs, where the maximum rate of the second track is only 1/3 of that of the first one. Other than that, the rest of the information is almost no significant difference from the characteristic information of the first track. Figure 13 demonstrates a detailed observation of the double-track temperature field and melts flow in the YZ cross section, and a comparative study with Fig. 9 reveals that the width of the second track is slightly wider. In addition, after the melt direction shifts from bottom to top, the first track undergoes four time periods (50 μs) to reach full solidification, while the second track takes five time periods. This is due to the presence of significant heat buildup in the powder bed after the forming of the first track, resulting in a longer dynamic time of the melt and an increased molten pool lifetime. In conclusion, the level of specimen forming can be significantly optimized by adjusting the laser power and hatch spacing.

FIG. 11.

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Double-track molten pool process: (a) t = 2050 ��⁠, (b) t = 2150 ��⁠, (c) t = 2300 ��⁠, (d) t = 2500 ��⁠.

FIG. 12.

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Vector plot of double-track molten pool velocity in XZ longitudinal section: (a) t = 2050 ��⁠, (b) t = 2150 ��⁠, (c) t = 2300 ��⁠, (d) t = 2500 ��⁠.

FIG. 13.

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Evolution of double-track molten pool temperature and melt flow in the YZ cross section: (a) t = 2250 ��⁠, (b) t = 2300 ��⁠, (c) t = 2350 ��⁠, (d) t = 2400 ��⁠, (e) t = 2450 ��⁠, (f) t = 2500 ��⁠, (g) t = 2550 ��⁠, (h) t = 2600 ��⁠, (i) t = 2650 ��⁠.

In order to quantitatively detect the molten pool dimensions as well as the remolten region dimensions, the molten pool characterization information in Fig. 14 is constructed by drawing the boundary on the YZ cross section based on the isothermal surface of the liquid phase line. It can be observed that the heights of the first track and second track are basically the same, but the depth of the second track increases relative to the first track. The molten pool width is mainly positively correlated with the laser power as well as the scanning speed (the laser line energy density �⁠). However, the remelted zone width is negatively correlated with the hatch spacing (the overlapping ratio). Overall, the forming quality of the specimens can be directly influenced by adjusting the laser power, scanning speed, and hatch spacing.

FIG. 14.

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Double-track molten pool characterization information on YZ cross section.

In order to study the variation rule of the temperature in the center of the molten pool with time, Fig. 15 demonstrates the temperature variation curves with time for two reference points, A and B. Among them, the red dotted line indicates the liquid phase line temperature of SS316L. From the figure, it can be seen that the maximum temperature at the center of the molten pool in the first track is lower than that in the second track, which is mainly due to the heat accumulation generated after passing through the first track. The maximum temperature gradient was calculated to be 1.69 × 10⁸ K/s. When the laser scanned the first track, the temperature in the center of the molten pool of the second track increased slightly. Similarly, when the laser scanned the second track, a similar situation existed in the first track. Since the temperature gradient in the second track is larger than that in the first track, the residence time of the liquid phase in the molten pool of the first track is longer than that of the second track.

FIG. 15.

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Temperature profiles as a function of time for two reference points A and B.

C. Simulation analysis of molten pool under different process parameters

In order to deeply investigate the effects of various process parameters on the mesoscopic-scale temperature field, molten pool characteristic information and defects of HP-LPBF, numerical simulation experiments on mesoscopic-scale laser power, scanning speed, and hatch spacing of double-track molten pools were carried out.

1. Laser power

Figure 16 shows the effects of different laser power on the morphology and temperature field of the double-track molten pool at a scanning speed of 800 mm/s and a hatch spacing of 0.06 mm. When P = 50 W, a smaller molten pool is formed due to the lower heat generated by the Gaussian light source per unit time. This leads to a smaller track width, which results in adjacent track not lapping properly and the presence of a large number of unmelted powder particles, resulting in an increase in the number of defects, such as pores in the specimen. The surface of the track is relatively flat, and the depth is small. In addition, the temperature gradient before and after the molten pool was large, and the depression location appeared at the biased front end in Fig. 16(a). When P = 100 W, the surface of the track is flat and smooth with excellent lap. Due to the Marangoni effect, the velocity field of the molten pool is in the form of “vortex,” and the melt has good fluidity, and the maximum velocity reaches 2.15 m/s in Fig. 16(b). When P = 200 W, the heat generated by the Gaussian light source per unit time is too large, resulting in the melt rapidly reaching the evaporation temperature, generating a huge recoil pressure, forming a large molten pool, and the surface of the track is obviously raised. The melt movement is intense, especially the closed loop at the center end of the molten pool. At this time, the depth and width of the molten pool are large, leading to the expansion of the remolten region and the increased chance of the appearance of porosity defects in Fig. 16(c). The results show that at low laser power, the surface tension in the molten pool is dominant. At high laser power, recoil pressure is its main role.

FIG. 16.

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Simulation results of double-track molten pool under different laser powers: (a) P = 50 W, (b) P = 100 W, (c) P = 200 W.

Table II shows the effect of different laser powers on the characteristic information of the double-track molten pool at a scanning speed of 800 mm/s and a hatch spacing of 0.06 mm. The negative overlapping ratio in the table indicates that the melt tracks are not lapped, and 26/29 indicates the melt depth of the first track/second track. It can be seen that with the increase in laser power, the melt depth, melt width, melt height, and remelted zone show a gradual increase. At the same time, the overlapping ratio also increases. Especially in the process of laser power from 50 to 200 W, the melting depth and melting width increased the most, which increased nearly 2 and 1.5 times, respectively. Meanwhile, the overlapping ratio also increases with the increase in laser power, which indicates that the melting and fusion of materials are better at high laser power. On the other hand, the dimensions of the molten pool did not change uniformly with the change of laser power. Specifically, the depth-to-width ratio of the molten pool increased from about 0.30 to 0.39 during the increase from 50 to 120 W, which further indicates that the effective heat transfer in the vertical direction is greater than that in the horizontal direction with the increase in laser power. This dimensional response to laser power is mainly affected by the recoil pressure and also by the difference in the densification degree between the powder layer and the metal substrate. In addition, according to the experimental results, the contact angle shows a tendency to increase and then decrease during the process of laser power increase, and always stays within the range of less than 33°. Therefore, in practical applications, it is necessary to select the appropriate laser power according to the specific needs in order to achieve the best processing results.

TABLE II.

Double-track molten pool characterization information at different laser powers.

Laser power (W)	Depth (μm)	Width (μm)	Height (μm)	Remolten region (μm)	Overlapping ratio (%)	Contact angle (°)
50	16	54	11	/	−10	23
100	26/29	74	14	18	23.33	33
200	37/45	116	21	52	93.33	28

2. Scanning speed

Figure 17 demonstrates the effect of different scanning speeds on the morphology and temperature field of the double-track molten pool at a laser power of 100 W and a hatch spacing of 0.06 mm. With the gradual increase in scanning speed, the surface morphology of the molten pool evolves from circular to elliptical. When � = 200 mm/s, the slow scanning speed causes the material to absorb too much heat, which is very easy to trigger the overburning phenomenon. At this point, the molten pool is larger and the surface morphology is uneven. This situation is consistent with the previously discussed scenario with high laser power in Fig. 17(a). However, when � = 1600 mm/s, the scanning speed is too fast, resulting in the material not being able to absorb sufficient heat, which triggers the powder particles that fail to melt completely to have a direct effect on the bonding of the melt to the substrate. At this time, the molten pool volume is relatively small and the neighboring melt track cannot lap properly. This result is consistent with the previously discussed case of low laser power in Fig. 17(b). Overall, the ratio of the laser power to the scanning speed (the line energy density �⁠) has a direct effect on the temperature field and surface morphology of the molten pool.

FIG. 17.

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Simulation results of double-track molten pool under different scanning speed: (a) � = 200 mm/s, (b) � = 1600 mm/s.

Table III shows the effects of different scanning speed on the characteristic information of the double-track molten pool under the condition of laser power of 100 W and hatch spacing of 0.06 mm. It can be seen that the scanning speed has a significant effect on the melt depth, melt width, melt height, remolten region, and overlapping ratio. With the increase in scanning speed, the melt depth, melt width, melt height, remelted zone, and overlapping ratio show a gradual decreasing trend. Among them, the melt depth and melt width decreased faster, while the melt height and remolten region decreased relatively slowly. In addition, when the scanning speed was increased from 200 to 800 mm/s, the decreasing speeds of melt depth and melt width were significantly accelerated, while the decreasing speeds of overlapping ratio were relatively slow. When the scanning speed was further increased to 1600 mm/s, the decreasing speeds of melt depth and melt width were further accelerated, and the un-lapped condition of the melt channel also appeared. In addition, the contact angle increases and then decreases with the scanning speed, and both are lower than 33°. Therefore, when selecting the scanning speed, it is necessary to make reasonable trade-offs according to the specific situation, and take into account the factors of melt depth, melt width, melt height, remolten region, and overlapping ratio, in order to achieve the best processing results.

TABLE III.

Double-track molten pool characterization information at different scanning speeds.

Scanning speed (mm/s)	Depth (μm)	Width (μm)	Height (μm)	Remolten region (μm)	Overlapping ratio (%)	Contact angle (°)
200	55/68	182	19/32	124	203.33	22
1600	13	50	11	/	−16.67	31

3. Hatch spacing

Figure 18 shows the effect of different hatch spacing on the morphology and temperature field of the double-track molten pool under the condition of laser power of 100 W and scanning speed of 800 mm/s. The surface morphology and temperature field of the first track and second track are basically the same, but slightly different. The first track shows a basically symmetric morphology along the scanning direction, while the second track shows a slight offset due to the difference in the heat transfer rate between the solidified material and the powder particles. When the hatch spacing is too small, the overlapping ratio increases and the probability of defects caused by remelting phenomenon grows. When the hatch spacing is too large, the neighboring melt track cannot overlap properly, and the powder particles are not completely melted, leading to an increase in the number of holes. In conclusion, the ratio of the line energy density � to the hatch spacing (the volume energy density E) has a significant effect on the temperature field and surface morphology of the molten pool.

FIG. 18.

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Simulation results of double-track molten pool under different hatch spacings: (a) H = 0.03 mm, (b) H = 0.12 mm.

Table IV shows the effects of different hatch spacing on the characteristic information of the double-track molten pool under the condition of laser power of 100 W and scanning speed of 800 mm/s. It can be seen that the hatch spacing has little effect on the melt depth, melt width, and melt height, but has some effect on the remolten region. With the gradual expansion of hatch spacing, the remolten region shows a gradual decrease. At the same time, the overlapping ratio also decreased with the increase in hatch spacing. In addition, it is observed that the contact angle shows a tendency to increase and then remain stable when the hatch spacing increases, which has a more limited effect on it. Therefore, trade-offs and decisions need to be made on a case-by-case basis when selecting the hatch spacing.

TABLE IV.

Double-track molten pool characterization information at different hatch spacings.

Hatch spacing (mm)	Depth (μm)	Width (μm)	Height (μm)	Remolten region (μm)	Overlapping ratio (%)	Contact angle (°)
0.03	25/27	82	14	59	173.33	30
0.12	26	78	14	/	−35	33

In summary, the laser power, scanning speed, and hatch spacing have a significant effect on the formation of the molten pool, and the correct selection of these three process parameters is crucial to ensure the forming quality. In addition, the melt depth of the second track is slightly larger than that of the first track at higher line energy density � and volume energy density E. This is mainly due to the fact that a large amount of heat accumulation is generated after the first track, forming a larger molten pool volume, which leads to an increase in the melt depth.

D. Simulation analysis of molten pool with powder particle size and laser focal spot diameter

Figure 19 demonstrates the effect of different powder particle sizes and laser focal spot diameters on the morphology and temperature field of the double-track molten pool under a laser power of 100 W, a scanning speed of 800 mm/s, and a hatch spacing of 0.06 mm. In the process of melting coarse powder with small laser focal spot diameter, the laser energy cannot completely melt the larger powder particles, resulting in their partial melting and further generating excessive pore defects. The larger powder particles tend to generate zigzag molten pool edges, which cause an increase in the roughness of the melt track surface. In addition, the molten pool is also prone to generate the present spatter phenomenon, which can directly affect the quality of forming. The volume of the formed molten pool is relatively small, while the melt depth, melt width, and melt height are all smaller relative to the fine powder in Fig. 19(a). In the process of melting fine powders with a large laser focal spot diameter, the laser energy is able to melt the fine powder particles sufficiently, even to the point of overmelting. This results in a large number of fine spatters being generated at the edge of the molten pool, which causes porosity defects in the melt track in Fig. 19(b). In addition, the maximum velocity of the molten pool is larger for large powder particle sizes compared to small powder particle sizes, which indicates that the temperature gradient in the molten pool is larger for large powder particle sizes and the melt motion is more intense. However, the size of the laser focal spot diameter has a relatively small effect on the melt motion. However, a larger focal spot diameter induces a larger melt volume with greater depth, width, and height. In conclusion, a small powder size helps to reduce the surface roughness of the specimen, and a small laser spot diameter reduces the minimum forming size of a single track.

FIG. 19.

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Simulation results of double-track molten pool with different powder particle size and laser focal spot diameter: (a) focal spot = 25 μm, coarse powder, (b) focal spot = 80 μm, fine powder.

Table V shows the maximum temperature gradient at the reference point for different powder sizes and laser focal spot diameters. As can be seen from the table, the maximum temperature gradient is lower than that of HP-LPBF for both coarse powders with a small laser spot diameter and fine powders with a large spot diameter, a phenomenon that leads to an increase in the heat transfer rate of HP-LPBF, which in turn leads to a corresponding increase in the cooling rate and, ultimately, to the formation of finer microstructures.

TABLE V.

Maximum temperature gradient at the reference point for different powder particle sizes and laser focal spot diameters.

Laser power (W)	Scanning speed (mm/s)	Hatch spacing (mm)	Average powder size (μm)	Laser focal spot diameter (μm)	Maximum temperature gradient (×10⁷ K/s)
100	800	0.06	31.7	25	7.89
100	800	0.06	11.5	80	7.11

IV. CONCLUSIONS

In this study, the geometrical characteristics of 3D coarse and fine powder particles were first calculated using DEM and then numerical simulations of single track and double track in the process of forming SS316L from monolayer HP-LPBF at mesoscopic scale were developed using CFD method. The effects of Marangoni convection, surface tension, recoil pressure, gravity, thermal convection, thermal radiation, and evaporative heat dissipation on the heat and mass transfer in the molten pool were considered in this model. The effects of laser power, scanning speed, and hatch spacing on the dynamics of the single-track and double-track molten pools, as well as on other characteristic information, were investigated. The effects of the powder particle size on the molten pool were investigated comparatively with the laser focal spot diameter. The main conclusions are as follows:

The results show that the temperature gradient at the front of the molten pool is significantly larger than that at the tail, and the molten pool exhibits a “comet” morphology. At the top of the molten pool, there is a slightly concave region, which is the result of the coupling of Marangoni convection, recoil pressure, and surface tension. The melt flow forms two closed loops, which are mainly influenced by temperature gradients and surface tension. This special dynamic behavior of the melt tends to form an “elliptical” molten pool and an almost “mountain” shape in single-track forming.
The basic characteristics of the three-dimensional morphology and temperature field of the second track are similar to those of the first track, but there are subtle differences. The first track exhibits a basically symmetrical shape; however, due to the difference in thermal diffusion rates between the solidified metal and the powder, a slight asymmetry in the molten pool morphology of the second track occurs. After forming through the first track, there is a significant heat buildup in the powder bed, resulting in a longer dynamic time of the melt, which increases the life of the molten pool. The heights of the first track and second track remained essentially the same, but the depth of the second track was greater relative to the first track. In addition, the maximum temperature gradient was 1.69 × 10⁸ K/s during HP-LPBF forming.
At low laser power, the surface tension in the molten pool plays a dominant role. At high laser power, recoil pressure becomes the main influencing factor. With the increase of laser power, the effective heat transfer in the vertical direction is superior to that in the horizontal direction. With the gradual increase of scanning speed, the surface morphology of the molten pool evolves from circular to elliptical. In addition, the scanning speed has a significant effect on the melt depth, melt width, melt height, remolten region, and overlapping ratio. Too large or too small hatch spacing will lead to remelting or non-lap phenomenon, which in turn causes the formation of defects.
When using a small laser focal spot diameter, it is difficult to completely melt large powder particle sizes, resulting in partial melting and excessive porosity generation. At the same time, large powder particles produce curved edges of the molten pool, resulting in increased surface roughness of the melt track. In addition, spatter occurs, which directly affects the forming quality. At small focal spot diameters, the molten pool volume is relatively small, and the melt depth, the melt width, and the melt height are correspondingly small. Taken together, the small powder particle size helps to reduce surface roughness, while the small spot diameter reduces the forming size.

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원본 바로가기

Figure 14. Defects: (a) Unmelt defects(Scheme NO.4);(b) Pores defects(Scheme NO.1); (c); Spattering defect (Scheme NO.3); (d) Low overlapping rate defects(Scheme NO.5).

Molten pool structure, temperature and velocity
flow in selective laser melting AlCu5MnCdVA alloy

용융 풀 구조, 선택적 온도 및 속도 흐름 레이저 용융 AlCu5MnCdVA 합금

Pan Lu1 , Zhang Cheng-Lin^2,6,Wang Liang³, Liu Tong⁴ and Liu Jiang-lin⁵
¹ Aviation and Materials College, Anhui Technical College of Mechanical and Electrical Engineering, Wuhu Anhui 241000, People’s
Republic of China 2 School of Engineering Science, University of Science and Technology of China, Hefei Anhui 230026, People’s Republic of China 3 Anhui Top Additive Manufacturing Technology Co., Ltd., Wuhu Anhui 241300, People’s Republic of China 4 Anhui Chungu 3D Printing Institute of Intelligent Equipment and Industrial Technology, Anhui 241300, People’s Republic of China 5 School of Mechanical and Transportation Engineering, Taiyuan University of Technology, Taiyuan Shanxi 030024, People’s Republic of
China 6 Author to whom any correspondence should be addressed.
E-mail: ahjdpanlu@126.com, jiao__zg@126.com, ahjdjxx001@126.com,tongliu1988@126.com and liujianglin@tyut.edu.cn

Keywords

SLM, molten pool, AlCu5MnCdVA alloy, heat flow, velocity flow, numerical simulation

Abstract

선택적 레이저 용융(SLM)은 열 전달, 용융, 상전이, 기화 및 물질 전달을 포함하는 복잡한 동적 비평형 프로세스인 금속 적층 제조(MAM)에서 가장 유망한 기술 중 하나가 되었습니다. 용융 풀의 특성(구조, 온도 흐름 및 속도 흐름)은 SLM의 최종 성형 품질에 결정적인 영향을 미칩니다. 이 연구에서는 선택적 레이저 용융 AlCu5MnCdVA 합금의 용융 풀 구조, 온도 흐름 및 속도장을 연구하기 위해 수치 시뮬레이션과 실험을 모두 사용했습니다.

그 결과 용융풀의 구조는 다양한 형태(깊은 오목 구조, 이중 오목 구조, 평면 구조, 돌출 구조 및 이상적인 평면 구조)를 나타냈으며, 용융 풀의 크기는 약 132 μm × 107 μm × 50 μm였습니다. : 용융풀은 초기에는 여러 구동력에 의해 깊이 15μm의 깊은 오목형상이었으나, 성형 후기에는 장력구배에 의해 높이 10μm의 돌출형상이 되었다. 용융 풀 내부의 금속 흐름은 주로 레이저 충격력, 금속 액체 중력, 표면 장력 및 반동 압력에 의해 구동되었습니다.

AlCu5MnCdVA 합금의 경우, 금속 액체 응고 속도가 매우 빠르며(3.5 × 10-4 S), 가열 속도 및 냉각 속도는 각각 6.5 × 107 K S-1 및 1.6 × 106 K S-1 에 도달했습니다. 시각적 표준으로 표면 거칠기를 선택하고, 낮은 레이저 에너지 AlCu5MnCdVA 합금 최적 공정 매개변수 창을 수치 시뮬레이션으로 얻었습니다: 레이저 출력 250W, 부화 공간 0.11mm, 층 두께 0.03mm, 레이저 스캔 속도 1.5m s-1 .

또한, 실험 프린팅과 수치 시뮬레이션과 비교할 때, 용융 풀의 폭은 각각 약 205um 및 약 210um이었고, 인접한 두 용융 트랙 사이의 중첩은 모두 약 65um이었다. 결과는 수치 시뮬레이션 결과가 실험 인쇄 결과와 기본적으로 일치함을 보여 수치 시뮬레이션 모델의 정확성을 입증했습니다.

Selective Laser Melting (SLM) has become one of the most promising technologies in Metal Additive Manufacturing (MAM), which is a complex dynamic non-equilibrium process involving heat transfer, melting, phase transition, vaporization and mass transfer. The characteristics of the molten pool (structure, temperature flow and velocity flow) have a decisive influence on the final forming quality of SLM. In this study, both numerical simulation and experiments were employed to study molten pool structure, temperature flow and velocity field in Selective Laser Melting AlCu5MnCdVA alloy. The results showed the structure of molten pool showed different forms(deep-concave structure, double-concave structure, plane structure, protruding structure and ideal planar structure), and the size of the molten pool was approximately 132 μm × 107 μm × 50 μm: in the early stage, molten pool was in a state of deep-concave shape with a depth of 15 μm due to multiple driving forces, while a protruding shape with a height of 10 μm duo to tension gradient in the later stages of forming. The metal flow inside the molten pool was mainly driven by laser impact force, metal liquid gravity, surface tension and recoil pressure. For AlCu5MnCdVA alloy, metal liquid solidification speed was extremely fast(3.5 × 10−4 S), the heating rate and cooling rate reached 6.5 × 107 K S−1 and 1.6 × 106 K S−1 , respectively. Choosing surface roughness as a visual standard, low-laser energy AlCu5MnCdVA alloy optimum process parameters window was obtained by numerical simulation: laser power 250 W, hatching space 0.11 mm, layer thickness 0.03 mm, laser scanning velocity 1.5 m s−1 . In addition, compared with experimental printing and numerical simulation, the width of the molten pool was about 205 um and about 210 um, respectively, and overlapping between two adjacent molten tracks was all about 65 um. The results showed that the numerical simulation results were basically consistent with the experimental print results, which proved the correctness of the numerical simulation model.

Figure 1. AlCu5MnCdVA powder particle size distribution.

Figure 3. Finite element model and calculation domains of SLM.

Figure 17. Two-pass molten tracks overlapping for Scheme NO.2.

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원본보기

Fig. 8 Distribution of solidification properties on the yz cross section at the maximum width of the melt pool.(a) thermal gradient G, (b) solidification velocity vT, (c) cooling rate G×vT, and (d) morphology factor G/vT. These profiles are calculated with a laser power 300 W and velocity 400 mm/s using (a1 through d1) analytical Rosenthal simulation and (a2 through d2) high-fidelity CFD simulation. The laser is moving out of the page from the upper left corner of each color map (Color figure online)

Quantifying Equiaxed vs Epitaxial Solidification in Laser Melting of CMSX-4 Single Crystal Superalloy

CMSX -4 단결정 초합금의 레이저 용융에서 등축 응고와 에피택셜 응고 정량화

본 논문은 독자의 편의를 위해 기계번역된 내용이어서 자세한 내용은 원문을 참고하시기 바랍니다.

원문보기

Runbo Jiang, Zhongshu Ren, Joseph Aroh, Amir Mostafaei, Benjamin Gould, Tao Sun & Anthony D. Rollett

Abstract

에피택셜 과 등축 응고 사이의 경쟁은 적층 제조에서 실행되는 레이저 용융 동안 CMSX-4 단결정 초합금에서 조사되었습니다. 단일 트랙 레이저 스캔은 레이저 출력과 스캐닝 속도의 여러 조합으로 방향성 응고된 CMSX-4 합금의 분말 없는 표면에서 수행되었습니다. EBSD(Electron Backscattered Diffraction) 매핑은 새로운 방향의 식별을 용이하게 합니다. 영역 분율 및 공간 분포와 함께 융합 영역 내에서 핵을 형성한 “스트레이 그레인”은 충실도가 높은 전산 유체 역학 시뮬레이션을 사용하여 용융 풀 내의 온도 및 유체 속도 필드를 모두 추정했습니다. 이 정보를 핵 생성 모델과 결합하여 용융 풀에서 핵 생성이 발생할 확률이 가장 높은 위치를 결정했습니다. 금속 적층 가공의 일반적인 경험에 따라 레이저 용융 트랙의 응고된 미세 구조는 에피택셜 입자 성장에 의해 지배됩니다. 더 높은 레이저 스캐닝 속도와 더 낮은 출력이 일반적으로 흩어진 입자 감소에 도움이 되지만,그럼에도 불구하고 길쭉한 용융 풀에서 흩어진 입자가 분명했습니다.

The competition between epitaxial vs. equiaxed solidification has been investigated in CMSX-4 single crystal superalloy during laser melting as practiced in additive manufacturing. Single-track laser scans were performed on a powder-free surface of directionally solidified CMSX-4 alloy with several combinations of laser power and scanning velocity. Electron backscattered diffraction (EBSD) mapping facilitated identification of new orientations, i.e., “stray grains” that nucleated within the fusion zone along with their area fraction and spatial distribution. Using high-fidelity computational fluid dynamics simulations, both the temperature and fluid velocity fields within the melt pool were estimated. This information was combined with a nucleation model to determine locations where nucleation has the highest probability to occur in melt pools. In conformance with general experience in metals additive manufacturing, the as-solidified microstructure of the laser-melted tracks is dominated by epitaxial grain growth; nevertheless, stray grains were evident in elongated melt pools. It was found that, though a higher laser scanning velocity and lower power are generally helpful in the reduction of stray grains, the combination of a stable keyhole and minimal fluid velocity further mitigates stray grains in laser single tracks.

Introduction

니켈 기반 초합금은 고온에서 긴 노출 시간 동안 높은 인장 강도, 낮은 산화 및 우수한 크리프 저항성을 포함하는 우수한 특성의 고유한 조합으로 인해 가스 터빈 엔진 응용 분야에서 광범위하게 사용됩니다. CMSX-4는 특히 장기 크리프 거동과 관련하여 초고강도의 2세대 레늄 함유 니켈 기반 단결정 초합금입니다. ^{[ 1 , 2 ]}입계의 존재가 크리프를 가속화한다는 인식은 가스 터빈 엔진의 고온 단계를 위한 단결정 블레이드를 개발하게 하여 작동 온도를 높이고 효율을 높이는 데 기여했습니다. 이러한 구성 요소는 사용 중 마모될 수 있습니다. 즉, 구성 요소의 무결성을 복원하고 단결정 미세 구조를 유지하는 수리 방법을 개발하기 위한 지속적인 작업이 있었습니다. ^{[ 3 , 4 , 5 ]}

적층 제조(AM)가 등장하기 전에는 다양한 용접 공정을 통해 단결정 초합금에 대한 수리 시도가 수행되었습니다. ^{균열 [ 6 , 7 ]} 및 흩어진 입자 ^{[ 8 , 9 ]} 와 같은 심각한 결함 이 이 수리 중에 자주 발생합니다. 일반적으로 “스트레이 그레인”이라고 하는 응고 중 모재의 방향과 다른 결정학적 방향을 가진 새로운 그레인의 형성은 니켈 기반 단결정 초합금의 수리 중 유해한 영향으로 인해 중요한 관심 대상입니다. ^{[ 3 , 10 ]}결과적으로 재료의 단결정 구조가 손실되고 원래 구성 요소에 비해 기계적 특성이 손상됩니다. 이러한 흩어진 입자는 특정 조건에서 에피택셜 성장을 대체하는 등축 응고의 시작에 해당합니다.

떠돌이 결정립 형성을 완화하기 위해 이전 작업은 용융 영역(FZ) 내에서 응고하는 동안 떠돌이 결정립 형성에 영향을 미치는 수지상 응고 거동 및 처리 조건을 이해하는 데 중점을 두었습니다. ^{[ 11 , 12 , 13 , 14 ]} 연구원들은 단결정 합금의 용접 중에 표류 결정립 형성에 대한 몇 가지 가능한 메커니즘을 제안했습니다. ^{[ 12 , 13 , 14 , 15 ]}응고 전단에 앞서 국부적인 구성 과냉각은 이질적인 핵 생성 및 등축 결정립의 성장을 유발할 수 있습니다. 또한 용융 풀에서 활발한 유체 흐름으로 인해 발생하는 덴드라이트 조각화는 용융 풀 경계 근처에서 새로운 결정립을 형성할 수도 있습니다. 두 메커니즘 모두에서, 표류 결정립 형성은 핵 생성 위치에 의존하며, 차이점은 수상 돌기 조각화는 수상 돌기 조각이 핵 생성 위치로 작용한다는 것을 의미하는 반면 다른 메커니즘은 재료, 예 를 들어 산화물 입자에서 발견되는 다른 유형의 핵 생성 위치를 사용한다는 것을 의미합니다. 잘 알려진 바와 같이, 많은 주물에 대한 반대 접근법은 TiB와 같은 핵제의 도입을 통해 등축 응고를 촉진하는 것입니다.22알루미늄 합금에서.

헌법적 과냉 메커니즘에서 Hunt ^[ 11 ] 는 정상 상태 조건에서 기둥에서 등축으로의 전이(CET)를 설명하는 모델을 개발했습니다. Gaumann과 Kurz는 Hunt의 모델을 수정하여 단결정이 응고되는 동안 떠돌이 결정립이 핵을 생성하고 성장할 수 있는 정도를 설명했습니다. ^{[ 12 , 14 ]} 이후 연구에서 Vitek은 Gaumann의 모델을 개선하고 출력 및 스캐닝 속도와 같은 용접 조건의 영향에 대한 보다 자세한 분석을 포함했습니다. Vitek은 또한 실험 및 모델링 기술을 통해 표류 입자 형성에 대한 기판 방향의 영향을 포함했습니다. ^{[ 3 , 10 ]}일반적으로 높은 용접 속도와 낮은 출력은 표류 입자의 양을 최소화하고 레이저 용접 공정 중 에피택셜 단결정 성장을 최대화하는 것으로 나타났습니다. ^{[ 3,10 ] 그러나 Vitek은 덴드라이트 조각화를 고려하지 않았으며 그의 연구는 불균질 핵형성이 레이저 용접된 CMSX} -4 단결정 합금에서 표류 결정립 형성을 이끄는 주요 메커니즘임을 나타냅니다. 현재 작업에서 Vitek의 수치적 방법이 채택되고 금속 AM의 급속한 특성의 더 높은 속도와 더 낮은 전력 특성으로 확장됩니다.

AM을 통한 금속 부품 제조 는 지난 10년 동안 급격한 인기 증가를 목격했습니다. ^[ 16 ] EBM(Electron Beam Melting)에 의한 CMSX-4의 제작 가능성은 자주 조사되었으나 ^{[ 17 , 18 , 19 , 20 , 21 ]} CMSX의 제조 및 수리에 대한 조사는 매우 제한적이었다. – 4개의 단결정 구성요소는 레이저 분말 베드 융합(LPBF)을 사용하며, AM의 인기 있는 하위 집합으로, 특히 표류 입자 형성을 완화하는 메커니즘과 관련이 있습니다. ^[ 22 ]이러한 조사 부족은 주로 이러한 합금 시스템과 관련된 처리 문제로 인해 발생합니다. ^{[ 2 , 19 , 22 , 23 , 24 ]} 공정 매개변수( 예: 열원 전력, 스캐닝 속도, 스폿 크기, 예열 온도 및 스캔 전략)의 엄격한 제어는 완전히 조밀한 부품을 만들고 유지 관리할 수 있도록 하는 데 필수적입니다. 단결정 미세구조. ^[ 25 ] EBM을 사용하여 단결정 합금의 균열 없는 수리가 현재 가능하지만 ^{[ 19 , 24 ]} 표류 입자를 생성하지 않는 수리는 쉽게 달성할 수 없습니다.^{[ 23 , 26 ]}

이 작업에서 LPBF를 대표하는 조건으로 레이저 용융을 사용하여 단결정 CMSX-4에서 표류 입자 완화를 조사했습니다. LPBF는 스캐닝 레이저 빔을 사용하여 금속 분말의 얇은 층을 기판에 녹이고 융합합니다. ^층별 증착에서 레이저 빔의 사용은 급격한 온도 구배, 빠른 가열/냉각 주기 및 격렬한 유체 흐름을 경험하는 용융 풀을 생성 ^합니다^.이것은 일반적으로 부품에 결함을 일으킬 수 있는 매우 동적인 물리적 현상으로 이어집니다. ^{[ 28 , 29 , 30 ]} 레이저 유도 키홀의 동역학( 예:, 기화 유발 반동 압력으로 인한 위상 함몰) 및 열유체 흐름은 AM 공정에서 응고 결함과 강하게 결합되고 관련됩니다. ^{[ 31 , 32 , 33 , 34 ]} 기하 구조의 급격한 변화가 발생하기 쉬운 불안정한 키홀은 다공성, 볼링, 스패터 형성 및 흔하지 않은 미세 구조 상을 포함하는 유해한 물리적 결함을 유발할 수 있습니다. 그러나 키홀 진화와 유체 흐름은 자연적으로 다음을 통해 포착 ^하기^어렵습니다 ^.전통적인 사후 특성화 기술. 고충실도 수치 모델링을 활용하기 위해 이 연구에서는 전산유체역학(CFD)을 적용하여 표면 아래의 레이저-물질 상호 작용을 명확히 했습니다. ^[ 36 ] 이것은 응고된 용융물 풀의 단면에 대한 오랫동안 확립된 사후 특성화와 비교하여 키홀 및 용융물 풀 유체 흐름 정량화를 실행합니다.

CMSX-4 구성 요소의 레이저 기반 AM 수리 및 제조를 위한 적절한 절차를 개발하기 위해 적절한 공정 창을 설정하고 응고 중 표류 입자 형성 경향에 대한 예측 기능을 개발하는 것부터 시작합니다. 다중 합금에 대한 단일 트랙 증착은 분말 층이 있거나 없는 AM 공정에서 용융 풀 형상 및 미세 구조의 정확한 분석을 제공하는 것으로 나타났습니다. ^{[ 37 , 38 , 39 ]}따라서 본 연구에서는 CMSX-4의 응고 거동을 알아보기 위해 분말을 사용하지 않는 단일 트랙 레이저 스캔 실험을 사용하였다. 이는 CMSX-4 단결정의 LPBF 제조를 위한 예비 실험 지침을 제공합니다. 또한 응고 모델링은 기존 용접에서 LPBF와 관련된 급속 용접으로 확장되어 표류 입자 감소를 위한 최적의 레이저 용융 조건을 식별했습니다. 가공 매개변수 최적화를 위한 추가 지침을 제공하기 위해 용융물 풀의 매우 동적인 유체 흐름을 모델링했습니다.

재료 및 방법

단일 트랙 실험

방전 가공(EDM)을 사용하여 CMSX-4 방향성 응고 단결정 잉곳으로부터 샘플을 제작했습니다. 샘플의 최종 기하학은 치수 20의 직육면체 형태였습니다.××20××6mm. 6개 중 하나⟨ 001 ⟩⟨001⟩잉곳의 결정학적 방향은 레이저 트랙이 이 바람직한 성장 방향을 따라 스캔되도록 절단 표면에 수직으로 위치했습니다. 단일 레이저 용융 트랙은 EOS M290 기계를 사용하여 분말이 없는 샘플 표면에 만들어졌습니다. 이 기계는 최대 출력 400W, 가우시안 빔 직경 100의 이터븀 파이버 레이저가 장착된 LPBF 시스템입니다. μμ초점에서 m. 실험 중에 직사각형 샘플을 LPBF 기계용 맞춤형 샘플 홀더의 포켓에 끼워 표면을 동일한 높이로 유지했습니다. 이 맞춤형 샘플 홀더에 대한 자세한 내용은 다른 곳에서 설명합니다. ^실험 은 아르곤 퍼지 분위기에서 수행되었으며 예열은 적용되지 ^{않았습니다}^.단일 트랙 레이저 용융 실험은 다양한 레이저 출력(200~370W)과 스캔 속도(0.4~1.4m/s)에서 수행되었습니다.

성격 묘사

레이저 스캐닝 후, 레이저 빔 스캐닝 방향에 수직인 평면에서 FZ를 통해 다이아몬드 톱을 사용하여 샘플을 절단했습니다. 그 후, 샘플을 장착하고 220 그릿 SiC 페이퍼로 시작하여 콜로이드 실리카 현탁액 광택제로 마무리하여 자동 연마했습니다. 결정학적 특성화는 20kV의 가속 전압에서 TESCAN MIRA 3XMH 전계 방출 주사 전자 현미경(SEM)에서 수행되었습니다. EBSD 지도는0.4μm _0.4μ미디엄단계 크기. Bruker 시스템을 사용하여 EBSD 데이터를 정리하고 분석했습니다. EBSD 클린업은 그레인을 접촉시키기 위한 그레인 확장 루틴으로 시작한 다음 인덱스되지 않은 회절 패턴과 관련된 검은색 픽셀을 해결하기 위해 이웃 방향 클린업 루틴으로 이어졌습니다. 용융 풀 형태를 분석하기 위해 단면을 광학 현미경으로 분석했습니다. 광학 특성화의 대비를 향상시키기 위해 10g CuSO로 구성된 Marbles 시약의 변형으로 샘플을 에칭했습니다.44, 50mL HCl 및 70mL H22영형.

응고 모델링

구조적 과냉 기준에 기반한 응고 모델링을 수행하여 표유 입자의 성향 및 분포에 대한 가공 매개변수의 영향을 평가했습니다. 이 분석 모델링 접근 방식에 대한 자세한 내용은 이전 작업에서 제공됩니다. ^{[ 3 , 10 ] 참고문헌}3 에 기술된 바와 같이 , 기본 재료의 결정학적 배향을 가진 용융 풀에서 총 표유 입자 면적 분율의 변화는 최소이므로 기본 재료 배향의 영향은 이 작업에서 고려되지 않았습니다. 우리의 LPBF 결과를 이전 작업과 비교하기 위해 Vitek의 작업에서 사용된 수학적으로 간단한 Rosenthal 방정식 ^[ 3 ]또한 레이저 매개변수의 함수로 용융 풀의 모양과 FZ의 열 조건을 계산하기 위한 기준으로 여기에서 채택되었습니다. Rosenthal 솔루션은 열이 일정한 재료 특성을 가진 반무한 판의 정상 상태 점원을 통해서만 전도를 통해 전달된다고 가정하며 일반적으로 다음과 같이 표현 됩니다 ^{[ 40 , 41 ]} .

티=티0+η피2 파이케이엑스2+와이2+지2———-√경험치[- 브이(엑스2+와이2+지2———-√− 엑스 )2α _] ,티=티0+η피2파이케이엑스2+와이2+지2경험치⁡[-V(엑스2+와이2+지2-엑스)2α],(1)

여기서 T 는 온도,티0티0본 연구에서 313K( 즉 , EOS 기계 챔버 온도)로 설정된 주변 온도, P 는 레이저 빔 파워, V 는 레이저 빔 스캐닝 속도,ηη는 레이저 흡수율, k 는 열전도율,αα베이스 합금의 열확산율입니다. x , y , z 는 각각 레이저 스캐닝 방향, 가로 방향 및 세로 방향의 반대 방향과 정렬된 방향입니다 . 이 직교 좌표는 참조 3 의 그림 1에 있는 시스템을 따랐습니다 . CMSX-4에 대한 고상선 온도(1603K)와 액상선 온도(1669K)의 등온선 평균으로 응고 프런트( 즉 , 고체-액체 계면)를 정의했습니다. ^{[ 42 , 43 , 44 ]} 시뮬레이션에 사용된 열물리적 특성은 표 I 에 나열되어 있습니다.표 I CMSX-4의 응고 모델링에 사용된 열물리적 특성

풀 사이즈 테이블

열 구배는 외부 열 흐름에 의해 결정되었습니다.∇ 티∇티^[ 45 ] 에 의해 주어진 바와 같이 :

지 = | ∇ 티| =∣∣∣∂티∂엑스나^^+∂티∂와이제이^^+∂티∂지케이^^∣∣∣=(∂티∂엑스)2+(∂티∂와이)2+(∂티∂지)2————————√,G=|∇티|=|∂티∂엑스나^^+∂티∂와이제이^^+∂티∂지케이^^|=(∂티∂엑스)2+(∂티∂와이)2+(∂티∂지)2,(2)

어디나^^나^^,제이^^제이^^, 그리고케이^^케이^^는 각각 x , y 및 z 방향 을 따른 단위 벡터 입니다. 응고 등온선 속도,V티V티는 다음 관계에 의해 레이저 빔 스캐닝 속도 V 와 기하학적으로 관련됩니다.

V티= V코사인θ =V∂티∂엑스(∂티∂엑스)2+(∂티∂와이)2+(∂티∂지)2——————-√,V티=V코사인⁡θ=V∂티∂엑스(∂티∂엑스)2+(∂티∂와이)2+(∂티∂지)2,(삼)

어디θθ는 스캔 방향과 응고 전면의 법선 방향( 즉 , 최대 열 흐름 방향) 사이의 각도입니다. 이 연구의 용접 조건과 같은 제한된 성장에서 수지상 응고 전면은 고체-액체 등온선의 속도로 성장하도록 강제됩니다.V티V티. ^[ 46 ]

응고 전선이 진행되기 전에 새로 핵 생성된 입자의 국지적 비율ΦΦ, 액체 온도 구배 G 에 의해 결정 , 응고 선단 속도V티V티및 핵 밀도N0N0. 고정된 임계 과냉각에서 모든 입자가 핵형성된다고 가정함으로써△티N△티N, 등축 결정립의 반경은 결정립이 핵 생성을 시작하는 시점부터 주상 전선이 결정립에 도달하는 시간까지의 성장 속도를 통합하여 얻습니다. 과냉각으로 대체 시간d (ΔT_) / dt = – _V티G디(△티)/디티=-V티G, 열 구배 G 사이의 다음 관계 , 등축 입자의 국부적 부피 분율ΦΦ, 수상 돌기 팁 과냉각ΔT _△티, 핵 밀도N0N0, 재료 매개변수 n 및 핵생성 과냉각△티N△티N, Gäumann 외 여러분 에 의해 파생되었습니다 . ^{[ 12 , 14 ]} Hunt의 모델 ^[ 11 ] 의 수정에 기반함 :

지 =1엔 + 1- 4π _N03 인치( 1 − Φ )———√삼ΔT _( 1 -△티엔 + 1N△티엔 + 1) .G=1N+1-4파이N0삼인⁡(1-Φ)삼△티(1-△티NN+1△티N+1).(4)

계산을 단순화하기 위해 덴드라이트 팁 과냉각을 전적으로 구성 과냉각의 것으로 추정합니다.△티씨△티씨, 멱법칙 형식으로 근사화할 수 있습니다.△티씨= ( _V티)1 / 엔△티씨=(ㅏV티)1/N, 여기서 a 와 n 은 재료 종속 상수입니다. CMSX-4의 경우 이 값은a = 1.25 ×106ㅏ=1.25×106 s K ^3.4m− 1-1,엔 = 3.4N=3.4, 그리고N0= 2 ×1015N0=2×1015미디엄− 3,-삼,참고문헌 3 에 의해 보고된 바와 같이 .△티N△티N2.5K이며 보다 큰 냉각 속도에서 응고에 대해 무시할 수 있습니다.106106 K/s. 에 대한 표현ΦΦ위의 방정식을 재배열하여 해결됩니다.

Φ= 1 -이자형에스\ 여기서\ S=- 4π _N0삼(1( 엔 + 1 ) (GN/ 아V티)1 / 엔)삼=−2.356×1019(vTG3.4)33.4.Φ=1−eS\ where\ S=−4πN03(1(n+1)(Gn/avT)1/n)3=−2.356×1019(vTG3.4)33.4.

(5)

As proposed by Hunt,^[11] a value of Φ≤0.66Φ≤0.66 pct represents fully columnar epitaxial growth condition, and, conversely, a value of Φ≥49Φ≥49 pct indicates that the initial single crystal microstructure is fully replaced by an equiaxed microstructure. To calculate the overall stray grain area fraction, we followed Vitek’s method by dividing the FZ into roughly 19 to 28 discrete parts (depending on the length of the melt pool) of equal length from the point of maximum width to the end of melt pool along the x direction. The values of G and vTvT were determined at the center on the melt pool boundary of each section and these values were used to represent the entire section. The area-weighted average of ΦΦ over these discrete sections along the length of melt pool is designated as Φ¯¯¯¯Φ¯, and is given by:

Φ¯¯¯¯=∑kAkΦk∑kAk,Φ¯=∑kAkΦk∑kAk,

(6)

where k is the index for each subsection, and AkAk and ΦkΦk are the areas and ΦΦ values for each subsection. The summation is taken over all the sections along the melt pool. Vitek’s improved model allows the calculation of stray grain area fraction by considering the melt pool geometry and variations of G and vTvT around the tail end of the pool.

수년에 걸쳐 용융 풀 현상 모델링의 정확도를 개선하기 위해 많은 고급 수치 방법이 개발되었습니다. 우리는 FLOW-3D와 함께 고충실도 CFD를 사용했습니다. FLOW-3D는 여러 물리 모델을 통합하는 상용 FVM(Finite Volume Method)입니다. ^{[ 47 , 48 ]} CFD는 유체 운동과 열 전달을 수치적으로 시뮬레이션하며 여기서 사용된 기본 물리 모델은 레이저 및 표면력 모델이었습니다. 레이저 모델에서는 레이 트레이싱 기법을 통해 다중 반사와 프레넬 흡수를 구현합니다. ^[ 36 ]먼저, 레이저 빔은 레이저 빔에 의해 조명되는 각 그리드 셀을 기준으로 여러 개의 광선으로 이산화됩니다. 그런 다음 각 입사 광선에 대해 입사 벡터가 입사 위치에서 금속 표면의 법선 벡터와 정렬될 때 에너지의 일부가 금속에 의해 흡수됩니다. 흡수율은 Fresnel 방정식을 사용하여 추정됩니다. ^나머지^{에너지는 반사광선 에 의해 유지되며}^, 반사광선은 재료 표면에 부딪히면 새로운 입사광선으로 처리됩니다. 두 가지 주요 힘이 액체 금속 표면에 작용하여 자유 표면을 변형시킵니다. 금속의 증발에 의해 생성된 반동 압력은 증기 억제를 일으키는 주요 힘입니다. 본 연구에서 사용된 반동 압력 모델은피아르 자형= 특급 _{ B ( 1- _티V/ 티) }피아르 자형=ㅏ경험치⁡{비(1-티V/티)}, 어디피아르 자형피아르 자형는 반동압력, A 와 B 는 재료의 물성에 관련된 계수로 각각 75와 15이다.티V티V는 포화 온도이고 T 는 키홀 벽의 온도입니다. 표면 흐름 및 키홀 형성의 다른 원동력은 표면 장력입니다. 표면 장력 계수는 Marangoni 흐름을 포함하기 위해 온도의 선형 함수로 추정되며,σ =1.79-9.90⋅10− 4( 티− 1654케이 )σ=1.79-9.90⋅10-4(티-1654년케이)엔엠− 1-1. ^[ 49 ] 계산 영역은 베어 플레이트의 절반입니다(2300 μμ미디엄××250 μμ미디엄××500 μμm) xz 평면 에 적용된 대칭 경계 조건 . 메쉬 크기는 8입니다. μμm이고 시간 단계는 0.15입니다. μμs는 계산 효율성과 정확성 간의 균형을 제공합니다.

결과 및 논의

용융 풀 형태

이 작업에 사용된 5개의 레이저 파워( P )와 6개의 스캐닝 속도( V )는 서로 다른 29개의 용융 풀을 생성했습니다.피- 브이피-V조합. P 와 V 값이 가장 높은 것은 그림 1 을 기준으로 과도한 볼링과 관련이 있기 때문에 본 연구에서는 분석하지 않았다 .

단일 트랙 용융 풀은 그림 1 과 같이 형상에 따라 네 가지 유형으로 분류할 수 있습니다 ^[ 39 ] : (1) 전도 모드(파란색 상자), (2) 키홀 모드(빨간색), (3) 전환 모드(마젠타), (4) 볼링 모드(녹색). 높은 레이저 출력과 낮은 스캐닝 속도의 일반적인 조합인 키홀 모드에서 용융물 풀은 일반적으로 너비/깊이( W / D ) 비율이 0.5보다 훨씬 큰 깊고 가느다란 모양을 나타냅니다 . 스캐닝 속도가 증가함에 따라 용융 풀이 얕아져 W / D 가 약 0.5인 반원형 전도 모드 용융 풀을 나타냅니다. W / D _전환 모드 용융 풀의 경우 1에서 0.5 사이입니다. 스캐닝 속도를 1200 및 1400mm/s로 더 높이면 충분히 큰 캡 높이와 볼링 모드 용융 풀의 특징인 과도한 언더컷이 발생할 수 있습니다.

힘과 속도의 함수로서의 용융 풀 깊이와 너비는 각각 그림 2 (a)와 (b)에 표시되어 있습니다. 용융 풀 폭은 기판 표면에서 측정되었습니다. 그림 2 (a)는 깊이가 레이저 출력과 매우 선형적인 관계를 따른다는 것을 보여줍니다. 속도가 증가함에 따라 깊이 대 파워 곡선의 기울기는 꾸준히 감소하지만 더 높은 속도 곡선에는 약간의 겹침이 있습니다. 이러한 예상치 못한 중첩은 종종 용융 풀 형태의 동적 변화를 유발하는 유체 흐름의 영향과 레이저 스캔당 하나의 이미지만 추출되었다는 사실 때문일 수 있습니다. 이러한 선형 동작은 그림 2 (b) 의 너비에 대해 명확하지 않습니다 . 그림 2(c)는 선형 에너지 밀도 P / V 의 함수로서 용융 깊이와 폭을 보여줍니다 . 선형 에너지 밀도는 퇴적물의 단위 길이당 에너지 투입량을 측정한 것입니다. ^[ 50 ] 용융 풀 깊이는 에너지 밀도에 따라 달라지며 너비는 더 많은 분산을 나타냅니다. 동일한 에너지 밀도가 준공 부품의 용융 풀, 미세 구조 또는 속성에서 반드시 동일한 유체 역학을 초래하지는 않는다는 점에 유의하는 것이 중요합니다. ^[ 50 ]

레이저 흡수율 평가

레이저 흡수율은 LPBF 조건에서 재료 및 가공 매개변수에 따라 크게 달라진다는 것은 잘 알려져 있습니다. ^{[ 31 , 51 , 52 ]} 적분구를 이용한 전통적인 흡수율의 직접 측정은 일반적으로 높은 비용과 구현의 어려움으로 인해 쉽게 접근할 수 없습니다. ^[ 51 ] 그 외 . ^[ 39 ] 전도 모드 용융 풀에 대한 Rosenthal 방정식을 기반으로 경험적 레이저 흡수율 모델을 개발했지만 기본 가정으로 인해 키홀 용융 풀에 대한 정확한 예측을 제공하지 못했습니다. ^[ 40 ] 최근 간외 . ^[ 53 ] Ti–6Al–4V에 대한 30개의 고충실도 다중 물리 시뮬레이션 사례를 사용하여 레이저 흡수에 대한 스케일링 법칙을 확인했습니다. 그러나 연구 중인 특정 재료에 대한 최소 흡수(평평한 용융 표면의 흡수율)에 대한 지식이 필요하며 이는 CMSX-4에 대해 알려지지 않았습니다. 다양한 키홀 모양의 용융 풀에 대한 레이저 흡수의 정확한 추정치를 얻기가 어렵기 때문에 상한 및 하한 흡수율로 분석 시뮬레이션을 실행하기로 결정했습니다. 깊은 키홀 모양의 용융 풀의 경우 대부분의 빛을 가두는 키홀 내 다중 반사로 인해 레이저 흡수율이 0.8만큼 높을 수 있습니다. 이것은 기하학적 현상이며 기본 재료에 민감하지 않습니다. ^{[ 51, 52 , 54 ]} 따라서 본 연구에서는 흡수율의 상한을 0.8로 설정하였다. 참고 문헌 51 에 나타낸 바와 같이 , 전도 용융 풀에 해당하는 최저 흡수율은 약 0.3이었으며, 이는 이 연구에서 합리적인 하한 값입니다. 따라서 레이저 흡수율이 스트레이 그레인 형성에 미치는 영향을 보여주기 위해 흡수율 값을 0.55 ± 0.25로 설정했습니다. Vitek의 작업에서는 1.0의 고정 흡수율 값이 사용되었습니다. ^[ 3 ]

퓨전 존 미세구조

그림 3 은 200~300W 및 600~300W 및 600~300W 범위의 레이저 출력 및 속도로 9가지 다른 처리 매개변수에 의해 생성된 CMSX-4 레이저 트랙의 yz 단면 에서 취한 EBSD 역극점도와 해당 역극점도를 보여 줍니다. 각각 1400mm/s. EBSD 맵에서 여러 기능을 쉽게 관찰할 수 있습니다. 스트레이 그레인은 EBSD 맵에서 그 방향에 해당하는 다른 RGB 색상으로 나타나고 그레인 경계를 묘사하기 위해 5도의 잘못된 방향이 사용되었습니다. 여기, 그림 3 에서 스트레이 그레인은 대부분 용융 풀의 상단 중심선에 집중되어 있으며, 이는 용접된 단결정 CMSX-4의 이전 보고서와 일치합니다. ^[ 10 ]역 극점도에서, 점 근처에 집중된 클러스터⟨ 001 ⟩⟨001⟩융합 경계에서 유사한 방향을 유지하는 단결정 기반 및 에피택셜로 응고된 덴드라이트를 나타냅니다. 그러나 흩어진 곡물은 식별할 수 있는 질감이 없는 흩어져 있는 점으로 나타납니다. 단결정 기본 재료의 결정학적 방향은 주로⟨ 001 ⟩⟨001⟩비록 샘플을 절단하는 동안 식별할 수 없는 기울기 각도로 인해 또는 단결정 성장 과정에서 약간의 잘못된 방향이 있었기 때문에 약간의 편차가 있지만. 용융 풀 내부의 응고된 수상 돌기의 기본 방향은 다시 한 번⟨ 001 ⟩⟨001⟩주상 결정립 구조와 유사한 에피택셜 성장의 결과. 그림 3 과 같이 용융 풀에서 수상돌기의 성장 방향은 하단의 수직 방향에서 상단의 수평 방향으로 변경되었습니다 . 이 전이는 주로 온도 구배 방향의 변화로 인한 것입니다. 두 번째 전환은 CET입니다. FZ의 상단 중심선 주변에서 다양한 방향의 흩어진 입자가 관찰되며, 여기서 안쪽으로 성장하는 수상돌기가 서로 충돌하여 용융 풀에서 응고되는 마지막 위치가 됩니다.

더 깊은 키홀 모양을 특징으로 하는 샘플에서 용융 풀의 경계 근처에 침전된 흩어진 입자가 분명합니다. 이러한 새로운 입자는 나중에 모델링 섹션에서 논의되는 수상돌기 조각화 메커니즘에 의해 잠재적으로 발생합니다. 결정립이 강한 열 구배에서 핵을 생성하고 성장한 결과, 대부분의 흩어진 결정립은 모든 방향에서 동일한 크기를 갖기보다는 장축이 열 구배 방향과 정렬된 길쭉한 모양을 갖습니다. 그림 3 의 전도 모드 용융 풀 흩어진 입자가 없는 것으로 입증되는 더 나은 단결정 품질을 나타냅니다. 상대적으로 낮은 출력과 높은 속도의 스캐닝 레이저에 의해 생성된 이러한 더 얕은 용융 풀에서 최소한의 결정립 핵형성이 발생한다는 것은 명백합니다. 더 큰 면적 분율을 가진 스트레이 그레인은 고출력 및 저속으로 생성된 깊은 용융 풀에서 더 자주 관찰됩니다. 국부 응고 조건에 대한 동력 및 속도의 영향은 후속 모델링 섹션에서 조사할 것입니다.

응고 모델링

서론에서 언급한 바와 같이 연구자들은 단결정 용접 중에 표류 결정립 형성의 가능한 메커니즘을 평가했습니다. ^{[ 12 , 13 , 14 , 15 , 55 ]}논의된 가장 인기 있는 두 가지 메커니즘은 (1) 응고 전단에 앞서 구성적 과냉각에 의해 도움을 받는 이종 핵형성 및 (2) 용융물 풀의 유체 흐름으로 인한 덴드라이트 조각화입니다. 첫 번째 메커니즘은 광범위하게 연구되었습니다. 이원 합금을 예로 들면, 고체는 액체만큼 많은 용질을 수용할 수 없으므로 응고 중에 용질을 액체로 거부합니다. 결과적으로, 성장하는 수상돌기 앞에서 용질 분할은 실제 온도가 국부 평형 액상선보다 낮은 과냉각 액체를 생성합니다. 충분히 광범위한 체질적으로 과냉각된 구역의 존재는 새로운 결정립의 핵형성 및 성장을 촉진합니다. ^[ 56 ]전체 과냉각은 응고 전면에서의 구성, 동역학 및 곡률 과냉각을 포함한 여러 기여의 합입니다. 일반적인 가정은 동역학 및 곡률 과냉각이 합금에 대한 용질 과냉각의 더 큰 기여와 관련하여 무시될 수 있다는 것입니다. ^[ 57 ]

서로 다른 기본 메커니즘을 더 잘 이해하려면피- 브이피-V조건에서 응고 모델링이 수행됩니다. 첫 번째 목적은 스트레이 그레인의 전체 범위를 평가하는 것입니다(Φ¯¯¯¯Φ¯) 처리 매개 변수의 함수로 국부적 표류 입자 비율의 변화를 조사하기 위해 (ΦΦ) 용융 풀의 위치 함수로. 두 번째 목적은 금속 AM의 빠른 응고 동안 응고 미세 구조와 표류 입자 형성 메커니즘 사이의 관계를 이해하는 것입니다.

그림 4 는 해석적으로 시뮬레이션된 표류 입자 비율을 보여줍니다.Φ¯¯¯¯Φ¯세 가지 레이저 흡수율 값에서 다양한 레이저 스캐닝 속도 및 레이저 출력에 대해. 결과는 스트레이 그레인 면적 비율이 흡수된 에너지에 민감하다는 것을 보여줍니다. 흡수율을 0.30에서 0.80으로 증가시키면Φ¯¯¯¯Φ¯약 3배이며, 이 효과는 저속 및 고출력 영역에서 더욱 두드러집니다. 다른 모든 조건이 같다면, 흡수된 전력의 큰 영향은 평균 열 구배 크기의 일반적인 감소와 용융 풀 내 평균 응고율의 증가에 기인합니다. 스캐닝 속도가 증가하고 전력이 감소함에 따라 평균 스트레이 그레인 비율이 감소합니다. 이러한 일반적인 경향은 Vitek의 작업에서 채택된 그림 5 의 파란색 영역에서 시뮬레이션된 용접 결과와 일치합니다 . ^[ 3 ] 더 큰 과냉각 구역( 즉, 지 /V티G/V티영역)은 용접 풀의 표유 입자의 면적 비율이 분홍색 영역에 해당하는 LPBF 조건의 면적 비율보다 훨씬 더 크다는 것을 의미합니다. 그럼에도 불구하고 두 데이터 세트의 일반적인 경향은 유사합니다. 즉 , 레이저 출력이 감소하고 레이저 속도가 증가함에 따라 표류 입자의 비율이 감소합니다. 또한 그림 5 에서 스캐닝 속도가 LPBF 영역으로 증가함에 따라 표유 입자 면적 분율에 대한 레이저 매개변수의 변화 효과가 감소한다는 것을 추론할 수 있습니다. 그림 6 (a)는 그림 3 의 EBSD 분석에서 나온 실험적 표류 결정립 면적 분율 과 그림 4 의 해석 시뮬레이션 결과를 비교합니다.. 열쇠 구멍 모양의 FZ에서 정확한 값이 다르지만 추세는 시뮬레이션과 실험 데이터 모두에서 일관되었습니다. 키홀 모양의 용융 풀, 특히 전력이 300W인 2개는 분석 시뮬레이션 예측보다 훨씬 더 많은 양의 흩어진 입자를 가지고 있습니다. Rosenthal 방정식은 일반적으로 열 전달이 순전히 전도에 의해 좌우된다는 가정으로 인해 열쇠 구멍 체제의 열 흐름을 적절하게 반영하지 못하기 때문에 이러한 불일치가 실제로 예상됩니다. ^{[ 39 , 40 ]} 그것은 또한 그림 4 의 발견 , 즉 키홀 모드 동안 흡수된 전력의 증가가 표류 입자 형성에 더 이상적인 조건을 초래한다는 것을 검증합니다. 그림 6 (b)는 실험을 비교Φ¯¯¯¯Φ¯수치 CFD 시뮬레이션Φ¯¯¯¯Φ¯. CFD 모델이 약간 초과 예측하지만Φ¯¯¯¯Φ¯전체적으로피- 브이피-V조건에서 열쇠 구멍 조건에서의 예측은 분석 모델보다 정확합니다. 전도 모드 용융 풀의 경우 실험 값이 분석 시뮬레이션 값과 더 가깝게 정렬됩니다.

모의 온도 구배 G 분포 및 응고율 검사V티V티분석 모델링의 쌍은 그림 7 (a)의 CMSX-4 미세 구조 선택 맵에 표시됩니다. 제공지 /V티G/V티( 즉 , 형태 인자)는 형태를 제어하고지 ×V티G×V티( 즉 , 냉각 속도)는 응고된 미세 구조의 규모를 제어하고 , ^{[ 58 , 59 ]}지 -V티G-V티플롯은 전통적인 제조 공정과 AM 공정 모두에서 미세 구조 제어를 지원합니다. 이 플롯의 몇 가지 분명한 특징은 등축, 주상, 평면 전면 및 이러한 경계 근처의 전이 영역을 구분하는 경계입니다. 그림 7 (a)는 몇 가지 선택된 분석 열 시뮬레이션에 대한 미세 구조 선택 맵을 나타내는 반면 그림 7 (b)는 수치 열 모델의 결과와 동일한 맵을 보여줍니다. 등축 미세구조의 형성은 낮은 G 이상 에서 명확하게 선호됩니다.V티V티정황. 이 플롯에서 각 곡선의 평면 전면에 가장 가까운 지점은 용융 풀의 최대 너비 위치에 해당하는 반면 등축 영역에 가까운 지점의 끝은 용융 풀의 후면 꼬리에 해당합니다. 그림 7 (a)에서 대부분의지 -V티G-V티응고 전면의 쌍은 원주형 영역에 속하고 점차 CET 영역으로 위쪽으로 이동하지만 용융 풀의 꼬리는 다음에 따라 완전히 등축 영역에 도달하거나 도달하지 않을 수 있습니다.피- 브이피-V조합. 그림 7 (a) 의 곡선 중 어느 것도 평면 전면 영역을 통과하지 않지만 더 높은 전력의 경우에 가까워집니다. 저속 레이저 용융 공정을 사용하는 이전 작업에서는 곡선이 평면 영역을 통과할 수 있습니다. 레이저 속도가 증가함에 따라 용융 풀 꼬리는 여전히 CET 영역에 있지만 완전히 등축 영역에서 멀어집니다. CET 영역으로 떨어지는 섹션의 수도 감소합니다.Φ¯¯¯¯Φ¯응고된 물질에서.

그만큼지 -V티G-V티CFD 모델을 사용하여 시뮬레이션된 응고 전면의 쌍이 그림 7 (b)에 나와 있습니다. 세 방향 모두에서 각 점 사이의 일정한 간격으로 미리 정의된 좌표에서 수행된 해석 시뮬레이션과 달리, 고충실도 CFD 모델의 출력은 불규칙한 사면체 좌표계에 있었고 G 를 추출하기 전에 일반 3D 그리드에 선형 보간되었습니다. 그리고V티V티그런 다음 미세 구조 선택 맵에 플롯됩니다. 일반적인 경향은 그림 7 (a)의 것과 일치하지만 이 방법으로 모델링된 매우 동적인 유체 흐름으로 인해 결과에 더 많은 분산이 있었습니다. 그만큼지 -V티G-V티분석 열 모델의 쌍 경로는 더 연속적인 반면 수치 시뮬레이션의 경로는 용융 풀 꼬리 모양의 차이를 나타내는 날카로운 굴곡이 있습니다(이는 G 및V티V티) 두 모델에 의해 시뮬레이션됩니다.

유체 흐름을 통합한 응고 모델링

수치 CFD 모델을 사용하여 유동 입자 형성 정도에 대한 유체 흐름의 영향을 이해하고 시뮬레이션 결과를 분석 Rosenthal 솔루션과 비교했습니다. 그림 8 은 응고 매개변수 G 의 분포를 보여줍니다.V티V티,지 /V티G/V티, 그리고지 ×V티G×V티yz 단면에서 x 는 FLOW-3D에서 (a1–d1) 분석 열 모델링 및 (a2–d2) FVM 방법을 사용하여 시뮬레이션된 용융 풀의 최대 폭입니다. 그림 8 의 값은 응고 전선이 특정 위치에 도달할 때 정확한 값일 수도 있고 아닐 수도 있지만 일반적인 추세를 반영한다는 의미의 임시 가상 값입니다. 이 프로파일은 출력 300W 및 속도 400mm/s의 레이저 빔에서 시뮬레이션됩니다. 용융 풀 경계는 흰색 곡선으로 표시됩니다. (a2–d2)의 CFD 시뮬레이션 용융 풀 깊이는 342입니다. μμm, 측정 깊이 352와 잘 일치 μμ일치하는 길쭉한 열쇠 구멍 모양과 함께 그림 1 에 표시된 실험 FZ의 m . 그러나 분석 모델은 반원 모양의 용융 풀을 출력하고 용융 풀 깊이는 264에 불과합니다. μμ열쇠 구멍의 경우 현실과는 거리가 멀다. CFD 시뮬레이션 결과에서 열 구배는 레이저 반사 증가와 불안정한 액체-증기 상호 작용이 발생하는 증기 함몰의 동적 부분 근처에 있기 때문에 FZ 하단에서 더 높습니다. 대조적으로 해석 결과의 열 구배 크기는 경계를 따라 균일합니다. 두 시뮬레이션 결과 모두 그림 8 (a1) 및 (a2) 에서 응고가 용융 풀의 상단 중심선을 향해 진행됨에 따라 열 구배가 점차 감소합니다 . 응고율은 그림 8 과 같이 경계 근처에서 거의 0입니다. (b1) 및 (b2). 이는 경계 영역이 응고되기 시작할 때 국부 응고 전면의 법선 방향이 레이저 스캐닝 방향에 수직이기 때문입니다. 이것은 드라이브θ → π/ 2θ→파이/2그리고V티→ 0V티→0식에서 [ 3 ]. 대조적으로 용융 풀의 상단 중심선 근처 영역에서 응고 전면의 법선 방향은 레이저 스캐닝 방향과 잘 정렬되어 있습니다.θ → 0θ→0그리고V티→ 브이V티→V, 빔 스캐닝 속도. G 와 _V티V티값이 얻어지면 냉각 속도지 ×V티G×V티및 형태 인자지 /V티G/V티계산할 수 있습니다. 그림 8 (c2)는 용융 풀 바닥 근처의 온도 구배가 매우 높고 상단에서 더 빠른 성장 속도로 인해 냉각 속도가 용융 풀의 바닥 및 상단 중심선 근처에서 더 높다는 것을 보여줍니다. 지역. 그러나 이러한 추세는 그림 8 (c1)에 캡처되지 않았습니다. 그림 8 의 형태 요인 (d1) 및 (d2)는 중심선에 접근함에 따라 눈에 띄게 감소합니다. 경계에서 큰 값은 열 구배를 거의 0인 성장 속도로 나누기 때문에 발생합니다. 이 높은 형태 인자는 주상 미세구조 형성 가능성이 높음을 시사하는 반면, 중앙 영역의 값이 낮을수록 등축 미세구조의 가능성이 더 크다는 것을 나타냅니다. Tanet al. 또한 키홀 모양의 용접 풀 ^[ 59 ] 에서 이러한 응고 매개변수의 분포 를 비슷한 일반적인 경향으로 보여주었습니다. 그림 3 에서 볼 수 있듯이 용융 풀의 상단 중심선에 있는 흩어진 입자는 낮은 특징을 나타내는 영역과 일치합니다.지 /V티G/V티그림 8 (d1) 및 (d2)의 값. 시뮬레이션과 실험 간의 이러한 일치는 용융 풀의 상단 중심선에 축적된 흩어진 입자의 핵 생성 및 성장이 등온선 속도의 증가와 온도 구배의 감소에 의해 촉진됨을 보여줍니다.

그림 9 는 유체 속도 및 국부적 핵형성 성향을 보여줍니다.ΦΦ300W의 일정한 레이저 출력과 400, 800 및 1200mm/s의 세 가지 다른 레이저 속도에 의해 생성된 3D 용융 풀 전체에 걸쳐. 그림 9 (d)~(f)는 로컬ΦΦ해당 3D 보기에서 밝은 회색 평면으로 표시된 특정 yz 단면의 분포. 이 yz 섹션은 가장 높기 때문에 선택되었습니다.Φ¯¯¯¯Φ¯용융 풀 내의 값은 각각 23.40, 11.85 및 2.45pct입니다. 이들은 그림 3 의 실험 데이터와 비교하기에 적절하지 않을 수 있는 액체 용융 풀의 과도 값이며Φ¯¯¯¯Φ¯그림 6 의 값은 이 값이 고체-액체 계면에 가깝지 않고 용융 풀의 중간에서 취해졌기 때문입니다. 온도가 훨씬 낮아서 핵이 생존하고 성장할 수 있기 때문에 핵 형성은 용융 풀의 중간이 아닌 고체-액체 계면에 더 가깝게 발생할 가능성이 있습니다.

그림 3 (a), (d), (g), (h)에서 위쪽 중심선에서 멀리 떨어져 있는 흩어진 결정립이 있었습니다. 그들은 훨씬 더 높은 열 구배와 더 낮은 응고 속도 필드에 위치하기 때문에 과냉각 이론은 이러한 영역에서 표류 입자의 형성에 대한 만족스러운 설명이 아닙니다. 이것은 떠돌이 결정립의 형성을 야기할 수 있는 두 번째 메커니즘, 즉 수상돌기의 팁을 가로지르는 유체 흐름에 의해 유발되는 수상돌기 조각화를 고려하도록 동기를 부여합니다. 유체 흐름이 열 구배를 따라 속도 성분을 갖고 고체-액체 계면 속도보다 클 때, 주상 수상돌기의 국지적 재용융은 용질이 풍부한 액체가 흐물흐물한 구역의 깊은 곳에서 액상선 등온선까지 이동함으로써 발생할 수 있습니다. . ^[ 55] 분리된 수상돌기는 대류에 의해 열린 액체로 운반될 수 있습니다. 풀이 과냉각 상태이기 때문에 이러한 파편은 고온 조건에서 충분히 오래 생존하여 길 잃은 입자의 핵 생성 사이트로 작용할 수 있습니다. 결과적으로 수상 돌기 조각화 과정은 활성 핵의 수를 효과적으로 증가시킬 수 있습니다.N0N0) 용융 풀 ^{[ 15 , 60 , 61 ]} 에서 생성된 미세 구조에서 표류 입자의 면적을 증가시킵니다.

그림 9 (a) 및 (b)에서 반동 압력은 용융 유체를 아래쪽으로 흐르게 하여 결과 흐름을 지배합니다. 유체 속도의 역방향 요소는 V = 400 및 800mm/s에 대해 각각 최대값 1.0 및 1.6m/s로 더 느려집니다 . 그림 9 (c)에서 레이저 속도가 더 증가함에 따라 증기 침하가 더 얕고 넓어지고 반동 압력이 더 고르게 분포되어 증기 침강에서 주변 영역으로 유체를 밀어냅니다. 역류는 최대값 3.5m/s로 더 빨라집니다. 용융 풀의 최대 너비에서 yz 단면 의 키홀 아래 평균 유체 속도는 그림에 표시된 경우에 대해 0.46, 0.45 및 1.44m/s입니다.9 (a), (b) 및 (c). 키홀 깊이의 변동은 각 경우의 최대 깊이와 최소 깊이의 차이로 정의되는 크기로 정량화됩니다. 240 범위의 강한 증기 내림 변동 μμm은 그림 9 (a)의 V = 400mm/s 경우에서 발견 되지만 이 변동은 그림 9 (c)에서 16의 범위로 크게 감소합니다.μμ미디엄. V = 400mm/s인 경우 의 유체장과 높은 변동 범위는 이전 키홀 동역학 시뮬레이션과 일치합니다. ^[ 34 ]

따라서 V = 400mm/s 키홀 케이스의 무질서한 변동 흐름이 용융 풀 경계를 따라 응고된 주상 수상돌기에서 분리된 조각을 구동할 가능성이 있습니다. V = 1200mm/s의 경우 강한 역류 는 그림 3 에서 관찰되지 않았지만 동일한 효과를 가질 수 있습니다. . 덴드라이트 조각화에 대한 유체 유동장의 영향에 대한 이 경험적 설명은 용융 풀 경계 근처에 떠돌이 입자의 존재에 대한 그럴듯한 설명을 제공합니다. 분명히 하기 위해, 우리는 이 가설을 검증하기 위해 이 현상에 대한 직접적인 실험적 관찰을 하지 않았습니다. 이 작업에서 표유 입자 면적 분율을 계산할 때 단순화를 위해 핵 생성 모델링에 일정한 핵 생성 수 밀도가 적용되었습니다. 이는 그림 9 의 표류 입자 영역 비율 이 수지상정 조각화가 발생하는 경우 이러한 높은 유체 흐름 용융 풀에서 발생할 수 있는 것, 즉 강화된 핵 생성 밀도를 반영하지 않는다는 것을 의미합니다.

위의 이유로 핵 형성에 대한 수상 돌기 조각화의 영향을 아직 배제할 수 없습니다. ^{그러나 단편화 이론은 용접 문헌 [ 62 ]} 에서 검증될 만큼 충분히 개발되지 않았 으므로 부차적인 중요성만 고려된다는 점에 유의해야 합니다. 1200mm/s를 초과하는 레이저 스캐닝 속도는 최소한의 표류 결정립 면적 분율을 가지고 있음에도 불구하고 분명한 볼링을 나타내기 때문에 단결정 수리 및 AM 처리에 적합하지 않습니다. 따라서 낮은 P 및 높은 V 에 의해 생성된 응고 전면 근처에서 키홀 변동이 최소화되고 유체 속도가 완만해진 용융 풀이 생성된다는 결론을 내릴 수 있습니다., 처리 창의 극한은 아니지만 흩어진 입자를 나타낼 가능성이 가장 적습니다.

마지막으로 단일 레이저 트랙의 응고 거동을 조사하면 에피택셜 성장 동안 표류 입자 형성을 더 잘 이해할 수 있다는 점에 주목하는 것이 중요합니다. 우리의 현재 결과는 최적의 레이저 매개변수에 대한 일반적인 지침을 제공하여 최소 스트레이 그레인을 달성하고 단결정 구조를 유지합니다. 이 가이드라인은 250W 정도의 전력과 600~800mm/s의 스캔 속도로 최소 흩어진 입자에 적합한 공정 창을 제공합니다. 각 처리 매개변수를 신중하게 선택하면 과거에 스테인리스강에 대한 거의 단결정 미세 구조를 인쇄하는 데 성공했으며 이는 CMSX-4 AM 빌드에 대한 가능성을 보여줍니다. ^[ 63 ]신뢰성을 보장하기 위해 AM 수리 프로세스를 시작하기 전에 보다 엄격한 실험 테스트 및 시뮬레이션이 여전히 필요합니다. 둘 이상의 레이저 트랙 사이의 상호 작용도 고려해야 합니다. 또한 레이저, CMSX-4 분말 및 벌크 재료 간의 상호 작용이 중요하며, 수리 중에 여러 층의 CMSX-4 재료를 축적해야 하는 경우 다른 스캔 전략의 효과도 중요한 역할을 할 수 있습니다. 분말이 포함된 경우 Lopez-Galilea 등 의 연구에서 제안한 바와 같이 분말이 주로 완전히 녹지 않았을 때 추가 핵 생성 사이트를 도입하기 때문에 단순히 레이저 분말과 속도를 조작하여 흩어진 입자 형성을 완화하기 어려울 수 있습니다 . ^[ 22 ]결과적으로 CMSX-4 단결정을 수리하기 위한 레이저 AM의 가능성을 다루기 위해서는 기판 재료, 레이저 출력, 속도, 해치 간격 및 층 두께의 조합을 모두 고려해야 하며 향후 연구에서 다루어야 합니다. CFD 모델링은 2개 이상의 레이저 트랙 사이의 상호작용과 열장에 미치는 영향을 통합할 수 있으며, 이는 AM 빌드 시나리오 동안 핵 생성 조건으로 단일 비드 연구의 지식 격차를 해소할 것입니다.

결론

LPBF 제조의 특징적인 조건 하에서 CMSX-4 단결정 의 에피택셜(기둥형) 대 등축 응고 사이의 경쟁을 실험적 및 이론적으로 모두 조사했습니다. 이 연구는 고전적인 응고 개념을 도입하여 빠른 레이저 용융의 미세 구조 특징을 설명하고 응고 조건과 표유 결정 성향을 예측하기 위해 해석적 및 수치적 고충실도 CFD 열 모델 간의 비교를 설명했습니다. 본 연구로부터 다음과 같은 주요 결론을 도출할 수 있다.

단일 레이저 트랙의 레이저 가공 조건은 용융 풀 형상, 레이저 흡수율, 유체 흐름 및 키홀 요동, 입자 구조 및 표류 입자 형성 민감성에 강한 영향을 미치는 것으로 밝혀졌습니다.
레이저 용접을 위해 개발된 이론적인 표유 결정립 핵형성 분석이 레이저 용융 AM 조건으로 확장되었습니다. 분석 모델링 결과와 단일 레이저 트랙의 미세구조 특성화를 비교하면 예측이 전도 및 볼링 조건에서 실험적 관찰과 잘 일치하는 반면 키홀 조건에서는 예측이 약간 과소하다는 것을 알 수 있습니다. 이러한 불일치는 레이저 트랙의 대표성이 없는 섹션이나 유체 속도 필드의 변화로 인해 발생할 수 있습니다. CFD 모델에서 추출한 열장에 동일한 표유 입자 계산 파이프라인을 적용하면 연구된 모든 사례에서 과대평가가 발생하지만 분석 모델보다 연장된 용융 풀의 실험 데이터와 더 정확하게 일치합니다.
이 연구에서 두 가지 표류 결정립 형성 메커니즘인 불균일 핵형성 및 수상돌기 조각화가 평가되었습니다. 우리의 결과는 불균일 핵형성이 용융 풀의 상단 중심선에서 새로운 결정립의 형성으로 이어지는 주요 메커니즘임을 시사합니다.지 /V티G/V티정권.
용융 풀 경계 근처의 흩어진 입자는 깊은 키홀 모양의 용융 풀에서 독점적으로 관찰되며, 이는 강한 유체 흐름으로 인한 수상 돌기 조각화의 영향이 이러한 유형의 용융 풀에서 고려하기에 충분히 강력할 수 있음을 시사합니다.
일반적으로 더 높은 레이저 스캐닝 속도와 더 낮은 전력 외에도 안정적인 키홀과 최소 유체 속도는 또한 흩어진 입자 형성을 완화하고 레이저 단일 트랙에서 에피택셜 성장을 보존합니다.

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Fig. 1 Multi-physics phenomena in the laser-material interaction zone

COMPARISON BETWEEN GREEN AND
INFRARED LASER IN LASER POWDER BED
FUSION OF PURE COPPER THROUGH HIGH
FIDELITY NUMERICAL MODELLING AT MESOSCALE

316-L 스테인리스강의 레이저 분말 베드 융합 중 콜드 스패터 형성의 충실도 높은 수치 모델링

W.E. ALPHONSO¹*, M. BAYAT¹ and J.H. HATTEL¹
*Corresponding author
1Technical University of Denmark (DTU), 2800, Kgs, Lyngby, Denmark

ABSTRACT

L-PBF(Laser Powder Bed Fusion)는 금속 적층 제조(MAM) 기술로, 기존 제조 공정에 비해 부품 설계 자유도, 조립품 통합, 부품 맞춤화 및 낮은 툴링 비용과 같은 여러 이점을 산업에 제공합니다.

전기 코일 및 열 관리 장치는 일반적으로 높은 전기 및 열 전도성 특성으로 인해 순수 구리로 제조됩니다. 따라서 순동의 L-PBF가 가능하다면 기하학적으로 최적화된 방열판과 자유형 전자코일을 제작할 수 있습니다.

그러나 L-PBF로 조밀한 순동 부품을 생산하는 것은 적외선에 대한 낮은 광 흡수율과 높은 열전도율로 인해 어렵습니다. 기존의 L-PBF 시스템에서 조밀한 구리 부품을 생산하려면 적외선 레이저의 출력을 500W 이상으로 높이거나 구리의 광흡수율이 높은 녹색 레이저를 사용해야 합니다.

적외선 레이저 출력을 높이면 후면 반사로 인해 레이저 시스템의 광학 구성 요소가 손상되고 렌즈의 열 광학 현상으로 인해 공정이 불안정해질 수 있습니다. 이 작업에서 FVM(Finite Volume Method)에 기반한 다중 물리학 중간 규모 수치 모델은 Flow-3D에서 개발되어 용융 풀 역학과 궁극적으로 부품 품질을 제어하는 물리적 현상 상호 작용을 조사합니다.

녹색 레이저 열원과 적외선 레이저 열원은 기판 위의 순수 구리 분말 베드에 단일 트랙 증착을 생성하기 위해 개별적으로 사용됩니다.

용융 풀 역학에 대한 레이저 열원의 유사하지 않은 광학 흡수 특성의 영향이 탐구됩니다. 수치 모델을 검증하기 위해 단일 트랙이 구리 분말 베드에 증착되고 시뮬레이션된 용융 풀 모양과 크기가 비교되는 실험이 수행되었습니다.

녹색 레이저는 광흡수율이 높아 전도 및 키홀 모드 용융이 가능하고 적외선 레이저는 흡수율이 낮아 키홀 모드 용융만 가능하다. 레이저 파장에 대한 용융 모드의 변화는 궁극적으로 기계적, 전기적 및 열적 특성에 영향을 미치는 열 구배 및 냉각 속도에 대한 결과를 가져옵니다.

Laser Powder Bed Fusion (L-PBF) is a Metal Additive Manufacturing (MAM) technology which offers several advantages to industries such as part design freedom, consolidation of assemblies, part customization and low tooling cost over conventional manufacturing processes. Electric coils and thermal management devices are generally manufactured from pure copper due to its high electrical and thermal conductivity properties. Therefore, if L-PBF of pure copper is feasible, geometrically optimized heat sinks and free-form electromagnetic coils can be manufactured. However, producing dense pure copper parts by L-PBF is difficult due to low optical absorptivity to infrared radiation and high thermal conductivity. To produce dense copper parts in a conventional L-PBF system either the power of the infrared laser must be increased above 500W, or a green laser should be used for which copper has a high optical absorptivity. Increasing the infrared laser power can damage the optical components of the laser systems due to back reflections and create instabilities in the process due to thermal-optical phenomenon of the lenses. In this work, a multi-physics meso-scale numerical model based on Finite Volume Method (FVM) is developed in Flow-3D to investigate the physical phenomena interaction which governs the melt pool dynamics and ultimately the part quality. A green laser heat source and an infrared laser heat source are used individually to create single track deposition on pure copper powder bed above a substrate. The effect of the dissimilar optical absorptivity property of laser heat sources on the melt pool dynamics is explored. To validate the numerical model, experiments were conducted wherein single tracks are deposited on a copper powder bed and the simulated melt pool shape and size are compared. As the green laser has a high optical absorptivity, a conduction and keyhole mode melting is possible while for the infrared laser only keyhole mode melting is possible due to low absorptivity. The variation in melting modes with respect to the laser wavelength has an outcome on thermal gradient and cooling rates which ultimately affect the mechanical, electrical, and thermal properties.

Keywords

Pure Copper, Laser Powder Bed Fusion, Finite Volume Method, multi-physics

Fig. 2 Framework for single laser track simulation model including powder bed and substrate (a) computational domain with boundaries (b) discretization of the domain with uniform quad mesh.

Fig. 3 2D melt pool contours from the numerical model compared to experiments [16] for (a) VED = 65 J/mm3 at 7 mm from the beginning of the single track (b) VED = 103 J/mm3 at 3 mm from the beginning of the single track (c) VED = 103 J/mm3 at 7 mm from the beginning of the single track. In the 2D contour, the non-melted region is indicated in blue, and the melted region is indicated by red and green when the VED is 65 J/mm3 and 103 J/mm3 respectively.

Fig. 4 3D temperature contour plots of during single track L-PBF process at time1.8 µs when (a) VED = 65 J/mm3 (b) VED = 103 J/mm3 along with 2D melt pool contours at 5 mm from the laser initial position. In the 2D contour, the non-melted region is indicated in blue, and the melted region is indicated by red and green when the VED is 65 J/mm3 and 103 J/mm3 respectively.

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Fig. 1. Schematic figure showing the PREP with additional gas flowing on the end face of electrode.

플라즈마 회전 전극 공정 중 분말 형성에 대한 공정 매개변수 및 냉각 가스의 영향

Yujie Cui^a Yufan Zhao^a1Haruko Numata^b Kenta Yamanaka^a Huakang Bian^aKenta Aoyagi^aAkihikoChiba^a
^aInstitute for Materials Research, Tohoku University, Sendai 980-8577, Japan^bDepartment of Materials Processing, Graduate School of Engineering, Tohoku University, Sendai 980-8577, Japan

Highlights

•The limitation of increasing the rotational speed in decreasing powder size was clarified.

•Cooling and disturbance effects varied with the gas flowing rate.

•Inclined angle of the residual electrode end face affected powder formation.

•Additional cooling gas flowing could be applied to control powder size.

Abstract

The plasma rotating electrode process (PREP) is rapidly becoming an important powder fabrication method in additive manufacturing. However, the low production rate of fine PREP powder limits the development of PREP. Herein, we investigated different factors affecting powder formation during PREP by combining experimental methods and numerical simulations. The limitation of increasing the rotation electrode speed in decreasing powder size is attributed to the increased probability of adjacent droplets recombining and the decreased tendency of granulation. The effects of additional Ar/He gas flowing on the rotational electrode on powder formation is determined through the cooling effect, the disturbance effect, and the inclined effect of the residual electrode end face simultaneously. A smaller-sized powder was obtained in the He atmosphere owing to the larger inclined angle of the residual electrode end face compared to the Ar atmosphere. Our research highlights the route for the fabrication of smaller-sized powders using PREP.

플라즈마 회전 전극 공정(PREP)은 적층 제조 에서 중요한 분말 제조 방법으로 빠르게 자리잡고 있습니다. 그러나 미세한 PREP 분말의 낮은 생산율은 PREP의 개발을 제한합니다. 여기에서 우리는 실험 방법과 수치 시뮬레이션을 결합하여 PREP 동안 분말 형성에 영향을 미치는 다양한 요인을 조사했습니다. 분말 크기 감소에서 회전 전극 속도 증가의 한계는 인접한 액적 재결합 확률 증가 및 과립화 경향 감소에 기인합니다.. 회전 전극에 흐르는 추가 Ar/He 가스가 분말 형성에 미치는 영향은 냉각 효과, 외란 효과 및 잔류 전극 단면의 경사 효과를 통해 동시에 결정됩니다. He 분위기에서는 Ar 분위기에 비해 잔류 전극 단면의 경사각이 크기 때문에 더 작은 크기의 분말이 얻어졌다. 우리의 연구는 PREP를 사용하여 더 작은 크기의 분말을 제조하는 경로를 강조합니다.

Keywords

Plasma rotating electrode process

Ti-6Al-4 V alloy, Rotating speed, Numerical simulation, Gas flowing, Powder size

Introduction

With the development of additive manufacturing, there has been a significant increase in high-quality powder production demand [1,2]. The initial powder characteristics are closely related to the uniform powder spreading [3,4], packing density [5], and layer thickness observed during additive manufacturing [6], thus determining the mechanical properties of the additive manufactured parts [7,8]. Gas atomization (GA) [9–11], centrifugal atomization (CA) [12–15], and the plasma rotating electrode process (PREP) are three important powder fabrication methods.

Currently, GA is the dominant powder fabrication method used in additive manufacturing [16] for the fabrication of a wide range of alloys [11]. GA produces powders by impinging a liquid metal stream to droplets through a high-speed gas flow of nitrogen, argon, or helium. With relatively low energy consumption and a high fraction of fine powders, GA has become the most popular powder manufacturing technology for AM.

The entrapped gas pores are generally formed in the powder after solidification during GA, in which the molten metal is impacted by a high-speed atomization gas jet. In addition, satellites are formed in GA powder when fine particles adhere to partially molten particles.

The gas pores of GA powder result in porosity generation in the additive manufactured parts, which in turn deteriorates its mechanical properties because pores can become crack initiation sites [17]. In CA, a molten metal stream is poured directly onto an atomizer disc spinning at a high rotational speed. A thin film is formed on the surface of the disc, which breaks into small droplets due to the centrifugal force. Metal powder is obtained when these droplets solidify.

Compared with GA powder, CA powder exhibits higher sphericity, lower impurity content, fewer satellites, and narrower particle size distribution [12]. However, very high speed is required to obtain fine powder by CA. In PREP, the molten metal, melted using the plasma arc, is ejected from the rotating rod through centrifugal force. Compared with GA powder, PREP-produced powders also have higher sphericity and fewer pores and satellites [18].

For instance, PREP-fabricated Ti6Al-4 V alloy powder with a powder size below 150 μm exhibits lower porosity than gas-atomized powder [19], which decreases the porosity of additive manufactured parts. Furthermore, the process window during electron beam melting was broadened using PREP powder compared to GA powder in Inconel 718 alloy [20] owing to the higher sphericity of the PREP powder.

In summary, PREP powder exhibits many advantages and is highly recommended for powder-based additive manufacturing and direct energy deposition-type additive manufacturing. However, the low production rate of fine PREP powder limits the widespread application of PREP powder in additive manufacturing.

Although increasing the rotating speed is an effective method to decrease the powder size [21,22], the reduction in powder size becomes smaller with the increased rotating speed [23]. The occurrence of limiting effects has not been fully clarified yet.

Moreover, the powder size can be decreased by increasing the rotating electrode diameter [24]. However, these methods are quite demanding for the PREP equipment. For instance, it is costly to revise the PREP equipment to meet the demand of further increasing the rotating speed or electrode diameter.

Accordingly, more feasible methods should be developed to further decrease the PREP powder size. Another factor that influences powder formation is the melting rate [25]. It has been reported that increasing the melting rate decreases the powder size of Inconel 718 alloy [26].

In contrast, the powder size of SUS316 alloy was decreased by decreasing the plasma current within certain ranges. This was ascribed to the formation of larger-sized droplets from fluid strips with increased thickness and spatial density at higher plasma currents [27]. The powder size of NiTi alloy also decreases at lower melting rates [28]. Consequently, altering the melting rate, varied with the plasma current, is expected to regulate the PREP powder size.

Furthermore, gas flowing has a significant influence on powder formation [27,29–31]. On one hand, the disturbance effect of gas flowing promotes fluid granulation, which in turn contributes to the formation of smaller-sized powder [27]. On the other hand, the cooling effect of gas flowing facilitates the formation of large-sized powder due to increased viscosity and surface tension. However, there is a lack of systematic research on the effect of different gas flowing on powder formation during PREP.

Herein, the authors systematically studied the effects of rotating speed, electrode diameter, plasma current, and gas flowing on the formation of Ti-6Al-4 V alloy powder during PREP as additive manufactured Ti-6Al-4 V alloy exhibits great application potential [32]. Numerical simulations were conducted to explain why increasing the rotating speed is not effective in decreasing powder size when the rotation speed reaches a certain level. In addition, the different factors incited by the Ar/He gas flowing on powder formation were clarified.

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원문보기

Fig. 1. Model geometry with the computational domain, extrusion nozzle, toolpath, and boundary conditions. The model is presented while printing the fifth layer.

재료 압출 적층 제조에서 증착된 층의 안정성 및 변형

Md Tusher Mollah Raphaël 사령관 Marcin P. Serdeczny David B. Pedersen Jon Spangenberg덴마크 공과 대학 기계 공학과, Kgs. 덴마크 링비

2020년 12월 22일 접수, 2021년 5월 1일 수정, 2021년 7월 15일 수락, 2021년 7월 21일 온라인 사용 가능, 기록 버전 2021년 8월 17일 .

Abstract

이 문서는 재료 압출 적층 제조 에서 여러 레이어를 인쇄하는 동안 증착 흐름의 전산 유체 역학 시뮬레이션 을 제공합니다 . 개발된 모델은 증착된 레이어의 형태를 예측하고 점소성 재료 를 인쇄하는 동안 레이어 변형을 캡처합니다 . 물리학은 일반화된 뉴턴 유체 로 공식화된 Bingham 구성 모델의 연속성 및 운동량 방정식에 의해 제어됩니다. . 증착된 층의 단면 모양이 예측되고 재료의 다양한 구성 매개변수에 대해 층의 변형이 연구됩니다. 층의 변형은 인쇄물의 정수압과 압출시 압출압력으로 인한 것임을 알 수 있다. 시뮬레이션에 따르면 항복 응력이 높을수록 변형이 적은 인쇄물이 생성되는 반면 플라스틱 점도 가 높을수록 증착된 레이어에서변형이 커 집니다 . 또한, 인쇄 속도, 압출 속도 의 영향, 층 높이 및 인쇄된 층의 변형에 대한 노즐 직경을 조사합니다. 마지막으로, 이 모델은 후속 인쇄된 레이어의 정수압 및 압출 압력을 지원하기 위해 증착 후 점소성 재료가 요구하는 항복 응력의 필요한 증가에 대한 보수적인 추정치를 제공합니다.

This paper presents computational fluid dynamics simulations of the deposition flow during printing of multiple layers in material extrusion additive manufacturing. The developed model predicts the morphology of the deposited layers and captures the layer deformations during the printing of viscoplastic materials. The physics is governed by the continuity and momentum equations with the Bingham constitutive model, formulated as a generalized Newtonian fluid. The cross-sectional shapes of the deposited layers are predicted, and the deformation of layers is studied for different constitutive parameters of the material. It is shown that the deformation of layers is due to the hydrostatic pressure of the printed material, as well as the extrusion pressure during the extrusion. The simulations show that a higher yield stress results in prints with less deformations, while a higher plastic viscosity leads to larger deformations in the deposited layers. Moreover, the influence of the printing speed, extrusion speed, layer height, and nozzle diameter on the deformation of the printed layers is investigated. Finally, the model provides a conservative estimate of the required increase in yield stress that a viscoplastic material demands after deposition in order to support the hydrostatic and extrusion pressure of the subsequently printed layers.

키워드

점성 플라스틱 재료, 재료 압출 적층 제조(MEX-AM), 다층 증착, 전산유체역학(CFD), 변형 제어
Viscoplastic Materials, Material Extrusion Additive Manufacturing (MEX-AM), Multiple-Layers Deposition, Computational Fluid Dynamics (CFD), Deformation Control

Introduction

Three-dimensional printing of viscoplastic materials has grown in popularity over the recent years, due to the success of Material Extrusion Additive Manufacturing (MEX-AM) [1]. Viscoplastic materials, such as ceramic pastes [2,3], hydrogels [4], thermosets [5], and concrete [6], behave like solids when the applied load is below their yield stress, and like a fluid when the applied load exceeds their yield stress [7]. Viscoplastic materials are typically used in MEX-AM techniques such as Robocasting [8], and 3D concrete printing [9,10]. The differences between these technologies lie in the processing of the material before the extrusion and in the printing scale (from microscale to big area additive manufacturing). In these extrusion-based technologies, the structure is fabricated in a layer-by-layer approach onto a solid surface/support [11, 12]. During the process, the material is typically deposited on top of the previously printed layers that may be already solidified (wet-on-dry printing) or still deformable (wet-on-wet printing) [1]. In wet-on-wet printing, control over the deformation of layers is important for the stability and geometrical accuracy of the prints. If the material is too liquid after the deposition, it cannot support the pressure of the subsequently deposited layers. On the other hand, the material flowability is a necessity during extrusion through the nozzle. Several experimental studies have been performed to analyze the physics of the extrusion and deposition of viscoplastic materials, as reviewed in Refs. [13–16]. The experimental measurements can be supplemented with Computational Fluid Dynamics (CFD) simulations to gain a more complete picture of MEX-AM. A review of the CFD studies within the material processing and deposition in 3D concrete printing was presented by Roussel et al. [17]. Wolfs et al. [18] predicted numerically the failure-deformation of a cylindrical structure due to the self-weight by calculating the stiffness and strength of the individual layers. It was found that the deformations can take place in all layers, however the most critical deformation occurs in the bottom layer. Comminal et al. [19,20] presented three-dimensional simulations of the material deposition in MEX-AM, where the fluid was approximated as Newtonian. Subsequently, the model was experimentally validated in Ref. [21] for polymer-based MEX-AM, and extended to simulate the deposition of multiple layers in Ref. [22], where the previously printed material was assumed solid. Xia et al. [23] simulated the influence of the viscoelastic effects on the shape of deposited layers in MEX-AM. A numerical model for simulating the deposition of a viscoplastic material was recently presented and experimentally validated in Refs. [24] and [25]. These studies focused on predicting the cross-sectional shape of a single printed layer for different processing conditions (relative printing speed, and layer height). Despite these research efforts, a limited number of studies have focused on investigating the material deformations in wet-on-wet printing when multiple layers are deposited on top of each other. This paper presents CFD simulations of the extrusion-deposition flow of a viscoplastic material for several subsequent layers (viz. three- and five-layers). The material is continuously printed one layer over another on a fixed solid surface. The rheology of the viscoplastic material is approximated by the Bingham constitutive equation that is formulated using the Generalized Newtonian Fluid (GNF) model. The CFD model is used to predict the cross-sectional shapes of the layers and their deformations while printing the next layers on top. Moreover, the simulations are used to quantify the extrusion pressure applied by the deposited material on the substrate, and the previously printed layers. Numerically, it is investigated how the process parameters (i.e., the extrusion speed, printing speed, nozzle diameter, and layer height) and the material rheology affect the deformations of the deposited layers. Section 2 describes the methodology of the study. Section 3 presents and discusses the results. The study is summarized and concluded in Section 4.

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원문 보기

Fluid Thermodynamic Simulation of Ti-6Al-4V Alloy in Laser Wire Deposition

Xiang Wang, Lin-Jie Zhang, Jie Ning, and Suck-Joo Na
Published Online:8 Apr 2022https://doi.org/10.1089/3dp.2021.0159

Abstract

A 3D numerical model of heat transfer and fluid flow of molten pool in the process of laser wire deposition was presented by computational fluid dynamics technique. The simulation results of the deposition morphology were also compared with the experimental results under the condition of liquid bridge transfer mode. Moreover, they showed a good agreement. Considering the effect of recoil pressure, the morphology of the deposit metal obtained by the simulation was similar to the experiment result. Molten metal at the wire tip was peeled off and flowed into the molten pool, and then spread to both sides of the deposition layer under the recoil pressure. In addition, the results of simulation and high-speed charge-coupled device presented that a wedge transition zone, with a length of ∼6 mm, was formed behind the keyhole in the liquid bridge transfer process, where the height of deposited metal decreased gradually. After solidification, metal in the transition zone retained the original melt morphology, resulting in a decrease in the height of the tail of the deposition layer.

Keywords

LWD, CFD, liquid bridge transfer, fluid dynamics, wedge transition zone

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Fig. 2: Scheme of the LED photo-crosslinking and 3D-printing section of the microfluidic/3D-printing device. The droplet train is transferred from the chip microchannel into a microtubing in a straight section with nearly identical inner channel and inner microtubing diameter. Further downstream, the microtubing passes an LED-section for fast photo cross-linking to generate the microgels. This section is contained in an aluminum encasing to avoid premature crosslinking of polymer precursor in upstream channel sections by stray light. Subsequently, the microtubing is integrated into a 3D-printhead, where the microgels are jammed into a filament that is directly 3D-printed into the scaffold.

On-Chip Fabrication and In-Flow 3D-Printing of Cell-Laden Microgel Constructs: From Chip to Scaffold Materials in One Integral Process

세포가 함유된 마이크로겔의 온칩 제작 및 인-플로우 3D 프린팅
구성:하나의 통합 프로세스에서 칩에서 스캐폴드 재료까지

Vollmer, Gültekin Tamgüney, Aldo Boccacini
Submitted date: 10/05/2021 • Posted date: 11/05/2021
Licence: CC BY-NC-ND 4.0

바이오프린팅은 세포가 실린 스캐폴드의 제조를 위한 유력한 기술로 발전했습니다. 바이오잉크는 바이오프린팅의 가장 중요한 구성요소입니다. 최근 마이크로겔은 세포 보호 및 세포 미세 환경 제어를 가능하게 하는 매우 유망한 바이오 잉크로 도입되었습니다. 그러나 이들의 미세유체 제작은 본질적으로 한계가 있는 것으로 보입니다.

여기에서 우리는 안정적인 스캐폴드에 직접 유입되는 바이오프린팅과 함께 세포가 실린 마이크로겔의 미세유체 생산을 위한 미세유체 및 3D 인쇄의 직접 결합을 소개합니다. 방법론은 세포를 단분산 미세 방울로 연속 온칩 캡슐화하여 후속 유입 교차 연결을 통해 세포가 함유된 마이크로겔을 생성할 수 있으며, 이는 미세관을 종료한 후 자동으로 얇은 연속 마이크로겔 필라멘트로 끼이게 됩니다.

3D 프린트 헤드로의 통합으로 독립형 3차원 스캐폴드에 필라멘트를 직접 유입 인쇄할 수 있습니다. 이 방법은 다양한 교차 연결 방법 및 세포주에 대해 설명됩니다. 이러한 발전으로 미세유체학은 더 이상 바이오 제조의 병목을 초래하는 현상이 아닙니다.

Bioprinting has evolved into a thriving technology for the fabrication of cell-laden scaffolds. Bioinks are the most critical component for bioprinting. Recently, microgels have been introduced as a very promising bioink enabling cell protection and the control of the cellular microenvironment. However, their microfluidic fabrication inherently seemed to be a limitation. Here we introduce a direct coupling of microfluidics and 3D-printing for the microfluidic production of cell-laden microgels with direct in-flow bioprinting into stable scaffolds. The methodology enables the continuous on-chip encapsulation of cells into monodisperse microdroplets with subsequent in-flow cross-linking to produce cell-laden microgels, which after exiting a microtubing are automatically jammed into thin continuous microgel filaments. The integration into a 3D printhead allows direct in-flow printing of the filaments into free-standing three-dimensional scaffolds. The method is demonstrated for different cross-linking methods and cell lines. With this advancement, microfluidics is no longer a bottleneck for biofabrication.

Fig. 1: Three-dimensional schematic view of the multilayer double 3D-focusing microfluidic channel system, (b) control of droplet diameter via the Capiilary number Ca, and accessible hydrodynamic regimes for droplet production: squeezing (c), dripping (d) and jetting (e). The scale bars are 200 µm.

Fig. 3: a) Photograph of a standard meander-shaped layer fabricated by microgel filament deposition printing. The lines have a thickness of 300 µm. b) photograph of a cross-bar pattern obtained by on-top deposition of several microgel filaments. The average linewidth is 1 mm. c) photograph of a donut-shaped microgel construct. The microgels have been fluorescently labelled by FITC-dextran to demonstrate the intrinsic microporosity corresponding to the black non-fluorescent regions, d) light microscopy image of a construct edge showing that fused adhesive microgels form a continuous, three-dimensional selfsupporting scaffold with intrinsic micropores.

Fig. 4: a) Scheme of the perfusion chamber consisting of an upstream and downstream chamber, perfusion ports, and removable scaffolds to stabilize the microgel construct during 3D-printing, b) photograph of a microgel construct in the perfusion chamber directly after printing and removal of the scaffolds, c) confocal microscopy image of the permeation front of a fluorescent dye, where the high dye concentration in the micropores can be clearly seen, d) confocal microscopy image of YFP-labelled HEK-cells within a microgel construct.

Fig. 5: a) Layer-by-layer printing of microgel construct with integrated perfusion channel. After printing of the first layer, a hollow perfusion channel is inserted. Subsequently, the second and third layers are printed. b) The construct is directly printed into a perfusion chamber. The perfusion chamber provides whole construct permeation via flows cin and cout, as well as independent flow through the perfusion channel via flows vin and vout. c) Photograph of a perfusion chamber containing the construct directly after printing. The flow of the fluorescein solution through the integrated PVA hollow channel is clearly visible.

Fig. 6: a) Photograph of an alginate capsule fiber formed after exiting the microtube. b) Confocal fluorescence microscopy image of part of a 3D-printed alginate capsule construct. The fluorescence arises from encapsulated fluorescently labelled polystyrene microbeads to demonstrate the integrity and stability of the alginate capsules.

Keywords

biomaterials, microgels, microfluidics, 3D printing, bioprinting

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Multiscale Process Modeling of Residual Deformation and Defect Formation for Laser Powder Bed Fusion Additive Manufacturing

Qian Chen, PhD
University of Pittsburgh, 2021

레이저 분말 베드 퓨전(L-PBF) 적층 제조(AM)는 우수한 기계적 특성으로 그물 모양에 가까운 복잡한 부품을 생산할 수 있습니다. 그러나 빌드 실패 및 다공성과 같은 결함으로 이어지는 원치 않는 잔류 응력 및 왜곡이 L-PBF의 광범위한 적용을 방해하고 있습니다.

L-PBF의 잠재력을 최대한 실현하기 위해 잔류 변형, 용융 풀 및 다공성 형성을 예측하는 다중 규모 모델링 방법론이 개발되었습니다. L-PBF의 잔류 변형 및 응력을 부품 규모에서 예측하기 위해 고유 변형 방법을 기반으로 하는 다중 규모 프로세스 모델링 프레임워크가 제안됩니다.

고유한 변형 벡터는 마이크로 스케일에서 충실도가 높은 상세한 다층 프로세스 시뮬레이션에서 추출됩니다. 균일하지만 이방성인 변형은 잔류 왜곡 및 응력을 예측하기 위해 준 정적 평형 유한 요소 분석(FEA)에서 레이어별로 L-PBF 부품에 적용됩니다.

부품 규모에서의 잔류 변형 및 응력 예측 외에도 분말 규모의 다중물리 모델링을 수행하여 공정 매개변수, 예열 온도 및 스패터링 입자에 의해 유도된 용융 풀 변동 및 결함 형성을 연구합니다. 이러한 요인과 관련된 용융 풀 역학 및 다공성 형성 메커니즘은 시뮬레이션 및 실험을 통해 밝혀졌습니다.

제안된 부품 규모 잔류 응력 및 왜곡 모델을 기반으로 경로 계획 방법은 큰 잔류 변형 및 건물 파손을 방지하기 위해 주어진 형상에 대한 레이저 스캐닝 경로를 조정하기 위해 개발되었습니다.

연속 및 아일랜드 스캐닝 전략을 위한 기울기 기반 경로 계획이 공식화되고 공식화된 컴플라이언스 및 스트레스 최소화 문제에 대한 전체 감도 분석이 수행됩니다. 이 제안된 경로 계획 방법의 타당성과 효율성은 AconityONE L-PBF 시스템을 사용하여 실험적으로 입증되었습니다.

또한 기계 학습을 활용한 데이터 기반 프레임워크를 개발하여 L-PBF에 대한 부품 규모의 열 이력을 예측합니다. 본 연구에서는 실시간 열 이력 예측을 위해 CNN(Convolutional Neural Network)과 RNN(Recurrent Neural Network)을 포함하는 순차적 기계 학습 모델을 제안합니다.

유한 요소 해석과 비교하여 100배의 예측 속도 향상이 달성되어 실제 제작 프로세스보다 빠른 예측이 가능하고 실시간 온도 프로파일을 사용할 수 있습니다.

Laser powder bed fusion (L-PBF) additive manufacturing (AM) is capable of producing complex parts near net shape with good mechanical properties. However, undesired residual stress and distortion that lead to build failure and defects such as porosity are preventing broader applications of L-PBF. To realize the full potential of L-PBF, a multiscale modeling methodology is developed to predict residual deformation, melt pool, and porosity formation. To predict the residual deformation and stress in L-PBF at part-scale, a multiscale process modeling framework based on inherent strain method is proposed.

Inherent strain vectors are extracted from detailed multi-layer process simulation with high fidelity at micro-scale. Uniform but anisotropic strains are then applied to L-PBF part in a layer-by-layer fashion in a quasi-static equilibrium finite element analysis (FEA) to predict residual distortion and stress. Besides residual distortion and stress prediction at part scale, multiphysics modeling at powder scale is performed to study the melt pool variation and defect formation induced by process parameters, preheating temperature and spattering particles. Melt pool dynamics and porosity formation mechanisms associated with these factors are revealed through simulation and experiments.

Based on the proposed part-scale residual stress and distortion model, path planning method is developed to tailor the laser scanning path for a given geometry to prevent large residual deformation and building failures. Gradient based path planning for continuous and island scanning strategy is formulated and full sensitivity analysis for the formulated compliance- and stress-minimization problem is performed.

The feasibility and effectiveness of this proposed path planning method is demonstrated experimentally using the AconityONE L-PBF system. In addition, a data-driven framework utilizing machine learning is developed to predict the thermal history at part-scale for L-PBF.

In this work, a sequential machine learning model including convolutional neural network (CNN) and recurrent neural network (RNN), long shortterm memory unit, is proposed for real-time thermal history prediction. A 100x prediction speed improvement is achieved compared to the finite element analysis which makes the prediction faster than real fabrication process and real-time temperature profile available.

Figure 1.1: Schematic Overview of Metal Laser Powder Bed Fusion Process [2]

Figure 1.2: Commercial Powder Bed Fusion Systems

Figure 1.3: Commercial Metal Components Fabricated by Powder Bed Fusion Additive Manufacturing: (a) GE Fuel Nozzle; (b) Stryker Hip Biomedical Implant.

Figure 2.1: Proposed Multiscale Process Simulation Framework

Figure 2.2: (a) Experimental Setup for In-situ Thermocouple Measurement in the EOS M290 Build Chamber; (b) Themocouple Locations on the Bottom Side of the Substrate.

Figure 2.3: (a) Finite Element Model for Single Layer Thermal Analysis; (b) Deposition Layer

Figure 2.4: Core-skin layer: (a) Surface Morphology; (b) Scanning Strategy; (c) Transient Temperature Distribution and Temperature History at (d) Point 1; (e) Point 2 and (f) Point 3

Figure 2.5: (a) Scanning Orientation of Each Layer; (b) Finite Element Model for Micro-scale Representative Volume

Figure 2.6: Bottom Layer (a) Thermal History; (b) Plastic Strain and (c) Elastic Strain Evolution History

Figure 2.7: Bottom Layer Inherent Strain under Default Process Parameters along Horizontal Scanning Path

Figure 2.8: Snapshots of the Element Activation Process

Figure 2.9: Double Cantilever Beam Structure Built by the EOS M290 DMLM Process (a) Before and (b) After Cutting off; (c) Faro Laser ScanArm V3 for Distortion Measurement

Figure 2.10: Square Canonical Structure Built by the EOS M290 DMLM Process

Figure 2.11: Finite Element Mesh for the Square Canonical and Snapshots of Element Activation Process

Figure 2.12: Simulated Distortion Field for the Double Cantilever Beam before Cutting off the Supports: (a) Inherent Strain Method; (b) Simufact Additive 3.1

Figure 3.10: Snapshots of Temperature Profile for Single Track in Keyhole Regime (P = 250W and V = 0.5m/s) at the Preheating Temperature of 100 °C

s) at the Preheating Temperature of 500 °C

Figure 3.15: Melt Pool Cross Section Comparison Between Simulation and Experiment for Single Track

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Figure 2. (a) Scanning electron microscopy images of Ti6Al4V powder particles and (b) simulated powder bed using discrete element modelling

Laser Powder Bed에서 Laser Drilling에 의한 Keyhole 형성 Ti6Al4V 생체 의학 합금의 융합: 메조스코픽 전산유체역학 시뮬레이션 대 경험적 검증을 사용한 수학적 모델링

Keyhole Formation by Laser Drilling in Laser Powder Bed Fusion of Ti6Al4V Biomedical Alloy: Mesoscopic Computational Fluid Dynamics Simulation versus Mathematical Modelling Using Empirical Validation

Asif Ur Rehman 1,2,3,*
,† , Muhammad Arif Mahmood 4,*
,† , Fatih Pitir 1
, Metin Uymaz Salamci 2,3
,
Andrei C. Popescu 4 and Ion N. Mihailescu 4

Abstract

LPBF(Laser Powder Bed fusion) 공정에서 작동 조건은 열 분포를 기반으로 레이저 유도 키홀 영역을 결정하는 데 필수적입니다. 얕은 구멍과 깊은 구멍으로 분류되는 이러한 영역은 LPBF 프로세스에서 확률과 결함 형성 강도를 제어합니다.

LPBF 프로세스의 핵심 구멍을 연구하고 제어하기 위해 수학적 및 CFD(전산 유체 역학) 모델이 제공됩니다. CFD의 경우 이산 요소 모델링 기법을 사용한 유체 체적 방법이 사용되었으며, 분말 베드 보이드 및 표면에 의한 레이저 빔 흡수를 포함하여 수학적 모델이 개발되었습니다.

동적 용융 풀 거동을 자세히 살펴봅니다. 실험적, CFD 시뮬레이션 및 분석적 컴퓨팅 결과 간에 정량적 비교가 수행되어 좋은 일치를 얻습니다.

LPBF에서 레이저 조사 영역 주변의 온도는 높은 내열성과 분말 입자 사이의 공기로 인해 분말층 주변에 비해 급격히 상승하여 레이저 횡방향 열파의 이동이 느려집니다. LPBF에서 키홀은 에너지 밀도에 의해 제어되는 얕고 깊은 키홀 모드로 분류될 수 있습니다. 에너지 밀도를 높이면 얕은 키홀 구멍 모드가 깊은 키홀 구멍 모드로 바뀝니다.

깊은 키홀 구멍의 에너지 밀도는 다중 반사와 키홀 구멍 내의 2차 반사 빔의 집중으로 인해 더 높아져 재료가 빠르게 기화됩니다.

깊은 키홀 구멍 모드에서는 온도 분포가 높기 때문에 액체 재료가 기화 온도에 가까우므로 얕은 키홀 구멍보다 구멍이 형성될 확률이 훨씬 높습니다. 온도가 급격히 상승하면 재료 밀도가 급격히 떨어지므로 비열과 융해 잠열로 인해 유체 부피가 증가합니다.

그 대가로 표면 장력을 낮추고 용융 풀 균일성에 영향을 미칩니다.

In the laser powder bed fusion (LPBF) process, the operating conditions are essential in determining laser-induced keyhole regimes based on the thermal distribution. These regimes, classified into shallow and deep keyholes, control the probability and defects formation intensity in the LPBF process. To study and control the keyhole in the LPBF process, mathematical and computational fluid dynamics (CFD) models are presented. For CFD, the volume of fluid method with the discrete element modeling technique was used, while a mathematical model was developed by including the laser beam absorption by the powder bed voids and surface. The dynamic melt pool behavior is explored in detail. Quantitative comparisons are made among experimental, CFD simulation and analytical computing results leading to a good correspondence. In LPBF, the temperature around the laser irradiation zone rises rapidly compared to the surroundings in the powder layer due to the high thermal resistance and the air between the powder particles, resulting in a slow travel of laser transverse heat waves. In LPBF, the keyhole can be classified into shallow and deep keyhole mode, controlled by the energy density. Increasing the energy density, the shallow keyhole mode transforms into the deep keyhole mode. The energy density in a deep keyhole is higher due to the multiple reflections and concentrations of secondary reflected beams within the keyhole, causing the material to vaporize quickly. Due to an elevated temperature distribution in deep keyhole mode, the probability of pores forming is much higher than in a shallow keyhole as the liquid material is close to the vaporization temperature. When the temperature increases rapidly, the material density drops quickly, thus, raising the fluid volume due to the specific heat and fusion latent heat. In return, this lowers the surface tension and affects the melt pool uniformity.

Keywords: laser powder bed fusion; computational fluid dynamics; analytical modelling; shallow
and deep keyhole modes; experimental correlation

Figure 1. Powder bed schematic with voids.

Figure 3. Temperature field contour formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.

Figure 4. Detailed view of shallow depth melt mode with temperature field at 0.695 ms

Figure 5. Melt flow stream traces formation at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms

Figure 6. Density evolution of the melt pool at various time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms.

Figure 7. Un-melted and melted regions at different time intervals (a) 0.695 ms, (b) 0.795 ms, (c) 0.995 ms and (d) 1.3 ms

Figure 8. Transformation from shallow depth melt flow to deep keyhole formation when laser power increased from (a) 170 W to (b) 200 W

Figure 9. Stream traces and laser beam multiple reflections in deep keyhole melt flow mode

Figure 10. A comparison between analytical and CFD simulation results for peak thermal distribution value in the deep keyhole formation

Figure 11. A comparison among experiments [49], CFD and analytical simulations for deep keyhole top width and bottom width

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nanomaterials-11-03284-v2 다운로드

Figure 6. Evolution of melt pool in the overhang region (θ = 45°, P = 100 W, v = 1000 mm/s, the streamlines are shown by arrows).

Experimental and numerical investigation of the origin of surface roughness in laser powder bed fused overhang regions

레이저 파우더 베드 융합 오버행 영역에서 표면 거칠기의 원인에 대한 실험 및 수치 조사

Shaochuan Feng ,Amar M. Kamat ,Soheil Sabooni &Yutao Pei Pages S66-S84 | Received 18 Jan 2021, Accepted 25 Feb 2021, Published online: 10 Mar 2021

ABSTRACT

Surface roughness of laser powder bed fusion (L-PBF) printed overhang regions is a major contributor to deteriorated shape accuracy/surface quality. This study investigates the mechanisms behind the evolution of surface roughness (R_a) in overhang regions. The evolution of surface morphology is the result of a combination of border track contour, powder adhesion, warp deformation, and dross formation, which is strongly related to the overhang angle (θ). When 0° ≤ θ ≤ 15°, the overhang angle does not affect R_a significantly since only a small area of the melt pool boundaries contacts the powder bed resulting in slight powder adhesion. When 15° < θ ≤ 50°, powder adhesion is enhanced by the melt pool sinking and the increased contact area between the melt pool boundary and powder bed. When θ > 50°, large waviness of the overhang contour, adhesion of powder clusters, severe warp deformation and dross formation increase R_a sharply.

레이저 파우더 베드 퓨전 (L-PBF) 프린팅 오버행 영역의 표면 거칠기는 형상 정확도 / 표면 품질 저하의 주요 원인입니다. 이 연구 는 오버행 영역에서 표면 거칠기 (R_a ) 의 진화 뒤에 있는 메커니즘을 조사합니다 . 표면 형태의 진화는 오버행 각도 ( θ ) 와 밀접한 관련이있는 경계 트랙 윤곽, 분말 접착, 뒤틀림 변형 및 드로스 형성의 조합의 결과입니다 . 0° ≤ θ ≤ 15° 인 경우 , 용융풀 경계의 작은 영역 만 분말 베드와 접촉하여 약간의 분말 접착이 발생하기 때문에 오버행 각도가 R _a에 큰 영향을 주지 않습니다 . 15° < θ 일 때 ≤ 50°, 용융 풀 싱킹 및 용융 풀 경계와 분말 베드 사이의 증가된 접촉 면적으로 분말 접착력이 향상됩니다. θ > 50° 일 때 오버행 윤곽의 큰 파형, 분말 클러스터의 접착, 심한 휨 변형 및 드 로스 형성이 R_a 급격히 증가 합니다.

KEYWORDS: Laser powder bed fusion (L-PBF), melt pool dynamics, overhang region, shape deviation, surface roughness

1. Introduction

레이저 분말 베드 융합 (L-PBF)은 첨단 적층 제조 (AM) 기술로, 집중된 레이저 빔을 사용하여 금속 분말을 선택적으로 융합하여 슬라이스 된 3D 컴퓨터 지원에 따라 층별로 3 차원 (3D) 금속 부품을 구축합니다. 설계 (CAD) 모델 (Chatham, Long 및 Williams 2019 ; Tan, Zhu 및 Zhou 2020 ). 재료가 인쇄 층 아래에 존재하는지 여부에 따라 인쇄 영역은 각각 솔리드 영역 또는 돌출 영역으로 분류 될 수 있습니다. 따라서 오버행 영역은 고체 기판이 아니라 분말 베드 바로 위에 건설되는 특수 구조입니다 (Patterson, Messimer 및 Farrington 2017). 오버행 영역은지지 구조를 포함하거나 포함하지 않고 구축 할 수 있으며, 지지대가있는 돌출 영역의 L-PBF는 지지체가 더 낮은 밀도로 구축된다는 점을 제외 하고 (Wang and Chou 2018 ) 고체 기판의 공정과 유사합니다 (따라서 기계적 강도가 낮기 때문에 L-PBF 공정 후 기계적으로 쉽게 제거 할 수 있습니다. 따라서지지 구조로 인쇄 된 오버행 영역은 L-PBF 공정 후 지지물 제거, 연삭 및 연마와 같은 추가 후 처리 단계가 필요합니다.

수평 내부 채널의 제작과 같은 일부 특정 경우에는 공정 후 지지대를 제거하기가 어려우므로 채널 상단 절반의 돌출부 영역을 지지대없이 건설해야합니다 (Hopkinson and Dickens 2000 ). 수평 내부 채널에 사용할 수없는지지 구조 외에도 내부 표면, 특히 등각 냉각 채널 (Feng, Kamat 및 Pei 2021 ) 에서 발생하는 복잡한 3D 채널 네트워크의 경우 표면 마감 프로세스를 구현하는 것도 어렵습니다 . 결과적으로 오버행 영역은 (i) 잔류 응력에 의한 변형, (ii) 계단 효과 (Kuo et al. 2020 ; Li et al. 2020 )로 인해 설계된 모양에서 벗어날 수 있습니다 .) 및 (iii) 원하지 않는 분말 소결로 인한 향상된 표면 거칠기; 여기서, 앞의 두 요소는 일반적으로 mm 길이 스케일에서 ‘매크로’편차로 분류되고 후자는 일반적으로 µm 길이 스케일에서 ‘마이크로’편차로 인식됩니다.

열 응력에 의한 변형은 오버행 영역에서 발생하는 중요한 문제입니다 (Patterson, Messimer 및 Farrington 2017 ). 국부적 인 용융 / 냉각은 용융 풀 내부 및 주변에서 큰 온도 구배를 유도하여 응고 된 층에 집중적 인 열 응력을 유발합니다. 열 응력에 의한 뒤틀림은 고체 영역을 현저하게 변형하지 않습니다. 이러한 영역은 아래의 여러 레이어에 의해 제한되기 때문입니다. 반면에 오버행 영역은 구속되지 않고 공정 중 응력 완화로 인해 상당한 변형이 발생합니다 (Kamat 및 Pei 2019 ). 더욱이 용융 깊이는 레이어 두께보다 큽니다 (이전 레이어도 재용 해되어 빌드 된 레이어간에 충분한 결합을 보장하기 때문입니다 [Yadroitsev et al. 2013 ; Kamath et al.2014 ]),응고 된 두께가 설계된 두께보다 크기 때문에형태 편차 (예 : 드 로스 [Charles et al. 2020 ; Feng et al. 2020 ])가 발생합니다. 마이크로 스케일에서 인쇄 된 표면 (R_a 및 S_a ∼ 10 μm)은 기계적으로 가공 된 표면보다 거칠다 (Duval-Chaneac et al. 2018 ; Wen et al. 2018 ). 이 문제는고형화 된 용융 풀의 가장자리에 부착 된 용융되지 않은 분말의 결과로 표면 거칠기 (R_a )가 일반적으로 약 20 μm인 오버행 영역에서 특히 심각합니다 (Mazur et al. 2016 ; Pakkanen et al. 2016 ).

오버행 각도 ( θ , 빌드 방향과 관련하여 측정)는 오버행 영역의 뒤틀림 편향과 표면 거칠기에 영향을 미치는 중요한 매개 변수입니다 (Kamat and Pei 2019 ; Mingear et al. 2019 ). θ ∼ 45 ° 의 오버행 각도 는 일반적으로지지 구조없이 오버행 영역을 인쇄 할 수있는 임계 값으로 합의됩니다 (Pakkanen et al. 2016 ; Kadirgama et al. 2018 ). θ 일 때이 임계 값보다 크면 오버행 영역을 허용 가능한 표면 품질로 인쇄 할 수 없습니다. 오버행 각도 외에도 레이저 매개 변수 (레이저 에너지 밀도와 관련된)는 용융 풀의 모양 / 크기 및 용융 풀 역학에 영향을줌으로써 오버행 영역의 표면 거칠기에 영향을줍니다 (Wang et al. 2013 ; Mingear et al . 2019 ).

용융 풀 역학은 고체 (Shrestha 및 Chou 2018 ) 및 오버행 (Le et al. 2020 ) 영역 모두에서 수행되는 L-PBF 공정을 포함한 레이저 재료 가공의 일반적인 물리적 현상입니다 . 용융 풀 모양, 크기 및 냉각 속도는 잔류 응력으로 인한 변형과 표면 거칠기에 모두 영향을 미치므로 처리 매개 변수와 표면 형태 / 품질 사이의 다리 역할을하며 용융 풀을 이해하기 위해 수치 시뮬레이션을 사용하여 추가 조사를 수행 할 수 있습니다. 거동과 표면 거칠기에 미치는 영향. 현재까지 고체 영역의 L-PBF 동안 용융 풀 동작을 시뮬레이션하기 위해 여러 연구가 수행되었습니다. 유한 요소 방법 (FEM)과 같은 시뮬레이션 기술 (Roberts et al. 2009 ; Du et al.2019 ), 유한 차분 법 (FDM) (Wu et al. 2018 ), 전산 유체 역학 (CFD) (Lee and Zhang 2016 ), 임의의 Lagrangian-Eulerian 방법 (ALE) (Khairallah and Anderson 2014 )을 사용하여 증발 반동 압력 (Hu et al. 2018 ) 및 Marangoni 대류 (Zhang et al. 2018 ) 현상을포함하는 열 전달 (온도 장) 및 물질 전달 (용융 흐름) 프로세스. 또한 이산 요소법 (DEM)을 사용하여 무작위 분산 분말 베드를 생성했습니다 (Lee and Zhang 2016 ; Wu et al. 2018 ). 이 모델은 분말 규모의 L-PBF 공정을 시뮬레이션했습니다 (Khairallah et al. 2016) 메조 스케일 (Khairallah 및 Anderson 2014 ), 단일 트랙 (Leitz et al. 2017 )에서 다중 트랙 (Foroozmehr et al. 2016 ) 및 다중 레이어 (Huang, Khamesee 및 Toyserkani 2019 )로.

그러나 결과적인 표면 거칠기를 결정하는 오버행 영역의 용융 풀 역학은 문헌에서 거의 관심을받지 못했습니다. 솔리드 영역의 L-PBF에 대한 기존 시뮬레이션 모델이 어느 정도 참조가 될 수 있지만 오버행 영역과 솔리드 영역 간의 용융 풀 역학에는 상당한 차이가 있습니다. 오버행 영역에서 용융 금속은 분말 입자 사이의 틈새로 아래로 흘러 용융 풀이 다공성 분말 베드가 제공하는 약한 지지체 아래로 가라 앉습니다. 이것은 중력과 표면 장력의 영향이 용융 풀의 결과적인 모양 / 크기를 결정하는 데 중요하며, 결과적으로 오버행 영역의 마이크로 스케일 형태의 진화에 중요합니다. 또한 분말 입자 사이의 공극, 열 조건 (예 : 에너지 흡수,2019 ; Karimi et al. 2020 ; 노래와 영 2020 ). 표면 거칠기는 (마이크로) 형상 편차를 증가시킬뿐만 아니라 주기적 하중 동안 미세 균열의 시작 지점 역할을함으로써 기계적 강도를 저하시킵니다 (Günther et al. 2018 ). 오버행 영역의 높은 표면 거칠기는 (마이크로) 정확도 / 품질에 대한 엄격한 요구 사항이있는 부품 제조에서 L-PBF의 적용을 제한합니다.

본 연구는 실험 및 시뮬레이션 연구를 사용하여 오버행 영역 (지지물없이 제작)의 미세 형상 편차 형성 메커니즘과 표면 거칠기의 기원을 체계적이고 포괄적으로 조사합니다. 결합 된 DEM-CFD 시뮬레이션 모델은 경계 트랙 윤곽, 분말 접착 및 뒤틀림 변형의 효과를 고려하여 오버행 영역의 용융 풀 역학과 표면 형태의 형성 메커니즘을 나타 내기 위해 개발되었습니다. 표면 거칠기 R의 시뮬레이션 및 단일 요인 L-PBF 인쇄 실험을 사용하여 오버행 각도의 함수로 연구됩니다. 용융 풀의 침몰과 관련된 오버행 영역에서 분말 접착의 세 가지 메커니즘이 식별되고 자세히 설명됩니다. 마지막으로, 인쇄 된 오버행 영역에서 높은 표면 거칠기 문제를 완화 할 수 있는 잠재적 솔루션에 대해 간략하게 설명합니다.

The shape and size of the L-PBF printed samples are illustrated in Figure 1

Figure 2. Borders in the overhang region depending on the overhang angle θ

Figure 3. (a) Profile of the volumetric heat source, (b) the model geometry of single-track printing on a solid substrate (unit: µm), and (c) the comparison of melt pool dimensions obtained from the experiment (right half) and simulation (left half) for a calibrated optical penetration depth of 110 µm (laser power 200 W and scan speed 800 mm/s, solidified layer thickness 30 µm, powder size 10–45 µm).

Figure 4. The model geometry of an overhang being L-PBF processed: (a) 3D view and (b) right view.

Figure 5. The cross-sectional contour of border tracks in a 45° overhang region.

Figure 7. The overhang contour is contributed by (a) only outer borders when θ ≤ 60° (b) both inner borders and outer borders when θ > 60°.

Figure 8. Schematic of powder adhesion on a 45° overhang region.

Figure 9. The L-PBF printed samples with various overhang angle (a) θ = 0° (cube), (b) θ = 30°, (c) θ = 45°, (d) θ = 55° and (e) θ = 60°.

Figure 10. Two mechanisms of powder adhesion related to the overhang angle: (a) simulation-predicted, θ = 45°; (b) simulation-predicted, θ = 60°; (c, e) optical micrographs, θ = 45°; (d, f) optical micrographs, θ = 60°. (e) and (f) are partial enlargement of (c) and (d), respectively.

Figure 11. Simulation-predicted surface morphology in the overhang region at different overhang angle: (a) θ = 15°, (b) θ = 30°, (c) θ = 45°, (d) θ = 60° and (e) θ = 80° (Blue solid lines: simulation-predicted contour; red dashed lines: the planar profile of designed overhang region specified by the overhang angles).

Figure 12. Effect of overhang angle on surface roughness Ra in overhang regions

Figure 13. Surface morphology of L-PBF printed overhang regions with different overhang angle: (a) θ = 15°, (b) θ = 30°, (c) θ = 45° and (d) θ = 60° (overhang border parameters: P = 100 W, v = 1000 mm/s).

Figure 14. Effect of (a) laser power (scan speed = 1000 mm/s) and (b) scan speed (lase power = 100 W) on surface roughness Ra in overhang regions (θ = 45°, laser power and scan speed referred to overhang border parameters, and the other process parameters are listed in Table 2).

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Full Text View

Fig. 1. Schematic description of the laser welding process considered in this study.

Analysis of molten pool dynamics in laser welding with beam oscillation and filler wire feeding

Won-Ik Cho, Peer Woizeschke
Bremer Institut für angewandte Strahltechnik GmbH, Klagenfurter Straße 5, Bremen 28359, Germany

Received 30 July 2020, Revised 3 October 2020, Accepted 18 October 2020, Available online 1 November 2020.

Abstract

Molten pool flow and heat transfer in a laser welding process using beam oscillation and filler wire feeding were calculated using computational fluid dynamics (CFD). There are various indirect methods used to analyze the molten pool dynamics in fusion welding. In this work, based on the simulation results, the surface fluctuation was directly measured to enable a more intuitive analysis, and then the signal was analyzed using the Fourier transform and wavelet transform in terms of the beam oscillation frequency and buttonhole formation. The 1^st frequency (2 x beam oscillation frequency, the so-called chopping frequency), 2^nd frequency (4 x beam oscillation frequency), and beam oscillation frequency components were the main components found. The 1^st and 2^nd frequency components were caused by the effect of the chopping process and lumped line energy. The beam oscillation frequency component was related to rapid, unstable molten pool behavior. The wavelet transform effectively analyzed the rapid behaviors based on the change of the frequency components over time.

Korea Abstract

빔 진동 및 필러 와이어 공급을 사용하는 레이저 용접 공정에서 용융 풀 흐름 및 열 전달은 CFD (전산 유체 역학)를 사용하여 계산되었습니다. 용융 용접에서 용융 풀 역학을 분석하는 데 사용되는 다양한 간접 방법이 있습니다.

본 연구에서는 시뮬레이션 결과를 바탕으로 보다 직관적 인 분석이 가능하도록 표면 변동을 직접 측정 한 후 빔 발진 주파수 및 버튼 홀 형성 측면에서 푸리에 변환 및 웨이블릿 변환을 사용하여 신호를 분석했습니다.

1 차 주파수 (2 x 빔 발진 주파수, 이른바 초핑 주파수), 2 차 주파수 (4 x 빔 발진 주파수) 및 빔 발진 주파수 성분이 발견 된 주요 구성 요소였습니다. 1 차 및 2 차 주파수 성분은 쵸핑 공정과 집중 라인 에너지의 영향으로 인해 발생했습니다.

빔 진동 주파수 성분은 빠르고 불안정한 용융 풀 동작과 관련이 있습니다. 웨이블릿 변환은 시간 경과에 따른 주파수 구성 요소의 변화를 기반으로 빠른 동작을 효과적으로 분석했습니다.

1 . 소개

융합 용접에서 용융 풀 역학은 용접 결함과 시각적 이음새 품질에 직접적인 영향을 미칩니다. 이러한 역학을 연구하기 위해 고속 카메라를 사용하는 직접 방법과 광학 또는 음향 신호를 사용하는 간접 방법과 같은 다양한 측정 방법을 사용하여 여러 실험 방법을 고려했습니다. 시간 도메인의 원래 신호는 특별히 주파수 도메인에서 변환 된 신호로 변환되어 용융 풀 동작에 영향을 미치는 주파수 성분을 분석합니다. Kotecki et al. (1972)는 고속 카메라를 사용하여 가스 텅스텐 아크 용접에서 용융 풀을 관찰했습니다. [1]. 그들은 120Hz 리플 DC 출력을 가진 용접 전원을 사용할 때 용융 풀 진동 주파수가 120Hz임을 보여주었습니다. 전원을 끈 후 진동 주파수는 용융 풀의 고유 주파수를 나타내는 용융 풀 크기와 관련이 있습니다. 진동은 응고 중에 용접 표면 스케일링을 생성했습니다. Zacksenhouse and Hardt (1983)는 레이저 섀도 잉 동작 측정 기술을 사용하여 가스 텅스텐 아크 용접에서 완전히 관통 된 용융 풀의 동작을 측정했습니다 [2] . 그들은 2.5mm 두께의 강판에서 6mm 풀 반경 (고정 용접)에 대해 용융 풀의 고유 주파수가 18.9Hz라는 것을 발견했습니다. Semak et al. (1995) 고속 카메라를 사용하여 레이저 스폿 용접에서 용융 풀 및 키홀 역학 조사 [3]. 그들은 깊이가 약 3mm이고 반경이 약 3mm 인 용융 풀에서 200Hz의 낮은 체적 진동 주파수를 관찰했습니다. 0.45mm Aendenroomer와 den Ouden (1998)은 강철의 펄스 가스 텅스텐 아크 용접에서 용융 풀 진동을보고했습니다 [4] . 그들은 침투 깊이에 따라 진동 모드 변화를 보였고 주파수는 50Hz에서 150Hz 사이에서 변화했습니다. 주파수는 완전히 침투 된 용융 풀에서 더 낮았습니다. Hermans와 den Ouden (1999)은 단락 가스 금속 아크 용접에서 용융 풀 진동을 분석했습니다. [5]. 그들은 용융 풀의 단락 주파수와 고유 주파수가 같을 때 부분적으로 침투 된 용융 풀의 경우 공정 안정성이 향상되었음을 보여주었습니다. Yudodibroto et al. (2004)는 가스 텅스텐 아크 용접에서 용융 풀 진동에 대한 필러 와이어의 영향을 조사했습니다 [6] . 그들은 금속 전달이 특히 부분적으로 침투 된 용융 풀에서 진동 거동을 방해한다는 것을 보여주었습니다. Geiger et al. (2009) 레이저 키홀 용접에서 발광 분석 [7]. 신호의 주파수 분석을 사용하여 용융 풀 (1.5kHz 미만)과 키홀 (약 3kHz)에 해당하는 진동 주파수 범위를 찾았습니다. Kägeler와 Schmidt (2010)는 레이저 용접에서 용융 풀 크기의 변화를 관찰하기 위해 고속 카메라를 사용했습니다 [8] . 그들은 용융 풀에서 지배적 인 저주파 진동 성분 (100Hz 미만)을 발견했습니다. Shi et al. (2015) 고속 카메라를 사용하여 펄스 가스 텅스텐 아크 용접에서 용융 풀 진동 주파수 분석 [9]. 그들은 용접 침투 깊이가 작을수록 용융 풀의 진동 빈도가 더 높다는 것을 보여주었습니다. 추출 된 진동 주파수는 완전 용입 용접의 경우 85Hz 미만 이었지만 부분 용입 용접의 경우 110Hz에서 125Hz 사이였습니다. Volpp와 Vollertsen (2016)은 레이저 키홀 역학을 분석하기 위해 광학 신호를 사용했습니다 [10] . 그들은 공간 레이저 강도 분포로 인해 0.8에서 154 kHz 사이의 고주파 범위에서 피크를 발견했습니다. 위에서 언급 한 실험적 접근법은 공정 조건, 측정 방법 및 측정 된 위치에 따라 수십 Hz에서 수십 kHz까지 광범위한 용융 풀 역학에 대한 결과를 보여 주었다는 점에 유의해야합니다.

융합 용접에서 용융 풀 역학을 연구하기 위해 분석 접근 방식도 사용되었습니다. Zacksenhouse와 Hardt (1983)는 2.5mm 두께의 강판에서 대칭형 완전 관통 용융 풀의 고유 진동수를 계산했습니다 [2] . 매스 스프링 해석 모델을 사용하여 용융 풀 반경 6mm (고정 용접)에 대해 20.4Hz (실험에서 18.9Hz)의 고유 진동수와 3mm 풀 반경 (연속 용접)에 대해 40Hz의 고유 진동수를 예측했습니다. ). Postacioglu et al. (1989)는 원통형 용융 풀과 키홀을 가정하여 레이저 용접의 용융 풀에서 키홀 진동의 고유 진동수를 계산했습니다 .. 특정 열쇠 구멍 모양의 경우 약 900Hz의 기본 주파수가 계산되었습니다. Postacioglu et al. (1991)은 또한 레이저 용접에서 용접 속도를 고려하기 위해 타원형 용융 풀의 고유 진동수를 계산했습니다 [12] . 그들은 타원형 용융 풀의 모양이 고유 진동수에 영향을 미친다는 것을 보여주었습니다. 고유 진동수는 축의 길이 비율이 낮았으며, 즉 타원의 반장 축과 반 단축의 비율이 낮았습니다. Kroos et al. (1993)은 축 대칭 용융 풀과 키홀을 가정하여 레이저 키홀 용접의 동적 거동에 대한 이론적 모델을 개발했습니다 .. 키홀 폐쇄 시간은 0.1ms였으며 안정성 분석은 약 500Hz의 주파수에서 공진과 같은 진동을 예측했습니다. Maruo와 Hirata (1993)는 완전 관통 아크 용접에서 용융 풀을 모델링했습니다 [14] . 그들은 녹은 웅덩이가 정적 타원 모양을 가지고 있다고 가정했습니다. 그들은 고유 진동수와 진동 모드 사이의 관계를 조사하고 용융 풀 크기가 감소함에 따라 고유 진동수가 증가한다는 것을 보여주었습니다. Klein et al. (1994)는 원통형 키홀 모양을 사용하여 완전 침투 레이저 용접에서 키홀 진동을 연구했습니다 [15] . 그들은 점성 감쇠로 인해 키홀 진동이 낮은 kHz 범위로 제한된다는 것을 보여주었습니다. Klein et al. (1996)은 또한 레이저 출력의 작은 변동이 강한 키홀 진동으로 이어질 수 있음을 보여주었습니다[16] . 그들은 키홀 진동의 주요 공진 주파수 범위가 500 ~ 3500Hz라는 것을 발견했습니다. Andersen et al. (1997)은 고정 가스 텅스텐 아크 용접 [17] 에서 고정 된 원통형 모양을 가정하여 용융 풀의 고유 진동수를 예측 했으며 완전 용입 용접에서 용융 풀 폭이 증가함에 따라 감소하는 것으로 나타났습니다. 3.175mm 두께의 강판의 경우 주파수는 20Hz ~ 100Hz 범위였습니다. 위에 표시된 분석 방법은 일반적으로 단순한 용융 풀 모양을 가정하고 고유 진동수를 계산했습니다. 이것은 단순한 용융 풀 모양으로 고정 용접 공정을 분석하는 데 충분하지만 대부분의 용접 사례를 설명하는 과도 용접 공정에서 용융 풀 역학 분석에는 적합하지 않습니다.

반면에 수치 접근 방식은 고온 및 강한 빛과 같은 실험적 제한없이 자세한 정보를 제공하기 때문에 용융 풀 역학을 분석하는 이점이 있습니다. 전산 유체 역학 (CFD)의 수치 시뮬레이션 기술이 발전함에 따라 용융 풀 역학 분석에 대한 많은 연구가 수행되었습니다. 실제 용융 표면 변화는 VOF (체적 부피) 방법을 사용하여 계산할 수 있습니다. Cho et al. (2010) CO ₂ 레이저-아크 하이브리드 용접 공정을 위한 수학적 모델 개발 [18], 구형 방울이 생성 된 금속 와이어의 용융 과정이 와이어 공급 속도와 일치한다고 가정합니다. 그들은 필러 와이어가 희석되는 용융 풀 동작을 보여주었습니다. Cho et al. (2012)는 높은 빔 품질과 높은 금속 흡수율로 인해 업계에서 널리 사용되는 디스크 레이저 키홀 용접으로 수학적 모델을 확장했습니다 [19] . 그들은 열쇠 구멍에서 레이저 광선 번들의 다중 반사를 고려하고 용융 풀에서 keyholing과 같은 빠른 표면 변화를 자세히보고했습니다. 최근 CFD 시뮬레이션은 험핑 (Otto et al., 2016 [20] ) 및 기공 (Lin et al., 2017 [21] )과 같은보다 구체적인 현상을 분석하는데도 사용되었습니다 .) 레이저 용접에서. 그러나 용융 풀 역학과 관련된 연구는 거의 수행되지 않았습니다. Ko et al. (2000)은 수치 시뮬레이션을 사용하여 가스 텅스텐 아크 용접 풀의 동적 거동을 조사했습니다 [22] . 그들은 완전히 침투 된 용융 풀이 부분적으로 침투 된 풀보다 낮은 주파수에서 진동한다는 것을 보여주었습니다. 진동은 수십 분의 1 초 내에 무시할 수있는 크기로 감쇠되었습니다. Geiger et al. (2009)는 또한 수치 시뮬레이션을 사용하여 레이저 용접에서 용융 풀 거동을 보여주었습니다 [7]. 그들은 계산 된 증발 속도를 주파수 분석에 사용하여 공정에서 나오는 빛의 실험 결과와 비교했습니다. 판금 레이저 용접에서 중요한 공간 빔 진동 및 추가 필러 재료가있는 공정에 대한 용융 풀 역학에 대한 연구도 불충분합니다. Hu et al. (2018)은 금속 전달 메커니즘을 밝히기 위해 전자빔 3D 프린팅에서 와이어 공급 모델링을 수행했습니다. 그들은 주로 열 입력에 의해 결정되는 액체 브리지 전이, 액적 전이 및 중간 전이의 세 가지 유형의 금속 전달 모드를 보여주었습니다 .. Meng et al. (2020)은 레이저 빔 용접에서 용융 풀에 필러 와이어에 의해 추가 된 추가 요소의 전자기 교반 효과를 모델링했습니다. 용가재의 연속적인 액체 브릿지 이동이 가정되었고, 그 결과 전자기 교반의 영향이 키홀 깊이에 미미한 반면 필러 와이어 혼합을 향상 시켰습니다 [24] . Cho et al. (2017) 용접 방향에 수직 인 1 차원 빔 진동과 용접 라인을 따라 공급되는 필러 와이어를 사용하여 레이저 용접을위한 시뮬레이션 모델 개발 [25]. 그들은 시뮬레이션을 사용하여 특정 용접 현상, 즉 용융 풀의 단추 구멍 형성을 보여주었습니다. Cho et al. (2018)은 다중 반사 수와 전력 흡수량의 푸리에 변환을 사용하여 주파수 영역에서 소위 쵸핑 주파수 (2 x 빔 발진 주파수) 성분을 발견했습니다 [26] . 그러나 그들은 용융 풀 역학을 분석하기 위해 간접 신호를 사용했습니다. 따라서보다 직관적 인 분석을 위해서는 표면의 변동을 직접 측정해야합니다.

이 연구는 이전 연구에서 개발 된 레이저 용접 모델을 사용하여 3 차원 과도 CFD 시뮬레이션을 수행하여 빔 진동 및 필러 와이어 공급을 포함한 레이저 용접 공정에서 용융 풀 역학을 조사합니다. 용융 된 풀 표면의 시간적 변화는 시뮬레이션 결과에서 추출되었습니다. 추출 된 데이터는 주파수 영역뿐만 아니라 시간-주파수 영역에서도 분석되었습니다. 신호 처리를 통해 도출 된 결과는 특징적인 용융 풀 역학을 나타내며 빔 진동 주파수 및 단추 구멍 형성 측면에서 레이저 용접의 역학을 줄일 수있는 잠재력을 제공합니다.

2 . 방법론

그림 1도 1은 용접 방향에 수직 인 1 차원 빔 진동과 용접 라인을 따라 공급되는 필러 와이어를 사용하는 레이저 용접 프로세스의 개략적 설명을 보여줍니다. 1mm 두께의 알루미늄 합금 (AlSi1MgMn) 시트는 시트 표면에 초점을 맞춘 멀티 kW 파이버 레이저 (YLR-8000S, IPG Photonics, USA)를 사용하여 용접되었습니다. 시트는 에어 갭이있는 맞대기 이음으로 정렬되었습니다. 1 차원 스캐너 (ILV DC-Scanner, Ingenieurbüro für Lasertechnik + Verschleiss-Schutz (ILV), 독일)를 사용하여 레이저 빔의 1 차원 정현파 진동을 실현했습니다. 이 스캔 시스템에서 최대 진동 폭은 250Hz의 진동 주파수에서 1.4mm입니다. 오정렬에 대한 공차를 개선하기 위해 동일한 최대 너비 값이 사용되었습니다. 와이어 공급 시스템은 1을 공급했습니다. 2mm 직경의 알루미늄 합금 (AlSi5) 필러 와이어를 일정한 공급 속도로 에어 갭을 채 웁니다. 1mm 에어 갭의 경우 와이어 이송 속도는 용접 속도의 1.5 배 값으로 설정되었으며 참조 실험 조건은 문헌에서 얻었습니다 (Schultz, 2015 참조).[27] ).

CFD 시뮬레이션은 레이저 용접에서 열 전달 및 용융 풀 동작을 계산하기 위해 수행되었습니다. 그림 2 는 CFD 시뮬레이션을위한 계산 영역을 보여줍니다. 실온에서 1.2mm 직경의 필러 와이어가 공급되고 레이저 빔이 진동했습니다. 1mm 두께의 공작물이 용접 속도로 왼쪽에서 오른쪽으로 이동했습니다. 0.1mm의 최소 메쉬 크기가 도메인에서 생성되었습니다. 침투 깊이가 더 깊은 이전 연구의 메쉬 테스트 결과는 0.2mm 이하의 메쉬 크기로 시뮬레이션 정확도가 확보 된 것으로 나타 났으므로 [28] 본 연구에서 사용 된 메쉬 크기가 적절할 수 있습니다. 도메인을 구성하는 세포의 수는 약 120 만 개였습니다. 1 번 테이블사용 된 레이저 용접 매개 변수를 보여줍니다. 용융 풀 역학 측면에서 다양한 진동 주파수와 에어 갭 크기가 고려되었으며 12 개의 용접 사례가 표 2 에 나와 있습니다. 표 3 은 시뮬레이션에 사용 된 알루미늄 합금과 순수 알루미늄 (Cho et al., 2018 [26] )의 표면 장력 계수를 제외하고 온도와 무관 한 열-물리적 재료 특성을 보여줍니다 . 여기서 표면 장력 계수는 액체 온도에서 온도와 표면 장력 계수 사이의 선형 관계를 가진 유일한 온도 의존적 특성이었습니다.

표 1 . . 레이저 용접 매개 변수.

레이저 용접 매개 변수	값
레이저 빔 파워	3.0kW
빔 허리 반경	50µm *
용접 속도	6.0m / 분
와이어 공급 속도	9.0m / 분
빔 진동 폭	1.4mm
빔 진동 주파수	100Hz, 150Hz, 200Hz, 250Hz
에어 갭 크기	0.8mm, 0.9mm, 1.0mm, 1.1mm

⁎

반경은 1.07μm의 파장, 4.2mm • mrad의 빔 품질, 시준 초점 거리 및 초점 렌즈 200mm, 광섬유 직경 100μm의 원형 빔을 가정하여 계산되었습니다.

표 2 . 이 연구에서 고려한 용접 사례.

에어 갭 크기 [mm]	진동 주파수 [Hz]
	100	150	200	250
0.9	사례 1	엑스	엑스	엑스
1.0	사례 2	사례 4	사례 7	사례 10
1.1	사례 3	사례 5	사례 8	사례 11
1.2	엑스	사례 6	사례 9	12면

표 3 . 시뮬레이션에 사용 된 열 물리적 재료 특성 (Cho et al., 2018 [26] ).

특성	상징	값
밀도	ρ	2700kg / m3
열 전도성	케이	1.7×102Wm K
점도	ν	1.15×10−삼kg / ms
표면 장력 계수 티엘*	γ엘	0.871 J / m2
표면 장력 온도 구배 *	ㅏ	−1.55×10−4J / m ² K
표면 장력 계수	γ	γ엘−ㅏ(티−티엘)
비열	씨	8.5×102J / kg K
융합 잠열	h에스엘	3.36×105J / kg
기화 잠열 *	hV	1.05×107J / kg
Solidus 온도	티에스	847K
Liquidus 온도	티엘	905K
끓는점 *	티비	2743K

⁎

순수한 알루미늄.

시뮬레이션을 위해 단상 뉴턴 유체와 비압축성 층류가 가정되었습니다. 질량, 운동량 및 에너지 보존의 지배 방정식을 해결하여 계산 영역에서 속도, 압력 및 온도 분포를 얻었습니다. VOF 방법은 자유 표면 경계를 찾는 데 사용되었습니다. 스칼라 보존 방정식을 추가로 도입하여 용융 풀에서 충전재의 부피 분율을 계산했습니다. 시뮬레이션에 사용 된 레이저 용접의 수학적 모델은 다음과 같습니다. 레이저 빔은 가우스와 같은 전력 밀도 분포를 기반으로 697 개의 광선 에너지 번들로 나뉩니다. 광선 추적 방법을 사용하여 다중 반사를 고려했습니다. 재료에 대한 레이저 빔의 반사 (또는 흡수) 에너지는 프레 넬 반사 모델을 사용하여 계산되었습니다. 온도에 따른 흡수율의 변화를 고려 하였다. 혼합물의 흡수율은베이스 및 충전제 물질 분획의 가중 평균을 사용하여 계산되었습니다. 반동 압력과 부력도 고려되었습니다. 경계 조건으로 에너지와 압력의 균형은 VOF 방법으로 계산 된 자유 표면에서 고려되었습니다. 레이저 용접 모델과 지배 방정식은 FLOW-3D v.11.2 (2017), Flow Science, Inc.에서 유한 차분 방법과 유한 체적 방법을 사용하여 이산화되고 해결되었습니다. 경계 조건으로 에너지와 압력의 균형은 VOF 방법으로 계산 된 자유 표면에서 고려되었습니다. 레이저 용접 모델과 지배 방정식은 FLOW-3D v.11.2 (2017), Flow Science, Inc.에서 유한 차분 방법과 유한 체적 방법을 사용하여 이산화되고 해결되었습니다. 경계 조건으로 에너지와 압력의 균형은 VOF 방법으로 계산 된 자유 표면에서 고려되었습니다. 레이저 용접 모델과 지배 방정식은 FLOW-3D v.11.2 (2017), Flow Science, Inc.에서 유한 차분 방법과 유한 체적 방법을 사용하여 이산화되고 해결되었습니다.[29] . 계산에는 48GB RAM이 장착 된 Intel® Xeon® 프로세서 E5649로 구성된 워크 스테이션이 사용되었습니다. 계산 시스템을 사용하여 0.2 초 레이저 용접을 시뮬레이션하는 데 약 18 시간이 걸렸습니다. 지배 방정식 (Cho and Woizeschke, 2020 [30] ) 및 레이저 용접 모델 (Cho et al., 2018 [26] )에 대한 자세한 설명은 부록 A 에서 확인할 수 있습니다 .

그림 3 은 용융 풀 변동의 직접 측정에 대한 개략적 설명을 보여줍니다. 용융 풀의 역학을 분석하기 위해 시뮬레이션 중에 용융 풀 표면의 시간적 변동 운동을 측정했습니다. 상단 및 하단 표면 모두에서 10kHz의 샘플링 주파수로 변동을 측정 한 반면, 측정 위치는 X 축의 레이저 빔 위치에서 2mm 떨어진 용접 중심선에있었습니다. 그림 4시간 신호를 분석하는 데 사용되는 푸리에 변환 및 웨이블릿 변환의 개략적 설명을 보여줍니다. 측정 된 시간 신호는 고속 푸리에 변환 (FFT) 방법을 사용하여 주파수 영역으로 변환되었습니다. 결과는 측정 기간 동안 평균화 된 주파수 성분의 크기를 보여줍니다. 웨이블릿 변환 방법은 시간-주파수 영역에서 국부적 인 특성을 찾는 데 사용되었습니다. 결과는 주파수 구성 요소의 크기뿐만 아니라 시간 변화도 보여줍니다.

3 . 결과

이 연구 에서는 표 2에 표시된 12 가지 용접 사례 를 시뮬레이션했습니다. 그림 5 는 3 차원 시뮬레이션 결과를 평면도 와 바닥면으로 보여줍니다. 결과는 용융 된 풀의 거동에 따라 분류 할 수 있습니다 : 단추 구멍 형성 없음 (녹색), 안정 또는 불안정 단추 구멍 있음 (파란색), 불안정한 단추 구멍으로 인한 구멍 결함 (빨간색). 일반적인 열쇠 구멍보다 훨씬 큰 직경을 가진 단추 구멍은 레이저 용접의 특정 진동 조건에서 나타날 수 있습니다 (Vollertsen, 2016 [31]). 진동 주파수가 증가함에 따라 용접 이음 부 코스 및 스케일링 측면에서 시각적 이음새 품질이 향상되었습니다. 고주파에서 스케일링은 무시할 수있을 정도 였고 코스는 균질했습니다. 언더컷 결함의 발생도 감소했습니다. 그러나 관통 결함 부족 (case 7, case 10)이 나타났다. 에어 갭은 단추 구멍 형성에 중요했습니다. 에어 갭 크기가 증가함에 따라 단추 구멍이 더 쉽게 형성되었지만 구멍 결함으로 더 쉽게 남아 있습니다. 안정적인 단추 구멍 형성은 고려 된 공극 조건의 좁은 영역에서만 나타납니다.

그림 6 은 시뮬레이션과 실험에서 융합 영역의 모양을 보여줍니다. 버튼 홀이없는 경우 1, 불안정한 버튼 홀 형성이있는 경우 8, 안정적인 버튼 홀 형성이있는 경우 11의 3 가지 경우에 대해 시뮬레이션 결과와 실험 결과를 비교하여 유사성을 나타냈다. 본 연구에서 고려한 용접 조건의 경우 표면 품질 결과는 Fig. 5 와 같이 큰 차이를 보였으 나 단면 융착 영역 [26] 과 형상은 큰 차이를 보이지 않았다.

무화과. 7 과 8 은 각각 100Hz와 250Hz의 진동 주파수에서 시뮬레이션 결과를 기반으로 분석 된 용융 풀 역학과 시뮬레이션 및 실험 결과를 보여줍니다. 이전 연구에서 볼 수 있듯이 레이저 빔의 진동 주파수는 단추 구멍 형성과 밀접한 관련이 있습니다 (Cho et al., 2018 [26] 참조 ). 그림 7 (a) 및 (b)는 각각 시뮬레이션 및 실험을 기반으로 한 진동 주파수 100Hz에서 대표적인 용융 풀 동작을 보여줍니다. 완전히 관통 된 키홀 및 버튼 홀 형성은 관찰되지 않았으며 응고 후 거친 비드 표면이 남았습니다. 그림 7(c)와 (d)는 각각 윗면과 바닥면의 표면 변동에 대한 시뮬레이션 결과를 기반으로 한 용융 풀 역학 분석을 보여줍니다. 샘플링 데이터는 상단 표면이 공작물의 상단 표면 위치에서 평균적으로 변동하는 반면 하단 표면은 공작물의 하단 표면 위치에서 평균적으로 변동하는 것으로 나타났습니다. 표면 변동의 푸리에 변환 및 웨이블릿 변환 결과는 명확한 1 ^차 주파수 (2 x 빔 발진 주파수, 이른바 초핑 주파수, Cho et al., 2018 [26] 참조 ) 및 2 ^차 주파수 (4 x 빔 발진)를 보여줍니다. 주파수) 두 표면의 구성 요소, 그러나 바닥 표면과 ^{첫 번째에} 대한 결과주파수 성분이 더 강합니다. 반면 그림 8 (a)와 (b)에서 보는 바와 같이 250Hz의 진동 주파수에서 시뮬레이션과 실험 결과는 안정된 버튼 홀 형성과 응고 후 매끄러운 비드 표면을 나타냈다. 그림 8 의 샘플링 신호의 진폭은 그림 7 의 진폭 보다 작으며 푸리에 변환 및 웨이블릿 변환의 결과에서 중요한 주파수 성분이 발견되지 않았습니다.

Fig. 9 는 진동 주파수 200Hz에서 시뮬레이션 결과를 바탕으로 분석 된 용융 풀 역학과 시뮬레이션 및 실험 결과를 보여준다. 이 주파수에서 Fig. 9 (a)와 (b) 에서 보는 바와 같이 , 시뮬레이션과 실험 모두에서 불안정한 buttonhole 거동이 관찰되었다. 바닥면에서 샘플링 데이터의 푸리에 변환 및 웨이블릿 변환의 결과 빔 발진 주파수 성분이 발견되었습니다.

4 . 토론

시뮬레이션 및 실험 결과는 비드 표면 품질이 향상되고 빔 진동 주파수가 증가함에 따라 버튼 홀이 형성되는 것으로 나타났습니다. 표면의 변동 데이터에 대한 푸리에 변환 및 웨이블릿 변환의 결과에 따라 다음과 같은 주요 주파수 구성 요소가 발견되었습니다. 1 ^차 및 2 ^차버튼 홀 형성이없는 주파수, 불안정한 용융 풀 거동이있는 빔 진동 주파수, 안정적인 버튼 홀 형성이있는 중요한 주파수 성분이 없습니다. 이들 중 불안정한 용융 풀 동작과 관련된 빔 진동 주파수 성분은 완전히 관통 된 키홀과 반복적으로 생성 및 붕괴되는 불안정한 버튼 홀의 특성으로 인해 웨이블릿 변환 결과에서 명확한 실선 형태로 나타나지 않았습니다. 분석 결과는 윗면보다 바닥면에서 더 분명했습니다. 이는 필러 와이어 공급 및 키홀 링 공정에서 강한 하향 흐름으로 인해 용융 풀 역학이 바닥 표면 영역에서 더 강했기 때문입니다. 진동 주파수가 증가함에 따라 용융 풀 역학과 상단 표면과 하단 표면 간의 차이가 감소했습니다.

첫 ^번째 주파수 (2 x 빔 진동 주파수)는이 연구에서 관찰 된 가장 분명한 구성 요소였습니다. Schultz et al. (2018)은 또한 실험을 통해 동일한 성분을 발견했습니다 [32] , 용융 풀 표면 운동에 대한 푸리에 분석을 수행했습니다. 첫 ^번째 주파수 성분은 빔 발진주기 당 두 개의 주요 이벤트가 있음을 의미합니다. 이것은 레이저 빔이 빔 진동주기 당 두 번 와이어를 절단하거나 절단하는 프로세스와 일치합니다. 용융 된 와이어 팁은 낮은 진동 주파수에서 고르지 않고 날카로운 모서리를 갖는 것으로 나타났습니다 (Cho et al., 2018 [26] ). 이것은 ^{첫 번째} 원인이 될 수 있습니다.용융 된 풀에서 지배적이되는 주파수 성분. 진동 주파수가 증가하면 용융 된 와이어 팁이 더 균일 해 지므로 효과가 감소합니다. 용접 방향으로의 정현파 횡 방향 빔 진동을 통한 에너지 집중도 빔 진동주기 당 두 번 발생합니다. 그림 10 은 발진 주파수에 따른 레이저 빔의 라인 에너지 (단위 길이 당 에너지)의 변화를 보여줍니다. 그림 10 b) 의 라인 에너지 는 레이저 출력을 공정 속도로 나누어 계산했습니다. 여기서 처리 속도는(w이자형엘디나는엔지에스피이자형이자형디)2+(디(에스나는엔유에스영형나는디ㅏ엘wㅏV이자형나는엔에프나는지.10ㅏ))디티)2. 낮은 발진 주파수에서 라인 에너지는 발진 폭의 양쪽 끝에 과도하게 집중됩니다. 이러한 집중된 에너지는 과도한 키홀 링 프로세스를 초래하므로 언더컷 결함이 나타날 수있는 높은 흐름 역학이 발생합니다. 진동 주파수가 증가함에 따라 집중 에너지는 더 작은 조각으로 나뉩니다. 따라서 높은 진동 주파수에서 과도한 키홀 링 및 수반되는 언더컷 결함의 발생이 감소되었습니다. 위에서 언급 한 두 가지 현상 (불균일 한 와이어 팁과 집중된 라인 에너지)은 빔 발진주기 당 두 번 발생하며 발진 주파수가 증가하면 그 효과가 감소합니다. 따라서 저주파 에서 2 ^차 주파수 성분 (4 x 빔 발진 주파수)이 나타나는 것은이 두 현상의 동시 작용입니다.

두 가지 현상 중 ^{첫 번째} 주파수 에 대한 주된 효과 는 집중된 라인 에너지입니다. Cho et al. (2018)은 전력 흡수 데이터를 푸리에 변환을 사용하여 분석했을 때 1 ^차 주파수 성분이 더 우세 해졌고, 2 ^차 주파수 성분은 발진 주파수가 증가함에 따라 상대적으로 약화 되었음을 보여주었습니다 [26] . 용융 된 와이어 팁은 또한 빈도가 증가함에 따라 더욱 균일 해졌습니다. 결과는 진동 주파수의 증가가 용융 풀에 대한 와이어의 영향을 제거하는 것으로 나타났습니다. 따라서 발진 주파수가 증가함에 따라 라인 에너지 집중의 영향 만 남을 수 있습니다. 그림 10 과 같이, 집중 선 에너지가 작은 조각으로 분할되기 때문에 효과도 감소하지만 최대 값이 변경되지 않았기 때문에 여전히 효과적입니다.

빔 진동 주파수 성분은 불안정한 단추 구멍 및 열쇠 구멍 붕괴를 수반하는 불안정한 용융 풀 동작과 관련이 있습니다. 언더컷 결함이있는 케이스 8 (발진 주파수 200Hz)에서 발진 주파수 성분이 관찰되었습니다. 이것은 특히 완전히 관통 된 열쇠 구멍과 불안정한 단추 구멍에서 불안정한 용융 풀 동작을 보여주었습니다. 경우 10 (진동 주파수 250Hz)의 경우 상대적으로 건강한 비드가 형성 되었으나, 도 11 (a) 와 같이 웨이블릿 변환 결과에서 t1의 시간 간격으로 진동 주파수 성분이 관찰되었다 . 이 시간 간격 t1의 용융 풀 거동은 그림 11에 나와 있습니다.(비). 완전히 관통 된 열쇠 구멍이 즉시 무너지는 것이 분명하게 관찰되었습니다. 이것은 진동 주파수 성분이 불안정한 용융 풀 거동과 밀접한 관련이 있음을 보여줍니다. 발견 된 주파수 성분으로부터 완전히 관통 된 열쇠 구멍과 같은 불안정한 용융 풀 거동을 예측할 수 있습니다. 완전히 관통 된 키홀이 반복적으로 붕괴되기 때문에 빔 진동 주파수 성분은 그림 9 (d) 와 같이 웨이블릿 변환 결과에서 명확한 실선 형태로 보이지 않습니다 .

Cho and Woizeschke (2020)에 따르면 단추 구멍 형성은 자체 지속 가능한 카테 노이드처럼 작용하기 때문에 용융 풀 역학을 감소시킬 수 있습니다 [30] . 그림 12 는 버튼 홀 형성 측면에서 t2의 시간 간격에서 용융 풀 거동의 변화를 보여줍니다. 단추 구멍은 t2의 간헐적 인 부분에만 형성되었습니다. ^1st 이후이 시간 동안 웨이블릿 변환의 결과로 주파수 성분이 사라졌고, 버튼 홀 형성은 용융 풀 역학을 줄이는 데 효과적이었습니다. 따라서, 웨이블릿 변환의 결과로 주파수 성분이 지워지는 것을 관찰함으로써 버튼 홀 형성을 예측할 수있다. 이와 관련하여 웨이블릿 변환 기술은 시간에 따른 용융 풀 변화를 나타낼 수 있습니다. 이 기술은 향후 용융 풀 동작을 모니터링하는 데 사용될 수 있습니다.

5 . 결론

CFD 시뮬레이션 결과를 사용하여 빔 진동 및 필러 와이어 공급을 통한 레이저 용접에서 용융 풀 역학을 분석 할 수있었습니다. 용융 풀 표면의 변동 데이터의 푸리에 변환 및 웨이블릿 변환은 여기서 용융 풀 역학을 분석하는 데 사용되었습니다. 결과는 다음과 같은 결론으로 이어집니다.1.

1 ^차 주파수 (2 x 빔 발진 주파수, 이른바 초핑 주파수), 2 ^차 주파수 (4 x 빔 발진 주파수) 및 빔 발진 주파수 성분은 푸리에 변환 및 웨이블릿 변환 분석에서 발견 된 주요 성분이었습니다.2.

1 ^차 주파수와 2 ^차 주파수 성분 의 출현은 두 가지 사건, 즉 레이저 빔에 의한 필러 와이어의 절단 공정과 집중된 레이저 라인 에너지의 효과의 결과였습니다. 이는 빔 진동주기 당 두 번 발생했습니다. 따라서 두 ^번째 주파수 성분은 동시 작용으로 인해 발생했습니다. 빔 진동 주파수 성분은 불안정한 용융 풀 동작과 관련이 있습니다. 구성 요소는 열쇠 구멍과 단추 구멍의 붕괴와 함께 나타났습니다.삼.

낮은 발진 주파수에서는 1 ^차 주파수와 2 ^차 주파수 성분이 함께 나타 났지만 발진 주파수가 증가함에 따라 그 크기가 함께 감소했습니다. 집중 선 에너지는 주파수가 증가함에 따라 최대 값이 변하지 않는 반면, 잘게 잘린 선단이 평평 해져 그 효과가 사라졌기 때문에 쵸핑 프로세스보다 더 큰 영향을 미쳤습니다.4.

용융 풀 거동의 빠른 시간적 변화는 웨이블릿 변환 방법을 사용하여 분석되었습니다. 따라서이 방법은 열쇠 구멍 및 단추 구멍의 형성 및 붕괴와 같은 일시적인 용융 풀 변화를 해석하는 데 사용할 수 있습니다.

CRediT 저자 기여 성명

조원익 : 개념화, 방법론, 소프트웨어, 검증, 형식 분석, 조사, 데이터 큐 레이션, 글쓰기-원고, 글쓰기-검토 및 편집. Peer Woizeschke : 감독, 프로젝트 관리, 작문-검토 및 편집.

경쟁 관심의 선언

저자는이 논문에보고 된 작업에 영향을 미칠 수있는 경쟁적인 재정적 이해 관계 나 개인적 관계가 없다고 선언합니다.

감사의 말

이 작업은 알루미늄 합금 용접 역량 센터 (Centr-Al)에서 수행되었습니다. Deutsche Forschungsgemeinschaft (DFG, 프로젝트 번호 290705638 , “용접 풀 캐비티를 생성하여 레이저 깊은 용입 용접에서 매끄러운 이음매 표면”) 의 자금은 감사하게도 인정됩니다.

부록 A . 사용 된 지배 방정식 및 레이저 용접 모델

1 . 지배 방정식 (Cho 및 Woizeschke [ 30 ])

-대량 보존 방정식,(A1)∇·V→=미디엄˙에스ρ어디, V→속도 벡터입니다. ρ밀도이고 미디엄˙에스필러 와이어를 공급하여 질량 소스의 비율입니다. 단위미디엄에스단위 부피당 질량입니다. WFS (와이어 공급 속도) 및 필러 와이어의 직경과 같은 매스 소스 및 필러 와이어 조건,디w계산 영역에서 다음과 같은 관계가 있습니다.(A2)미디엄=∫미디엄에스디V=미디엄0+씨×ρ×W에프에스×π디w24×티어디, 미디엄총 질량, 미디엄0초기 총 질량, V볼륨입니다.씨단위 변환 계수입니다. 티시간입니다.

-운동량 보존 방정식,(A3)∂V→∂티+V→·∇V→=−1ρ∇피+ν∇2V→−케이V→+미디엄˙에스ρ(V에스→−V→)+지어디, 피압력입니다. ν동적 점도입니다. 케이뭉툭한 영역의 다공성 매체 모델에 대한 항력 계수, V에스→질량 소스에 대한 속도 벡터입니다. 지신체 힘으로 인한 신체 가속도입니다.

-에너지 절약 방정식,(A4)∂h∂티+V→·∇h=1ρ∇·(케이∇티)+h˙에스어디, h특정 엔탈피입니다. 케이열전도율, 티온도이고 h˙에스특정 엔탈피 소스로, Eq 의 질량 소스와 연관됩니다 . (A1) . 계산 영역의 총 에너지,이자형다음과 같이 계산됩니다.(A5)이자형=∫미디엄에스h에스디V=∫미디엄에스씨Vw티w디V어디, 씨Vw질량 원의 비열, 티w질량 소스의 온도입니다.

또한, 엔탈피 기반 연속체 모델을 사용하여 고체-액체 상 전이를 고려했습니다.

-VOF 방정식,(A6)∂에프∂티+∇·(V→에프)=에프˙에스어디, 에프유체가 차지하는 부피 분율이며 0과 1 사이의 값을 가지며 에프˙에스질량의 소스와 연결된 유체의 체적 분율의 비율 식. (A1) . 질량 공급원에 해당하는 부피 분율은 다음에 할당됩니다.에프에스.

-스칼라 보존 방정식,(A7)∂Φ∂티+∇·(V→Φ)=Φ˙에스어디, Φ필러 와이어의 스칼라 값입니다. 셀의 유체가 전적으로 필러 와이어로 구성된 경우Φ1이고 유체에 대한 필러 와이어의 부피 분율에 따라 0과 1 사이에서 변경됩니다. Φ˙에스Eq 에서 질량 소스에 연결된 스칼라 소스의 비율입니다 . (A1) . 스칼라 소스는 전적으로 필러 와이어이기 때문에 1에 할당됩니다. 확산 효과는 고려되지 않았습니다.

2 . 레이저 용접 모델 (Cho et al. [26] )

흡수율을 계산하기 위해 프레 넬 반사 모델을 사용했습니다. ㅏ=1−ρ씨재료의 표면 상에 도시 된 바와 같이 수학 식. (A8) 원 편광 빔의 경우.(A8)ㅏ=1−ρ씨=1−12(ρ에스+ρ피)어디,ρ에스=(엔1씨영형에스θ−피)2+큐2(엔1씨영형에스θ+피)2+큐2,ρ에스=(피−엔1에스나는엔θ티ㅏ엔θ)2+큐2(피+엔1에스나는엔θ티ㅏ엔θ)2+큐2,피2=12{[엔22−케이22−(엔1에스나는엔θ)2]2+2엔22케이22+[엔22−케이22−(엔1에스나는엔θ)2]},큐2=12{[엔22−케이22−(엔1에스나는엔θ)2]2+2엔22케이22−[엔22−케이22−(엔1에스나는엔θ)2]}.어디, 복잡한 인덱스 엔1과 케이1반사 지수와 공기의 흡수 지수이며 엔2과 케이2공작물을위한 것입니다. θ입사각입니다. 도시 된 바와 같이 수학 식. (A9)에서 , 혼합물의 흡수율은 식에서 얻은 모재 및 필러 와이어 분획의 가중 평균이됩니다 . (A7) .(A9)ㅏ미디엄나는엑스티유아르 자형이자형=Φㅏw나는아르 자형이자형+(1−Φ)ㅏ비ㅏ에스이자형어디, ㅏ비ㅏ에스이자형과 ㅏw나는아르 자형이자형각각 비금속과 필러 와이어의 흡수율입니다.

자유 표면 경계에서의 반동 압력 에이 싱은 Eq. (A10) .(A10)피아르 자형(티)≅0.54피에스ㅏ티(티)=0.54피0이자형엑스피(엘V티−티비아르 자형¯티티비)어디, 피에스ㅏ티포화 압력, 피0대기압입니다. 엘V기화의 잠열, 티비끓는 온도이고 아르 자형¯보편적 인 기체 상수입니다.

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Review on the evolution and technology of State-of-the-Art metal additive manufacturing processes

최첨단 금속 적층 제조 공정의 진화 및 기술 검토

S.Pratheesh Kumar
S.ElangovanR.Mohanraj
J.R.Ramakrishna

Abstract

Nowadays, the requirements of customers undergo dynamic changes and industries are heading towards the manufacturing of customized end-user products, making market fluctuations extremely unpredictable. This demands the production industries to shift towards instantaneous product development strategies that can deliver products on the shortest lead time without compromise in the quality and accuracy. Direct metal deposition is one such evolving additive manufacturing (AM) technique that has found its application from rapid prototyping to production of real-time industrial components. In addition, the process is ideal for just-in-time manufacturing, producing parts-on-demand while offering the potential to reduce cost, energy consumption, and carbon footprint. The evolution of this advanced manufacturing technique had drastically reduced the manufacturing constraints and greatly improved the product versatility. This review provides insight into the evolution, current status, and challenges of metal additive manufacturing (MAM) techniques, starting from powder bed fusion and direct metal deposition. In addition to this, the review explores the variants of metal additive manufacturing with its process mechanism, merits, demerits, and applications. The efficiency of the processes is finally analysed using a time–cost triangle and the mechanical properties are comprehensively compared. The review will enhance the basic understanding of MAM and thus broaden the scope of research and development.

오늘날 고객의 요구 사항은 역동적 인 변화를 겪고 있으며 산업은 맞춤형 최종 사용자 제품의 제조로 향하고있어 시장 변동을 예측할 수 없게 만듭니다. 따라서 생산 산업은 품질과 정확성을 타협하지 않고 최단 리드 타임에 제품을 제공 할 수있는 즉각적인 제품 개발 전략으로 전환해야합니다. 직접 금속 증착은 쾌속 프로토 타이핑에서 실시간 산업 부품 생산에 이르기까지 응용 분야를 발견 한 진화하는 적층 제조 (AM) 기술 중 하나입니다. 또한이 프로세스는 적시 제조에 이상적이며 주문형 부품을 생산하는 동시에 비용, 에너지 소비 및 탄소 발자국을 줄일 수있는 잠재력을 제공합니다. 이 고급 제조 기술의 발전으로 제조 제약이 크게 줄어들고 제품의 다양성이 크게 향상되었습니다. 이 리뷰는 분말 베드 융합 및 직접 금속 증착에서 시작하여 금속 적층 제조 (MAM) 기술의 발전, 현재 상태 및 과제에 대한 통찰력을 제공합니다. 이 외에도이 리뷰에서는 프로세스 메커니즘, 장점, 단점 및 응용 프로그램과 함께 금속 적층 제조의 변형을 탐색합니다. 프로세스의 효율성은 마지막으로 시간-비용 삼각형을 사용하여 분석되고 기계적 특성이 포괄적으로 비교됩니다. 검토는 MAM에 대한 기본적인 이해를 높이고 연구 개발 범위를 넓힐 것입니다.

Keywords: Metal additive manufacturing, 3D Printing, Direct energy deposition, Electron beam meltingRapid prototyping

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Figure 4. Calculate and simulate the injection of water in a single-channel injection chamber with a nozzle diameter of 60 μm and a thickness of 50 μm, at an operating frequency of 5 KHz, in the X-Y two-dimensional cross-sectional view, at 10, 20, 30, 40 and 200 μs.

DNA Printing Integrated Multiplexer Driver Microelectronic Mechanical System Head (IDMH) and Microfluidic Flow Estimation

DNA 프린팅 통합 멀티플렉서 드라이버 Microelectronic Mechanical System Head (IDMH) 및 Microfluidic Flow Estimation

by Jian-Chiun Liou^1,*,Chih-Wei Peng¹ ,Philippe Basset² andZhen-Xi Chen¹¹School of Biomedical Engineering, Taipei Medical University, Taipei 11031, Taiwan²ESYCOM, Université Gustave Eiffel, CNRS, CNAM, ESIEE Paris, F-77454 Marne-la-Vallée, France^*Author to whom correspondence should be addressed.

Abstract

The system designed in this study involves a three-dimensional (3D) microelectronic mechanical system chip structure using DNA printing technology. We employed diverse diameters and cavity thickness for the heater. DNA beads were placed in this rapid array, and the spray flow rate was assessed. Because DNA cannot be obtained easily, rapidly deploying DNA while estimating the total amount of DNA being sprayed is imperative. DNA printings were collected in a multiplexer driver microelectronic mechanical system head, and microflow estimation was conducted. Flow-3D was used to simulate the internal flow field and flow distribution of the 3D spray room. The simulation was used to calculate the time and pressure required to generate heat bubbles as well as the corresponding mean outlet speed of the fluid. The “outlet speed status” function in Flow-3D was used as a power source for simulating the ejection of fluid by the chip nozzle. The actual chip generation process was measured, and the starting voltage curve was analyzed. Finally, experiments on flow rate were conducted, and the results were discussed. The density of the injection nozzle was 50, the size of the heater was 105 μm × 105 μm, and the size of the injection nozzle hole was 80 μm. The maximum flow rate was limited to approximately 3.5 cc. The maximum flow rate per minute required a power between 3.5 W and 4.5 W. The number of injection nozzles was multiplied by 100. On chips with enlarged injection nozzle density, experiments were conducted under a fixed driving voltage of 25 V. The flow curve obtained from various pulse widths and operating frequencies was observed. The operating frequency was 2 KHz, and the pulse width was 4 μs. At a pulse width of 5 μs and within the power range of 4.3–5.7 W, the monomer was injected at a flow rate of 5.5 cc/min. The results of this study may be applied to estimate the flow rate and the total amount of the ejection liquid of a DNA liquid.

이 연구에서 설계된 시스템은 DNA 프린팅 기술을 사용하는 3 차원 (3D) 마이크로 전자 기계 시스템 칩 구조를 포함합니다. 히터에는 다양한 직경과 캐비티 두께를 사용했습니다. DNA 비드를 빠른 어레이에 배치하고 스프레이 유속을 평가했습니다.

DNA를 쉽게 얻을 수 없기 때문에 DNA를 빠르게 배치하면서 스프레이 되는 총 DNA 양을 추정하는 것이 필수적입니다. DNA 프린팅은 멀티플렉서 드라이버 마이크로 전자 기계 시스템 헤드에 수집되었고 마이크로 플로우 추정이 수행되었습니다.

Flow-3D는 3D 스프레이 룸의 내부 유동장과 유동 분포를 시뮬레이션 하는데 사용되었습니다. 시뮬레이션은 열 거품을 생성하는데 필요한 시간과 압력뿐만 아니라 유체의 해당 평균 출구 속도를 계산하는데 사용되었습니다.

Flow-3D의 “출구 속도 상태”기능은 칩 노즐에 의한 유체 배출 시뮬레이션을 위한 전원으로 사용되었습니다. 실제 칩 생성 프로세스를 측정하고 시작 전압 곡선을 분석했습니다. 마지막으로 유속 실험을 하고 그 결과를 논의했습니다. 분사 노즐의 밀도는 50, 히터의 크기는 105μm × 105μm, 분사 노즐 구멍의 크기는 80μm였다. 최대 유량은 약 3.5cc로 제한되었습니다. 분당 최대 유량은 3.5W에서 4.5W 사이의 전력이 필요했습니다. 분사 노즐의 수에 100을 곱했습니다. 분사 노즐 밀도가 확대 된 칩에 대해 25V의 고정 구동 전압에서 실험을 수행했습니다. 얻은 유동 곡선 다양한 펄스 폭과 작동 주파수에서 관찰되었습니다. 작동 주파수는 2KHz이고 펄스 폭은 4μs입니다. 5μs의 펄스 폭과 4.3–5.7W의 전력 범위 내에서 단량체는 5.5cc / min의 유속으로 주입되었습니다. 이 연구의 결과는 DNA 액체의 토 출액의 유량과 총량을 추정하는 데 적용될 수 있습니다.

Keywords: DNA printing; flow estimation; MEMS

Introduction

잉크젯 프린트 헤드 기술은 매우 중요하며, 잉크젯 기술의 거대한 발전은 주로 잉크젯 프린트 헤드 기술의 원리 개발에서 시작되었습니다. 잉크젯 인쇄 연구를 위한 대규모 액적 생성기 포함 [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8]. 연속 식 잉크젯 시스템은 고주파 응답과 고속 인쇄의 장점이 있습니다. 그러나이 방법의 잉크젯 프린트 헤드의 구조는 더 복잡하고 양산이 어려운 가압 장치, 대전 전극, 편향 전계가 필요하다. 주문형 잉크젯 시스템의 잉크젯 프린트 헤드는 구조가 간단하고 잉크젯 헤드의 다중 노즐을 쉽게 구현할 수 있으며 디지털화 및 색상 지정이 쉽고 이미지 품질은 비교적 좋지만 일반적인 잉크 방울 토출 속도는 낮음 [ 9 , 10 , 11 ].

핫 버블 잉크젯 헤드의 총 노즐 수는 수백 또는 수천에 달할 수 있습니다. 노즐은 매우 미세하여 풍부한 조화 색상과 부드러운 메쉬 톤을 생성할 수 있습니다. 잉크 카트리지와 노즐이 일체형 구조를 이루고 있으며, 잉크 카트리지 교체시 잉크젯 헤드가 동시에 업데이트되므로 노즐 막힘에 대한 걱정은 없지만 소모품 낭비가 발생하고 상대적으로 높음 비용. 주문형 잉크젯 기술은 배출해야 하는 그래픽 및 텍스트 부분에만 잉크 방울을 배출하고 빈 영역에는 잉크 방울이 배출되지 않습니다. 이 분사 방법은 잉크 방울을 충전할 필요가 없으며 전극 및 편향 전기장을 충전할 필요도 없습니다. 노즐 구조가 간단하고 노즐의 멀티 노즐 구현이 용이하며, 출력 품질이 더욱 개선되었습니다. 펄스 제어를 통해 디지털화가 쉽습니다. 그러나 잉크 방울의 토출 속도는 일반적으로 낮습니다. 열 거품 잉크젯, 압전 잉크젯 및 정전기 잉크젯의 세 가지 일반적인 유형이 있습니다. 물론 다른 유형이 있습니다.

압전 잉크젯 기술의 실현 원리는 인쇄 헤드의 노즐 근처에 많은 소형 압전 세라믹을 배치하면 압전 크리스탈이 전기장의 작용으로 변형됩니다. 잉크 캐비티에서 돌출되어 노즐에서 분사되는 패턴 데이터 신호는 압전 크리스탈의 변형을 제어한 다음 잉크 분사량을 제어합니다. 압전 MEMS 프린트 헤드를 사용한 주문형 드롭 하이브리드 인쇄 [ 12]. 열 거품 잉크젯 기술의 실현 원리는 가열 펄스 (기록 신호)의 작용으로 노즐의 발열체 온도가 상승하여 근처의 잉크 용매가 증발하여 많은 수의 핵 형성 작은 거품을 생성하는 것입니다. 내부 거품의 부피는 계속 증가합니다. 일정 수준에 도달하면 생성된 압력으로 인해 잉크가 노즐에서 분사되고 최종적으로 기판 표면에 도달하여 패턴 정보가 재생됩니다 [ 13 , 14 , 15 , 16 , 17 , 18 ].

“3D 제품 프린팅”및 “증분 빠른 제조”의 의미는 진화했으며 모든 증분 제품 제조 기술을 나타냅니다. 이는 이전 제작과는 다른 의미를 가지고 있지만, 자동 제어 하에 소재를 쌓아 올리는 3D 작업 제작 과정의 공통적 인 특징을 여전히 반영하고 있습니다 [ 19 , 20 , 21 , 22 , 23 , 24 ].

이 개발 시스템은 열 거품 분사 기술입니다. 이 빠른 어레이에 DNA 비드를 배치하고 스프레이 유속을 평가하기 위해 다른 히터 직경과 캐비티 두께를 설계하는 것입니다. DNA 제트 칩의 부스트 회로 시스템은 큰 흐름을 구동하기위한 신호 소스입니다. 목적은 분사되는 DNA 용액의 양과 출력을 조정하는 것입니다. 입력 전압을 더 높은 출력 전압으로 변환해야 하는 경우 부스트 컨버터가 유일한 선택입니다. 부스트 컨버터는 내부 금속 산화물 반도체 전계 효과 트랜지스터 (MOSFET)를 통해 전압을 충전하여 부스트 출력의 목적을 달성하고, MOSFET이 꺼지면 인덕터는 부하 정류를 통해 방전됩니다.

인덕터의 충전과 방전 사이의 변환 프로세스는 인덕터를 통한 전압의 방향을 반대로 한 다음 점차적으로 입력 작동 전압보다 높은 전압을 증가시킵니다. MOSFET의 스위칭 듀티 사이클은 확실히 부스트 비율을 결정합니다. MOSFET의 정격 전류와 부스트 컨버터의 부스트 비율은 부스트 컨버터의 부하 전류의 상한을 결정합니다. MOSFET의 정격 전압은 출력 전압의 상한을 결정합니다. 일부 부스트 컨버터는 정류기와 MOSFET을 통합하여 동기식 정류를 제공합니다. 통합 MOSFET은 정확한 제로 전류 턴 오프를 달성하여 부스트 변압기를 보다 효율적으로 만듭니다. 최대 전력 점 추적 장치를 통해 입력 전력을 실시간으로 모니터링합니다. 입력 전압이 최대 입력 전력 지점에 도달하면 부스트 컨버터가 작동하기 시작하여 부스트 컨버터가 최대 전력 출력 지점으로 유리 기판에 DNA 인쇄를 하는 데 적합합니다. 일정한 온 타임 생성 회로를 통해 온 타임이 온도 및 칩의 코너 각도에 영향을 받지 않아 시스템의 안정성이 향상됩니다.

잉크젯 프린트 헤드에 사용되는 기술은 매우 중요합니다. 잉크젯 기술의 엄청난 발전은 주로 잉크젯 프린팅에 사용되는 대형 액적 이젝터 [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]를 포함하여 잉크젯 프린트 헤드 기술의 이론 개발에서 시작되었습니다 . 연속 잉크젯 시스템은 고주파 응답과 고속 인쇄의 장점을 가지고 있습니다. 잉크젯 헤드의 총 노즐 수는 수백 또는 수천에 달할 수 있으며 이러한 노즐은 매우 복잡합니다. 노즐은 풍부하고 조화로운 색상과 부드러운 메쉬 톤을 생성할 수 있습니다 [ 9 , 10 ,11 ]. 잉크젯은 열 거품 잉크젯, 압전 잉크젯 및 정전 식 잉크젯의 세 가지 주요 유형으로 분류할 수 있습니다. 다른 유형도 사용 중입니다. 압전 잉크젯의 기능은 다음과 같습니다. 많은 소형 압전 세라믹이 잉크젯 헤드 노즐 근처에 배치됩니다. 압전 결정은 전기장 아래에서 변형됩니다. 그 후, 잉크는 잉크 캐비티에서 압착되어 노즐에서 배출됩니다. 패턴의 데이터 신호는 압전 결정의 변형을 제어한 다음 분사되는 잉크의 양을 제어합니다. 압전 마이크로 전자 기계 시스템 (MEMS) 잉크젯 헤드는 하이브리드 인쇄에 사용됩니다. [ 12]. 열 버블 잉크젯 기술은 다음과 같이 작동합니다. 가열 펄스 (즉, 기록 신호) 하에서 노즐의 가열 구성 요소의 온도가 상승하여 근처의 잉크 용매를 증발시켜 많은 양의 작은 핵 기포를 생성합니다. 내부 기포의 부피가 지속적으로 증가합니다. 압력이 일정 수준에 도달하면 노즐에서 잉크가 분출되고 잉크가 기판 표면에 도달하여 패턴과 메시지가 표시됩니다 [ 13 , 14 , 15 , 16 , 17 , 18 ].

3 차원 (3D) 제품 프린팅 및 빠른 프로토 타입 기술의 발전에는 모든 빠른 프로토 타입의 생산 기술이 포함됩니다. 래피드 프로토 타입 기술은 기존 생산 방식과는 다르지만 3D 제품 프린팅 생산 과정의 일부 특성을 공유합니다. 구체적으로 자동 제어 [ 19 , 20 , 21 , 22 , 23 , 24 ] 하에서 자재를 쌓아 올립니다 .

이 연구에서 개발된 시스템은 열 기포 방출 기술을 사용했습니다. 이 빠른 어레이에 DNA 비드를 배치하기 위해 히터에 대해 다른 직경과 다른 공동 두께가 사용되었습니다. 그 후, 스프레이 유속을 평가했다. DNA 제트 칩의 부스트 회로 시스템은 큰 흐름을 구동하기위한 신호 소스입니다. 목표는 분사되는 DNA 액체의 양과 출력을 조정하는 것입니다. 입력 전압을 더 높은 출력 전압으로 수정해야하는 경우 승압 컨버터가 유일한 옵션입니다. 승압 컨버터는 내부 금속 산화물 반도체 전계 효과 트랜지스터 (MOSFET)를 충전하여 출력 전압을 증가시킵니다. MOSFET이 꺼지면 부하 정류를 통해 인덕턴스가 방전됩니다. 충전과 방전 사이에서 인덕터를 변경하는 과정은 인덕터를 통과하는 전압의 방향을 변경합니다. 전압은 입력 작동 전압을 초과하는 지점까지 점차적으로 증가합니다. MOSFET 스위치의 듀티 사이클은 부스트 비율을 결정합니다. MOSFET의 승압 컨버터의 정격 전류와 부스트 비율은 승압 컨버터의 부하 전류의 상한을 결정합니다. MOSFET의 정격 전류는 출력 전압의 상한을 결정합니다. 일부 승압 컨버터는 정류기와 MOSFET을 통합하여 동기식 정류를 제공합니다. 통합 MOSFET은 정밀한 제로 전류 셧다운을 실현할 수 있으므로 셋업 컨버터의 효율성을 높일 수 있습니다. 최대 전력 점 추적 장치는 입력 전력을 실시간으로 모니터링하는 데 사용되었습니다. 입력 전압이 최대 입력 전력 지점에 도달하면 승압 컨버터가 작동을 시작합니다. 스텝 업 컨버터는 DNA 프린팅을 위한 최대 전력 출력 포인트가 있는 유리 기판에 사용됩니다.

MEMS Chip Design for Bubble Jet

이 연구는 히터 크기, 히터 번호 및 루프 저항과 같은 특정 매개 변수를 조작하여 5 가지 유형의 액체 배출 챔버 구조를 설계했습니다. 표 1 은 측정 결과를 나열합니다. 이 시스템은 다양한 히터의 루프 저항을 분석했습니다. 100 개 히터 설계를 완료하기 위해 2 세트의 히터를 사용하여 각 단일 회로 시리즈를 통과하기 때문에 100 개의 히터를 설계할 때 총 루프 저항은 히터 50 개의 총 루프 저항보다 하나 더 커야 합니다. 이 연구에서 MEMS 칩에서 기포를 배출하는 과정에서 저항 층의 면저항은 29 Ω / m ^2입니다. 따라서 모델 A의 총 루프 저항이 가장 컸습니다. 일반 사이즈 모델 (모델 B1, C, D, E)의 두 배였습니다. 모델 B1, C, D 및 E의 총 루프 저항은 약 29 Ω / m ² 입니다. 표 1 에 따르면 오류 범위는 허용된 설계 값 이내였습니다. 따라서야 연구에서 설계된 각 유형의 단일 칩은 동일한 생산 절차 결과를 가지며 후속 유량 측정에 사용되었습니다.

Table 1. List of resistance measurement of single circuit resistance.

DNA를 뿌린 칩의 파워가 정상으로 확인되면 히터 버블의 성장 특성을 테스트하고 검증했습니다. DNA 스프레이 칩의 필름 두께와 필름 품질은 히터의 작동 조건과 스프레이 품질에 영향을 줍니다. 따라서 기포 성장 현상과 그 성장 특성을 이해하면 본 연구에서 DNA 스프레이 칩의 특성과 작동 조건을 명확히 하는 데 도움이 됩니다.

설계된 시스템은 기포 성장 조건을 관찰하기 위해 개방형 액체 공급 방법을 채택했습니다. 이미지 관찰을 위해 발광 다이오드 (LED, Nichia NSPW500GS-K1, 3.1V 백색 LED 5mm)를 사용하는 동기식 플래시 방식을 사용하여 동기식 지연 광원을 생성했습니다. 이 시스템은 또한 전하 결합 장치 (CCD, Flir Grasshopper3 GigE GS3-PGE-50S5C-C)를 사용하여 이미지를 캡처했습니다. 그림 1핵 형성, 성장, 거품 생성에서 소산에 이르는 거품의 과정을 보여줍니다. 이 시스템은 기포의 성장 및 소산 과정을 확인하여 시작 전압을 관찰하는 데 사용할 수 있습니다. 마이크로 채널의 액체 공급 방법은 LED가 깜빡이는 시간을 가장 큰 기포 발생에 필요한 시간 (15μs)으로 설정했습니다. 이 디자인은 부적합한 깜박임 시간으로 인한 잘못된 판단과 거품 이미지 캡처 불가능을 방지합니다.

Figure 1. The system uses CCD to capture images.

<내용 중략>…….

Table 2. Open pool test starting voltage results.

Figure 2. Serial input parallel output shift registers forms of connection.

Figure 3. The geometry of the jet cavity. (a) The actual DNA liquid chamber, (b) the three-dimensional view of the microfluidic single channel. A single-channel jet cavity with 60 μm diameter and 50 μm thickness, with an operating frequency of 5 KHz, in (a) three-dimensional side view (b) X-Z two-dimensional cross-sectional view, at 10, 20, 30, 40 and 200 μs injection conditions.

Figure 5 depicts the calculation results of the 2D X-Z cross section. At 100 μs and 200 μs, the fluid injection orifice did not completely fill the chamber. This may be because the size of the single-channel injection cavity was unsuitable for the highest operating frequency of 10 KHz. Thus, subsequent calculation simulations employed 5 KHz as the reference operating frequency. The calculation simulation results were calculated according to the operating frequency of the impact. Figure 6 illustrates the injection cavity height as 60 μm and 30 μm and reveals the 2D X-Y cross section. At 100 μs and 200 μs, the fluid injection orifice did not completely fill the chamber. In those stages, the fluid was still filling the chamber, and the flow field was not yet stable.

Figure 6. Calculate and simulate water in a single-channel spray chamber with a spray hole diameter of 60 μm and a thickness of 50 μm, with an operating frequency of 10 KHz, in an XY cross-sectional view, at 10, 20, 30, 40, 100, 110, 120, 130, 140 and 200 μs injection situation.

Figure 7. The DNA printing integrated multiplexer driver MEMS head (IDMH).

Figure 8. The initial voltage diagrams of chip number A,B,C,D,E type.

Figure 9. The initial energy diagrams of chip number A,B,C,D,E type.

Figure 17. E1 type ejection rate relationship.

Conclusions

이 연구는 DNA 프린팅 IDMH를 제공하고 미세 유체 흐름 추정을 수행했습니다. 설계된 DNA 스프레이 캐비티와 20V의 구동 전압에서 다양한 펄스 폭의 유동 성능이 펄스 폭에 따라 증가하는 것으로 밝혀졌습니다.

E1 유형 유량 테스트는 해당 유량이 3.1cc / min으로 증가함에 따라 유량이 전력 변화에 영향을 받는 것으로 나타났습니다. 동력이 증가함에 따라 유량은 0.75cc / min에서 3.5cc / min으로 최대 6.5W까지 증가했습니다. 동력이 더 증가하면 유량은 에너지와 함께 증가하지 않습니다. 이것은 이 테이블 디자인이 가장 크다는 것을 보여줍니다. 유속은 3.5cc / 분이었다.
작동 주파수가 2KHz이고 펄스 폭이 4μs 및 5μs 인 특수 설계된 DNA 스프레이 룸 구조에서 다양한 전력 조건 하에서 유량 변화를 관찰했습니다. 4.3–5.87 W의 출력 범위 내에서 주입 된 모노머의 유속은 5.5cc / 분이었습니다. 이것은 힘이 증가해도 변하지 않았습니다. DNA는 귀중하고 쉽게 얻을 수 없습니다. 이 실험을 통해 우리는 DNA가 뿌려진 마이크로 어레이 바이오칩의 수천 개의 지점에 필요한 총 DNA 양을 정확하게 추정 할 수 있습니다.

<내용 중략>…….

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Full Text View

CASE2-실험 결과와 FLOW-3D WELD에 의한 해석 결과와의 비교(단면 형상)

FLOW-3D WELD 용접 사례

FLOW-3D WELD를 이용한 용접 해석 사례를 소개합니다.

열전도 형 용접 (레이저)
　 두께가 다른 모재 맞대기
하이브리드
　 레이저 / 아크 하이브리드
깊이 용해 형 (키 홀)
　 알루미늄 평판에 의한 용해 깊이, 형상 확인
레이저 고기 모듬
　 파우더 공급 및 용해
아크 용접
　 오버레이 피팅 관통 평가
레이저 용접 (무릎 관절)
　 무릎 관절의 실험과의 비교
Selective Laser Sintering (3D printing)
　 3 차원 프린터에의 응용

레이저 용접의 특징

열전도 형 용접

열전도 형 용접 결과

하이브리드

강판의 레이저 / 아크 하이브리드 용접의 분석을 실시했습니다.

분석 조건

메쉬

해석과 실험과의 비교

온도의 단위는 [K]입니다.

깊이 용해형 (키 홀)

레이저 금속 침전 Laser Metal Deposition (LMD)

파우더 공급 레이저에 의한 용해

아크 용접

분석 조건

분석 결과 : 온도 등고선 [K]

분석 결과 : 용융 부의 교반

분석 결과 : 용융 부 교반 유속 벡터

분석 결과 : 구슬 모양

분석 결과 : 고출력의 경우 온도 등고선 [K]

레이저 용접 (무릎 관절)

분석 결과와 실제의 단면 비교

Selective Laser Sintering (3D printing)

선택적 레이저 용융 분석

Liquid Metal 3D Printing

This article was contributed by V.Sukhotskiy^1,2, I. H. Karampelas³, G. Garg ¹, A. Verma¹, M. Tong ¹, S. Vader², Z. Vader², and E. P. Furlani^1
1University at Buffalo SUNY, ²Vader Systems, ³Flow Science, Inc.

Drop-on-demand 잉크젯 인쇄는 상업 및 소비자 이미지 재생을 위한 잘 정립 된 방법입니다. 이 기술을 주도하는 동일한 원리는 인쇄 및 적층 제조 분야에도 적용될 수 있습니다. 기존의 잉크젯 기술은 폴리머에서 살아있는 세포에 이르기까지 다양한 재료를 증착하고 패턴화하여 다양한 기능성 매체, 조직 및 장치를 인쇄하는 데 사용되었습니다 [1, 2]. 이 작업의 초점은 잉크젯 기반 기술을 3D 솔리드 금속 구조 인쇄로 확장하는 데 있습니다 [3, 4]. 현재 대부분의 3D 금속 프린팅 응용 프로그램은 고체 물체를 형성하기 위해 레이저 [6] 또는 전자 빔 [7]과 같은 외부 지향 에너지 원의 영향을 받아 증착 된 금속 분말 소결 또는 용융을 포함합니다. 그러나 이러한 방법은 비용 및 프로세스 복잡성 측면에서 단점이 있습니다. 예를 들어, 3D 프린팅 프로세스에 앞서 분말을 생성하기 위해 시간과 에너지 집약적인 기술이 필요합니다.

이 기사에서는 MHD (자기 유체 역학) drop-on-demand 방출 및 움직이는 기판에 액체 방울 증착을 기반으로 3D 금속 구조의 적층 제조에 대한 새로운 접근 방식에 대해 설명합니다. 프로세스의 각 부분을 연구하기 위해 많은 시뮬레이션이 수행되었습니다.

단순화를 위해 이 연구는 두 부분으로 나뉘었습니다.

첫 번째 부분에서는 MHD 분석을 사용하여 프린트 헤드 내부의 Lorentz 힘 밀도에 의해 생성 된 압력을 추정 한 다음 FLOW-3D 모델의 경계 조건으로 사용됩니다. 액적 방출 역학을 연구하는 데 사용되었습니다.

두 번째 부분에서는 이상적인 액적 증착 조건을 식별하기 위해 FLOW-3D 매개 변수 분석을 수행했습니다. 모델링 노력의 결과는 그림 1에 표시된 장치의 설계를 안내하는데 사용되었습니다.

코일은 배출 챔버를 둘러싸고 전기적으로 펄스되어 액체 금속을 투과하고 폐쇄 루프를 유도하는 과도 자기장을 생성합니다. 그 안에 일시적인 전기장. 전기장은 순환 전류 밀도를 발생시키고, 이는 과도장에 역 결합되고 챔버 내에서 자홍 유체 역학적 로렌츠 힘 밀도를 생성합니다. 힘의 방사형 구성 요소는 오리피스에서 액체 금속 방울을 분출하는 역할을 하는 압력을 생성합니다. 분출된 액적은 기질로 이동하여 결합 및 응고되어 확장된 고체 구조를 형성합니다. 임의의 형태의 3 차원 구조는 입사 액적의 정확한 패턴 증착을 가능하게 하는 움직이는 기판을 사용하여 층별로 인쇄 될 수 있습니다. 이 기술은 상표명 MagnetoJet으로 Vader Systems (www.vadersystems.com)에 의해 특허 및 상용화되었습니다.

MagnetoJet 프린팅 공정의 장점은 상대적으로 높은 증착 속도와 낮은 재료 비용으로 임의 형상의 3D 금속 구조를 인쇄하는 것입니다 [8, 9]. 또한 고유한 금속 입자 구조가 존재하기 때문에 기계적 특성이 개선된 부품을 인쇄 할 수 있습니다.

프로토타입 디바이스 개발

Vader Systems의 3D 인쇄 시스템의 핵심 구성 요소는 두 부분의 노즐과 솔레노이드 코일로 구성된 프린트 헤드 어셈블리입니다. 액체화는 노즐의 상부에서 발생합니다. 하부에는 직경이 100μm ~ 500μm 인 서브 밀리미터 오리피스가 있습니다. 수냉식 솔레노이드 코일은 위 그림에 표시된 바와 같이 오리피스 챔버를 둘러싸고있습니다 (냉각 시스템은 도시되지 않음). 다수의 프린트 헤드 디자인의 반복적인 개발은 액체 금속 배출 거동뿐만 아니라, 액체 금속 충전 거동에 대한 사출 챔버 기하적인 효과를 분석하기 위해 연구되었습니다.

이 프로토타입 시스템은 일반적인 알루미늄 합금으로 만들어진 견고한 3D 구조를 성공적으로 인쇄했습니다 (아래 그림 참조). 액적 직경, 기하학, 토출 빈도 및 기타 매개 변수에 따라 직경이 50 μm에서 500 μm까지 다양합니다. 짧은 버스트에서 최대 5000 Hz까지 40-1000 Hz의 지속적인 방울 분사 속도가 달성 되었습니다.

Computational Models

프로토 타입 장치 개발의 일환으로, 성능 (예 : 액적 방출 역학, 액적-공기 및 액적-기질 상호 작용)에 대한 설계 개념을 스크리닝하기 위해 프로토타입 제작 전에 계산 시뮬레이션을 수행했습니다. 분석을 단순화하기 위해 CFD 분석 뿐만 아니라 컴퓨터 전자기(CE)를 사용하는 두 가지 다른 보완 모델이 개발되었습니다. 첫 번째 모델에서는 2 단계 CE 및 CFD 분석을 사용하여 MHD 기반 액적 분출 거동과 효과적인 압력 생성을 연구했습니다. 두 번째 모델에서는 열-유체 CFD 분석을 사용하여 기판상의 액적 패턴화, 유착 및 응고를 연구했습니다.

MHD 분석 후, 첫 번째 모델에서 등가 압력 프로파일을 추출하여 액적 분출 및 액적-기질 상호 작용의 과도 역학을 탐구하도록 설계된 FLOW-3D 모델의 입력으로 사용되었습니다. FLOW-3D 시뮬레이션은 액적 분출에 대한 오리피스 안과 주변의 습윤 효과를 이해하기 위해 수행되었습니다. 오리피스 내부와 외부 모두에서 유체 초기화 수준을 변경하고 펄스 주파수에 의해 결정된 펄스 사이의 시간을 허용함으로써 크기 및 속도를 포함하여 분출 된 액 적의 특성 차이를 식별 할 수있었습니다.

Droplet 생성

MagnetoJet 인쇄 프로세스에서, 방울은 전압 펄스 매개 변수에 따라 일반적으로 1 – 10m/s 범위의 속도로 배출되고 기판에 충돌하기 전에 비행 중에 약간 냉각됩니다. 기판상의 액적들의 패터닝 및 응고를 제어하는 능력은 정밀한 3D 솔리드 구조의 형성에 중요합니다. 고해상도 3D 모션베이스를 사용하여 패터닝을 위한 정확한 Droplet 배치가 이루어집니다. 그러나 낮은 다공성과 원하지 않는 레이어링 artifacts가 없는 잘 형성된 3D 구조를 만들기 위해 응고를 제어하는 것은 다음과 같은 제어를 필요로하기 때문에 어려움이 있습니다.

냉각시 액체 방울로부터 주변 물질로의 열 확산,
토출된 액적의 크기,
액적 분사 빈도 및
이미 형성된 3D 물체로부터의 열 확산.

이들 파라미터를 최적화 함으로써, 인쇄된 형상의 높은 공간 분해능을 제공하기에 충분히 작으며, 인접한 액적들 및 층들 사이의 매끄러운 유착을 촉진하기에 충분한 열 에너지를 보유 할 것입니다. 열 관리 문제에 직면하는 한 가지 방법은 가열된 기판을 융점보다 낮지만 상대적으로 가까운 온도에서 유지하는 것입니다. 이는 액체 금속 방울과 그 주변 사이의 온도 구배를 감소시켜 액체 금속 방울로부터의 열의 확산을 늦춤으로써 유착을 촉진시키고 고형화하여 매끄러운 입체 3D 덩어리를 형성합니다. 이 접근법의 실행 가능성을 탐구하기 위해 FLOW-3D를 사용한 파라 메트릭 CFD 분석이 수행되었습니다.

액체 금속방울 응집과 응고

우리는 액체 금속방울 분사 주파수뿐만 아니라 액체 금속방울 사이의 중심 간 간격의 함수로서 가열된 기판에서 내부 층의 금속방울 유착 및 응고를 조사했습니다. 이 분석에서 액체 알루미늄의 구형 방울은 3mm 높이에서 가열 된 스테인리스 강 기판에 충돌합니다. 액적 분리 거리 (100)로 변화 될 때 방울이 973 K의 초기 온도를 가지고, 기판이 다소 943 K.도 3의 응고 온도보다 900 K로 유지됩니다. 실선의 인쇄 중에 액적 유착 및 응고를 도시 50㎛의 간격으로 500㎛에서 400㎛까지 연속적으로 유지하고, 토출 주파수는 500Hz에서 일정하게 유지 하였습니다.

방울 분리가 250μm를 초과하면 선을 따라 입자가 있는 응고된 세그먼트가 나타납니다. 350μm 이상의 거리에서는 세그먼트가 분리되고 선이 채워지지 않은 간극이 있어 부드러운 솔리드 구조를 형성하는데 적합하지 않습니다. 낮은 온도에서 유지되는 기질에 대해서도 유사한 분석을 수행했습니다(예: 600K, 700K 등). 3D 구조물이 쿨러 기질에 인쇄될 수 있지만, 그것들은 후속적인 퇴적 금속 층들 사이에 강한 결합의 결여와 같은 바람직하지 않은 공예품을 보여주는 것이 관찰되었습니다. 이는 침전된 물방울의 열 에너지 손실률이 증가했기 때문입니다. 기판 온도의 최종 선택은 주어진 용도에 대해 물체의 허용 가능한 인쇄 품질에 따라 결정될 수 있습니다. 인쇄 중에 부품이 커짐에 따라 더 높은 열 확산에 맞춰 동적으로 조정할 수도 있습니다.

FLOW-3D 결과 검증

위 그림은 가열된 기판 상에 인쇄된 컵 구조 입니다. 인쇄 과정에서 가열된 인쇄물의 온도는 인쇄된 부분의 순간 높이를 기준으로 실시간으로 733K (430 ° C)에서 833K (580 ° C)로 점차 증가했습니다. 이것은 물체 표면적이 증가함에 따라 국부적인 열 확산의 증가를 극복하기 위해 행해졌습니다. 알루미늄의 높은 열전도율은 국부적인 온도 구배에 대한 조정이 신속하게 이루어져야 하기 때문에 특히 어렵습니다. 그렇지 않으면 온도가 빠르게 감소하고 층내 유착을 저하시킵니다.

결론

시뮬레이션 결과를 바탕으로, Vader System의 프로토타입 마그네슘 유체 역학 액체 금속 Drop-on-demand 3D 프린터 프로토 타입은 임의의 형태의 3D 솔리드 알루미늄 구조를 인쇄할 수 있었습니다. 이러한 구조물은 서브 밀리미터의 액체 금속방울을 층 단위로 패턴화하여 성공적으로 인쇄되었습니다. 시간당 540 그램 이상의 재료 증착 속도는 오직 하나의 노즐을 사용하여 달성 되었습니다.

이 기술의 상업화는 잘 진행되고 있지만 처리량, 효율성, 해상도 및 재료 선택면에서 최적의 인쇄 성능을 실현하는 데는 여전히 어려움이 있습니다. 추가 모델링 작업은 인쇄 과정 중 과도 열 영향을 정량화하고, 메니스커스 동작뿐만 아니라 인쇄된 부품의 품질을 평가하는 데 초점을 맞출 것입니다.

References
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[2] Sirringhaus, H., Kawase, T., Friend, R.H., Shimoda, T., Inbasekaran, M., Wu, W. and Woo, E.P., “High-resolution inkjet printing of all-polymer transistor circuits,” Science 290(5499), 2123-2126 (2000).

[3] Tseng, A.A., Lee, M.H. and Zhao, B., “Design and operation of a droplet deposition system for freeform fabrication of metal parts,” Transactions-American Society of Mechanical Engineers Journal of Engineering Materials and Technology 123(1), 74-84 (2001).

[4] Suter, M., Weingärtner, E. and Wegener, K., “MHD printhead for additive manufacturing of metals,” Procedia CIRP 2, 102-106 (2012).

[5] Loh, L.E., Chua, C.K., Yeong, W.Y., Song, J., Mapar, M., Sing, S.L., Liu, Z.H. and Zhang, D.Q., “Numerical investigation and an effective modelling on the Selective Laser Melting (SLM) process with aluminium alloy 6061,” International Journal of Heat and Mass Transfer 80, 288-300 (2015).

[6] Simchi, A., “Direct laser sintering of metal powders: Mechanism, kinetics and microstructural features,” Materials Science and Engineering: A 428(1), 148-158 (2006).

[7] Murr, L.E., Gaytan, S.M., Ramirez, D.A., Martinez, E., Hernandez, J., Amato, K.N., Shindo, P.W., Medina, F.R. and Wicker, R.B., “Metal fabrication by additive manufacturing using laser and electron beam melting technologies,” Journal of Materials Science & Technology, 28(1), 1-14 (2012).

[8] J. Jang and S. S. Lee, “Theoretical and experimental study of MHD (magnetohydrodynamic) micropump,” Sensors & Actuators: A. Physical, 80(1), 84-89 (2000).

[9] M. Orme and R. F. Smith, “Enhanced aluminum properties by means of precise droplet deposition,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, 122(3), 484-493, (2000)

igure 1:Essential componentsof the MHD printhead (a) cross-sectional view of printhead showing flow of liquid metal.(b) simulation model showing the magneticfield generated by a pulsed magnetic coil as well as an ejecteddroplet of liquid aluminum.

Timeline of molten metal droplet ejection

용융 금속 액적 분출 타임 라인

Keywords: Magnetohydrodynamicdroplet ejection, droplet on demandprinting, 3D printing of molten metal, additive manufacturing, thermo-fluidic analysis, molten aluminum.

우리는 액체 금속 방울을 사용하여 3D 고체 금속 구조의 DOD (drop-on-demand) 프린팅을 위한 새로운 방법을 제시합니다. 이 방법은 MHD (Magnetohydrodynamic) 기반 방울 생성에 의존합니다. 특히, 외부 코일에 의해 공급되는 맥동 자기장은 액체 금속으로 채워진 분사 챔버 내에서 MHD 기반 힘 밀도를 유도하여 물방울이 노즐을 통해 분사되도록 합니다.

임의의 모양의 3 차원 (3D) 고체 금속 구조는 드롭 방식의 유착 및 응고와 함께 방울의 층별 패턴 증착을 통해 인쇄 할 수 있습니다. 샘플 인쇄 구조와 함께 이 프로토 타입 MHD 인쇄 시스템을 소개합니다. 또한 드롭 생성을 제어하는 기본 물리학에 대해 논의하고 장치 성능을 예측하기 위한 계산 모델을 소개합니다.

주문형 잉크젯 인쇄는 상업용 및 소비자용 이미지 재생을 위한 잘 정립된 방법입니다. 이 기술을 주도하는 동일한 원리는 기능 인쇄 및 적층 제조 분야에도 적용될 수 있습니다. 기존의 잉크젯 기술은 폴리머에서 살아있는 세포에 이르기까지 다양한 재료를 증착하고 패턴 화하여 다양한 기능성 매체, 조직 및 장치를 인쇄하는 데 사용되어 왔습니다 [1, 2]. 이 작업의 초점은 잉크젯 기반 기술을 3D 솔리드 금속 구조 프린팅으로 확장하는 데 있습니다 [3, 4]. 현재 대부분의 3D 금속 프린팅 응용 분야에는 레이저 (예 : 선택적 레이저 소결 [5] 및 직접 레이저 금속 소결 [6]) 또는 전자 빔 (예 : 레이저 소결 [6])과 같은 외부 지향 에너지 원의 영향으로 증착 된 금속 분말 소결 또는 용융이 포함됩니다. 전자빔 용융 [7])을 사용하여 고체 물체를 형성합니다. 그러나 이러한 방법은 비용 및 복잡성 측면에서 단점이 있습니다. 3D 프린팅 프로세스에 앞서 금속을 밀링해야 합니다.

이 작업에서 우리는 자기 유체 역학의 원리에 기반한 금속 구조물의 적층 제조에 대한 새로운 접근 방식을 소개합니다. 이 방법에서는 감긴 고체 금속 와이어가 MHD 프린트 헤드의 아세라 미치 팅 챔버에 연속적으로 공급되고 용융되어 그림 1에 표시된 것처럼 모세관 힘을 통해 배출 챔버에 공급되는 액체 금속 저장소를 형성합니다. 코일이 배출 챔버를 둘러싸고 있습니다. 액체 금속 내에서 과도 전기장을 유도하는 과도 자기장을 생성하도록 전기적으로 펄스됩니다. 전기장은 유도 된 순환 전류 밀도를 생성하며, 이는 적용된 자기장과 결합하여 챔버 내에서 오리피스의 액체 금속 방울을 방출하는 역할을하는 로렌츠 힘 밀도 (fMHD)를 생성합니다. 분출 된 액 적은 기질로 이동하여 결합 및 응고되어 확장 된 고체 구조를 형성합니다. 임의의 형태의 3 차원 구조는 입사 액 적의 정확한 패턴 증착을 가능하게하는 움직이는 기판을 사용하여 층별로 인쇄 될 수 있습니다. 이 기술은 Vader Systems (www.vadersystems.com)에서 MagnetoJet이라는 상표명으로 개척하고 상용화했습니다. MagnetoJet 인쇄 공정의 장점은 상대적으로 높은 증착 속도와 낮은 재료 비용으로 임의의 모양의 3D 금속 구조를 인쇄하는 것입니다. 이 작업에서는 MagnetoJet 프로토 타입 프린팅 프로세스에 대해 논의하고 샘플 3D 프린팅 구조를 시연하며 합리적인 설계 및 장치 성능 예측을 가능하게하는 계산 모델을 소개합니다.

Figure 2:Printed 3D structures: (a) ring showing as printed base and processed upper portion, and (b) cat

논문 원문 보기

Laser Metal Deposition and Fluid Particles

FLOW-3D는 신규 모듈을 개발 하면서, 입자 모델의 새로운 입자 클래스 중 하나인 유체 입자의 기능에 초점을 맞출 것입니다. 유체 입자는 증발 및 응고를 포함하여 유체 속성을 본질적으로 부여합니다. 유체 입자가 비교적 간단한 강우 모델링(아래의 애니메이션)에서 복잡한 레이저 증착(용접) 모델링에 이르기까지 다양한 사례가 있을 수 있습니다.

Fluid Particles

FLOW-3D에서 유체 입자 옵션이 활성화 되면 사용자는 다양한 직경과 밀도로 다양한 유체 입자 종을 설정할 수 있습니다. 또한 유체 입자의 동력학은 확산 계수, 항력 계수, 난류 슈미트 수, 반발 계수 및 응고된 반발 계수와 같은 특성에 의해 제어 될 수 있습니다. 유체 입자는 열적 및 전기적 특성을 지정할 수 있습니다.

사용자는 유체 입자 생성을 위해 여러 소스를 설정할 수 있습니다. 각 소스는 이전에 정의 된 모든 유체 입자 종 또는 일부 유체 입자 종의 혼합을 가질 수 있습니다. 또한 사용자는 무작위 또는 균일한 입자 생성을 선택하고 입자가 소스에서 방출되는 속도를 정의 할 수 있습니다.

Laser Metal Deposition

레이저 금속 증착은 미세한 금속 분말을 함께 융합하여 3차원 금속 부품을 제작하는 3D printing 공정입니다. 레이저 금속 증착은 항공 우주 및 의료 정형 외과 분야에서 다양한 응용 분야에 적용됩니다. 레이저 금속 증착의 개략도는 아래와 같습니다. 전력 강도 분포, 기판의 이동 속도, 차폐 가스 압력 및 용융/응고, 상 변화 및 열전달과 같은 물리적 제어와 같은 제어 매개 변수가 함께 작동하여 레이저 금속 증착을 효과적인 적층 제조 공정으로 만듭니다.

Setting Up Laser Metal Deposition

새로운 유체 입자 모델은 분말 강도 분포를 할당하고 용융 풀 내부 및 주변에서 발생하는 복잡한 입자 – 기판 상호 작용을 포착하기 때문에 레이저 금속 증착 시뮬레이션을 설정하는 데 없어서는 안될 부분입니다.

일반 사용자들은 FLOW-3D에서 시뮬레이션을 쉽게 설정할 수 있다는 것을 알고 있습니다. 레이저 금속 증착 설정의 경우에도 다른 점은 없습니다. IN-718의 물리적 특성, 형상 생성, 입자 분말 강도 분포, 메쉬 생성 및 시뮬레이션 실행과 같은 모든 설정 단계가 간단하고 사용자 친화적입니다.

IN-718의 물성은 기판과 응고된 유체 입자 모두에 사용됩니다. 40 미크론 유체 입자가 무작위 방식으로 초당 500,000의 속도로 입자 영역에서 계산 영역으로 주입됩니다. 입자 빔은 기판의 운동 방향이 변화 될 때마다 순간적으로 정지되어 용융 풀이 급격한 속도 변화에 적응하도록 합니다.

이렇게 하면 기판에서 입자가 반사되는 것을 방지 할 수 있습니다. 기판이 5초마다 회전하기 때문에 입자 생성 속도는 아래 그림과 같이 5 초마다 0으로 떨어집니다. 기판 이동 자체는 표 형식의 속도 데이터를 사용하여 FLOW-3D에 지정됩니다. 입자는 응고된 유체 입자로 주입되어 고온의 용융 풀에 부딪혀 녹아 용융 풀 유체의 일부가 됩니다.

Substrate velocity

입자 모델 외에도 FLOW-3D의 밀도 평가, 열 전달, 표면 장력, 응고 및 점도 모델이 사용됩니다. 보다 구체적으로, 온도에 따른 표면 장력은 증착된 층의 형태에 큰 영향을 주는 Marangoni 효과를 일으킵니다.

레이저를 복제하기 위해 100 % 다공성 구성 요소가 있는 매우 기본적인 설정이 열원으로 사용됩니다. 100 % 다공성은 구성 요소 주변의 유동 역학에 영향을 미치지 않습니다. 오히려 그것은 특정 영역의 기판에 열을 효과적으로 추가합니다. 이 예비 가열 메커니즘을 자회사인 Flow Science Japan이 개발한 고급 레이저 모듈로 교체하는 작업이 현재 본격적으로 진행 중입니다. 가열 다공성 구성 요소는 각각의 층이 동일한 양의 열을 얻도록 각 층이 증착된 후에 약간 위로 이동됩니다.

Results and discussion

아래 애니메이션은 다중 층 증착을 이용한 레이저 금속 증착 공정을 보여줍니다. 기판이 방향을 변경할 때마다 입자 빔 모션이 일시적으로 중지됩니다. 또한 층이 증착됨에 따라 다공성 열원에서 각 층에 불균등 한 열이 추가되어 새로운 층의 모양이 변경됩니다. 각 층을 증착 한 후에 열원을 위로 이동해야 하는 양을 측정하는 것은 현재의 기능에서는 어렵습니다. 다만 준비중인 Flow Science Japan의 레이저 모듈은 이 문제를 해결할 수 있습니다.

전반적으로 입자 모델은 레이저 금속 증착에서 매우 중요한 공정 매개 변수인 분말 강도 분포를 정확하게 재현합니다. 입자 모델에 대한 이러한 수준의 제어 및 정교함은 적층 제조 분야의 사용자와 공급자 모두가 제조 공정을 미세 조정하는 데 도움이 될 것으로 기대합니다.

Fig. 3. Comparison of SEM photographs and simulation results of two neighboring aluminum droplets from (a) top view, (b) side view and (c) bottom view. The scale bar is 100 µm.

Effect of the surface morphology of solidified droplet on remelting
between neighboring aluminum droplets

Abstract

인접한 물방울 사이의 좋은 야금학적 결합은 droplet 기반 3D 프린팅에서 필수적입니다. 그러나 재용해 메커니즘이 명확하게 마스터되었지만, 콜드 랩은 균일한 알루미늄 액적 증착 제조에서 형성된 부품의 일반적인 내부 결함이며, 이는 응고된 액 적의 표면 형태를 간과하기 때문입니다.

여기에서 처음으로 물방울 사이의 융합에 대한 잔물결과 응고각의 차단 효과가 드러났습니다. 재용해의 자세한 과정을 조사하기 위해 VOF (체적 부피) 방법을 기반으로 3D 수치 모델을 개발했습니다. 실험과 시뮬레이션을 통해 인접한 액적 간의 재 용융 공정은 두 번째 액 적과 기판 사이의 과도 접촉에 따라 두 단계로 나눌 수 있음을 보여줍니다.

첫 번째 단계에서는 재용해 조건이 이론적으로 충족 되더라도 콜드 랩이 형성 될 수 있다는 직관적이지 않은 결과가 관찰됩니다. 이전에 증착된 액적 표면의 잔물결은 새로운 액적과의 직접 접촉을 차단합니다. 두 번째 단계에서는 응고 각도가 90 °보다 클 때 액체 금속이 불완전하게 채워져 바닥 표면에 콜드랩이 형성됩니다. 또한 이러한 콜드 랩은 온도 매개 변수를 개선하여 완전히 피하는 것이 어렵습니다.

이 문제를 해결하기 위해 기판의 열전도 계수를 감소시키는 새로운 전략이 제안 되었습니다. 이 방법은 잔물결을 제거하고 응고 각도를 줄임으로써 물방울 사이의 재용해를 효과적으로 촉진합니다.

Keywords: 3D printing; aluminum droplets; metallurgical bonding; ripples; solidification angle.

Fig. 1. Schematic diagram of (a) experimental setup and (b) process principle of uniform aluminum droplet deposition manufacturing.

Fig. 2. Schematic diagram of the numerical model of two droplets successively depositing on the substrate.

Fig. 4. Experimental and simulation images of shape evolution during two neighboring droplets successively impacting at (a) t, (b) t+0.5 ms, (c) t+1 ms, (d) t+2 ms, (e) t+3 ms and (f) t+5 ms.

Fig. 5. SEM observation of (a) side view and (b) bottom view of successive deposition of aluminum droplets; (c) enlarged side view of the section of the printed metal trace in (a); (d) fracture of two neighboring droplets; (e) cross-section of two droplets successive deposition; (f) enlarged view of the selected section in (e).

Fig. 6. Simulation results of (a) shape evolution and solid fraction distribution in Y- Z middle cross-section of two successively-deposited droplets; (b) temperature variation with time at three points (labeled A-C) on the surface of the first droplet during the deposition of the second droplet.

논문 원본 바로가기

References

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JOURNAL OF HEAT TRANSFER, 116 (1994) 436-436.

FLOW-3D Weld

FLOW-3D WELD 2025R1 변경점

워크플로우 향상

User Interface 통일

FLOW-3D WELD 2025R1은 FLOW-3D와 FLOW-3D WELD의 기능을 원활하게 통합하는 사용자 인터페이스를 도입합니다. 사용자는 단일 애플리케이션 내에서 모든 관련 물리 모델을 활성화하고 단일 합금 또는 이종 금속 용접 애플리케이션에 필요한 모든 재료 특성을 정의할 수 있습니다.

신규 프로세스 탬플릿

새로운 사전 로드 레이저 용접 템플릿은 시뮬레이션 설정을 그 어느 때보다 쉽게 만듭니다.

Restrat 워크플로우 향상

FLOW-3D(x)에서는 전용 FLOW-3D WELD 노드를 추가하고 성능을 크게 향상시킴으로써 매개변수 연구와 최적화 루틴을 구성하는 데 시간이 많이 소요될 수 있습니다. 사용자는 모델 검증을 간소화하고, 프로세스 창을 식별하며, 매개변수 민감도 분석을 수행하고, 레이저 입력과 빔 특성을 최적화하기 위한 워크플로우를 설정할 수 있습니다.

FLOW-3D WELD 는 레이저 용접 공정에 대한 강력한 통찰력을 제공하여 공정 최적화를 달성합니다. 더 나은 공정 제어를 통해 다공성, 열 영향 영역을 최소화하고, 미세 구조 변화를 제어 할 수 있습니다. 레이저 용접 프로세스를 정확하게 시뮬레이션하기 위해 FLOW-3D WELD 는 레이저 열원, 레이저-재료 상호 작용, 유체 흐름, 열 전달, 표면 장력, 응고, 다중 레이저 반사 및 위상 변화와 같은 모든 관련 물리학을 구현합니다.

퍼포먼스 향상

FLOW-3D WELD 2025R1은 고성능 컴퓨팅(HPC) 플랫폼을 지원하여 전례 없는 시뮬레이션 속도를 제공합니다. 코어 솔버의 고급 OpenMP – MPI 기능을 활용하여 HPC 플랫폼에서의 레이저 용접 시뮬레이션은 이제 표준 워크스테이션 구성에 비해 최대 약 9배 더 빠릅니다.

Scaling comparisons for a high-resolution single track laser melting simulation.

Solver 향상

반사 모델 향상

용융 표면의 에너지 반사는 특히 키홀 영역을 시뮬레이션할 때 중요한 요소가 될 수 있습니다. FLOW-3D WELD의 개선된 반사 모델은 레이저 반사를 보다 정확하게 표현할 수 있습니다.

열원 통합 및 개선

업그레이드된 열원 정의 옵션을 통해 사용자는 나선형 및 스큐 라인과 같은 복잡한 레이저 경로를 더 정밀하게 정의할 수 있습니다. 추가적인 제어 기능을 통해 다중 소스 시뮬레이션을 위한 열원 속성을 전송할 수 있어 시간을 절약하고 오류 발생 가능성을 줄입니다.

FLOW-3D POST 지원

유체, 용융 영역, 열원, 반사 및 입자를 위한 새로운 사전 구성 객체는 FLOW-3D WELD 시뮬레이션의 시각화를 용이하게 합니다. 일반적으로 사용되는 출력의 주석은 FLOW-3D POST에서 결과 파일을 열면 자동으로 제공되므로 후처리 워크플로우가 가속화됩니다.

FLOW-3D WELD Brochure Download

낮은 열 입력, 뛰어난 생산성, 속도는 기존의 용접 방법을 대체하는 레이저 용접 프로세스로 이어집니다. 레이저 용접이 제공하는 장점 중 일부는 더 나은 용접 강도, 더 작은 열 영향 영역, 더 정밀한 정밀도, 최소 변형 및 강철, 알루미늄, 티타늄 및 이종 금속을 포함한 광범위한 금속 / 합금을 용접 할 수있는 능력을 포함합니다.

공정 최적화

FLOW-3D WELD 는 레이저 용접 공정에 대한 강력한 통찰력을 제공하고 궁극적으로 공정 최적화를 달성하는 데 도움이됩니다. 더 나은 공정 제어로 다공성을 최소화하고 열 영향을받는 영역을 제한하며 미세 구조 변화를 제어 할 수 있습니다. FLOW-3D WELD 는 자유 표면 추적 알고리즘으로 인해 매우 복잡한 용접 풀을 시뮬레이션하는 데 매우 적합합니다. FLOW-3D WELD 는 관련 물리적 모델을 FLOW-3D 에 추가로 통합하여 개발되었습니다. 레이저 소스에 의해 생성된 열유속, 용융 금속의 증발 압력, 차폐 가스 효과, 용융 풀의 반동 압력 및 키홀 용접의 다중 레이저 반사. 현실적인 공정 시뮬레이션을 위해 모든 관련 물리 현상을 포착하는 것이 중요합니다.

얕은 용입 용접 (왼쪽 상단); 실드 가스 효과가 있는 깊은 용입 용접 (오른쪽 상단); 쉴드 가스 및 증발 압력을 사용한 심 용입 용접 (왼쪽 하단); 쉴드 가스, 증발 압력 및 다중 레이저 반사 효과 (오른쪽 하단)를 사용한 깊은 침투 용접.

FLOW-3D WELD 는 레이저 용접의 전도 모드와 키홀 모드를 모두 시뮬레이션 할 수 있습니다. 전 세계의 연구원들은 FLOW-3D WELD 를 사용하여 용융 풀 역학을 분석하고 공정 매개 변수를 최적화하여 다공성을 최소화하며 레이저 용접 수리 공정에서 결정 성장을 예측합니다.

완전 관통 레이저 용접 실험

한국의 KAIST와 독일의 BAM은 16K kW 레이저를 사용하여 10mm 강판에 완전 침투 레이저 용접 실험을 수행했습니다. CCD 카메라의 도움으로 그들은 완전 침투 레이저 용접으로 인해 형성된 상단 및 하단 용융 풀 역학을 포착 할 수있었습니다. 그들은 또한 FLOW-3D WELD 에서 프로세스를 시뮬레이션하고 시뮬레이션과 실험 결과 사이에 좋은 일치를 얻었습니다.

레이저 용접 다공성 사례 연구

General Motors, Michigan 및 Shanghai University는 중국의 공정 매개 변수, 즉 용접 속도 및 용접 경사각이 키홀 용접에서 다공성 발생에 미치는 영향을 이해하기 위해 상세한 연구를 공동으로 진행했습니다.

연구원들은 FLOW-3D WELD를 사용 하여 증발 및 반동 압력, 용융풀 역학, 온도 의존적 표면 장력 및 키홀 내에서 여러 번의 레이저 반사 동안 프레넬 흡수를 포함한 모든 중요한 물리적 현상을 설명했습니다.

시뮬레이션 모델을 기반으로 연구진은 키홀 용접에서 유도 다공성의 주요 원인으로 불안정한 키홀을 식별했습니다. 아래 이미지에서 볼 수 있듯이 후방 용융 풀의 과도한 재순환으로 인해 후방 용융 풀이 전방 용융 풀 벽에서 붕괴되고 공극이 발생하여 다공성이 발생합니다. 이러한 갇힌 공극이 진행되는 응고 경계에 의해 포착되었을 때 다공성이 유도되었습니다.

높은 용접 속도에서는 더 큰 키홀 개구부가 있으며 이는 일반적으로 더 안정적인 키홀 구성을 가져옵니다. 사용 FLOW-3D 용접 , 연구진은 그 높은 용접 속도와 경사도 완화 다공성의 큰 용접 각도를 예측했습니다.

FLOW Weld

FLOW Weld 모듈은 용접 해석에 필요한 모델을 FLOW-3D 에 추가하는 추가 모듈입니다.

FLOW-3D 의 표면 장력 자유 표면 분석, 용융, 응고, 증발, 상 변화 모델 등의 기본 기능을

응용하여 각종 용접 현상을 분석 할 수 있습니다.

주요 기능 :	열원 모델 (출력 지정, 가우스분포, 디 포커스 등) 열원의 자유로운 이동 증발 압력 (그에 따른 반력) 실드 가스 압력 다중 반사 용접에 관한 대표적인 출력 (온도 구배 냉각 속도, 에너지 분포 등)
분석 용도 :	높은 방사선 강도와 고온에 의해 직접 관찰이 어려운 현상을 시각화 온도, 열, 용접 속도, 위치 관계, 재료 물성 등의 매개 변수 연구 결함 예측 (기공, 응고, 수축 등)

FLOW -3D Weld 분석 기능

열원 모델의 이동
　 출력량 지정, 가우스분포
에너지 밀도의 분포 , 가공 속도
　 가우스 테이블 입력
증발 압력
　 온도 의존성
다중 반사
　 용해 깊이에 미치는 영향
결과 처리
　 용해 모양, 에너지 분포, 온도 구배 냉각 속도
다양항형상의 레이저와 거동 (+ csv 파일로드)
　 다양한 모양을 csv 파일 형식으로 정의 회전 + 이동
　 임의 형상 이동을 csv 파일로 로드 (나선형)
이종 재료
　 이종 재료의 용접
3D Printing Method
　 Cladding 적층공정

1. 열원 모델의 이동

2. 에너지 밀도의 분포, 가공 속도

열 플럭스 r 방향의 분포 단면은 원형으로, r 방향으로 열유속 분포를 제공합니다.

에너지 밀도의 공간적 분포

가우스 : 원추형의 경우는 조사 방향으로 변화하고 열유속의 면적 분은 동일합니다.

가공 속도

가공 노즐을 x, y, z 방향, 시간 – 속도의 테이블에서 지정합니다.
또한 노즐 (광원) 위치 좌표 조사 방향 벡터 성분을 지정합니다.

3. 증발 압력

에너지 밀도가 높은 경우, 용융 부 계면이 증발하고 그 반력에 의해 계면에 함몰이 발생합니다.
특히 깊은 용융부를 포함한 레이저 용접은 증발 압력을 고려한 모델링이 필요합니다.

증발 압력의 평가는 일반적인 수학적 모델이 없기 때문에 다음 모델 식을 사용합니다.

증발 가스의 상승 효과 (키 홀, 스퍼터 등)

증기의 상승 흐름의 영향을 동압, 전단력으로 평가합니다.

4. 다중 반사

키홀 거동의 비교

다중 반사를 고려한 레이저

5. 결과 처리

용접 기능에 관한 대표적인 출력 예입니다.

6. 다양한 형상의 레이저와 거동 (+ csv 파일 읽기)

7. 이종 재료

이종 재료 간이 분석

재료 : 철, 구리

이종 재료를 이용한 레이저 용접

재료 : 구리, 철

8. 금속 3D 프린팅 기법

– 적층 제조 (Additive Manufacturing) 공정

– DED(Direct Energy Deposition) 공정

Additive manufacturing

LPBF 시뮬레이션 순서

Powder settling

Powder spreading

Laser scan tracks on a powder bed

선택적 레이저 용해(Melting) : 단일 트랙 모델링

Power Bed spreading : 파우더 베드(Bed)압축의 파라메트릭 분석
– 블레이드(Blade) 모션
– 롤러(Roller) 속도와 방향

용융 풀(Melt pool) 모델링

용융 풀의 진화(Evolution of the melt pool)
시뮬레이션 및 실험적 단면(Cross-section) 검증

다층 SLM프로세스 : TU덴마크

추가 특성 – 고객 요청

두 재료의 온도 의존성 재료 특성

유체 영역과 고체 영역 사이의 접촉각 설정

Phase change-heat transfer-thermodynamics-Solidification

Thermodynamic reactors… – on-a-chip

^{David Sinton – Mechanical and Industrial Engineering – University of Toronto}

Phase change

Heat and pressure control phase change and nucleation
Example of formation of void at heated plate nucleation site

Solidification

Welding
Metal deposition
3D printing – metal

Particle DEM modeling

Courtecy Flow Science Japan

Particle DEM modeling + laser sintering

Microfluidic Flow Modeling | FLOW-3D Webinar

금속 3D 프린팅 파우더 베드 해석(PBF해석)

Laser Powder Bed Additive Manufacturing

Heat transfer and fluid flow modeling

레이저 파우더 베드 퓨전 (L-PBF) 첨가제 제조에는 복잡한 물리적 공정이 필요합니다. 특히, 흡수 된 레이저 빔 에너지는 입자를 녹여 강한 유체 흐름이 표면 장력 기울기 (또는 Marangoni 전단 응력)에 의해 주로 발생하는 용융 풀을 형성합니다. 열 전달 및 유체 유동은 분말 베드 내의 분말 입자의 국부적 배열에 의해 영향을 받으며, 이는 위치에 따라 다를 수 있습니다. 매우 일시적인 유체 흐름으로 인해 용융 된 풀 표면 (자유 표면)의 형상이 끊임없이 진화하여 최종 표면 품질에 영향을 미칩니다.

Numerical modeling approach

본 연구에서는 분말 포장 특성, 공정 변수 및 용융 풀 역학이 표면 품질에 미치는 영향을 정량적으로 이해하기 위해 두 가지 모델을 순서대로 사용합니다. 첫 번째 모델은 오픈 소스 이산 요소 방법 (DEM) 코드 인 Yade를 기반으로 개발 된 분말 입자 포장 모델입니다. 입자 적층 정보 (예를 들어, 개별 입자의 위치 및 반경)를 제공한다. 이러한 정보는 FLOW-3D를 기반으로 한 3D 과도 용융 풀 모델 인 두 번째 모델에 입력됩니다. 두 모델의 세부 사항은 문헌 [1]에 나와있다. FLOW-3D를 기반으로 한 용융 풀 모델의 특징을 요약하면 다음과 같습니다.

과도 유체 흐름 시뮬레이션은 그림 1에서와 같이 1000 μm (길이), 270 μm (너비) 및 190 μm (높이) 치수의 3D 계산 영역에서 수행됩니다. 도메인은 50 μm 두께의 층 의 분말 입자를 90㎛ 두께의 기판 위에 놓았다. 도메인의 미리 알림은 처음에는 무효로 채워집니다. 분말 층 형상은 DEM 시뮬레이션의 결과를 사용하여 초기화됩니다. 총 셀 수를 줄이면서 공간 분해능을 극대화하기 위해 메쉬 크기가 기판 / 파우더 레이어 인터페이스를 향하여 기판에서 9 μm에서 3 μm까지 연속적으로 감소하는 편향 메쉬가 사용됩니다. 메쉬 크기는 파우더 레이어와 그 위의 빈 공간에서 3 μm로 일정하게 유지됩니다. 총 셀 수는 143 만입니다.

경계 조건의 경우, 가우시안 분포에 기초한 소정의 열유속이 분말 층의 상부 표면에 부과되어 X 방향을 따라 이동하는 레이저로부터의 열 입력을 나타낸다. 온도에 따른 표면 장력은 FLOW-3D에서 사용 가능한 개선 된 표면 장력 모델을 사용하여 포함됩니다. 다른 열 – 물리적 특성의 경우, FLOW-3D 데이터베이스에서 사용 가능한 IN718 합금에 대한 데이터가 사용됩니다.

Result and discussion

그림 2는 시간 = 55 μs에서 용융 풀 내의 온도 등면 및 속도 벡터의 종단면도 (즉, 레이저 이동 방향에 평행 한 단면)를 도시한다. 용융 된 풀 경계는 1608.15 K에서 등온선으로 표시되며, IN718의 액상 선 온도입니다. 이 그림의 오른쪽에 표시된 것처럼 입자는 부분적으로 용융 풀로 용융됩니다. 용융 된 풀 표면 근처에서, 용융 금속은 레이저 빔 바로 밑의 중심 위치에서 풀의 후단으로 당겨진다. 풀 표면 근처의 용융 금속의 이와 같은 역류는 풀의 후단을 향해 고비를 형성하는 동안 레이저 빔 아래에서 움푹 들어간 표면 프로파일을 생성한다. 다음에서 논의되는 바와 같이, 혹 모양은 볼 결함의 형성을 초래할 수 있습니다.

볼링(balling)은 그림 3에서와 같이 용융 풀이 불연속으로 분리되어 분리 된 섬으로 갈라질 때 발생할 수있는 결함입니다.이 그림에서 알 수 있듯이 레이저 빔 바로 아래의 용융 풀은 안정적이지 않으며 후단이 빠르게 분리됩니다 정면에서 분리 된 섬을 형성합니다. 분리는 그림 3 (c)와 같이 용융 풀의 중간에있는 보이드에서 시작된다. 이 공극은 레이저가 앞으로 계속 이동하면서 팽창하여 결국 용융 된 풀을 두 부분으로 나눕니다. 도 3 (e) 및 (f). 공극의 형성과 그 팽창은 표면 장력 구배 (Marangoni 효과)에 의해 강한 후진 유동에 의해 유발됩니다.

Summary

L-PBF에서의 열 전달 및 유체 흐름의 3D 과도 시뮬레이션은 볼 결함의 형성을 정량적으로 이해하기 위해 수행됩니다. 단순한 선형 트랙 만 시뮬레이션되었지만, 본 모델은 최종 빌드 품질의 중요한 속성 인 용융 풀 표면 프로파일 및 볼링 결함 형성을 연구 할 때 분말 레벨 시뮬레이션의 중요성을 보여줍니다.

뿐만 아니라 위의 금속 분말 소결 시뮬레이션은 금속 3D 프린팅(Metal 3D Printing) 산업의 핵심 기술이며 차후 많은 연구와 응용이 기대되는 분야가 될 것입니다.

Acknowledgements

이 자료는 수상 번호 N00014-14-1-0688하에 미해군 연구소(ONR)가 지원하는 연구과제에 기초로 작성되었습니다.

References

[1] Y.S. Lee and W. Zhang, Mesoscopic Simulation of Heat Transfer and Fluid Flow in Laser Powder Bed Additive Manufacturing, In: 2015 Annual International Solid Freeform Fabrication Symposium, Austin, TX, pp. 1154-1165, August 2015.

해석예제 및 적용사례

당사에서 오랜 기간 동안 FLOW-3D를 적용한 분야별 프로젝트 적용사례와 간단한 소개 자료를 제공합니다.
아래 분야별 적용사례 다운로드 링크를 클릭하여 자유롭게 활용하시기 바랍니다.

Laser Metal Deposition and Fluid Particles

FLOW-3D의 신규 모듈 개발을 하면서, 입자 모델의 새로운 입자 부류 중 하나인 유체 입자의 기능에 초점을 맞출 것입니다. 유체 입자는 증발 및 응고를 포함하여 유체 속성을 본질적으로 부여합니다. 유체 입자가 비교적 간단한 강우 모델링에서 복잡한 레이저 증착(용접) 모델링에 이르기까지 다양한 사례가 있을 수 있습니다.

Fluid Particles

FLOW-3D에서 유체 입자 옵션이 활성화 되면 사용자는 다양한 직경과 밀도의 다양한 유체 입자 종을 설정할 수 있습니다. 또한 유체 입자의 동력학은 확산 계수, 항력 계수, 난류 슈미트 수, 반발 계수 및 응고 된 반발 계수와 같은 특성에 의해 제어 될 수 있습니다. 유체 입자는 열적 및 전기적 특성을 부여 받을 수도 있습니다.

사용자는 유체 입자 생성을 위해 여러 소스를 설정할 수 있습니다. 각 소스는 이전에 정의 된 모든 유체 입자 종 또는 일부 유체 입자 종의 혼합을 가질 수 있습니다. 또한 사용자는 무작위 또는 균일 한 파티클 생성을 선택하고 파티클이 소스에서 추출되는 속도를 정의 할 수 있습니다.

Laser Metal Deposition

레이저 금속 증착은 함께 미세한 금속 분말을 융합하여 입체 금속 부품을 제작하는 3D printing 공정이다. 레이저 금속 증착는 항공 우주 및 의료 정형 외과 분야에서 다양한 응용 프로그램을 찾습니다. 레이저 금속 증착의 개략도는 아래와 같습니다. 전력 밀도 분포, 기판의 이동 속도, 차폐 가스 압력 및 용융 / 응고, 상 변화 및 열전달과 같은 물리적 제어와 같은 제어 매개 변수가 함께 작동하여 레이저 금속 증착을 효과적인 첨가제 제조 공정으로 만듭니다.

Setting Up Laser Metal Deposition

새로운 유체 입자 모델은 분말 강도 분포를 할당하고 용융 풀 주변에서 발생하는 복잡한 입자 – 기판 상호 작용을 포착하기 때문에 레이저 금속 증착 시뮬레이션을 설정하는 데 없어서는 안될 부분입니다.

일반의 사용자들은 FLOW-3D에서 시뮬레이션을 쉽게 설정할 수 있다는 점을 계속 알고 있을 것입니다. 레이저 금속 증착 설정의 경우에도 다른 점은 없습니다. IN-718의 물리적 특성, 형상 생성, 입자 분말 강도 분포, 메쉬 생성 및 시뮬레이션 실행과 같은 모든 설정 단계는 직접적이고 사용자 친화적입니다.

IN-718의 물성은 기판과 응고 된 유체 입자 모두에 사용됩니다. 40 미크론 유체 입자가 무작위 방식으로 초당 500,000의 속도로 입자 영역에서 계산 영역으로 주입됩니다. 입자 빔은 기판의 운동 방향이 변화 될 때마다 순간적으로 정지되어 용융 풀이 급격한 속도 변화에 적응하도록 합니다. 이렇게 하면 기판에서 입자가 반사되는 것을 방지 할 수 있습니다. 매 5 초마다 기판이 회전하기 때문에 입자 생성 속도는 아래 그림과 같이 5 초마다 0으로 떨어집니다. 기판 이동 자체는 표 형식의 속도 데이터를 사용하여 FLOW-3D에 지정됩니다. 입자는 응고 된 유체 입자로 주입되어 고온의 용융 풀에 부딪혀 녹아 용융 풀 유체의 일부가 됩니다.

Substrate velocity

입자 모델 외에도 FLOW-3D의 밀도 평가, 열 전달, 표면 장력, 응고 및 점도 모델이 사용됩니다. 보다 구체적으로, 온도에 따른 표면 장력은 증착 된 층의 형태에 큰 영향을주는 Marangoni 효과를 일으킵니다.

레이저를 복제하기 위해 100 % 다공성 구성 요소가있는 매우 기본적인 설정이 열원으로 사용됩니다. 100 % 다공성은 구성 요소 주변의 유동 역학에 영향을 미치지 않습니다. 오히려 그것은 특정 영역의 기판에 열을 효과적으로 부가한다. 이 예비 가열 메커니즘을 자회사인 Flow Science Japan이 개발 한 고급 레이저 모듈로 교체하는 작업이 현재 본격적으로 진행 중입니다. 가열 다공성 구성 요소는 각각의 층이 동일한 양의 열을 얻도록 각 층이 증착 된 후에 약간 위로 이동됩니다.

Results and discussion

아래 애니메이션은 다중 층 증착을 이용한 레이저 금속 증착 공정을 보여줍니다. 기판이 방향을 바꿀 때마다 입자 빔 동작의 일시적인 정지를 확인하십시오. 또한, 층이 증착됨에 따라, 새로운 층의 형상은 다공성 열원으로부터 각 층에 열의 불균등 한 첨가로 인해 변화됩니다. 각 층을 증착 한 후에 열원을 위로 이동해야 하는 양을 측정하는 것은 현재의 기능에서는 어렵습니다. 다만 준비중인 Flow Science Japan의 레이저 모듈은 이 문제를 해결할 수 있습니다.

전반적으로 입자 모델은 레이저 금속 증착에서 매우 중요한 공정 매개 변수 인 분말 강도 분포를 정확하게 재현합니다. 입자 모델과 같은 수준의 제어와 정교함은 첨가제 제조 분야의 사용자와 공급자 모두가 제조 프로세스를 미세 조정하는 데 도움이 될 것으로 기대합니다.

금속 3D 프린팅 파우더 베드 수치해석

Laser Powder Bed Additive Manufacturing

Heat transfer and fluid flow modeling

Numerical modeling approach

약 600 마이크로 초 길이의 L-PBF의 과도 시뮬레이션은 약 40 시간의 클럭 시간이 소요되었으며 인텔 ® 제온 ® 프로세서 E5335 및 4GB RAM의 중간 정도급의 워크 스테이션에서 수행되었습니다.

Result and discussion

Summary

Acknowledgements

이 자료는 수상 번호 N00014-14-1-0688하에 미해군 연구소(ONR)가 지원하는 연구과제에 기초로 작성되었습니다.

References

Additive Manufacturing & Welding Bibliography

다음은 적층 제조 및 용접 참고 문헌의 기술 문서 모음입니다. 이 모든 논문에는 FLOW-3D AM 결과가 나와 있습니다. FLOW-3D AM을 사용하여 적층 제조, 레이저 용접 및 기타 용접 기술에서 발견되는 프로세스를 성공적으로 시뮬레이션하는 방법에 대해 자세히 알아보십시오.

2024년 11월 20일 update

121-24 Lovejoy Mutswatiwa, Lauren Katch, Nathan John Kizer, Judith Anne Todd, Tao Sun, Samuel James Clark, Kamel Fezzaa, Jordan Lum, David Matthew Stobbe, Griffin Jones, Kenneth Charles Meinert Jr., Andrea Paola Argüelles, Christopher Micheal Kube, High-speed synchrotron X-ray imaging of melt pool dynamics during ultrasonic melt processing of Al6061, Communications Materials, 5; 143, 2024. doi.org/10.1038/s43246-024-00584-3

120-24 Mysore Nagaraja Kishore, Dong Qian, Masakazu Soshi, Wei Li, Conforming mesh modeling of multi-physics effect on residual stress in multi-layer powder bed fusion process, Journal of Manufacturing Processes, 124; pp. 793-804, 2024. doi.org/10.1016/j.jmapro.2024.06.033

113-24 Yusufu Ekubaru, Takuya Nakabayashi, Tomoharu Fujiwara, Behrang Poorganji, Processing windows of Ni625 alloy fabricated using direct energy deposition, Advanced Engineering Materials, 2024. doi.org/10.1002/adem.202400962

111-24 Ruijie Liu, Melt pool dynamic modelling for the titanium-based metal additive manufacturing process, Thesis, The University of Auckland, 2024.

104-24 Ju Wang, Meng Li, Huarong Zhang, Zhe Liu, Xiaodan Li, Dengzhi Yao, Yuhang Wu, Qiong Wu, Xizhong An, Shujun Li, Jian Wang, Xing Zhang , Cumulative effects of powder beds and melted areas on pore defects in electron beam powder bed fusion of tungsten, Powder Technology, 443; 119971, 2024. doi.org/10.1016/j.powtec.2024.119971

100-24 Xuesong Gao, Aryan Aryan, Wei Zhang, Numerical analysis of rotating scans’ effect on surface roughness in laser-powder bed fusion, Journal of Materials Research and Technology, 30; pp. 8671-8682, 2024. doi.org/10.1016/j.jmrt.2024.05.214

95-24 Yongbiao Wang, Yue Zhang, Junjie Jiang, Yang Zhang, Hongyang Cui, Xintian Liu, Yujuan Wu, Cross-scale simulation of macro/microstructure evolution during selective laser melting of Mg–Gd–Y alloy, Metallurgical and Materials Transactions B , 2024. doi.org/10.1007/s11663-024-03104-3

94-24 Yang Chu, Haichuan Shi, Peilei Zhang, Zhishui Yu, Hua Yan, Qinghua Lu, Shijie Song, Kaichang Yu, Simulation-assisted parameter optimization and tribological behavior of graphene reinforced IN718 matrix composite prepared by SLM, Intermetallics, 170; 108307, 2024. doi.org/10.1016/j.intermet.2024.108307

92-24 Ying Wei, Song Han, Shiwei Yu, Ziwei Chen, Ziang Li, Hailong Wang, Wenbo Cheng, Mingzhe An , Parameter impact on 3D concrete printing from single to multi-layer stacking, Automation in Construction, 164; 105449, 2024. doi.org/10.1016/j.autcon.2024.105449

90-24 Chuanbin Du, Yuewei Ai, Yiyuan Wang, Chenglong Ye, The effect mechanism of laser beam defocusing on the surface quality of IN718 alloy prepared by laser powder bed fusion, Powder Technology, 443; 119841, 2024. doi.org/10.1016/j.powtec.2024.119841

88-24 Arash Samaei, Joseph P. Leonor, Zhengtao Gan, Zhongsheng Sang, Xiaoyu Xie, Brian J. Simonds, Wing Kam Liu, Gregory J. Wagner, Benchmark study of melt pool and keyhole dynamics, laser absorptance, and porosity in additive manufacturing of Ti-6Al-4V, Progress in Additive Manufacturing, 2024. doi.org/10.1007/s40964-024-00637-6

83-24 Ao Fu, Zhonghao Xie, Jian Wang, Yuankui Cao, Bingfeng Wang, Jia Li, Qihong Fang, Xiaofeng Li, Bin Liu, Yong Liu, Controlling of cellular substructure and its effect on mechanical properties of FeCoCrNiMo0.2 high entropy alloy fabricated by selective laser melting, Materials Science and Engineering: A, 901; 146547, 2024. doi.org/10.1016/j.msea.2024.146547

82-24 Fatemeh Bodaghi, Mojtaba Movahedi, Suck-Joo Na, Lin-Jie Zhang, Amir Hossein Kokabi, Effect of welding current and speed on solidification cracking susceptibility in gas tungsten arc fillet welding of dissimilar aluminum alloys: Coupling a weld simulation and a cracking criterion, Journal of Materials Research and Technology, 30: pp. 4777-4785, 2024. doi.org/10.1016/j.jmrt.2024.04.195

81-24 Myeonghwan Choi, Dae-Won Cho, Kwang-Hyeon Lee, Seonghoon Yoo, Sangyong Nam, Namhyun Kang, Severe Mn vaporization for partial-penetrated laser keyhole welds of high-manganese cryogenic steel, International Journal of Heat and Mass Transfer, 227; 125567, 2024. doi.org/10.1016/j.ijheatmasstransfer.2024.125567

78-24 An Wang, Qianglong Wei, Zijue Tang, J.P. Oliviera, Chu Lun Alex Leung, Pengyuan Ren, Xiaolin Zhang, Yi Wu, Haowei Wang, Hongze Wang, Effects of hatch spacing on pore segregation and mechanical properties during blue laser directed energy deposition of AlSi10Mg, Additive Manufacturing, 85; 104147, 2024. doi.org/10.1016/j.addma.2024.104147

77-24 Jeongho Yang, Seonghun Ji, Du-Rim Eo, Jongcheon Yoon, Parviz Kahhal, Hyub Lee, Sang Hu Park, Effect of abnormal powder feeding on mechanical properties of fabricated part in directed energy deposition, International Journal of Precision Engineering and Manufacturing – Green Technology, 2024. doi.org/10.1007/s40684-024-00620-0

72-24 Minglei Qu, Jiandong Yuan, Ali Nabaa, Junye Huang, Chihpin Andrew Chuang, Lianyi Chen, Melting and solidification dynamics during laser melting of reaction-based metal matrix composites uncovered by in-situ synchrotron X-ray diffraction, Acta Materialia, 271; 119875, 2024. doi.org/10.1016/j.actamat.2024.119875

71-24 Chenze Li, Manish Jain, Qian Liu, Zhuohan Cao, Michael Ferry, Jamie J. Kruzic, Bernd Gludovatz, Xiaopeng Li, Multi-scale microstructure manipulation of an additively manufactured CoCrNi medium entropy alloy for superior mechanical properties and tunable mechanical anisotropy, Additive Manufacturing, 84; 104104, 2024. doi.org/10.1016/j.addma.2024.104104

68-24 Jialu Wang, Shuaicheng Zhu, Miaojin Jiang, Yunwei Gui, Huadong Fu, Jianxin Xie, Solidification track morphology, residual stress behavior, and microstructure evolution mechanism of FGH96-R nickel-based superalloys during laser powder bed fusion process, Journal of Materials Engineering and Performance, 2024. doi.org/10.1007/s11665-024-09326-5

66-24 Erik Holmen Olofsson, Ashley Dan, Michael Roland, Ninna Halberg Jokil, Rohit Ramachandran, Jesper Henri Hattel, Numerical modeling of fill-level and residence time in starve-fed single-screw extrusion: a dimensionality reduction study from a 3D CFD model to a 2D convection-diffusion model, The International Journal of Advanced Manufacturing Technology, 132; pp. 1111-1125, 2024. doi.org/10.1007/s00170-024-13378-1

64-24 Feipeng An, Linjie Zhang, Wei Ma, Suck Joo Na, Influences of the powder size and process parameters on the quasi-stability of molten pool shape in powder bed fusion-laser beam of molybdenum, Journal of Materials Engineering and Performance, 2024. doi.org/10.1007/s11665-024-09328-3

63-24 Haodong Chen, Xin Lin, Yajing Sun, Shuhao Wang, Kunpeng Zhu, Binbin Dan, Revealing formation mechanism of end of process depression in laser powder bed fusion by multi-physics meso-scale simulation, Virtual and Physical Prototyping, 19.1; e2326599, 2024. doi.org/10.1080/17452759.2024.2326599

57-24 Masayuki Okugawa, Kenji Saito, Haruki Yoshima, Katsuhiko Sawaizumi, Sukeharu Nomoto, Makoto Watanabe, Takayoshi Nakano, Yuichiro Koizumi, Solute segregation in a rapidly solidified Hastelloy-X Ni-based superalloy during laser powder bed fusion investigated by phase-field and computational thermal-fluid dynamics simulations, Additive Manufacturing, 84; 104079, 2024. doi.org/10.1016/j.addma.2024.104079

51-24 Jeongho Yang, Dongseok Kang, Si Mo Yeon, Yong Son, Sang Hu Park, Interval island laser-scanning strategy of Ti–6Al–4V part additively manufactured for anisotropic stress reduction, International Journal of Precision Engineering and Manufacturing, 25; pp. 1087-1099, 2024. doi.org/10.1007/s12541-024-00967-z

50-24 James Lamb, Ruben Ochoa, Adriana Eres-Castellanos, Jonah Klemm-Toole, McLean P. Echlin, Tao Sun, Kamel Fezzaa, Amy Clarke, Tresa M. Pollack, Quantification of melt pool dynamics and microstructure during simulated additive manufacturing, Scripta Materialia, 245; 116036, 2024. doi.org/10.1016/j.scriptamat.2024.116036

41-24 Xiong Zhang, Chunjin Wang, Benny C.F. Cheung, Gaoyang Mi, Chunming Wang, Ultrafast laser ablation of tungsten carbide: Quantification of threshold range and interpretation of feature transition, Journal of the American Ceramic Society, 107.6; pp. 3724-3734, 2024. doi.org/10.1111/jace.19718

38-24 Hao-Ping Yeh, Mohamad Bayat, Amirhossein Arzani, Jesper H. Hattel, Accelerated process parameter selection of polymer-based selective laser sintering via hybrid physics-informed neural network and finite element surrogate modelling, Applied Mathematical Modelling, 130; pp. 693-712, 2024. doi.org/10.1016/j.apm.2024.03.030

34-24 Khalid El Abbaoui, Issam Al Korachi, Mostapha El Jai, Berin Šeta, Md. Tusher Mollah, 3D concrete printing using computational fluid dynamics: Modeling of material extrusion with slip boundaries, Journal of Manufacturing Processes, 118; pp. 448-459, 2024. doi.org/10.1016/j.jmapro.2024.03.042

33-24 Hao Lu, Lida Zhu, Pengsheng Xue, Boling Yan, Yanpeng Hao, Zhichao Yang, Jinsheng Ning, Chuanliang Shi, Hao Wang, Ultrasonic machining response and improvement mechanism for differentiated bio-CoCrMo alloys manufactured by directed energy deposition, Journal of Materials Science & Technology, 193; pp. 226-243, 2024. doi.org/10.1016/j.jmst.2023.12.037

32-24 Yinghang Liu, Zhe Song, Yi Guo, Gaoming Zhu, Yunhao Fan, Huamiao Wang, Wentao Yan, Xiaoqin Zeng, Leyun Wang, Simultaneously enhancing strength and ductility of LPBF Ti alloy via trace Y2O3 nanoparticle addition, Journal of Materials Science & Technology, 191; pp. 146-156, 2024. doi.org/10.1016/j.jmst.2024.01.011

27-24 Zehui Liu, Yiyang Hu, Mingyang Zhang, Wei Zhang, Jun Wang, Wenbo Lei, Chunming Wang, Surface morphology evolution mechanisms of pulse laser polishing mold steel, International Journal of Mechanical Sciences, 269; 109039, 2024. doi.org/10.1016/j.ijmecsci.2024.109039

25-24 Muhammad Arif Mahmood, Kashif Ishfaq, Marwan Khraisheh, Inconel-718 processing windows by directed energy deposition: a framework combining computational fluid dynamics and machine learning models with experimental validation, The International Journal of Advanced Manufacturing Technology, 130; pp. 3997-4011, 2024. doi.org/10.1007/s00170-024-12980-7

24-24 Jinsheng Ning, Lida Zhu, Shuhao Wang, Zhichao Yang, Peihua Xu, Pengsheng Xue, Hao Lu, Miao Yu, Yunhang Zhao, Jiachen Li, Susmita Bose, Amit Bandyopadhyay, Printability disparities in heterogeneous material combinations via laser directed energy deposition: a comparative study, International Journal of Extreme Manufacturing, 6; 025001, 2024. doi.org/10.1088/2631-7990/ad172f

18-24 Delong Jia, Dong Zhou, Peng Yi, Chuanwei Zhang, Junru Li, Yankuo Guo, Shengyue Zhang, Yanhui Li, Splat deposition stress formation mechanism of droplets impacting onto texture, International Journal of Mechanical Sciences, 266; 109002, 2024. doi.org/10.1016/j.ijmecsci.2024.109002

11-24 Dae Gune Jung, Ji Young Park, Choong Mo Ryu, Jong Jin Hwang, Seung Jae Moon, Numerical study of laser welding of 270 μm thick silicon-steel sheets for electrical motors, Metals, 14.1; 24, 2024. doi.org/10.3390/met14010024

8-24 Zhifu Yao, Longke Bao, Mujin Yang, Yuechao Chen, Minglin He, Jiang Yi, Xintong Yang, Tao Yang, Yilu Zhao, Cuiping Wang, Zheng Zhong, Shuai Wang, Xingjun Liu, Thermally stabe strong <101> texture in additively manufactured cobalt-based superalloys, Scripta Materialia, 242; 115942, 2024. doi.org/10.1016/j.scriptamat.2023.115942

5-24 Xi Shu, Chunyu Wang, Guoqing Chen, Chunju Wang, Lining Sun, Pre-melted electron beam freeform fabrication additive manufacturing: modeling and numerical simulation, Welding in the World, 68; pp. 163-176, 2024. doi.org/10.1007/s40194-023-01647-8

4-24 Lin Gao, Andrew C. Chuang, Peter Kenesei, Zhongshu Ren, Lilly Balderson, Tao Sun, An operando synchrotron study on the effect of wire melting state on solidification microstructures of Inconel 718 in wire-laser directed energy deposition, International Journal of Machine Tools and Manufacture, 194; 104089, 2024. doi.org/10.1016/j.ijmachtools.2023.104089

3-24 Kunjie Dai, Xing He, Decheng Kong, Chaofang Dong, Multi-physical field simulation to yield defect-free IN718 alloy fabricated by laser powder bed fusion, Materials Letters, 355; 135437, 2024. doi.org/10.1016/j.matlet.2023.135437

2-24 You Wang, Yinkai Xie, Huaixue Li, Caiyou Zeng, Ming Xu, Hongqiang Zhang, In-situ monitoring plume, spattering behavior and revealing their relationship with melt flow in laser powder bed fusion of nickel-based superalloy, Journal of Materials Science & Technology, 177; pp. 44-58, 2024. doi.org/10.1016/j.jmst.2023.07.068

1-24 Yukai Chen, Hongtu Xu, Yu Lu, Yin Wang, Shuangyuzhou Wang, Ke Huang, Qi Zhang, Prediction of microstructure for Inconel 718 laser welding process using multi-scale model, Proceedings of the 14th International Conference on the Technology of Plasticity – Current Trends in the Technology of Plasticity, pp. 713-722, 2024. doi.org/10.1007/978-3-031-41341-4_75

211-23 Giovanni Chianese, Qamar Hayat, Sharhid Jabar, Pasquale Franciosa, Darek Ceglarek, Stanislao Patalano, A multi-physics CFD study to investigate the impact of laser beam shaping on metal mixing and molten pool dynamics during laser welding of copper to steel for battery terminal-to-casing connections, Journal of Materials Processing Technology, 322; 118202, 2023. doi.org/10.1016/j.jmatprotec.2023.118202

207-23 Dong Liu, Jiaqi Pei, Hua Hou, Xiaofeng Niu, Yuhong Zhao, Optimizing solidification dendrites and process parameters for laser powder bed fusion additive manufacturing of GH3536 superalloy by finite volume and phase-field method, Journal of Materials Research and Technology, 27; pp. 3323-3338, 2023. doi.org/10.1016/j.jmrt.2023.10.188

206-23 Houshang Yin, Jingfan Yang, Ralf D. Fischer, Zilong Zhang, Bart Prorok, Lang Yuan, Xiaoyuan Lou, Pulsed laser additive manufacturing for 316L stainless steel: a new approach to control subgrain cellular structure, JOM, 75; pp. 5027-5036, 2023. doi.org/10.1007/s11837-023-06177-8

205-23 Francis Ogoke, William Lee, Ning-Yu Kao, Alexander Myers, Jack Beuth, Jonathan Malen, Amir Barati Farimani, Convolutional neural networks for melt depth prediction and visualization in laser powder bed fusion, The International Journal of Advanced Manufacturing Technology, 129; pp. 3047-3062, 2023. doi.org/10.1007/s00170-023-12384-z

202-23 Habib Hamed Zargari, Kazuhiro Ito, Abhay Sharma, Effect of workpiece vibration frequency on heat distribution and material flow in the molten pool in tandem-pulsed gas metal arc welding, The International Journal of Advanced Manufacturing Technology, 129; pp. 2507-2522, 2023. doi.org/10.1007/s00170-023-12424-8

199-23 Yukai Chen, Yin Wang, Hao Li, Yu Lu, Bin Han, Qi Zhang, Effects of process parameters on the microstructure of Inconel 718 during powder bed fusion based on cellular automata approach, Virtual and Physical Prototyping, 18.1; e2251032, 2023. doi.org/10.1080/17452759.2023.2251032

197-23 Qiong Wu, Chuang Qiao, Yuhang Wu, Zhe Liu, Xiaodan Li, Ju Wang, Xizhong An, Aijun Huang, Chao Voon Samuel Lim, Numerical investigation on the reuse of recycled powders in powder bed fusion additive manufacturing, Additive Manufacturing, 77; 103821, 2023. doi.org/10.1016/j.addma.2023.103821

196-23 Daicong Zhang, Chunhui Jing, Wei Guo, Yuan Xiao, Jun Luo, Lehua Qi, Microchannels formed using metal microdroplets, Micromachines, 14.10; 1922, 2023. doi.org/10.3390/mi14101922

195-23 Trong-Nhan Le, Santosh Rauniyar, V.H. Nismath, Kevin Chou, An investigation into the effects of contouring process parameters on the up-skin surface characteristics in laser powder-bed fusion process, Manufacturing Letters, 35; Supplement, pp. 707-716, 2023. doi.org/10.1016/j.mfglet.2023.08.085

194-23 Kyubok Lee, Teresa J. Rinker, Masoud M. Pour, Wayne Cai, Wenkang Huang, Wenda Tan, Jennifer Bracey, Jingjing Li, A study on cracks and IMCs in laser welding of Al and Cu, Manufacturing Letters, 35; Supplement, pp. 221-231, 2023. doi.org/10.1016/j.mfglet.2023.08.026

192-23 Kunjie Dai, Xing He, Wei Zhang, Decheng Kong, Rong Guo, Minlei Hu, Ketai He, Chaofang Dong, Tailoring the microstructure and mechanical properties for Hastelloy X alloy by laser powder bed fusion via scanning strategy, Materials & Design, 235; 112386, 2023. doi.org/10.1016/j.matdes.2023.112386

191-23 Jun Du, Daqing Wang, Jimiao He, Yongheng Zhang, Zhike Peng, Influence of droplet size and ejection frequency on molten pool dynamics and deposition morphology in TIG-aided droplet deposition manufacturing, International Communications in Heat and Mass Transfer, 148; 107075, 2023. doi.org/10.1016/j.icheatmasstransfer.2023.107075

188-23 Jin-Hyeong Park, Du-Song Kim, Dae-Won Cho, Jaewoong Kim, Changmin Pyo, Influence of thermal flow and predicting phase transformation on various welding positions, Heat and Mass Transfer, 2023. doi.org/10.1007/s00231-023-03429-w

184-23 Lin Gao, Jishnu Bhattacharyya, Wenhao Lin, Zhongshu Ren, Andrew C. Chuang, Pavel D. Shevchenko, Viktor Nikitin, Ji Ma, Sean R. Agnew, Tao Sun, Tailoring material microstructure and property in wire-laser directed energy deposition through a wiggle deposition strategy, Additive Manufacturing, 77; 103801, 2023. doi.org/10.1016/j.addma.2023.103801

182-23 Liping Guo, Hanjie Liu, Hongze Wang, Qianglong Wei, Jiahui Zhang, Yingyan Chen, Chu Lun Alex Leung, Qing Lian, Yi Wu, Yu Zou, Haowei Wang, A high-fidelity comprehensive framework for the additive manufacturing printability assessment, Journal of Manufacturing Processes, 105; pp. 219-231, 2023. doi.org/10.1016/j.jmapro.2023.09.041

172-23 Liping Guo, Hanjie Liu, Hongze Wang, Qianglong Wei, Yakai Xiao, Zijue Tang, Yi Wu, Haowei Wang, Identifying the keyhole stability and pore formation mechanisms in laser powder bed fusion additive manufacturing, Journal of Materials Processing Technology, 321; 118153, 2023. doi.org/10.1016/j.jmatprotec.2023.118153

171-23 Yuhang Wu, Qiong Wu, Meng Li, Ju Wang, Dengzhi Yao, Hao Luo, Xizhong An, Haitao Fu, Hao Zhang, Xiaohong Yang, Qingchuan Zou, Shujun Li, Haibin Ji, Xing Zhang, Numerical investigation on effects of operating conditions and final dimension predictions in laser powder bed fusion of molybdenum, Additive Manufacturing, 76; 103783, 2023. doi.org/10.1016/j.addma.2023.103783

158-23 K. El Abbaoui, I. Al Korachi, M.T. Mollah, J. Spangenberg, Numerical modelling of planned corner deposition in 3D concrete printing, Archives of Materials Science and Engineering, 121.2; pp. 71-79, 2023. doi.org/10.5604/01.3001.0053.8488

156-23 Liping Guo, Hanjie Liu, Hongze Wang, Valentino A.M. Cristino, C.T. Kwok, Qianglong Wei, Zijue Tang, Yi Wu, Haowei Wang, Deepening the scientific understanding of different phenomenology in laser powder bed fusion by an integrated framework, International Journal of Heat and Mass Transfer, 216; 124596, 2023. doi.org/10.1016/j.ijheatmasstransfer.2023.124596

154-23 Zhiyong Li, Xiuli He, Shaoxia Li, Xinfeng Kan, Yanjun Yin, Gang Yu, Sulfur-induced transitions of thermal behavior and flow dynamics in laser powder bed fusion of 316L powders, Thermal Science and Engineering Progress, 45; 102072, 2023. doi.org/10.1016/j.tsep.2023.102072

149-23 Shardul Kamat, Wayne Cai, Teresa J. Rinker, Jennifer Bracey, Liang Xi, Wenda Tan, A novel integrated process-performance model for laser welding of multi-layer battery foils and tabs, Journal of Materials Processing Technology, 320; 118121, 2023. doi.org/10.1016/j.jmatprotec.2023.118121

148-23 Reza Ghomashchi, Shahrooz Nafisi, Solidification of Al12Si melt pool in laser powder bed fusion, Journal of Materials En gineering and Performance, 2023. doi.org/10.1007/s11665-023-08502-3

133-23 Hesam Moghadasi, Md Tusher Mollah, Deepak Marla, Hamid Saffari, Jon Spangenberg, Computational fluid dynamics modeling of top-down digital light processing additive manufacturing, Polymers, 15.11; 2459, 2023. doi.org/10.3390/polym15112459

131-23 Luca Santoro, Raffaella Sesana, Rosario Molica Nardo, Francesca Curà, In line defect detection in steel welding process by means of thermography, Experimental Mechanics in Engineering and Biomechanics – Proceedings ICEM20, 19981, 2023.

128-23 Md Tusher Mollah, Raphaël Comminal, Wilson Ricardo Leal da Silva, Berin Šeta, Jon Spangenberg, Computational fluid dynamics modelling and experimental analysis of reinforcement bar integration in 3D concrete printing, Cement and Concrete Research, 173; 107263, 2023. doi.org/10.1016/j.cemconres.2023.107263

123-23 Arash Samaei, Zhongsheng Sang, Jennifer A. Glerum, Jon-Erik Mogonye, Gregory J. Wagner, Multiphysics modeling of mixing and material transport in additive manufacturing with multicomponent powder beds, Additive Manufacturing, 67; 103481, 2023. doi.org/10.1016/j.addma.2023.103481

122-23 Chu Han, Ping Jiang, Shaoning Geng, Lingyu Guo, Kun Liu, Inhomogeneous microstructure distribution and its formation mechanism in deep penetration laser welding of medium-thick aluminum-lithium alloy plates, Optics & Laser Technology, 167; 109783, 2023. doi.org/10.1016/j.optlastec.2023.109783

111-23 Alexander J. Myers, Guadalupe Quirarte, Francis Ogoke, Brandon M. Lane, Syed Zia Uddin, Amir Barati Farimani, Jack L. Beuth, Jonathan A. Malen, High-resolution melt pool thermal imaging for metals additive manufacturing using the two-color method with a color camera, Additive Manufacturing, 73; 103663, 2023. doi.org/10.1016/j.addma.2023.103663

107-23 M. Mohsin Raza, Yu-Lung Lo, Hua-Bin Lee, Chang Yu-Tsung, Computational modeling of laser welding for aluminum–copper joints using a circular strategy, Journal of Materials Research and Technology, 25; pp. 3350-3364, 2023. doi.org/10.1016/j.jmrt.2023.06.122

106-23 H.Z. Lu, L.H. Liu, X. Luo, H.W. Ma, W.S. Cai, R. Lupoi, S. Yin, C. Yang, Formation mechanism of heterogeneous microstructures and shape memory effect in NiTi shape memory alloy fabricated via laser powder bed fusion, Materials & Design, 232; 112107, 2023. doi.org/10.1016/j.matdes.2023.112107

105-23 Harun Kahya, Hakan Gurun, Gokhan Kucukturk, Experimental and analytical investigation of the re-melting effect in the manufacturing of 316L by direct energy deposition (DED) method, Metals, 13.6; 1144, 2023. doi.org/10.3390/met13061144

100-23 Dongju Chen, Gang Li, Peng Wang, Zhiqiang Zeng, Yuhang Tang, Numerical simulation of melt pool size and flow evolution for laser powder bed fusion of powder grade Ti6Al4V, Finite Elements in Analysis and Design, 223; 103971, 2023. doi.org/10.1016/j.finel.2023.103971

97-23 Mahyar Khorasani, Martin Leary, David Downing, Jason Rogers, Amirhossein Ghasemi, Ian Gibson, Simon Brudler, Bernard Rolfe, Milan Brandt, Stuart Bateman, Numerical and experimental investigations on manufacturability of Al–Si–10Mg thin wall structures made by LB-PBF, Thin-Walled Structures, 188; 110814, 2023. doi.org/10.1016/j.tws.2023.110814

95-23 M.S. Serdeczny, Laser welding of dissimilar materials – simulation driven optimization of process parameters, IOP Conference Series: Materials Science and Engineering, 1281; 012018, 2023. doi.org/10.1088/1757-899X/1281/1/012018

90-23 Lin Liu, Tubin Liu, Xi Dong, Min Huang, Fusheng Cao, Mingli Qin, Numerical simulation of thermal dynamic behavior and morphology evolution of the molten pool of selective laser melting BN/316L stainless steel composite, Journal of Materials Engineering and Performance, 2023. doi.org/10.1007/s11665-023-08210-y

89-23 M. P. Serdeczny, A. Jackman, High fidelity modelling of bead geometry in directed energy deposition – simulation driven optimization, Journal of Physics: Conference Series, NOLAMP19, 2023.

88-23 Lu Wang, Shuhao Wang, Yanming Zhang, Wentao Yan, Multi-phase flow simulation of powder streaming in laser-based directed energy deposition, International Journal of Heat and Mass Transfer, 212; 124240, 2023. doi.org/10.1016/j.ijheatmasstransfer.2023.124240

80-23 Mahyar Khorasani, AmirHossein Ghasemi, Martin Leary, David Downing, Ian Gibson, Elmira G. Sharabian, Jithin Kozuthala Veetil, Milan Brandt, Stuart Batement, Bernard Rolfe, Benchmark models for conduction and keyhole modes in laser-based powder bed fusion of Inconel 718, Optics & Laser Technology, 164; 109509, 2023. doi.org/10.1016/j.optlastec.2023.109509

78-23 Md. Tusher Mollah, Raphaël Comminal, Marcin P. Serdeczny, Berin Šeta, Jon Spangenberg, Computational analysis of yield stress buildup and stability of deposited layers in material extrusion additive manufacturing, Additive Manufacturing, 71; 103605, 2023. doi.org/10.1016/j.addma.2023.103605

76-23 Asif Ur Rehman, Kashif Azher, Abid Ullah, Celal Sami Tüfekci, Metin Uymaz Salamci, Binder jetting of SS316L: a computational approach for droplet-powder interaction, Rapid Prototyping Journal, 2023. doi.org/10.1108/RPJ-08-2022-0264

75-23 Dengzhi Yao, Ju Wang, Hao Luo, Yuhang Wu, Xizhong An, Thermal behavior and control during multi-track laser powder bed fusion of 316 L stainless steel, Additive Manufacturing, 70; 103562, 2023. doi.org/10.1016/j.addma.2023.103562

61-23 Yaqing Hou, Hang Su, Hao Zhang, Fafa Li, Xuandong Wang, Yazhou He, Dupeng He, An integrated simulation model towards laser powder bed fusion in-situ alloying technology, Materials & Design, 228; 111795, 2023. doi.org/10.1016/j.matdes.2023.111795

56-23 Maohong Yang, Guiyi Wu, Xiangwei Li, Shuyan Zhang, Honghong Wang, Jiankang Huang, Influence of heat source model on the behavior of laser cladding pool, Journal of Laser Applications, 35.2; 2023. doi.org/10.2351/7.0000963

45-23 Daniel Martinez, Philip King, Santosh Reddy Sama, Jay Sim, Hakan Toykoc, Guha Manogharan, Effect of freezing range on reducing casting defects through 3D sand-printed mold designs, The International Journal of Advanced Manufacturing Technology, 2023. doi.org/10.1007/s00170-023-11112-x

39-23 Peter S. Cook, David J. Ritchie, Determining the laser absorptivity of Ti-6Al-4V during laser powder bed fusion by calibrated melt pool simulation, Optics & Laser Technology, 162; 109247. 2023. doi.org/10.1016/j.optlastec.2023.109247

36-23 Yixuan Chen, Weihao Wang, Yao Ou, Yingna Wu, Zirong Zhai, Rui Yang, Impact of laser power and scanning velocity on microstructure and mechanical properties of Inconel 738LC alloys fabricated by laser powder bed fusion, TMS 2023 152nd Annual Meeting & Exhibition Supplemental Proceedings, pp. 138-149, 2023. doi.org/10.1007/978-3-031-22524-6_15

34-23 Chao Kang, Ikki Ikeda, Motoki Sakaguchi, Recoil and solidification of a paraffin droplet impacted on a metal substrate: Numerical study and experimental verification, Journal of Fluids and Structures, 118; 103839, 2023. doi.org/10.1016/j.jfluidstructs.2023.103839

30-23 Fei Wang, Tiechui Yuan, Ruidi Li, Shiqi Lin, Zhonghao Xie, Lanbo Li, Valentino Cristino, Rong Xu, Bing liu, Comparative study on microstructures and mechanical properties of ultra ductility single-phase Nb40Ti40Ta20 refractory medium entropy alloy by selective laser melting and vacuum arc melting, Journal of Alloys and Compounds, 942; 169065, 2023. doi.org/10.1016/j.jallcom.2023.169065

29-23 Haejin Lee, Yeonghwan Song, Seungkyun Yim, Kenta Aoyagi, Akihiko Chiba, Byoungsoo Lee, Influence of linear energy on side surface roughness in powder bed fusion electron beam melting process: Coupled experimental and simulation study, Powder Technology, 418; 118292, 2023.

27-23 Yinan Chen, Bo Li, Double-phase refractory medium entropy alloy NbMoTi via selective laser melting (SLM) additive manufacturing, Journal of Physics: Conference Series, 2419; 012074, 2023. doi.org/10.1088/1742-6596/2419/1/012074

23-23 Yunwei Gui, Kenta Aoyagi, Akihiko Chiba, Development of macro-defect-free PBF-EB-processed Ti–6Al–4V alloys with superior plasticity using PREP-synthesized powder and machine learning-assisted process optimization, Materials Science and Engineering: A, 864; 144595, 2023. doi.org/10.1016/j.msea.2023.144595

21-23 Tatsuhiko Sakai, Yasuhiro Okamoto, Nozomi Taura, Riku Saito, Akira Okada, Effect of scanning speed on molten metal behaviour under angled irradiation with a continuous-wave laser, Journal of Materials Processing Technology, 313; 117866, 2023. doi.org/10.1016/j.jmatprotec.2023.117866

19-23 Gianna M. Valentino, Arunima Banerjee, Alexander lark, Christopher M. Barr, Seth H. Myers, Ian D. McCue, Influence of laser processing parameters on the density-ductility tradeoff in additively manufactured pure tantalum, Additive Manufacturing Letters, 4; 100117, 2023. doi.org/10.1016/j.addlet.2022.100117

15-23 Runbo Jiang, Zhongshu Ren, Joseph Aroh, Amir Mostafaei, Benjamin Gould, Tao Sun, Anthony D. Rollett, Quantifying equiaxed vs epitaxial solidification in laser melting of CMSX-4 single crystal superalloy, Metallurgical and Materials Transactions A, 54; pp. 808-822, 2023. doi.org/10.1007/s11661-022-06929-2

14-23 Nguyen Thi Tien, Yu-Lung Lo, M. Mohsin Raza, Cheng-Yen Chen, Chi-Pin Chiu, Optimization of processing parameters for pulsed laser welding of dissimilar metal interconnects, Optics & Laser Technology, 159; 109022, 2023. doi.org/10.1016/j.optlastec.2022.109022

9-23 Hou Yi Chia, Wentao Yan, High-fidelity modeling of metal additive manufacturing, Additive Manufacturing Technology: Design, Optimization, and Modeling, Ed. Kun Zhou, 2023.

8-23 Akash Aggarwal, Yung C. Shin, Arvind Kumar, Investigation of the transient coupling between the dynamic laser beam absorptance and the melt pool – vapor depression morphology in laser powder bed fusion process, International Journal of Heat and Mass Transfer, 201.2; 123663, 2023. doi.org/10.1016/j.ijheatmasstransfer.2022.123663

199-22 Md. Tusher Mollah, Raphaël Comminal, Marcin P. Serdeczny, David B. Pedersen, Jon Spangenberg, Numerical predictions of bottom layer stability in material extrusion additive manufacturing, JOM, 74; pp. 1096-1101, 2022. doi.org/10.1007/s11837-021-05035-9

198-22 Md. Tusher Mollah, Amirpasha Moetazedian, Andy Gleadall, Jiongyi Yan, Wayne Edgar Alphonso, Raphael Comminal, Berin Seta, Tony Lock, Jon Spangenberg, Investigation on corner precision at different corner angles in material extrusion additive manufacturing: An experimental and computational fluid dynamics analysis, Proceedings of the 33rd Annual Solid Freeform Fabrication Symposium, 2022.

197-22 Md. Tusher Mollah, Marcin P. Serdeczny, Raphaël Comminal, Berin Šeta, Marco Brander, David B. Pedersen, Jon Spangenberg, A numerical investigation of the inter-layer bond and surface roughness during the yield stress buildup in wet-on-wet material extrusion additive manufacturing, ASPE and euspen Summer Topical Meeting, 77, 2022.

182-22 Chan Kyu Kim, Dae-Won Cho, Seok Kim, Sang Woo Song, Kang Myung Seo, Young Tae Cho, High-throughput metal 3D printing pen enabled by a continuous molten droplet transfer, Advanced Science, 2205085, 2022. doi.org/10.1002/advs.202205085

180-22 Xu Kaikai, Gong Yadong, Zhang Qiang, Numerical simulation of dynamic analysis of molten pool in the process of direct energy deposition, The International Journal of Advanced Manufacturing Technology, 2022. doi.org/10.1007/s00170-022-10271-7

179-22 Yasuhiro Okamoto, Nozomi Taura, Akira Okada, Study on laser drilling process of solid metal on its liquid, International Journal of Electrical Machining, 27; 2022. doi.org/10.2526/ijem.27.35

175-22 Lu Min, Xhi Xiaojie, Lu Peipei, Wu Meiping, Forming quality and wettability of surface texture on CuSn10 fabricated by laser powder bed fusion, AIP Advances, 12.12; 125114, 2022. doi.org/10.1063/5.0122076

174-22 Thinus Van Rhijn, Willie Du Preez, Maina Maringa, Dean Kouprianoff, An investigation into the optimization of the selective laser melting process parameters for Ti6Al4V through numerical modelling, JOM, 2022. doi.org/10.1007/s11837-022-05608-2

171-22 Jonathan Yoshioka, Mohsen Eshraghi, Temporal evolution of temperature gradient and solidification rate in laser powder bed fusion additive manufacturing, Heat and Mass Transfer, 2022. doi.org/10.1007/s00231-022-03318-8

170-22 Subin Shrestha and Kevin Chou, Residual heat effect on the melt pool geometry during the laser powder bed fusion process, Journal of Manufacturing and Materials Processing, 6.6; 153, 2022. doi.org/10.3390/jmmp6060153

169-22 Aryan Aryan, Obinna Chukwubuzo, Desmond Bourgeois, Wei Zhang, Hardness prediction by incorporating heat transfer and molten pool fluid flow in a mult-pass, multi-layer weld for onsite repair of Grade 91 steel, U.S. Department of Energy Office of Scientific and Technical Information, DOE-OSU-0032067, 2022. doi.org/10.2172/1898594

158-22 Dafan Du, Lu Wang, Anping Dong, Wentao Yan, Guoliang Zhu, Baode Sun, Promoting the densification and grain refinement with assistance of static magnetic field in laser powder bed fusion, International Journal of Machine Tools and Manufacture, 183; 103965, 2022. doi.org/10.1016/j.ijmachtools.2022.103965

157-22 Han Chu, Jiang Ping, Geng Shaoning, Liu Kun, Nucleation mechanism in oscillating laser welds of 2024 aluminium alloy: A combined experimental and numerical study, Optics & Laser Technology, 158.A; 108812, 2022. doi.org/10.1016/j.optlastec.2022.108812

153-22 Zixiang Li, Yinan Cui, Baohua Chang, Guan Liu, Ze Pu, Haoyu Zhang, Zhiyue Liang, Changmeng Liu, Li Wang, Dong Du, Manipulating molten pool in in-situ additive manufacturing of Ti-22Al-25 Nb through alternating dual-electron beams, Additive Manufacturing, 60.A; 103230, 2022. doi.org/10.1016/j.addma.2022.103230

149-22 Qian Chen, Yao Fu, Albert C. To, Multiphysics modeling of particle spattering and induced defect formation mechanism in Inconel 718 laser powder bed fusion, The International Journal of Advanced Manufacturing Technology, 123; pp. 783-791, 2022. doi.org/10.1007/s00170-022-10201-7

146-22 Zixuan Wan, Hui-ping Wang, Jingjing Li, Baixuan Yang, Joshua Solomon, Blair Carlson, Effect of welding mode on remote laser stitch welding of zinc-coated steel with different sheet thickness combinations, Journal of Manufacturing Science and Engineering, MANU-21-1598, 2022. doi.org/10.1115/1.4055792

143-22 Du-Rim Eo, Seong-Gyu Chung, JeongHo Yang, Won Tae Cho, Sun-Hong Park, Jung-Wook Cho, Surface modification of high-Mn steel via laser-DED: Microstructural characterization and hot crack susceptibility of clad layer, Materials & Design, 223; 111188, 2022. doi.org/10.1016/j.matdes.2022.111188

142-22 Zichuan Fu, Xiangman Zhou, Bin Luo, Qihua Tian, Numerical simulation study of the effect of weld current on WAAM welding pool dynamic and weld bead morphology, International Conference on Mechanical Design and Simulation, Proceedings, 12261; 122614G, 2022. doi.org/10.1117/12.2639074

132-22 Yiyu Huang, Zhonghao Xie, Wenshu Li, Haoyu Chen, Bin Liu, Bingfeng Wang, Dynamic mechanical properties of the selective laser melting NiCrFeCoMo0.2 high entropy alloy and the microstructure of molten pool, Journal of Alloys and Compounds, 927; 167011, 2022. doi.org/10.1016/j.jallcom.2022.167011

126-22 Jingqi Zhang, Yingang Liu, Gang Sha, Shenbao Jin, Ziyong Hou, Mohamad Bayat, Nan Yang, Qiyang Tan, Yu Yin, Shiyang Liu, Jesper Henri Hattel, Matthew Dargusch, Xiaoxu Huang, Ming-Xing Zhang, Designing against phase and property heterogeneities in additively manufactured titanium alloys, Nature Communications, 13; 4660, 2022. doi.org/10.1038/s41467-022-32446-2

119-22 Xu Kaikai, Gong Yadong, Zhao Qiang, Numerical simulation on molten pool flow of Inconel718 alloy based on VOF during additive manufacturing, Materials Today Communications, 33; 104147, 2022. doi.org/10.1016/j.mtcomm.2022.104147

118-22 AmirPouya Hemmasian, Francis Ogoke, Parand Akbari, Jonathan Malen, Jack Beuth, Amir Barati Farimani, Surrogate modeling of melt pool thermal field using deep learning, SSRN, 2022. doi.org/10.2139/ssrn.4190835

117-22 Chiara Ransenigo, Marialaura Tocci, Filippo Palo, Paola Ginestra, Elisabetta Ceretti, Marcello Gelfi, Annalisa Pola, Evolution of melt pool and porosity during laser powder bed fusion of Ti6Al4V alloy: Numerical modelling and experimental validation, Lasers in Manufacturing and Materials Processing, 2022. doi.org/10.1007/s40516-022-00185-3

112-22 Chris Jasien, Alec Saville, Chandler Gus Becker, Jonah Klemm-Toole, Kamel Fezzaa, Tao Sun, Tresa Pollock, Amy J. Clarke, In situ x-ray radiography and computational modeling to predict grain morphology in β-titanium during simulated additive manufacturing, Metals, 12.7; 1217, 2022. doi.org/10.3390/met12071217

110-22 Haotian Zhou, Haijun Su, Yinuo Guo, Peixin Yang, Yuan Liu, Zhonglin Shen, Di Zhao, Haifang Liu, Taiwen Huang, Min Guo, Jun Zhang, Lin Liu, Hengzhi Fu, Formation and evolution mechanisms of pores in Inconel 718 during selective laser melting: Meso-scale modeling and experimental investigations, Journal of Manufacturing Processes, 81; pp. 202-213, 2022. doi.org/10.1016/j.jmapro.2022.06.072

109-22 Yufan Zhao, Huakang Bian, Hao Wang, Aoyagi Kenta, Yamanaka Kenta, Akihiko Chiba, Non-equilibrium solidification behavior associated with powder characteristics during electron beam additive manufacturing, Materials & Design, 221; 110915, 2022. doi.org/10.1016/j.matdes.2022.110915

107-22 Dan Lönn, David Spångberg, Study of process parameters in laser beam welding of copper hairpins, Thesis, University of Skövde, 2022.

106-22 Liping Guo, Hongze Wang, Qianglong Wei, Hanjie Liu, An Wang, Yi Wu, Haowei Wang, A comprehensive model to quantify the effects of additional nano-particles on the printability in laser powder bed fusion of aluminum alloy and composite, Additive Manufacturing, 58; 103011, 2022. doi.org/10.1016/j.addma.2022.103011

104-22 Hongjiang Pan, Thomas Dahmen, Mohamad Bayat, Kang Lin, Xiaodan Zhang, Independent effects of laser power and scanning speed on IN718’s precipitation and mechanical properties produced by LBPF plus heat treatment, Materials Science and Engineering: A, 849; 143530, 2022. doi.org/10.1016/j.msea.2022.143530

101-22 Yufan Zhao, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, A survey on basic influencing factors of solidified grain morphology during electron beam melting, Materials & Design, 221; 110927, 2022. doi.org/10.1016/j.matdes.2022.110927

98-22 Jon Spangenberg, Wilson Ricardo Leal da Silva, Md. Tusher Mollah, Raphaël Comminal, Thomas Juul Andersen, Henrik Stang, Integrating reinforcement with 3D concrete printing: Experiments and numerical modelling, Third RILEM International Conference on Concrete and Digital Fabrication, Eds. Ana Blanco, Peter Kinnell, Richard Buswell, Sergio Cavalaro, pp. 379-384, 2022.

93-22 Minglei Qu, Qilin Guo, Luis I. Escano, Samuel J. Clark Kamel Fezzaa, Lianyi Chen, Mitigating keyhole pore formation by nanoparticles during laser powder bed fusion additive manufacturing, Additive Manufacturing Letters, 100068, 2022. doi.org/10.1016/j.addlet.2022.100068

86-22 Patiparn Ninpetch, Prasert Chalermkarnnon, Pruet Kowitwarangkul, Multiphysics simulation of thermal-fluid behavior in laser powder bed fusion of H13 steel: Influence of layer thickness and energy input, Metals and Materials International, 2022. doi.org/10.1007/s12540-022-01239-z

85-22 Merve Biyikli, Taner Karagoz, Metin Calli, Talha Muslim, A. Alper Ozalp, Ali Bayram, Single track geometry prediction of laser metal deposited 316L-Si via multi-physics modelling and regression analysis with experimental validation, Metals and Materials International, 2022. doi.org/10.1007/s12540-022-01243-3

76-22 Zhichao Yang, Shuhao Wang, Lida Zhu, Jinsheng Ning, Bo Xin, Yichao Dun, Wentao Yan, Manipulating molten pool dynamics during metal 3D printing by ultrasound, Applied Physics Reviews, 9; 021416, 2022. doi.org/10.1063/5.0082461

73-22 Yu Sun, Liqun Li, Yu Hao, Sanbao Lin, Xinhua Tang, Fenggui Lu, Numerical modeling on formation of periodic chain-like pores in high power laser welding of thick steel plate, Journal of Materials Processing Technology, 306; 117638, 2022. doi.org/10.1016/j.jmatprotec.2022.117638

67-22 Yu Hao, Hiu-Ping Wang, Yu Sun, Liqun Li, Yihan Wu, Fenggui Lu, The evaporation behavior of zince and its effect on spattering in laser overlap welding of galvanized steels, Journal of Materials Processing Technology, 306; 117625, 2022. doi.org/10.1016/j.jmatprotec.2022.117625

65-22 Yanhua Zhao, Chuanbin Du, Peifu Wang, Wei Meng, Changming Li, The mechanism of in-situ laser polishing and its effect on the surface quality of nickel-based alloy fabricated by selective laser melting, Metals, 12.5; 778, 2022. doi.org/10.3390/met12050778

58-22 W.E. Alphonso, M. Bayat, M. Baier, S. Carmignato, J.H. Hattel, Multi-physics numerical modelling of 316L Austenitic stainless steel in laser powder bed fusion process at meso-scale, 17th UK Heat Transfer Conference (UKHTC2021), Manchester, UK, April 4-6, 2022.

57-22 Brandon Hayes, Travis Hainsworth, Robert MacCurdy, Liquid-solid co-printing of multi-material 3D fluidic devices via material jetting, Additive Manufacturing, in press, 102785, 2022. doi.org/10.1016/j.addma.2022.102785

55-22 Xiang Wang, Lin-Jie Zhang, Jie Ning, Suck-joo Na, Fluid thermodynamic simulation of Ti-6Al-4V alloy in laser wire deposition, 3D Printing and Additive Manufacturing, 2022. doi.org/10.1089/3dp.2021.0159

54-22 Junhao Zhao, Binbin Wang, Tong Liu, Liangshu Luo, Yanan Wang, Xiaonan Zheng, Liang Wang, Yanqing Su, Jingjie Guo, Hengzhi Fu, Dayong Chen, Study of in situ formed quasicrystals in Al-Mn based alloys fabricated by SLM, Journal of Alloys and Compounds, 909; 164847, 2022. doi.org/10.1016/j.jallcom.2022.164847

48-22 Yueming Sun, Jianxing Ma, Fei Peng, Konstantin G. Kornev, Making droplets from highly viscous liquids by pushing a wire through a tube, Physics of Fluids, 34; 032119, 2022. doi.org/10.1063/5.0082003

46-22 H.Z. Lu, T. Chen, H. Liu, H. Wang, X. Luo, C.H. Song, Constructing function domains in NiTi shape memory alloys by additive manufacturing, Virtual and Physical Prototyping, 17.3; 2022. doi.org/10.1080/17452759.2022.2053821

42-22 Islam Hassan, P. Ravi Selvaganapathy, Microfluidic printheads for highly switchable multimaterial 3D printing of soft materials, Advanced Materials Technologies, 2101709, 2022. doi.org/10.1002/admt.202101709

41-22 Nan Yang, Youping Gong, Honghao Chen, Wenxin Li, Chuanping Zhou, Rougang Zhou, Huifeng Shao, Personalized artificial tibia bone structure design and processing based on laser powder bed fusion, Machines, 10.3; 205, 2022. doi.org/10.3390/machines10030205

31-22 Bo Shen, Raghav Gnanasambandam, Rongxuan Wang, Zhenyu (James) Kong, Multi-Task Gaussian process upper confidence bound for hyperparameter tuning and its application for simulation studies of additive manufacturing, IISE Transactions, 2022. doi.org/10.1080/24725854.2022.2039813

27-22 Lida Zhu, Shuhao Wang, Hao Lu, Dongxing Qi, Dan Wang, Zhichao Yang, Investigation on synergism between additive and subtractive manufacturing for curved thin-walled structure, Virtual and Physical Prototyping, 17.2; 2022. doi.org/10.1080/17452759.2022.2029009

24-22 Hoon Sohn, Peipei Liu, Hansol Yoon, Kiyoon Yi, Liu Yang, Sangjun Kim, Real-time porosity reduction during metal directed energy deposition using a pulse laser, Journal of Materials Science & Technology, 116; pp. 214-223. doi.org/10.1016/j.jmst.2021.12.013

18-22 Yaohong Xiao, Zixuan Wan, Pengwei Liu, Zhuo Wang, Jingjing Li, Lei Chen, Quantitative simulations of grain nucleation and growth at additively manufactured bimetallic interfaces of SS316L and IN625, Journal of Materials Processing Technology, 302; 117506, 2022. doi.org/10.1016/j.jmatprotec.2022.117506

06-22 Amal Charles, Mohamad Bayat, Ahmed Elkaseer, Lore Thijs, Jesper Henri Hattel, Steffen Scholz, Elucidation of dross formation in laser powder bed fusion at down-facing surfaces: Phenomenon-oriented multiphysics simulation and experimental validation, Additive Manufacturing, 50; 102551, 2022. doi.org/10.1016/j.addma.2021.102551

05-22 Feilong Ji, Xunpeng Qin, Zeqi Hu, Xiaochen Xiong, Mao Ni, Mengwu Wu, Influence of ultrasonic vibration on molten pool behavior and deposition layer forming morphology for wire and arc additive manufacturing, International Communications in Heat and Mass Transfer, 130; 105789, 2022. doi.org/10.1016/j.icheatmasstransfer.2021.105789

150-21 Daniel Knüttel, Stefano Baraldo, Anna Valente, Konrad Wegener, Emanuele Carpanzano, Model based learning for efficient modelling of heat transfer dynamics, Procedia CIRP, 102; pp. 252-257, 2021. doi.org/10.1016/j.procir.2021.09.043

149-21 T. van Rhijn, W. du Preez, M. Maringa, D. Kouprianoff, Towards predicting process parameters for selective laser melting of titanium alloys through the modelling of melt pool characteristics, Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie, 40.1; 2021.

148-21 Qian Chen, Multiscale process modeling of residual deformation and defect formation for laser powder bed fusion additive manufacturing, Thesis, University of Pittsburgh, Pittsburgh, PA USA, 2021.

147-21 Pareekshith Allu, Developing process parameters through CFD simulations, Lasers in Manufacturing Conference, 2021.

143-21 Asif Ur Rehman, Muhammad Arif Mahmood, Fatih Pitir, Metin Uymaz Salamci, Andrei C. Popescu, Ion N. Mihailescu, Spatter formation and splashing induced defects in laser-based powder bed fusion of AlSi10Mg alloy: A novel hydrodynamics modelling with empirical testing, Metals, 11.12; 2023, 2021. doi.org/10.3390/met11122023

142-21 Islam Hassan, Ponnambalam Ravi Selvaganapathy, A microfluidic printhead with integrated hybrid mixing by sequential injection for multimaterial 3D printing, Additive Manufacturing, 102559, 2021. doi.org/10.1016/j.addma.2021.102559

137-21 Ting-Yu Cheng, Ying-Chih Liao, Enhancing drop mixing in powder bed by alternative particle arrangements with contradictory hydrophilicity, Journal of the Taiwan Institute of Chemical Engineers, 104160, 2021. doi.org/10.1016/j.jtice.2021.104160

134-21 Asif Ur Rehman, Muhammad Arif Mahmood, Fatih Pitir, Metin Uymaz Salamci, Andrei C. Popescu, Ion N. Mihailescu, Keyhole formation by laser drilling in laser powder bed fusion of Ti6Al4V biomedical alloy: Mesoscopic computational fluid dynamics simulation versus mathematical modelling using empirical validation, Nanomaterials, 11.2; 3284, 2021. doi.org/10.3390/nano11123284

128-21 Sang-Woo Han, Won-Ik Cho, Lin-Jie Zhang, Suck-Joo Na, Coupled simulation of thermal-metallurgical-mechanical behavior in laser keyhole welding of AH36 steel, Materials & Design, 212; 110275, 2021. doi.org/10.1016/j.matdes.2021.110275

127-21 Jiankang Huang, Zhuoxuan Li, Shurong Yu, Xiaoquan Yu, Ding Fan, Real-time observation and numerical simulation of the molten pool flow and mass transfer behavior during wire arc additive manufacturing, Welding in the World, 2021. doi.org/10.1007/s40194-021-01214-z

123-21 Boxue Song, Tianbiao Yu, Xingyu Jiang, Wenchao Xi, Xiaoli Lin, Zhelun Ma, ZhaoWang, Development of the molten pool and solidification characterization in single bead multilayer direct energy deposition, Additive Manufacturing, 102479, 2021. doi.org/10.1016/j.addma.2021.102479

112-21 Kathryn Small, Ian D. McCue, Katrina Johnston, Ian Donaldson, Mitra L. Taheri, Precision modification of microstructure and properties through laser engraving, JOM, 2021. doi.org/10.1007/s11837-021-04959-6

111-21 Yongki Lee, Jason Cheon, Byung-Kwon Min, Cheolhee Kim, Modelling of fume particle behaviour and coupling glass contamination during vacuum laser beam welding, Science and Technology of Welding and Joining, 2021. doi.org/10.1080/13621718.2021.1990658

110-21 Menglin Liu, Hao Yi, Huajun Cao, Rufeng Huang, Le Jia, Heat accumulation effect in metal droplet-based 3D printing: Evolution mechanism and elimination strategy, Additive Manufacturing, 48.A; 102413, 2021. doi.org/10.1016/j.addma.2021.102413

108-21 Nozomi Taura, Akiya Mitsunobu, Tatsuhiko Sakai, Yasuhiro Okamoto, Akira Okada, Formation and its mechanism of high-speed micro-grooving on metal surface by angled CW laser irradiation, Journal of Laser Micro/Nanoengineering, 16.2, 2021. doi.org/10.2961/jlmn.2021.02.2006

105-21 Jon Spangenberg, Wilson Ricardo Leal da Silva, Raphaël Comminal, Md. Tusher Mollah, Thomas Juul Andersen, Henrik Stang, Numerical simulation of multi-layer 3D concrete printing, RILEM Technical Letters, 6; pp. 119-123, 2021. doi.org/10.21809/rilemtechlett.2021.142

104-21 Lin Chen, Chunming Wang, Gaoyang Mi, Xiong Zhang, Effects of laser oscillating frequency on energy distribution, molten pool morphology and grain structure of AA6061/AA5182 aluminum alloys lap welding, Journal of Materials Research and Technology, 15; pp. 3133-3148, 2021. doi.org/10.1016/j.jmrt.2021.09.141

101-21 R.J.M. Wolfs, T.A.M. Salet, N. Roussel, Filament geometry control in extrusion-based additive manufacturing of concrete: The good, the bad and the ugly, Cement and Concrete Research, 150; 106615, 2021. doi.org/10.1016/j.cemconres.2021.106615

89-21 Wenlin Ye, Jin Bao, Jie Lei, Yichang Huang, Zhihao Li, Peisheng Li, Ying Zhang, Multiphysics modeling of thermal behavior of commercial pure titanium powder during selective laser melting, Metals and Materials International, 2021. doi.org/10.1007/s12540-021-01019-1

81-21 Lin Chen, Gaoyang Mi, Xiong Zhang, Chunming Wang, Effects of sinusoidal oscillating laser beam on weld formation, melt flow and grain structure during aluminum alloys lap welding, Journals of Materials Processing Technology, 298; 117314, 2021. doi.org/10.1016/j.jmatprotec.2021.117314

77-21 Yujie Cui, Yufan Zhao, Haruko Numata, Kenta Yamanaka, Huakang Bian, Kenta Aoyagi, Akihiko Chiba, Effects of process parameters and cooling gas on powder formation during the plasma rotating electrode process, Powder Technology, 393; pp. 301-311, 2021. doi.org/10.1016/j.powtec.2021.07.062

76-21 Md Tusher Mollah, Raphaël Comminal, Marcin P. Serdeczny, David B. Pedersen, Jon Spangenberg, Stability and deformations of deposited layers in material extrusion additive manufacturing, Additive Manufacturing, 46; 102193, 2021. doi.org/10.1016/j.addma.2021.102193

72-21 S. Sabooni, A. Chabok, S.C. Feng, H. Blaauw, T.C. Pijper, H.J. Yang, Y.T. Pei, Laser powder bed fusion of 17–4 PH stainless steel: A comparative study on the effect of heat treatment on the microstructure evolution and mechanical properties, Additive Manufacturing, 46; 102176, 2021. doi.org/10.1016/j.addma.2021.102176

71-21 Yu Hao, Nannan Chena, Hui-Ping Wang, Blair E. Carlson, Fenggui Lu, Effect of zinc vapor forces on spattering in partial penetration laser welding of zinc-coated steels, Journal of Materials Processing Technology, 298; 117282, 2021. doi.org/10.1016/j.jmatprotec.2021.117282

67-21 Lu Wang, Wentao Yan, Thermoelectric magnetohydrodynamic model for laser-based metal additive manufacturing, Physical Review Applied, 15.6; 064051, 2021. doi.org/10.1103/PhysRevApplied.15.064051

61-21 Ian D. McCue, Gianna M. Valentino, Douglas B. Trigg, Andrew M. Lennon, Chuck E. Hebert, Drew P. Seker, Salahudin M. Nimer, James P. Mastrandrea, Morgana M. Trexler, Steven M. Storck, Controlled shape-morphing metallic components for deployable structures, Materials & Design, 208; 109935, 2021. doi.org/10.1016/j.matdes.2021.109935

60-21 Mahyar Khorasani, AmirHossein Ghasemi, Martin Leary, William O’Neil, Ian Gibson, Laura Cordova, Bernard Rolfe, Numerical and analytical investigation on meltpool temperature of laser-based powder bed fusion of IN718, International Journal of Heat and Mass Transfer, 177; 121477, 2021. doi.org/10.1016/j.ijheatmasstransfer.2021.121477

57-21 Dae-Won Cho, Yeong-Do Park, Muralimohan Cheepu, Numerical simulation of slag movement from Marangoni flow for GMAW with computational fluid dynamics, International Communications in Heat and Mass Transfer, 125; 105243, 2021. doi.org/10.1016/j.icheatmasstransfer.2021.105243

55-21 Won-Sang Shin, Dae-Won Cho, Donghyuck Jung, Heeshin Kang, Jeng O Kim, Yoon-Jun Kim, Changkyoo Park, Investigation on laser welding of Al ribbon to Cu sheet: Weldability, microstructure and mechanical and electrical properties, Metals, 11.5; 831, 2021. doi.org/10.3390/met11050831

50-21 Mohamad Bayat, Venkata K. Nadimpalli, Francesco G. Biondani, Sina Jafarzadeh, Jesper Thorborg, Niels S. Tiedje, Giuliano Bissacco, David B. Pedersen, Jesper H. Hattel, On the role of the powder stream on the heat and fluid flow conditions during directed energy deposition of maraging steel—Multiphysics modeling and experimental validation, Additive Manufacturing, 43;102021, 2021. doi.org/10.1016/j.addma.2021.102021

47-21 Subin Shrestha, Kevin Chou, An investigation into melting modes in selective laser melting of Inconel 625 powder: single track geometry and porosity, The International Journal of Advanced Manufacturing Technology, 2021. doi.org/10.1007/s00170-021-07105-3

34-21 Haokun Sun, Xin Chu, Cheng Luo, Haoxiu Chen, Zhiying Liu, Yansong Zhang, Yu Zou, Selective laser melting for joining dissimilar materials: Investigations ofiInterfacial characteristics and in situ alloying, Metallurgical and Materials Transactions A, 52; pp. 1540-1550, 2021. doi.org/10.1007/s11661-021-06178-9

32-21 Shanshan Zhang, Subin Shrestha, Kevin Chou, On mesoscopic surface formation in metal laser powder-bed fusion process, Supplimental Proceedings, TMS 150th Annual Meeting & Exhibition (Virtual), pp. 149-161, 2021. doi.org/10.1007/978-3-030-65261-6_14

22-21 Patiparn Ninpetch, Pruet Kowitwarangkul, Sitthipong Mahathanabodee, Prasert Chalermkarnnon, Phadungsak Rattanadecho, Computational investigation of thermal behavior and molten metal flow with moving laser heat source for selective laser melting process, Case Studies in Thermal Engineering, 24; 100860, 2021. doi.org/10.1016/j.csite.2021.100860

19-21 M.B. Abrami, C. Ransenigo, M. Tocci, A. Pola, M. Obeidi, D. Brabazon, Numerical simulation of laser powder bed fusion processes, La Metallurgia Italiana, February; pp. 81-89, 2021.

16-21 Wenjun Ge, Jerry Y.H. Fuh, Suck Joo Na, Numerical modelling of keyhole formation in selective laser melting of Ti6Al4V, Journal of Manufacturing Processes, 62; pp. 646-654, 2021. doi.org/10.1016/j.jmapro.2021.01.005

11-21 Mohamad Bayat, Venkata K. Nadimpalli, David B. Pedersen, Jesper H. Hattel, A fundamental investigation of thermo-capillarity in laser powder bed fusion of metals and alloys, International Journal of Heat and Mass Transfer, 166; 120766, 2021. doi.org/10.1016/j.ijheatmasstransfer.2020.120766

10-21 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, Thermal properties of powder beds in energy absorption and heat transfer during additive manufacturing with electron beam, Powder Technology, 381; pp. 44-54, 2021. doi.org/10.1016/j.powtec.2020.11.082

9-21 Subin Shrestha, Kevin Chou, A study of transient and steady-state regions from single-track deposition in laser powder bed fusion, Journal of Manufacturing Processes, 61; pp. 226-235, 2021. doi.org/10.1016/j.jmapro.2020.11.023

6-21 Qian Chen, Yunhao Zhao, Seth Strayer, Yufan Zhao, Kenta Aoyagi, Yuichiro Koizumi, Akihiko Chiba, Wei Xiong, Albert C. To, Elucidating the effect of preheating temperature on melt pool morphology variation in Inconel 718 laser powder bed fusion via simulation and experiment, Additive Manufacturing, 37; 101642, 2021. doi.org/10.1016/j.addma.2020.101642

04-21 Won-Ik Cho, Peer Woizeschke, Analysis of molten pool dynamics in laser welding with beam oscillation and filler wire feeding, International Journal of Heat and Mass Transfer, 164; 120623, 2021. doi.org/10.1016/j.ijheatmasstransfer.2020.120623

128-20 Mahmood Al Bashir, Rajeev Nair, Martina M. Sanchez, Anil Mahapatro, Improving fluid retention properties of 316L stainless steel using nanosecond pulsed laser surface texturing, Journal of Laser Applications, 32.4, 2020. doi.org/10.2351/7.0000199

127-20 Eric Riedel, Niklas Bergedieck, Stefan Scharf, CFD simulation based investigation of cavitation cynamics during high intensity ultrasonic treatment of A356, Metals, 10.11; 1529, 2020. doi.org/10.3390/met10111529

126-20 Benjamin Himmel, Material jetting of aluminium: Analysis of a novel additive manufacturing process, Thesis, Technical University of Munich, Munich, Germany, 2020.

121-20 Yufan Zhao, Yujie Cui, Haruko Numata, Huakang Bian, Kimio Wako, Kenta Yamanaka, Kenta Aoyagi, Akihiko Chiba, Centrifugal granulation behavior in metallic powder fabrication by plasma rotating electrode process, Scientific Reports, 10; 18446, 2020. doi.org/10.1038/s41598-020-75503-w

116-20 Raphael Comminal, Wilson Ricardo Leal da Silva, Thomas Juul Andersen, Henrik Stang, Jon Spangenberg, Modelling of 3D concrete printing based on computational fluid dynamics, Cement and Concrete Research, 138; 106256, 2020. doi.org/10.1016/j.cemconres.2020.106256

112-20 Peng Liu, Lijin Huan, Yu Gan, Yuyu Lei, Effect of plate thickness on weld pool dynamics and keyhole-induced porosity formation in laser welding of Al alloy, The International Journal of Advanced Manufacturing Technology, 111; pp. 735-747, 2020. doi.org/10.1007/s00170-020-05818-5

108-20 Fan Chen, Wentao Yan, High-fidelity modelling of thermal stress for additive manufacturing by linking thermal-fluid and mechanical models, Materials & Design, 196; 109185, 2020. doi.org/10.1016/j.matdes.2020.109185

104-20 Yunfu Tian, Lijun Yang, Dejin Zhao, Yiming Huang, Jiajing Pan, Numerical analysis of powder bed generation and single track forming for selective laser melting of SS316L stainless steel, Journal of Manufacturing Processes, 58; pp. 964-974, 2020. doi.org/10.1016/j.jmapro.2020.09.002

100-20 Raphaël Comminal, Sina Jafarzadeh, Marcin Serdeczny, Jon Spangenberg, Estimations of interlayer contacts in extrusion additive manufacturing using a CFD model, International Conference on Additive Manufacturing in Products and Applications (AMPA), Zurich, Switzerland, September 1-3: Industrializing Additive Manufacturing, pp. 241-250, 2020. doi.org/10.1007/978-3-030-54334-1_17

97-20 Paree Allu, CFD simulation for metal Additive Manufacturing: Applications in laser- and sinter-based processes, Metal AM, 6.4; pp. 151-158, 2020.

95-20 Yufan Zhao, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, Role of operating and environmental conditions in determining molten pool dynamics during electron beam melting and selective laser melting, Additive Manufacturing, 36; 101559, 2020. doi.org/10.1016/j.addma.2020.101559

94-20 Yan Zeng, David Himmler, Peter Randelzhofer, Carolin Körner, Processing of in situ Al3Ti/Al composites by advanced high shear technology: influence of mixing speed, The International Journal of Advanced Manufacturing Technology, 110; pp. 1589-1599, 2020. doi.org/10.1007/s00170-020-05956-w

93-20 H. Hamed Zargari, K. Ito, M. Kumar, A. Sharma, Visualizing the vibration effect on the tandem-pulsed gas metal arc welding in the presence of surface tension active elements, International Journal of Heat and Mass Transfer, 161; 120310, 2020. doi.org/10.1016/j.ijheatmasstransfer.2020.120310

90-20 Guangxi Zhao, Jun Du, Zhengying Wei, Siyuan Xu, Ruwei Geng, Numerical analysis of aluminum alloy fused coating process, Journal of the Brazilian Society of Mechanical Science and Engineering, 42; 483, 2020. doi.org/10.1007/s40430-020-02569-y

85-20 Wenkang Huang, Hongliang Wang, Teresa Rinker, Wenda Tan, Investigation of metal mixing in laser keyhold welding of dissimilar metals, Materials & Design, 195; 109056, 2020. doi.org/10.1016/j.matdes.2020.109056

82-20 Pan Lu, Zhang Cheng-Lin, Wang Liang, Liu Tong, Liu Jiang-lin, Molten pool structure, temperature and velocity flow in selective laser melting AlCu5MnCdVA alloy, Materials Research Express, 7; 086516, 2020. doi.org/10.1088/2053-1591/abadcf

80-20 Yujie Cui, Yufan Zhao, Haruko Numata, Huakang Bian, Kimio Wako, Kento Yamanaka, Kenta Aoyagi, Chen Zhang, Akihiko Chiba, Effects of plasma rotating electrode process parameters on the particle size distribution and microstructure of Ti-6Al-4 V alloy powder, Powder Technology, 376; pp. 363-372, 2020. doi.org/10.1016/j.powtec.2020.08.027

78-20 F.Q. Liu, L. Wei, S.Q. Shi, H.L. Wei, On the varieties of build features during multi-layer laser directed energy deposition, Additive Manufacturing, 36; 101491, 2020. doi.org/10.1016/j.addma.2020.101491

75-20 Nannan Chen, Zixuan Wan, Hui-Ping Wang, Jingjing Li, Joshua Solomon, Blair E. Carlson, Effect of Al single bond Si coating on laser spot welding of press hardened steel and process improvement with annular stirring, Materials & Design, 195; 108986, 2020. doi.org/10.1016/j.matdes.2020.108986

72-20 Yujie Cui, Kenta Aoyagi, Yufan Zhao, Kenta Yamanaka, Yuichiro Hayasaka, Yuichiro Koizumi, Tadashi Fujieda, Akihiko Chiba, Manufacturing of a nanosized TiB strengthened Ti-based alloy via electron beam powder bed fusion, Additive Manufacturing, 36; 101472, 2020. doi.org/10.1016/j.addma.2020.101472

64-20 Dong-Rong Liu, Shuhao Wang, Wentao Yan, Grain structure evolution in transition-mode melting in direct energy deposition, Materials & Design, 194; 108919, 2020. doi.org/10.1016/j.matdes.2020.108919

61-20 Raphael Comminal, Wilson Ricardo Leal da Silva, Thomas Juul Andersen, Henrik Stang, Jon Spangenberg, Influence of processing parameters on the layer geometry in 3D concrete printing: Experiments and modelling, 2nd RILEM International Conference on Concrete and Digital Fabrication, RILEM Bookseries, 28; pp. 852-862, 2020. doi.org/10.1007/978-3-030-49916-7_83

60-20 Marcin P. Serdeczny, Raphaël Comminal, Md. Tusher Mollah, David B. Pedersen, Jon Spangenberg, Numerical modeling of the polymer flow through the hot-end in filament-based material extrusion additive manufacturing, Additive Manufacturing, 36; 101454, 2020. doi.org/10.1016/j.addma.2020.101454

58-20 H.L. Wei, T. Mukherjee, W. Zhang, J.S. Zuback, G.L. Knapp, A. De, T. DebRoy, Mechanistic models for additive manufacturing of metallic components, Progress in Materials Science, 116; 100703, 2020. doi.org/10.1016/j.pmatsci.2020.100703

55-20 Masoud Mohammadpour, Experimental study and numerical simulation of heat transfer and fluid flow in laser welded and brazed joints, Thesis, Southern Methodist University, Dallas, TX, US; Available in Mechanical Engineering Research Theses and Dissertations, 24, 2020.

48-20 Masoud Mohammadpour, Baixuan Yang, Hui-Ping Wang, John Forrest, Michael Poss, Blair Carlson, Radovan Kovacevica, Influence of laser beam inclination angle on galvanized steel laser braze quality, Optics and Laser Technology, 129; 106303, 2020. doi.org/10.1016/j.optlastec.2020.106303

34-20 Binqi Liu, Gang Fang, Liping Lei, Wei Liu, A new ray tracing heat source model for mesoscale CFD simulation of selective laser melting (SLM), Applied Mathematical Modeling, 79; pp. 506-520, 2020. doi.org/10.1016/j.apm.2019.10.049

27-20 Xuesong Gao, Guilherme Abreu Farira, Wei Zhang and Kevin Wheeler, Numerical analysis of non-spherical particle effect on molten pool dynamics in laser-powder bed fusion additive manufacturing, Computational Materials Science, 179, art. no. 109648, 2020. doi.org/10.1016/j.commatsci.2020.109648

26-20 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka and Akihiko Chiba, Isothermal γ → ε phase transformation behavior in a Co-Cr-Mo alloy depending on thermal history during electron beam powder-bed additive manufacturing, Journal of Materials Science & Technology, 50, pp. 162-170, 2020. doi.org/10.1016/j.jmst.2019.11.040

21-20 Won-Ik Cho and Peer Woizeschke, Analysis of molten pool behavior with buttonhole formation in laser keyhole welding of sheet metal, International Journal of Heat and Mass Transfer, 152, art. no. 119528, 2020. doi.org/10.1016/j.ijheatmasstransfer.2020.119528

06-20 Wei Xing, Di Ouyang, Zhen Chen and Lin Liu, Effect of energy density on defect evolution in 3D printed Zr-based metallic glasses by selective laser melting, Science China Physics, Mechanics & Astronomy, 63, art. no. 226111, 2020. doi.org/10.1007/s11433-019-1485-8

04-20 Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

02-20 Dongsheng Wu, Shinichi Tashiro, Ziang Wu, Kazufumi Nomura, Xueming Hua, and Manabu Tanaka, Analysis of heat transfer and material flow in hybrid KPAW-GMAW process based on the novel three dimensional CFD simulation, International Journal of Heat and Mass Transfer, 147, art. no. 118921, 2020. doi.org/10.1016/j.ijheatmasstransfer.2019.118921

01-20 Xiang Huang, Siying Lin, Zhenxiang Bu, Xiaolong Lin, Weijin Yi, Zhihong Lin, Peiqin Xie, and Lingyun Wang, Research on nozzle and needle combination for high frequency piezostack-driven dispenser, International Journal of Adhesion and Adhesives, 96, 2020. doi.org/10.1016/j.ijadhadh.2019.102453

88-19 Bo Cheng and Charles Tuffile, Numerical study of porosity formation with implementation of laser multiple reflection in selective laser melting, Proceedings Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing, ASME 2019 14th International Manufacturing Science and Engineering Conference, Erie, Pennsylvania, USA, June 10-14, 2019. doi.org/10.1115/MSEC2019-2891

87-19 Shuhao Wang, Lida Zhu, Jerry Ying His Fuh, Haiquan Zhang, and Wentao Yan, Multi-physics modeling and Gaussian process regression analysis of cladding track geometry for direct energy deposition, Optics and Lasers in Engineering, 127:105950, 2019. doi.org/10.1016/j.optlaseng.2019.105950

78-19 Bo Cheng, Lukas Loeber, Hannes Willeck, Udo Hartel, and Charles Tuffile, Computational investigation of melt pool process dynamics and pore formation in laser powder bed fusion, Journal of Materials Engineering and Performance, 28:11, 6565-6578, 2019. doi.org/10.1007/s11665-019-04435-y

77-19 David Souders, Pareekshith Allu, Anurag Chandorkar, and Ruendy Castillo, Application of computational fluid dynamics in developing process parameters for additive manufacturing, Additive Manufacturing Journal, 9th International Conference on 3D Printing and Additive Manufacturing Technologies (AM 2019), Bangalore, India, September 7-9, 2019.

75-19 Raphaël Comminal, Marcin Piotr Serdeczny, Navid Ranjbar, Mehdi Mehrali, David Bue Pedersen, Henrik Stang, Jon Spangenberg, Modelling of material deposition in big area additive manufacturing and 3D concrete printing, Proceedings, Advancing Precision in Additive Manufacturing, Nantes, France, September 16-18, 2019.

73-19 Baohua Chang, Zhang Yuan, Hao Cheng, Haigang Li, Dong Du 1, and Jiguo Shan, A study on the influences of welding position on the keyhole and molten pool behavior in laser welding of a titanium alloy, Metals, 9:1082, 2019. doi.org/10.3390/met9101082

57-19 Shengjie Deng, Hui-Ping Wang, Fenggui Lu, Joshua Solomon, and Blair E. Carlson, Investigation of spatter occurrence in remote laser spiral welding of zinc-coated steels, International Journal of Heat and Mass Transfer, Vol. 140, pp. 269-280, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.06.009

53-19 Mohamad Bayat, Aditi Thanki, Sankhya Mohanty, Ann Witvrouw, Shoufeng Yang, Jesper Thorborg, Niels Skat Tieldje, and Jesper Henri Hattel, Keyhole-induced porosities in Laser-based Powder Bed Fusion (L-PBF) of Ti6Al4V: High-fidelity modelling and experimental validation, Additive Manufacturing, Vol. 30, 2019. doi.org/10.1016/j.addma.2019.100835

51-19 P. Ninpetch, P. Kowitwarangkul, S. Mahathanabodee, R. Tongsri, and P. Ratanadecho, Thermal and melting track simulations of laser powder bed fusion (L-PBF), International Conference on Materials Research and Innovation (ICMARI), Bangkok, Thailand, December 17-21, 2018. IOP Conference Series: Materials Science and Engineering, Vol. 526, 2019. doi.org/10.1088/1757-899X/526/1/012030

46-19 Hongze Wang and Yu Zou, Microscale interaction between laser and metal powder in powder-bed additive manufacturing: Conduction mode versus keyhole mode, International Journal of Heat and Mass Transfer, Vol. 142, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.118473

45-19 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Kenta Yamanaka, and Akihiko Chiba, Manipulating local heat accumulation towards controlled quality and microstructure of a Co-Cr-Mo alloy in powder bed fusion with electron beam, Materials Letters, Vol. 254, pp. 269-272, 2019. doi.org/10.1016/j.matlet.2019.07.078

44-19 Guoxiang Xu, Lin Li, Houxiao Wang, Pengfei Li, Qinghu Guo, Qingxian Hu, and Baoshuai Du, Simulation and experimental studies of keyhole induced porosity in laser-MIG hybrid fillet welding of aluminum alloy in the horizontal position, Optics & Laser Technology, Vol. 119, 2019. doi.org/10.1016/j.optlastec.2019.105667

38-19 Subin Shrestha and Y. Kevin Chou, A numerical study on the keyhole formation during laser powder bed fusion process, Journal of Manufacturing Science and Engineering, Vol. 141, No. 10, 2019. doi.org/10.1115/1.4044100

34-19 Dae-Won Cho, Jin-Hyeong Park, and Hyeong-Soon Moon, A study on molten pool behavior in the one pulse one drop GMAW process using computational fluid dynamics, International Journal of Heat and Mass Transfer, Vol. 139, pp. 848-859, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.05.038

30-19 Mohamad Bayat, Sankhya Mohanty, and Jesper Henri Hattel, Multiphysics modelling of lack-of-fusion voids formation and evolution in IN718 made by multi-track/multi-layer L-PBF, International Journal of Heat and Mass Transfer, Vol. 139, pp. 95-114, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.05.003

29-19 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Daixiu Wei, Kenta Yamanaka, and Akihiko Chiba, Comprehensive study on mechanisms for grain morphology evolution and texture development in powder bed fusion with electron beam of Co–Cr–Mo alloy, Materialia, Vol. 6, 2019. doi.org/10.1016/j.mtla.2019.100346

28-19 Pareekshith Allu, Computational fluid dynamics modeling in additive manufacturing processes, The Minerals, Metals & Materials Society (TMS) 148th Annual Meeting & Exhibition, San Antonio, Texas, USA, March 10-14, 2019.

24-19 Simulation Software: Use, Advantages & Limitations, The Additive Manufacturing and Welding Magazine, Vol. 2, No. 2, 2019

22-19 Hunchul Jeong, Kyungbae Park, Sungjin Baek, and Jungho Cho, Thermal efficiency decision of variable polarity aluminum arc welding through molten pool analysis, International Journal of Heat and Mass Transfer, Vol. 138, pp. 729-737, 2019. doi.org/10.1016/j.ijheatmasstransfer.2019.04.089

07-19 Guangxi Zhao, Jun Du, Zhengying Wei, Ruwei Geng and Siyuan Xu, Numerical analysis of arc driving forces and temperature distribution in pulsed TIG welding, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 41, No. 60, 2019. doi.org/10.1007/s40430-018-1563-0

04-19 Santosh Reddy Sama, Tony Badamo, Paul Lynch and Guha Manogharan, Novel sprue designs in metal casting via 3D sand-printing, Additive Manufacturing, Vol. 25, pp. 563-578, 2019. doi.org/10.1016/j.addma.2018.12.009

03-19 Dongsheng Wu, Anh Van Nguyen, Shinichi Tashiro, Xueming Hua and Manabu Tanaka, Elucidation of the weld pool convection and keyhole formation mechanism in the keyhold plasma arc welding, International Journal of Heat and Mass Transfer, Vol. 131, pp. 920-931, 2019. doi.org/10.1016/j.ijheatmasstransfer.2018.11.108

97-18 Wentao Yan, Ya Qian, Wenjun Ge, Stephen Lin, Wing Kam Liu, Feng Lin, Gregory J. Wagner, Meso-scale modeling of multiple-layer fabrication process in Selective Electron Beam Melting: Inter-layer/track voids formation, Materials & Design, 2018. doi.org/10.1016/j.matdes.2017.12.031

84-18 Bo Cheng, Xiaobai Li, Charles Tuffile, Alexander Ilin, Hannes Willeck and Udo Hartel, Multi-physics modeling of single track scanning in selective laser melting: Powder compaction effect, Proceedings of the 29th Annual International Solid Freeform Fabrication Symposium, pp. 1887-1902, 2018.

81-18 Yufan Zhao, Yuichiro Koizumi, Kenta Aoyagi, Daixiu Wei, Kenta Yamanaka and Akihiko Chiba, Molten pool behavior and effect of fluid flow on solidification conditions in selective electron beam melting (SEBM) of a biomedical Co-Cr-Mo alloy, Additive Manufacturing, Vol. 26, pp. 202-214, 2019. doi.org/10.1016/j.addma.2018.12.002

77-18 Jun Du and Zhengying Wei, Numerical investigation of thermocapillary-induced deposited shape in fused-coating additive manufacturing process of aluminum alloy, Journal of Physics Communications, Vol. 2, No. 11, 2018. doi.org/10.1088/2399-6528/aaedc7

76-18 Yu Xiang, Shuzhe Zhang, Zhengying We, Junfeng Li, Pei Wei, Zhen Chen, Lixiang Yang and Lihao Jiang, Forming and defect analysis for single track scanning in selective laser melting of Ti6Al4V, Applied Physics A, 124:685, 2018. doi.org/10.1007/s00339-018-2056-9

74-18 Paree Allu, CFD simulations for laser welding of Al Alloys, Proceedings, Die Casting Congress & Exposition, Indianapolis, IN, October 15-17, 2018.

72-18 Hunchul Jeong, Kyungbae Park, Sungjin Baek, Dong-Yoon Kim, Moon-Jin Kang and Jungho Cho, Three-dimensional numerical analysis of weld pool in GMAW with fillet joint, International Journal of Precision Engineering and Manufacturing, Vol. 19, No. 8, pp. 1171-1177, 2018. doi.org/10.1007/s12541-018-0138-4

60-18 R.W. Geng, J. Du, Z.Y. Wei and G.X. Zhao, An adaptive-domain-growth method for phase field simulation of dendrite growth in arc preheated fused-coating additive manufacturing, IOP Conference Series: Journal of Physics: Conference Series 1063, 012077, 2018. doi.org/10.1088/1742-6596/1063/1/012077

59-18 Guangxi Zhao, Jun Du, Zhengying Wei, Ruwei Geng and Siyuan Xu, Coupling analysis of molten pool during fused coating process with arc preheating, IOP Conference Series: Journal of Physics: Conference Series 1063, 012076, 2018. doi.org/10.1088/1742-6596/1063/1/012076 (Available at http://iopscience.iop.org/article/10.1088/1742-6596/1063/1/012076/pdf and in shared drive)

58-18 Siyuan Xu, Zhengying Wei, Jun Du, Guangxi Zhao and Wei Liu, Numerical simulation and analysis of metal fused coating forming, IOP Conference Series: Journal of Physics: Conference Series 1063, 012075, 2018. doi.org/10.1088/1742-6596/1063/1/012075

55-18 Jason Cheon, Jin-Young Yoon, Cheolhee Kim and Suck-Joo Na, A study on transient flow characteristic in friction stir welding with realtime interface tracking by direct surface calculation, Journal of Materials Processing Tech., vol. 255, pp. 621-634, 2018.

54-18 V. Sukhotskiy, P. Vishnoi, I. H. Karampelas, S. Vader, Z. Vader, and E. P. Furlani, Magnetohydrodynamic drop-on-demand liquid metal additive manufacturing: System overview and modeling, Proceedings of the 5th International Conference of Fluid Flow, Heat and Mass Transfer, Niagara Falls, Canada, June 7 – 9, 2018; Paper no. 155, 2018. doi.org/10.11159/ffhmt18.155

52-18 Michael Hilbinger, Claudia Stadelmann, Matthias List and Robert F. Singer, Temconex® – Kontinuierliche Pulverextrusion: Verbessertes Verständnis mit Hilfe der numerischen Simulation, Hochleistungsmetalle und Prozesse für den Leichtbau der Zukunft, Tagungsband 10. Ranshofener Leichtmetalltage, 13-14 Juni 2018, Linz, pp. 175-186, 2018.

38-18 Zhen Chen, Yu Xiang, Zhengying Wei, Pei Wei, Bingheng Lu, Lijuan Zhang and Jun Du, Thermal dynamic behavior during selective laser melting of K418 superalloy: numerical simulation and experimental verification, Applied Physics A, vol. 124, pp. 313, 2018. doi.org/10.1007/s00339-018-1737-8

19-18 Chenxiao Zhu, Jason Cheon, Xinhua Tang, Suck-Joo Na, and Haichao Cui, Molten pool behaviors and their influences on welding defects in narrow gap GMAW of 5083 Al-alloy, International Journal of Heat and Mass Transfer, vol. 126:A, pp.1206-1221, 2018. doi.org/10.1016/j.ijheatmasstransfer.2018.05.132

16-18 P. Schneider, V. Sukhotskiy, T. Siskar, L. Christie and I.H. Karampelas, Additive Manufacturing of Microfluidic Components via Wax Extrusion, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 162 – 165, 2018.

09-18 The Furlani Research Group, Magnetohydrodynamic Liquid Metal 3D Printing, Department of Chemical and Biological Engineering, © University at Buffalo, May 2018.

08-18 Benjamin Himmel, Dominik Rumschöttel and Wolfram Volk, Thermal process simulation of droplet based metal printing with aluminium, Production Engineering, March 2018 © German Academic Society for Production Engineering (WGP) 2018.

07-18 Yu-Che Wu, Cheng-Hung San, Chih-Hsiang Chang, Huey-Jiuan Lin, Raed Marwan, Shuhei Baba and Weng-Sing Hwang, Numerical modeling of melt-pool behavior in selective laser melting with random powder distribution and experimental validation, Journal of Materials Processing Tech. 254 (2018) 72–78.

60-17 Pei Wei, Zhengying Wei, Zhen Chen, Yuyang He and Jun Du, Thermal behavior in single track during selective laser melting of AlSi10Mg powder, Applied Physics A: Materials Science & Processing, 123:604, 2017. doi.org/10.1007/z00339-017-1194-9

51-17 Koichi Ishizaka, Keijiro Saitoh, Eisaku Ito, Masanori Yuri, and Junichiro Masada, Key Technologies for 1700°C Class Ultra High Temperature Gas Turbine, Mitsubishi Heavy Industries Technical Review, vol. 54, no. 3, 2017.

49-17 Yu-Che Wu, Weng-Sing Hwang, Cheng-Hung San, Chih-Hsiang Chang and Huey-Jiuan Lin, Parametric study of surface morphology for selective laser melting on Ti6Al4V powder bed with numerical and experimental methods, International Journal of Material Forming, © Springer-Verlag France SAS, part of Springer Nature 2017. doi.org/10.1007/s12289-017-1391-2.

37-17 V. Sukhotskiy, I. H. Karampelas, G. Garg, A. Verma, M. Tong, S. Vader, Z. Vader, and E. P. Furlani, Magnetohydrodynamic Drop-on-Demand Liquid Metal 3D Printing, Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference

15-17 I.H. Karampelas, S. Vader, Z. Vader, V. Sukhotskiy, A. Verma, G. Garg, M. Tong and E.P. Furlani, Drop-on-Demand 3D Metal Printing, Informatics, Electronics and Microsystems TechConnect Briefs 2017, Vol. 4

14-17 Jason Cheon and Suck-Joo Na, Prediction of welding residual stress with real-time phase transformation by CFD thermal analysis, International Journal of Mechanical Sciences 131–132 (2017) 37–51.

91-16 Y. S. Lee and D. F. Farson, Surface tension-powered build dimension control in laser additive manufacturing process, Int J Adv Manuf Technol (2016) 85:1035–1044, doi.org/10.1007/s00170-015-7974-5.

84-16 Runqi Lin, Hui-ping Wang, Fenggui Lu, Joshua Solomon, Blair E. Carlson, Numerical study of keyhole dynamics and keyhole-induced porosity formation in remote laser welding of Al alloys, International Journal of Heat and Mass Transfer 108 (2017) 244–256, Available online December 2016.

68-16 Dongsheng Wu, Xueming Hua, Dingjian Ye and Fang Li, Understanding of humping formation and suppression mechanisms using the numerical simulation, International Journal of Heat and Mass Transfer, Volume 104, January 2017, Pages 634–643, Published online 2016.

39-16 Chien-Hsun Wang, Ho-Lin Tsai, Yu-Che Wu and Weng-Sing Hwang, Investigation of molten metal droplet deposition and solidification for 3D printing techniques, IOP Publishing, J. Micromech. Microeng. 26 (2016) 095012 (14pp), doi: 10.1088/0960-1317/26/9/095012, July 8, 2016

29-16 Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Advances in Magnetohydrodynamic Liquid Metal Jet Printing, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

26-16 Y.S. Lee and W. Zhang, Modeling of heat transfer, fluid flow and solidification microstructure of nickel-base superalloy fabricated by laser powder bed fusion, S2214-8604(16)30087-2, doi.org/10.1016/j.addma.2016.05.003, ADDMA 86.

123-15 Koji Tsukimoto, Masashi Kitamura, Shuji Tanigawa, Sachio Shimohata, and Masahiko Mega, Laser welding repair for single crystal blades, Proceedings of International Gas Turbine Congress, pp. 1354-1358, 2015.

122-15 Y.S. Lee, W. Zhang, Mesoscopic simulation of heat transfer and fluid flow in laser powder bed additive manufacturing, Proceedings, 26th Solid Freeform Fabrication Symposium, Austin, Texas, 2015.

116-15 Yousub Lee, Simulation of Laser Additive Manufacturing and its Applications, Ph.D. Thesis: Graduate Program in Welding Engineering, The Ohio State University, 2015, Copyright by Yousub Lee 2015

103-15 Ligang Wu, Jason Cheon, Degala Venkata Kiran, and Suck-Joo Na, CFD Simulations of GMA Welding of Horizontal Fillet Joints based on Coordinate Rotation of Arc Models, Journal of Materials Processing Technology, Available online December 29, 2015

96-15 Jason Cheon, Degala Venkata Kiran, and Suck-Joo Na, Thermal metallurgical analysis of GMA welded AH36 steel using CFD – FEM framework, Materials & Design, Volume 91, February 5 2016, Pages 230-241, published online November 2015

86-15 Yousub Lee and Dave F. Farson, Simulation of transport phenomena and melt pool shape for multiple layer additive manufacturing, J. Laser Appl. 28, 012006 (2016). doi: 10.2351/1.4935711, published online 2015.

63-15 Scott Vader, Zachary Vader, Ioannis H. Karampelas and Edward P. Furlani, Magnetohydrodynamic Liquid Metal Jet Printing, TechConnect World Innovation Conference & Expo, Washington, D.C., June 14-17, 2015

46-15 Adwaith Gupta, 3D Printing Multi-Material, Single Printhead Simulation, Advanced Qualification of Additive Manufacturing Materials Workshop, July 20 – 21, 2015, Santa Fe, NM

25-15 Dae-Won Cho and Suck-Joo Na, Molten pool behaviors for second pass V-groove GMAW, International Journal of Heat and Mass Transfer 88 (2015) 945–956.

21-15 Jungho Cho, Dave F. Farson, Kendall J. Hollis and John O. Milewski, Numerical analysis of weld pool oscillation in laser welding, Journal of Mechanical Science and Technology 29 (4) (2015) 1715~1722, www.springerlink.com/content/1738-494x, doi.org/10.1007/s12206-015-0344-2.

82-14 Yousub Lee, Mark Nordin, Sudarsanam Suresh Babu, and Dave F. Farson, Effect of Fluid Convection on Dendrite Arm Spacing in Laser Deposition, Metallurgical and Materials Transactions B, August 2014, Volume 45, Issue 4, pp 1520-1529

59-14 Y.S. Lee, M. Nordin, S.S. Babu, and D.F. Farson, Influence of Fluid Convection on Weld Pool Formation in Laser Cladding, Welding Research/ August 2014, VOL. 93

18-14 L.J. Zhang, J.X. Zhang, A. Gumenyuk, M. Rethmeier, and S.J. Na, Numerical simulation of full penetration laser welding of thick steel plate with high power high brightness laser, Journal of Materials Processing Technology (2014), doi.org/10.1016/j.jmatprotec.2014.03.016.

36-13 Dae-Won Cho,Woo-Hyun Song, Min-Hyun Cho, and Suck-Joo Na, Analysis of Submerged Arc Welding Process by Three-Dimensional Computational Fluid Dynamics Simulations, Journal of Materials Processing Technology, 2013. doi.org/10.1016/j.jmatprotec.2013.06.017

12-13 D.W. Cho, S.J. Na, M.H. Cho, J.S. Lee, A study on V-groove GMAW for various welding positions, Journal of Materials Processing Technology, April 2013, doi.org/10.1016/j.jmatprotec.2013.02.015.

01-13 Dae-Won Cho & Suck-Joo Na & Min-Hyun Cho & Jong-Sub Lee, Simulations of weld pool dynamics in V-groove GTA and GMA welding, Weld World, doi.org/10.1007/s40194-012-0017-z, © International Institute of Welding 2013.

63-12 D.W. Cho, S.H. Lee, S.J. Na, Characterization of welding arc and weld pool formation in vacuum gas hollow tungsten arc welding, Journal of Materials Processing Technology, doi.org/10.1016/j.jmatprotec.2012.09.024, September 2012.

77-10 Lim, Y. C.; Yu, X.; Cho, J. H.; et al., Effect of magnetic stirring on grain structure refinement Part 1-Autogenous nickel alloy welds, Science and Technology of Welding and Joining, Volume: 15 Issue: 7, Pages: 583-589, doi.org/10.1179/136217110X12720264008277, October 2010

18-10 K Saida, H Ohnishi, K Nishimoto, Fluxless laser brazing of aluminium alloy to galvanized steel using a tandem beam–dissimilar laser brazing of aluminium alloy and steels, Welding International, 2010

58-09 Cho, Jung-Ho; Farson, Dave F.; Milewski, John O.; et al., Weld pool flows during initial stages of keyhole formation in laser welding, Journal of Physics D-Applied Physics, Volume: 42 Issue: 17 Article Number: 175502 ; doi.org/10.1088/0022-3727/42/17/175502, September 2009

57-09 Lim, Y. C.; Farson, D. F.; Cho, M. H.; et al., Stationary GMAW-P weld metal deposit spreading, Science and Technology of Welding and Joining, Volume: 14 Issue: 7 ;Pages: 626-635, doi.org/10.1179/136217109X441173, October 2009

1-09 J.-H. Cho and S.-J. Na, Three-Dimensional Analysis of Molten Pool in GMA-Laser Hybrid Welding, Welding Journal, February 2009, Vol. 88

52-07 Huey-Jiuan Lin and Wei-Kuo Chang, Design of a sheet forming apparatus for overflow fusion process by numerical simulation, Journal of Non-Crystalline Solids 353 (2007) 2817–2825.

50-07 Cho, Min Hyun; Farson, Dave F., Understanding bead hump formation in gas metal arc welding using a numerical simulation, Metallurgical and Mateials Transactions B-Process Metallurgy and Materials Processing Science, Volume: 38, Issue: 2, Pages: 305-319, doi.org/10.1007/s11663-007-9034-5, April 2007

49-07 Cho, M. H.; Farson, D. F., Simulation study of a hybrid process for the prevention of weld bead hump formation, Welding Journal Volume: 86, Issue: 9, Pages: 253S-262S, September 2007

48-07 Cho, M. H.; Farson, D. F.; Lim, Y. C.; et al., Hybrid laser/arc welding process for controlling bead profile, Science and Technology of Welding and Joining, Volume: 12 Issue: 8, Pages: 677-688, doi.org/10.1179/174329307X236878, November 2007

47-07 Min Hyun Cho, Dave F. Farson, Understanding Bead Hump Formation in Gas Metal Arc Welding Using a Numerical Simulation, Metallurgical and Materials Transactions B, Volume 38, Issue 2, pp 305-319, April 2007

36-06 Cho, M. H.; Lim, Y. C.; Farson, D. F., Simulation of weld pool dynamics in the stationary pulsed gas metal arc welding process and final weld shape, Welding Journal, Volume: 85 Issue: 12, Pages: 271S-283S, December 2006

금속 3D 프린팅 / 적층 제조 수치해석(FLOW-3D Weld/DEM)

Additive Manufacturing

적층 제조법은 3D프린팅이라고도 하며, 일반적으로 분말 또는 와이어를 층별로 적층제조하는 방법입니다. 금속기반 적층제조공정에 대한 관심이 지난 몇년간 크게 높아졌습니다. 오늘날 사용되는 3개의 주요 금속 적층 제조 공정은 파우더 베드 융접(PBF), 직접 에너지 증착(DED) 및 바인더 분사 공정입니다. FLOW-3D는 이러한 각 프로세스에 대해 높은 정확도의 시뮬레이션 기능을 제공합니다

파우더 베드 융합 및 직접 에너지 증착 공정에서는 레이저나 전자 빔을 열원으로 사용할 수 있습니다. 두 경우 모두 PBF의 분말 형태와 DED공정의 분말 또는 와이어 형태의 금속은 완전히 용해되고 함께 융합되어 층별 부품을 형성합니다. 그러나, 결합제 분사에서, 결합제로서 작용하는 수지는 금속 분말 위에 선택적으로 증착되어 층별로 부품을 형성합니다. 그런 다음 이러한 부품을 소결하여 밀도를 높이게 됩니다.

FLOW-3D의 자유 표면 추적 알고리즘과 물리적 모델은 이러한 각 프로세스를 매우 정확하게 시뮬레이션할 수 있습니다. 레이저 파우더 베드 퓨전(L-LPF)모델링 단계에 대해서는 여기에서 자세히 설명합니다. DED 및 바인더 분사 프로세스에 대한 개념 증명 시뮬레이션도 몇 가지 보여줍니다.

Laser-powder bed fusion processes

L-PBF 공정은 유체 유동, 열 전달, 표면 장력, 상 변화 및 응고와 같은 복잡한 다중 물리 현상을 포함하며, 이는 공정 및 궁극적으로 품질에 영향을 미칩니다. FLOW-3D의 물리적 모델은 질량, 운동량 및 에너지 보존 방정식을 동시에 해결하면서, 입자 크기 분포 및 충진 분율을 고려하여 메소 스케일에서 용융 풀 현상을 시뮬레이션합니다.

FLOW-3D의 추가 모듈인 DEM및 WELD는 전체 파우더 베드 융접 프로세스를 시뮬레이션하는데 사용됩니다. L-PBF 공정의 다양한 단계는 분말 베드 적층, 분말 용융 및 응고 및이어서 이전에 응고 된 층 상에 새로운 분말을 놓고 새로운 층을 이전 층에 다시 융해 및 융합시키는 단계입니다. FLOW-3D는 이러한 각 단계를 시뮬레이션하는 데 사용할 수 있습니다.

Powder bed laying process

FLOW-3D와 통합된 DEM모듈을 사용하면 다음과 같이 입자의 랜덤화된 분포를 삭제하고 포장하여 파우더 베드 배치 프로세스를 아래 영상처럼 시뮬레이션 할 수 있습니다.

One way to achieve different powder bed compactions is to choose different particle size distributions while laying the bed. As seen below, there are three different sized particle size distributions, which result in varying powder bed compactions with Case 2 giving the highest compaction.

입자-입자 상호 작용, 유체-입자 결합 및 입자 이동 객체 상호 작용은 DEM 모듈을 사용하여 자세히 분석할 수 있습니다. 또한 입자 간 힘을 특정하여 분말을 넓게 펴서 뿌리는 현상에 대한 응용을 보다 정확하게 연구할 수도 있습니다.

FLOW-3D 시뮬레이션은 DEM 모델을 사용하여, 회전하는 원통형 롤러로 인해 퍼지는 분말을 해석한 결과입니다. 비디오의 시작 부분에서는 분말 저장소가 아래로 이동하는 반면, 빌드 플랫폼은 위쪽으로 이동합니다. 그 직후 롤러는 초기 위치에 따라 컬러 분말 입자를 빌드 플랫폼으로 분산시켜 다음 층의 용해 및 제작을 준비합니다. 그러한 시뮬레이션은 탱크에서 빌드 플랫폼으로 전달되는 분말 입자의 바람직한 크기에 대한 예측할 수 있습니다.

Powder bed melting

파우더 베드를 배치한 후에는 FLOW-3D WELD에 레이저 빔 프로세스 매개 변수를 지정하여 고 밀도의 용융 풀 시뮬레이션을 수행할 수 있습니다. 온도, 속도, 고상 분율, 온도 구배 및 응고 속도에 대한 그림을 자세히 분석할 수 있습니다

Melt pool analysis of the powder bed under a laser power output of 200W, scan speed of 3.0m/s and a spot radius of 100μm.

용해 풀이 굳으면 FLOW-3D 압력 및 온도 데이터를 Abaqus 또는 MSC Nastran과 같은 FEA 도구로 데이터를 가져와 응력 등고선 및 변위 프로필을 분석할 수 있습니다.

Multi-layer additive manufacturing

첫번째 용해 층이 굳으면 두번째 입자 층이 응고된 베드에 쌓입니다. 새로운 분말 입자 층에 레이저 프로세스 파라미터를 지정함으로써, 용해 풀 시뮬레이션을 다시 수행할 수 있습니다. 이 과정을 여러 번 반복하여 연속적으로 형성된 층 사이의 결합을 평가할 수 있습니다.

Binder jetting

바인더 분사 시뮬레이션은 모세관 힘의 영향을 받는 파우더 베드의 바인더 확산 및 침투에 대한 통찰력을 제공합니다. 공정 매개 변수와 재료 특성은 증착 및 확산 공정에 직접적인 영향을 미친다.

Direct energy deposition

FLOW-3D의 Particle 모델을 사용하여 직접 에너지 축적 프로세스를 시뮬레이션 할 수도 있습니다. 고체 기판에 분말 주입 속도와 열유속 입사를 지정함으로써, 고체 입자는 용융풀을 통해 질량, 추진력 및 에너지를 추가할 수 있습니다. 다음 동영상에서는 용융풀을 통해 고체 금속 입자가 주입되고 이어서 기판에 용융풀응 응고시키는 과정이 관찰됩니다.

Additive Manufacturing

적층 가공법은 3D프린팅이라고도 하며, 일반적으로 분말 또는 와이어를 층별로 적층제조하는 방법입니다. 금속기반 적층제조공정에 대한 관심이 지난 몇년간 크게 높아졌습니다. 오늘날 사용되는 3개의 주요 금속 적층 제조 공정은 파우더 베드 융접(PBF), 직접 에너지 증착(DED) 및 바인더 분사 공정입니다. FLOW-3D는 이러한 각 프로세스에 대해 고유한 시뮬레이션 통찰력을 제공합니다

파우더 베드 융합 및 직접 에너지 증착 공정에서는 레이저나 전자 빔을 열원으로 사용할 수 있습니다. 두 경우 모두 PBF의 분말 형태와 DED공정의 분말 또는 와이어 형태의 금속은 완전히 용해되고 함께 융합되어 층별 부품을 형성합니다. 그러나 바인더 분사에서, 결합제 역할을 하는 수지는 금속 분말에 선택적으로 침전되어 층별로 부품을 형성합니다. 이들 부품은 더 나은 밀도를 얻기 위해 중앙에 배치됩니다.

Laser-powder bed fusion processes

Powder bed laying process

FLOW-3D 시뮬레이션은 DEM 모델을 사용하여, 회전하는 원통형 롤러로 인해 퍼지는 분말을 해석한 결과입니다. 영상의 시작 부분에서는 분말 저장소가 아래로 이동하는 반면, 빌드 플랫폼은 위쪽으로 이동합니다. 그 직후 롤러는 초기 위치에 따라 컬러 분말 입자를 빌드 플랫폼으로 분산시켜 다음 층의 용해 및 제작을 준비합니다. 그러한 시뮬레이션은 탱크에서 빌드 플랫폼으로 전달되는 분말 입자의 바람직한 크기에 대해 예측할 수 있습니다.

Powder bed melting

파우더 베드를 배치한 후에는 FLOW-3D 에 레이저 빔 프로세스 매개 변수를 지정하여 고 밀도의 용융지 시뮬레이션을 수행할 수 있습니다. 온도, 속도, 고상 분율, 온도 구배 및 응고 속도에 대한 그림을 자세히 분석할 수 있습니다

Melt pool analysis of the powder bed under a laser power output of 200W, scan speed of 3.0m/s and a spot radius of 100μm.

Multi-layer additive manufacturing

첫번째 용해 층이 굳으면 두번째 입자 층이 응고된 베드에 쌓입니다. 새로운 분말 입자 층에 레이저 프로세스 파라미터를 지정함으로써, 용해지 시뮬레이션을 다시 수행할 수 있습니다. 이 과정을 여러 번 반복하여 연속적으로 형성된 층 사이의 결합을 평가할 수 있습니다.

Binder jetting

Direct energy deposition

FLOW-3D의 Particle 모델을 사용하여 직접 에너지 증착 공정을 시뮬레이션 할 수도 있습니다. 고체 기판에 분말 주입 속도와 열유속 입사를 지정함으로써, 고체 입자는 용융지를 통해 질량, 추진력 및 에너지를 추가할 수 있습니다. 다음 동영상에서는 용융지을 통해 고체 금속 입자가 주입되고 이어서 기판에 용융지를 응고시키는 과정이 관찰됩니다.

Kodak Develops New Printhead Design in 1/3rd the Time

Kodak Develops New Printhead Design in 1/3rd the Time

Eastman Kodak Company가 잉크젯 프린팅 시장에 진입했을 때 회사는 낭비 할 시간이 없었습니다. Kodak은 프리미엄 안료-기반 잉크를 사용하여 잉크 카트리지를 교체하지 않고 프린트 헤드를 프린터에 통합하여 수명이 오래 지속되고 잉크 비용을 크게 절감하는 생생한 컬러의 사진 인쇄물을 제공함으로써 비즈니스 진출을 계획했습니다. 이러한 발전으로 완전히 새로운 프린트 헤드 개발이 필요했습니다. 업계 경험에 따르면 완전히 새로운 잉크젯 프린터 기술을 개발하는데 보통 8-10 년이 걸렸습니다. Kodak 연구원은 프로젝트의 시간 제약조건을 충족시키기 위해 매우 정확한 시뮬레이션 소프트웨어와 설계를 최적화하는 체계적인 방법이 모두 필요하다는 것을 알고 있었습니다. FLOW-3D 및 D-optimal 설계 실험 (DOE)을 사용하여 Kodak 연구원은 불과 3 년 만에 작업을 완료했습니다.

KODAK EASYSHARE 5500 all-in-one printer

Innovative Ink Leads to New Printer Line

Kodak의 EASYSHARE 프린터 라인을 형성하는 핵심 혁신은 염료-기반 잉크와 동일한 수준의 광택을 제공하지만 훨씬 오래 지속되는 안료-기반 잉크의 개발이었습니다. Kodak 엔지니어는 이 새로운 잉크를 용지 및 기타 용지에 정확하게 공급하면서 기존의 프린트 헤드보다 훨씬 긴 수명을 제공 할 수 있는 프린트 헤드를 개발할 필요가 있었습니다.

FLOW-3D는 우수한 경향 예측뿐만 아니라 시각적 관찰도 우수한 질적인 결과를 제공합니다. 마찬가지로 소프트웨어는 설계 민감도를 정확하게 예측합니다. 결과적으로, FLOW-3D는 Kodak의 첨단 연구 및 개발 활동을 지원하는 귀중한 통찰력을 제공했습니다.

– Christopher Delametter, Senior Research Scientist, Eastman Kodak Company

Simulation Spotlight: Homogeneous Bubble Model

Homogenous Thermal Bubble model

Kodak 엔지니어는 유체 및 열 흐름에 증기 기포의 형성을 연결하는 균일한 버블 모델을 통합하는 FLOW-3D를 사용하여 프린트 헤드 작동을 정확하게 시뮬레이션 했습니다. thin-film stack내의 열원은 고체 구조내에서의 전도 및 유체 / 고체 계면에서의 열전달에 의해 잉크로 운반됩니다. 유체에서 과열 온도에 이르면 증기 거품이 폭발적으로 형성됩니다. 기포는 균질 한 압력과 온도를 갖는 것으로 가정되며, 그 동역학은 증기에 대한 Clapeyron 방정식에 의해 지배된다. 기포 / 액체 계면에서의 질량 및 열교환은 기포가 팽창함에 따라 계속되고, 질량 유속은 운동 이론에 따라 결정됩니다. 표면장력과 점성영역도 시뮬레이션에 포함됩니다. 모델에서의 힘과 플럭스의 적절한 구현은 자유 표면의 정확한 추적에 달려 있는데, 이는 TruVOF 방법론을 사용하여 달성되며 계산의 중요한 부분입니다.

Design of Experiments

DOE를 사용하여 CFD를 구동함으로써 Kodak 연구원은 경쟁사보다 훨씬 짧은 시간에 프린트 헤드 디자인을 최적화 할 수 있었습니다. 시뮬레이션의 장점은 연구원이 색상 중 하나에 대해보다 최적의 잉크 배합을 발견했을 때 나타나며 프로젝트 후반부에 분명했습니다. 잉크는 이러한 장점을 이용하기 위해 신속하게 재구성되었습니다. 그러나 프린트 헤드를 1 년 이상 재설계해야 할 필요가 있을까요? 다행히 Kodak 연구원은 이미 잉크 특성에 대한 민감성 연구를 수행했으므로 단일 시뮬레이션을 추가하지 않고도 기존의 프린트 헤드 설계가 제대로 작동 할 것이라고 신속하게 판단했습니다. 따라서 EASYSHARE 프린터 제품군은 프로젝트가 시작된 지 불과 3 년 만에 출시되었으며, 일반적으로 새로운 잉크젯 기술을 시장에 출시하는 데 소요되는 시간은 약 3 분의 1입니다.

Comparison between physical experiments and simulation—Early experimental device configuration.

Learn more about the power and versatility of modeling microfluidic applications with FLOW-3D >

Non-Newtonian Ink Additive Manufacturing

Non-Newtonian 잉크 프린팅은 적층 제조의 미래입니다. 추가적인 열은 필요하지 않습니다. 대신에 두꺼운 농도의 재료를 제작 플랫폼으로 배출하기 위해 펌프에 의존합니다. 그러나 다른 3D프린팅 방법과 마찬가지로 동일한 제품에 여러 잉크를 사용할 경우 문제가 발생합니다. 여러 개의 압출기를 사용하면 압출기의 전이와 관련된 고유의 부정확함이 수반됩니다. 해결책은 프로그램 가능한 방식으로 단일 프린터 헤드를 사용하는 것입니다. 이 예에서는 FLOW-3D를 사용하여 잉크를 프린팅하기 위해 two-ink microfluidic 프린트 헤드를 시뮬레이션한 것입니다.

Multi-material T-junction microfluidic printhead

프린터 헤드의 설정은 그림(a)에 나와 있습니다. 잉크의 점도는 변형률 비율에 따라 달라지므로, FLOW-3D를 사용하면 변형률-점도 곡선을 나타내는 표 형식으로 유체 특성을 지정할 수 있습니다. 그림(b)는 프린터 헤드의 양 끝에서 잉크의 유입 배출 속도에 대한 프로그램 가능한 제어를 보여줍니다. FLOW-3D에서는 정확한 경계 조건이 지정되었습니다. FLOW-3D의 Moving Objects모델은 지정된 속도로 프린팅 플랫폼의 이동을 시뮬레이션 합니다. 이 경우 설정 값은 2.65 mm/sec입니다. 정량적으로, 1600 micro-liter /min의 최대 방전 비율에 대해, 잉크의 액체 비율이 1에서 0으로 감소하는 거리가 1mm이내인 것으로 나타났습니다. 0.5mm 정도의 프린팅된 잉크 줄무늬가 완전히 변하는 것이 관찰됩니다. 이 값은 실험 결과와 일치합니다.

위의 애니메이션은 프로그래밍 방식으로 한 잉크에서 다른 잉크로 전환하는 과정을 보여줍니다. 파란 색에서 빨간 색 잉크로의 전환 길이는 1mm이내이며, 실험 결과에 따라 급격한 전환이 될 수 있습니다.

This study is based on the paper from James O. Hardin, Thomas J. Ober, Alexander D. Valentine, Jennifer A. Lewis. Microfluidic Printheads for Multimaterial 3D Printing of Viscoelastic Inks, 2015. Figures (a) and (b) have been extracted from this paper.

Learn more about simulating additive manufacturing processes with FLOW-3D >

FLOW-3D의 활용 및 설계 적용 사례 (4)

코팅 분야의 활용

코팅(coating)이란 기판 위의 공기를 액상의 코팅액으로 대체하는 것을 말하고, 코팅 공정은 고분자 용액, 현탁액, 등의 코팅액이 기판 위에 도포됨으로써 마이크로 단위 이하의 액막을 만드는 공정이라고 할 수 있다.

<슬롯(Slot)코팅의 유동 전개>

코팅 공정의 산업적 응용 범위는 매우 넓다. 예를 들면, LCD/PDP 등과 같은 디스플레이, 자성 또는 광학 디스크, 노광, 고집적회로 기판, 광섬유 등의 일반 산업용 및 가정용에 이르기까지 코팅 공정은 모든 산업의 핵심이 되는 분야라고 할 수 있다.
최근 그 중요성으로 인해 코팅 분야에 대한 연구가 활발히 진행되고 있는데, 생산 속도를 빠르게 하면서 얇고 정밀한 코팅 제품을 생산하기 위한 최적화, 안정성 연구, 다층 필름 및 동시 양면 코팅 제품 등과 같이 코팅의 기능성을 부여하는 신기술 개발, 환경 문제를 해결하기 위한 비 뉴턴(Non-Newtonian) 특성의 코팅액 개발, 최적의 공정 조건을 도출하고자 하는 이론적, 실험적 연구 등이 그것이다.
코팅 공정을 안정되게 유지하기 위해서는 코팅 비드(bead) 내에 작용하는 관성력, 점성력, 모세관력 그리고 외력의 상관관계를 고찰할 필요가 있다. 이를 위해서는 코팅액의 물성, 다이의 구조, 운전 조건 등의 공정 조건을 체계적으로 분석하여야 한다. 코팅액의 유변학적 물성 중에는 점도, 표면장력, 밀도 등이 동특성에 큰 영향을 준다.
또한 코팅액이 웹(web)에 닿을 때의 접촉각, 웹 특성도 공정 해석에 주요 인자이다. 장비의 설계에 있어서는 다이의 기하구조와 더불어 다이 립(lip)의 배치도 중요하다. 이들은 코팅액의 유동과 압력 분포에 크게 영향을 미치기 때문에 여러 형태의 다이 구조가 제안되고 있다. 운전변수는 생산성을 결정짓는 웹 속도를 비롯하여 필름 두께, 공급 유량, 코팅 갭 등이 있다. 이외에도 외력으로 작용하는 전자기력 또는 상류흐름(upstream) 영역 밖에서 가해주는 압력 강하도 고려되어야 한다. 다양한 공정 변수들의 효과는 여러 무차원수로 표현하여 설명하는 것이 적절하다. 가장 중요한 무차원 변수는 카필러리 수(Capillary number)라고 할 수 있는데 용액의 점도와 웹 속도에 대한 표면장력의 비로 정의된다.

참고로 유체역학에서 모세관수는 서로 섞이지 않는 두 유체의 점도와 표면 장력의 상대적 영향력을 나타내는 무차원 수이다.
한편, 코팅 방식의 종류는 코팅 대상물의 구조 및 종류, 코팅액, 코팅 두께 등에 따라 다음과 같이 분류된다.

■ 슬라이드(Slide) 코팅
■ 딥(Dip) 코팅
■ 스핀(Spin) 코팅
■ 커튼(Curtain) 코팅
■ 슬롯(Slot) 코팅
■ 롤(Roll) 코팅
■ 그라비어(Gravure) 코팅

이번 호에서는 FLOW-3D를 이용하여 다양한 코팅방식에 대한 간단한 예제 및 코팅 공정에서 발생하는 불량의 해석 사례를 제시하고자 하며, 또한 최근에 가공 시간 및 공정 단축을 위해 자주 시도되고 있는 다층 동시 코팅에 대해서도 간략하게 언급하고자 한다.

다운로드 : [ 4회_201804_analysis_flow3d ]

작성자 | 홍규선_에스티아이C&D 솔루션 사업부 이사
이메일 | flow3d@stikorea.co.kr
홈페이지 | www.flow3d.co.kr

출처 : CAD&Graphics 2018년 04월호

Microfluidics Bibliography

다음은 Microfluidics Bibliography의 기술 문서 모음입니다.
이 모든 논문은 FLOW-3D 결과를 특징으로 합니다. 미세 유체 공정 및 장치 를 성공적으로 시뮬레이션하기 위해 FLOW-3D 를 사용 하는 방법에 대해 자세히 알아보십시오 .

2024년 11월 20일 Update

109-24 Dileep Karnam, Yu-Lung Lo, Chia-Hua Yang, Spray mist-assisted drilling of through silicon vias (TSV) using nanosecond laser: Influence of CNT nanofluid, Journal of Materials Research and Technology, 31; pp. 679-688, 2024. doi.org/10.1016/j.jmrt.2024.06.109

22-24 Bin-Jie Lai, Li-Tao Zhu, Zhe Chen, Bo Ouyang, Zheng-Hong Luo, Review on blood flow dynamics in lab-on-a-chip systems: an engineering perspective, Chem & Bio Engineering, 1.1; pp. 26-43, 2024. doi.org/10.1021/cbe.3c00014

196-23 Daicong Zhang, Chunhui Jing, Wei Guo, Yuan Xiao, Jun Luo, Lehua Qi, Microchannels formed using metal microdroplets, Micromachines, 14.10; 1922, 2023. doi.org/10.3390/mi14101922

121-23 Feng Lin Ng, Zhanhong Cen, Yi-Chin Toh, Lay Poh Tan, A 3D-printed micro-perfused culture device with embedded 3D fibrous scaffold for enhanced biomimicry, International Journal of Bioprinting, 2023. doi.org/10.36922/ijb.0226

104-23 Cristina González-Fernández, Jenifer Gómez-Pastora, Eugenio Bringas, Inmaculada Ortiz, Computer-aided design of magnetophoretic microfluidic systems for enhanced recovery of target products, 33rd European Symposium on Computer-Aided Engineering (ESCAPE), 2023.

64-23 Tihomir Tjankov, Dimitar Trifonov, Conceptual design and 3D modeling of a microfluidic device for liver cells investigation, Industry 4.0, 8.2; pp. 39-41, 2023.

64-22 Babatunde Aramide, Computational modelling of electrohydrodynamic jetting (Taylor cone formation, dripping & jet evolution): Case study of electrospinning, Thesis, University College London, 2022.

138-21 Enver Guler, Mine Eti, Aydin Cihanoglu, Esra Altiok, Kadriye Ozlem Hamaloglu, Burcu Gokcal, Ali Tuncel, Nalan Kabay, Ion exchange membranes with enhanced antifouling properties to produce energy from renewable sources, Proceedings of the 6th International Symposium on Green and Smart Technologies for a Sustainable Society, Santander, Cantabria, Spain, December 9-10, 2021.

45-21 Navid Tonekaboni, Mahdi Feizbahr, Nima Tonekaboni, Guang-Jun Jiang, Hong-Xia Chen, Optimization of solar CCHP systems with collector enhanced by porous media and nanofluid, Mathematical Problems in Engineering, 2021; 9984940, 2021. doi.org/10.1155/2021/9984840

40-21 B. Hayes, G.L. Whiting, R. MacCurdy, Modeling of contactless bubble–bubble interactions in microchannels with integrated inertial pumps, Physics of Fluids, 33.4; 042002, 2021. doi.org/10.1063/5.0041924

Below is a collection of technical papers in our Microfluidics Bibliography. All of these papers feature FLOW-3D results. Learn more about how FLOW-3D can be used to successfully simulate microfluidic processes and devices.

14-21 Jian-Chiun Liou, Chih-Wei Peng, Philippe Basset, Zhen-Xi Chen, DNA printing integrated multiplexer driver microelectronic mechanical system head (IDMH) and microfluidic flow estimation, Micromachines, 12.1; 25, 2021. doi.org/10.3390/mi12010025

08-20 Li Yong-Qiang, Dong Jun-Yan and Rui Wei, Numerical simulation for capillary driven flow in capsule-type vane tank with clearances under microgravity, Microgravity Science and Technology, 2020. doi.org/10.1007/s12217-019-09773-z

89-19 Tim Dreckmann, Julien Boeuf, Imke-Sonja Ludwig, Jorg Lumkemann, and Jorg Huwyler, Low volume aseptic filling: impact of pump systems on shear stress, European Journal of Pharmeceutics and Biopharmeceutics, in press, 2019. doi:10.1016/j.ejpb.2019.12.006

88-19 V. Amiri Roodan, J. Gomez-Pastora, C. Gonzalez-Fernandez, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, CFD analysis of the generation and manipulation of ferrofluid droplets, TechConnect Briefs, pp. 182-185, 2019. TechConnect World Innovation Conference & Expo, Boston, Massachussetts, USA, June 17-19, 2019.

55-19 Julio Aleman, Sunil K. George, Samuel Herberg, Mahesh Devarasetty, Christopher D. Porada, Aleksander Skardal, and Graça Almeida‐Porada, Deconstructed microfluidic bone marrow on‐a‐chip to study normal and malignant hemopoietic cell–niche interactions, Small, 2019. doi: 10.1002/smll.201902971

37-19 Feng Lin Ng, Miniaturized 3D fibrous scaffold on stereolithography-printed microfluidic perfusion culture, Doctoral Thesis, Nanyang Technological University, Singapore, 2019.

32-19 Jenifer Gómez-Pastora, Ioannis H. Karampelas, Eugenio Bringas, Edward P. Furlani, and Inmaculada Ortiz, Numerical analysis of bead magnetophoresis from flowing blood in a continuous-flow microchannel: Implications to the bead-fluid interactions, Nature: Scientific Reports, Vol. 9, No. 7265, 2019. doi: 10.1038/s41598-019-43827-x

01-19 Jelena Dinic and Vivek Sharma, Computational analysis of self-similar capillary-driven thinning and pinch-off dynamics during dripping using the volume-of-fluid method, Physics of Fluids, Vol. 31, 2019. doi: 10.1063/1.5061715

75-18 Tobias Ladner, Sebastian Odenwald, Kevin Kerls, Gerald Zieres, Adeline Boillon and Julien Bœuf, CFD supported investigation of shear induced by bottom-mounted magnetic stirrer in monoclonal antibody formulation, Pharmaceutical Research, Vol. 35, 2018. doi: 10.1007/s11095-018-2492-4

53-18 Venoos Amiri Roodan, Jenifer Gómez-Pastora, Aditi Verma, Eugenio Bringas, Inmaculada Ortiz and Edward P. Furlani, Computational analysis of magnetic droplet generation and manipulation in microfluidic devices, Proceedings of the 5th International Conference of Fluid Flow, Heat and Mass Transfer, Niagara Falls, Canada, June 7 – 9, 2018; Paper no. 154, 2018. doi: 10.11159/ffhmt18.154

35-18 Jenifer Gómez-Pastora, Cristina González Fernández, Marcos Fallanza, Eugenio Bringas and Inmaculada Ortiz, Flow patterns and mass transfer performance of miscible liquid-liquid flows in various microchannels: Numerical and experimental studies, Chemical Engineering Journal, vol. 344, pp. 487-497, 2018. doi: 10.1016/j.cej.2018.03.110

15-18 J. Gómez-Pastora, I.H. Karampelas, A.Q. Alorabi, M.D. Tarn, E. Bringas, A. Iles, V.N. Paunov, N. Pamme, E.P. Furlani, I. Ortiz, CFD analysis and experimental validation of magnetic droplet generation and deflection across multilaminar flow streams, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 182-185, 2018.

14-18 J. Gómez-Pastora, C. González-Fernández, I.H. Karampelas, E. Bringas, E.P. Furlani, and I. Ortiz, Design of Magnetic Blood Cleansing Microdevices through Experimentally Validated CFD Modeling, Biotech, Biomaterials and Biomedical TechConnect Briefs, vol. 3, pp. 170-173, 2018.

10-18 A. Gupta, I.H. Karampelas, J. Kitting, Numerical modeling of the formation of dynamically configurable L2 lens in a microchannel, Biotech, Biomaterials and Biomedical TechConnect Briefs, Vol. 3, pp. 186 – 189, 2018.

17-17 I.H. Karampelas, J. Gómez-Pastora, M.J. Cowan, E. Bringas, I. Ortiz and E.P. Furlani, Numerical Analysis of Acoustophoretic Discrete Particle Focusing in Microchannels, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

16-17 J. Gómez-Pastora, I.H. Karampelas, E. Bringas, E.P. Furlani and I. Ortiz, CFD analysis of particle magnetophoresis in multiphase continuous-flow bioseparators, Biotech, Biomaterials and Biomedical TechConnect Briefs 2017, Vol. 3

102-16 J. Brindha, RA.G. Privita Edwina, P.K. Rajesh and P.Rani, “Influence of rheological properties of protein bio-inks on printability: A simulation and validation study,” Materials Today: Proceedings, vol. 3, no.10, pp. 3285-3295, 2016. doi: 10.1016/j.matpr.2016.10.010

99-16 Ioannis H. Karampelas, Kai Liu, Fatema Alali, and Edward P. Furlani, Plasmonic Nanoframes for Photothermal Energy Conversion, J. Phys. Chem. C, 2016, 120 (13), pp 7256–7264

98-16 Jelena Dinic and Vivek Sharma, Drop formation, pinch-off dynamics and liquid transfer of simple and complex fluids, http://meetings.aps.org/link/BAPS.2016.MAR.B53.12, APS March Meeting 2016, Volume 61, Number 2, March 14–18, 2016, Baltimore, Maryland

67-16 Vahid Bazargan and Boris Stoeber, Effect of substrate conductivity on the evaporation of small sessile droplets, PHYSICAL REVIEW E 94, 033103 (2016), doi: 10.1103/PhysRevE.94.033103

57-16 Ioannis Karampelas, Computational analysis of pulsed-laser plasmon-enhanced photothermal energy conversion and nanobubble generation in the nanoscale, PhD Dissertation: Department of Chemical and Biological Engineering, University at Buffalo, State University of New York, July 2016

44-16 Takeshi Sawada et al., Prognostic impact of circulating tumor cell detected using a novel fluidic cell microarray chip system in patients with breast cancer, EBioMedicine, Available online 27 July 2016, doi: 10.1016/j.ebiom.2016.07.027.

30-16 Ioannis H. Karampelas, Kai Liu and Edward P. Furlani, Plasmonic Nanocages as Photothermal Transducers for Nanobubble Cancer Therapy, Nanotech 2016 Conference & Expo, May 22-25, Washington, DC.

02-16 Stephen D. Hoath (Editor), Fundamentals of Inkjet Printing: The Science of Inkjet and Droplets, ISBN: 978-3-527-33785-9, 472 pages, February 2016 (see chapters 2 and 3 for FLOW-3D results)

125-15 J. Berthier, K.A. Brakke, E.P. Furlani, I.H. Karampelas, V. Poher, D. Gosselin, M. Cubinzolles and P. Pouteau, Whole blood spontaneous capillary flow in narrow V-groove microchannels, Sensors and Actuators B: Chemical, 206, pp. 258-267, 2015.

77-15 Ho-Lin Tsai, Weng-Sing Hwang, Jhih-Kai Wang, Wen-Chih Peng and Shin-Hau Chen, Fabrication of Microdots Using Piezoelectric Dispensing Technique for Viscous Fluids, Materials 2015, 8(10), 7006-7016. doi: 10.3390/ma8105355

46-15 Adwaith Gupta, 3D Printing Multi-Material, Single Printhead Simulation, Advanced Qualification of Additive Manufacturing Materials Workshop, July 20 – 21, 2015, Santa Fe, NM

28-15 Yongqiang Li, Mingzhu Hu, Ling Liu, Yin-Yin Su, Li Duan, and Qi Kang, Study of Capillary Driven Flow in an Interior Corner of Rounded Wall Under Microgravity, Microgravity Science and Technology, June 2015

20-15 Pamela J. Waterman, Diversity in Medical Simulation Applications, Desktop Engineering, May 2015, pp 22-26,

16-15 Saurabh Singh, Ann Junghans, Erik Watkins, Yash Kapoor, Ryan Toomey, and Jaroslaw Majewski, Effects of Fluid Shear Stress on Polyelectrolyte Multilayers by Neutron Scattering Studies, © 2015 American Chemical Society, DOI: 10.1021/acs.langmuir.5b00037, Langmuir 2015, 31, 2870−2878, February 17, 2015

11-15 Cheng-Han Wu and Weng-Sing Hwang, The effect of process condition of the ink-jet printing process on the molten metallic droplet formation through the analysis of fluid propagation direction, Canadian Journal of Physics, 2015. doi: 10.1139/cjp-2014-0259

03-15 Hanchul Cho, Sivasubramanian Somu, Jin Young Lee, Hobin Jeong and Ahmed Busnaina, High-Rate Nanoscale Offset Printing Process Using Directed Assembly and Transfer of Nanomaterials, Adv. Materials, doi: 10.1002/adma.201404769, February 2015

122-14 Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastián D’hers and Noel M Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Research Gate, doi: 10.1007/s13346-014-0198-7, July 2014

113-14 Cihan Yilmaz, Arif E. Cetin, Georgia Goutzamanidis, Jun Huang, Sivasubramanian Somu, Hatice Altug, Dongguang Wei and Ahmed Busnaina, Three-Dimensional Crystalline and Homogeneous Metallic Nanostructures Using Directed Assembly of Nanoparticles, 10.1021/nn500084g, © 2014 American Chemical Society, April 2014

110-14 Koushik Ponnuru, Jincheng Wu, Preeti Ashok, Emmanuel S. Tzanakakis and Edward P. Furlani, Analysis of Stem Cell Culture Performance in a Microcarrier Bioreactor System, Nanotech, Washington, D.C., June 15-18, 2014

109-14 Ioannis H. Karampelas, Young Hwa Kim and Edward P. Furlani, Numerical Analysis of Laser Induced Photothermal Effects using Colloidal Plasmonic Nanostructures, Nanotech, Washington, D.C., June 15-18, 2014

108-14 Chenxu Liu, Xiaozheng Xue and Edward P. Furlani, Numerical Analysis of Fully-Coupled Particle-Fluid Transport and Free-Flow Magnetophoretic Sorting in Microfluidic Systems, Nanotech, Washington, D.C., June 15-18, 2014

95-14 Cheng-Han Wu, Weng-Sing Hwang, The effect of the echo-time of a bipolar pulse waveform on molten metallic droplet formation by squeeze mode piezoelectric inkjet printing, Accepted November 2014, Microelectronics Reliability (2014) , © 2014 Elsevier Ltd. All rights reserved.

61-14 Chenxu Liu, A Computational Model for Predicting Fully-Coupled Particle-Fluid Dynamics and Self-Assembly for Magnetic Particle Applications, Master’s Thesis: State University of New York at Buffalo, 2014, 75 pages; 1561583, http://gradworks.umi.com/15/61/1561583.html

41-14 Albert Chi, Sebastian Curi, Kevin Clayton, David Luciano, Kameron Klauber, Alfredo Alexander-Katz, Sebastian D’hers, and Noel M. Elman, Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics, Drug Deliv. and Transl. Res., DOI 10.1007/s13346-014-0198-7, # Controlled Release Society 2014. Available for purchase online at SpringerLink.

21-14 Suk-Hee Park, Ung Hyun Koh, Mina Kim, Dong-Yol Yang, Kahp-Yang Suh and Jennifer Hyunjong Shin, Hierarchical multilayer assembly of an ordered nanofibrous scaffold via thermal fusion bonding, Biofabrication 6 (2014) 024107 (10pp), doi:10.1088/1758-5082/6/2/024107, IOP Publishing, 2014. Available for purchase online at IOP.

17-14 Vahid Bazargan, Effect of substrate cooling and droplet shape and composition on the droplet evaporation and the deposition of particles, Ph.D. Thesis: Department of Mechanical Engineering, The University of British Columbia, March 2014, © Vahid Bazargan, 2014

73-13 Oliver G. Harlen, J. Rafael Castrejón-Pita, and Arturo Castrejon-Pita, Asymmetric Detachment from Angled Nozzles Plates in Drop-on Demand Inkjet Printing, NIP & Digital Fabrication Conference, 2013 International Conference on Digital Printing Technologies. Pages 253-549, pp. 277-280(4)

63-13 Fatema Alali, Ioannis H. Karampelas, Young Hwa Kim, and Edward P. Furlani, Photonic and Thermofluidic Analysis of Colloidal Plasmonic Nanorings and Nanotori for Pulsed-Laser Photothermal Applications, J. Phys. Chem. C, Article ASAP, DOI: 10.1021/jp406986y, Copyright © 2013 American Chemical Society, September 2013.

25-13 Sudhir Srivastava, Theo Driessen, Roger Jeurissen, Herma Wijshoff, and Federico Toschi, Lattice Boltzmann Method to Study the Contraction of a Viscous Ligament, International Journal of Modern Physics © World Scientific Publishing Company, May 2013.

11-13 Li-Chieh Hsu, Yong-Jhih Chen, Jia-Huang Liou, Numerical Investigation in the Factors on the Pool Boiling, Applied Mechanics and Materials Vol. 311 (2013) pp 456-461, © (2013) Trans Tech Publications, Switzerland, doi:10.4028/www.scientific.net/AMM.311.456. Available for purchase online at Scientific.Net.

10-13 Pamela J. Waterman, CFD: Shaping the Medical World, Desktop Engineering, April 2013. Full article available online at Desktop Engineering.

90-12 Charles R. Ortloff and Martin Vogel, Spray Cooling Heat Transfer- Test and CFD Analysis, Electronics Cooling, June 2012. Available online at Electronics Cooling.

79-12 Daniel Parsaoran Siregar, Numerical simulation of evaporation and absorption of inkjet printed droplets, Ph.D. Thesis: Technische Universiteit Eindhoven, September 18, 2012, Copyright 2012 by D.P. Siregar, ISBN: 978-90-386-3190-5.

71-12 Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, Seungwan Lee, and Woonbae Kim, Varifocal liquid lens based on microelectrofluidic technology, Optics Letters, Vol. 37, Issue 21, pp. 4377-4379 (2012) http://dx.doi.org/10.1364/OL.37.004377

70-12 Jong-hyeon Chang, Kyu-Dong Jung, Eunsung Lee, Minseog Choi, and Seunwan Lee, Microelectrofluidic Iris for Variable Aperture, Proc. SPIE 8252, MOEMS and Miniaturized Systems XI, 82520O (February 9, 2012); doi:10.1117/12.906587

69-12 Jong-hyeon Chang, Eunsung Lee, Kyu-Dong Jung, Seungwan Lee, Minseog Choi, and Woonbae Kim, Microelectrofluidic Lens for Variable Curvature, Proc. SPIE 8486, Current Developments in Lens Design and Optical Engineering XIII, 84860X (October 11, 2012); doi:10.1117/12.925852.

61-12 Biddut Bhattacharjee, Study of Droplet Splitting in an Electrowetting Based Digital Microfluidic System, Thesis: Doctor of Philosophy in the College of Graduate Studies (Applied Sciences), The University of British Columbia, September 2012, © Biddut Bhattacharjee.

55-12 Hejun Li, Pengyun Wang, Lehua Qi, Hansong Zuo, Songyi Zhong, Xianghui Hou, 3D numerical simulation of successive deposition of uniform molten Al droplets on a moving substrate and experimental validation, Computational Materials Science, Volume 65, December 2012, Pages 291–301. Available for purchase online at SciVerse.

54-12 Edward P. Furlani, Anthony Nunez, Gianmarco Vizzeri, Modeling Fluid Structure-Interactions for Biomechanical Analysis of the Human Eye, Nanotech Conference & Expo, June 18-21, 2012, Santa Clara, CA.

53-12 Xinyun Wu, Richard D. Oleschuk and Natalie M. Cann, Characterization of microstructured fibre emitters in pursuit of improved nano electrospray ionization performance, The Royal Society of Chemistry 2012, http://pubs.rsc.org, DOI: 10.1039/c2an35249d, May 2012

25-12 Edward P. Furlani, Ioannis H. Karampelas and Qian Xie, Analysis of Pulsed Laser Plasmon-assisted Photothermal Heating and Bubble Generation at the Nanoscale, Lab on a Chip, 10.1039/C2LC40495H, Received 01 May 2012, Accepted 07 Jun 2012. First published on the web 13 Jun 2012.

22-12 R.A. Sultanov, D. Guster, Numerical Modeling and Simulations of Pulsatile Human Blood Flow in Different 3D-Geometries, Book chapter #21 in Fluid Dynamics, Computational Modeling and Applications (2012), ISBN: 978-953-51-0052-2, p. 475 [18 pages]. Available online at INTECH.

21-12 Guo-Wei Huang, Tzu-Yi Hung, and Chin-Tai Chen, Design, Simulation, and Verification of Fluidic Light-Guide Chips with Various Geometries of Micro Polymer Channels, NEMS 2012, Kyoto, Japan, March 5-8, 2012. Available for purchase online at IEEE.

103-11 Suk-Hee Park, Development of Three-Dimensional Scaffolds containing Electrospun Nanofibers and their Applications to Tissue Regeneration, Ph.D. Thesis: School of Mechanical, Aersospace and Systems Engineering, Division of Mechanical Engineering, KAIST, 2011.

81-11 Xinyun Wu, Modeling and Characterization of Microfabricated Emitters-In Pursuit of Improved ESI-MS Performance, thesis: Department of Chemistry, Queen’s University, December 2011, Copyright © Xinyun Wu, 2011

77-11 Ge Bai, W. Thomas Leach, Computational fluid dynamics (CFD) insights into agitation stress methods in biopharmaceutical development, International Journal of Pharmaceutics, Available online 8 December 2011, ISSN 0378-5173, 10.1016/j.ijpharm.2011.11.044. Available online at SciVerse.

72-11 M.R. Barkhudarov, C.W. Hirt, D. Milano, and G. Wei, Comments on a Comparison of CFD Software for Microfluidic Applications, Flow Science Technical Note #93, FSI-11-TN93, December 2011

45-11 Chang-Wei Kang, Jiak Kwang Tan, Lunsheng Pan, Cheng Yee Low and Ahmed Jaffar, Numerical and experimental investigations of splat geometric characteristics during oblique impact of plasma spraying, Applied Surface Science, In Press, Corrected Proof, Available online 20 July 2011, ISSN 0169-4332, DOI: 10.1016/j.apsusc.2011.06.081. Available to purchase online at SciVers

33-11 Edward P. Furlani, Mark T. Swihart, Natalia Litchinitser, Christopher N. Delametter and Melissa Carter, Modeling Nanoscale Plasmon-assisted Bubble Nucleation and Applications, Nanotech Conference and Expo 2011, Boston, MA, June 13-16, 2011

32-11 Lu, Cong and Mills, James K., Three cell separation design for realizing automatic cell injection, Complex Medical Engineering (CME), 2011 IEEE/ICME, pp: 599 – 603, Harbin, China, 10.1109/ICCME.2011.5876811, June 2011. Available online at IEEEXplore.

25-11 Issam M. Bahadur, James K. Mills, Fluidic vacuum-based biological cell holding device with piezoelectrically induced vibration, Complex Medical Engineering (CME), 2011 IEEE/ICME International Conference on, 22-25 May 2011, pp: 85 – 90, Harbin, China. Available online at: IEEE Xplore.

14-11 Edward P. Furlani, Roshni Biswas, Alexander N. Cartwright and Natalia M. Litchinitser, Antiresonant guiding optofluidic biosensor, doi:10.1016/j.optcom.2011.04.014, Optics Communication, April 2011

05-11 Hyeju Eom and Keun Park, Integrated numerical analysis to evaluate replication characteristics of micro channels in a locally heated mold by selective induction, International Journal of Precision Engineering and Manufacturing, Volume 12, Number 1, 53-60, DOI: 10.1007/s12541-011-0007-x, 2011. Available online at: SpringerLink.

70-10 I.N. Volnov, V.S. Nagornyi, Modeling Processes for Generation of Streams of Monodispersed Fluid Droplets in Electro-inkjet Applications, Science and Technology News, St. Petersburg State Polytechnic University, 4, pp 294-300, 2010. In Russian.

62-10 F. Mobadersani, M. Eskandarzade, S. Azizi and S. Abbasnezhad, Effect of Ambient Pressure on Bubble Growth in Micro-Channel and Its Pumping Effect, ESDA2010-24436, pp. 577-584, doi:10.1115/ESDA2010-24436, ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis (ESDA2010), Istanbul, Turkey, July 12–14, 2010. Available online at the ASME Digital Library.

58-10 Tsung-Yi Ho, Jun Zeng, and Chakrabarty, K, Digital microfluidic biochips: A vision for functional diversity and more than moore, Computer-Aided Design (ICCAD), 2010 IEEE/ACM International Conference on, DOI: 10.1109/ICCAD.2010.5654199, © IEEE, November 2010. Available online at IEEE Explore.

51-10 Regina Bleul, Marion Ritzi-Lehnert, Julian Höth, Nico Scharpfenecker, Ines Frese, Dominik Düchs, Sabine Brunklaus, Thomas E. Hansen-Hagge, Franz-Josef Meyer-Almes, Klaus S. Drese, Compact, cost-efficient microfluidics-based stopped-flow device, Anal Bioanal Chem, DOI 10.1007/s00216-010-4446-5, Available online at Springer, November 2010

22-10 Krishendu Chakrabarty, Richard B. Fair and Jun Zeng, Design Tools for Digital Microfluidic Biochips Toward Functional Diversification and More than Moore, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 29, No. 7, July 2010

14-10 E. P. Furlani and M. S. Hanchak, Nonlinear analysis of the deformation and breakup of viscous microjets using the method of lines, International Journal for Numerical Methods in Fluids (2010), © 2010 John Wiley & Sons, Ltd., Published online in Wiley InterScience. DOI: 10.1002/fld.2205

55-09 R.A. Sultanov, and D. Guster, Computer simulations of pulsatile human blood flow through 3D models of the human aortic arch, vessels of simple geometry and a bifurcated artery, Proceedings of the 31st Annual International Conference of the IEEE EMBS (Engineering in Medicine and Biology Society), Minneapolis, September 2-6, 2009, p.p. 4704-4710.

30-09 Anurag Chandorkar and Shayan Palit, Simulation of Droplet Dynamics and Mixing in Microfluidic Devices using a VOF-Based Method, Sensors & Transducers journal, ISSN 1726-5479 © 2009 by IFSA, Vol.7, Special Issue “MEMS: From Micro Devices to Wireless Systems,” October 2009, pp. 136-149.

13-09 E.P. Furlani, M.C. Carter, Analysis of an Electrostatically Actuated MEMS Drop Ejector, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

12-09 A. Chandorkar, S. Palit, Simulation of Droplet-Based Microfluidics Devices Using a Volume-of-Fluid Approach, Presented at Nanotech Conference & Expo 2009, Houston, Texas, USA, May 3-7, 2009

3-09 Christopher N. Delametter, FLOW-3D Speeds MEMS Inkjet Development, Desktop Engineering, January 2009

42-08 Tien-Li Chang, Jung-Chang Wang, Chun-Chi Chen, Ya-Wei Lee, Ta-Hsin Chou, A non-fluorine mold release agent for Ni stamp in nanoimprint process, Microelectronic Engineering 85 (2008) 1608–1612

26-08 Pamela J. Waterman, First-Pass CFD Analyses – Part 2, Desktop Engineering, November 2008

09-08 M. Ren and H. Wijshoff, Thermal effect on the penetration of an ink droplet onto a porous medium, Proc. Eurotherm2008 MNH, 1 (2008)

04-08 Delametter, Christopher N., MEMS development in less than half the time, Small Times, Online Edition, May 2008

02-08 Renat A. Sultanov, Dennis Guster, Brent Engelbrekt and Richard Blankenbecler, 3D Computer Simulations of Pulsatile Human Blood Flows in Vessels and in the Aortic Arch – Investigation of Non-Newtonian Characteristics of Human Blood, The Journal of Computational Physics, arXiv:0802.2362v1 [physics.comp-ph], February 2008

01-08 Herman Wijshoff, thesis: University of Twente, Structure- and fluid dynamics in piezo inkjet printheads, ISBN 978-90-365-2582-4, Venlo, The Netherlands January 2008.

30-07 A. K. Sen, J. Darabi, and D. R. Knapp, Simulation and parametric study of a novel multi-spray emitter for ESI–MS applications, Microfluidics and Nanofluidics, Volume 3, Number 3, June 2007, pp. 283-298(16)

28-07 Dan Soltman and Vivek Subramanian, Inkjet-Printed Line Morphologies and Temperature Control of the Coffee Ring Effect, Langmuir; 2008; ASAP Web Release Date: 16-Jan-2008; (Research Article) DOI: 10.1021/la7026847

23-07 A K Sen and J Darabi, Droplet ejection performance of a monolithic thermal inkjet print head, Journal of Micromechanical and Microengineering,vol.17, pp.1420-1427 (2007) doi:10.1088/0960-1317/17/8/002; Abstract only.

18-07 Herman Wisjhoff, Better Printheads Via Simulation, Desktop Engineering, October 2007, Vol. 13, Issue 2

17-07 Jos de Jong, Ph.D. Thesis: University of Twente, Air entrapment in piezo inkjet printing, ISBN 978-90-365-2483-4, April 2007

15-07 Krishnendu Chakrabarty and Jun Zeng, (Ed.), Design Automation Methods and Tools for Microfluidics-Based Biochips, Springer, September 2006.

14-07 Fei Su and Jun Zeng, Computer-aided design and test for digital microfluidics, IEEE Design & Test of Computers, 24(1), 2007, 60-70.

13-07 Jun Zeng, Modeling and simulation of electrified droplets and its application to computer-aided design of digital microfluidics, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(2), 2006, 224-233.

12-07 Krishnendu Chakrabarty and Jun Zeng, (2005), Automated top-down design for microfluidic biochips, ACM Journal on Emerging Technologies in Computing Systems, 1(3), 2005, 186–223.

01-07 Wijshoff, Herman, Drop formation mechanisms in piezo-acoustic inkjet, NSTI-Nanotech 2007, ISBN 1420061844 Vol. 3, 2007)

23-06 John J. Uebbing, Stephan Hengstler, Dale Schroeder, Shalini Venkatesh, and Rick Haven, Heat and Fluid Flow in an Optical Switch Bubble, Journal of Microelectromechanical Systems, Vol. 15, No. 6, December 2006

21-06 Wijshoff, Herman, Manipulating Drop Formation in Piezo Acoustic Inkjet, Proc. IS&T’s NIP22, 79 (2006)

20-06 J. de Jong, H. Reinten, M. van den Berg, H. Wijshoff, M. Versluis, G. de Bruin, A. Prosperetti and D. Lohse, Air entrapment in piezo-driven inkjet printheads, J. Acoust. Soc. Am. 120(3), 1257 (2006)

11-06 A. K. Sen, J. Darabi, D. R. Knapp and J. Liu, Modeling and Characterization of a Carbon Fiber Emitter for Electrospray Ionization, 1 MEMS and Microsystems Laboratory, Department of Mechanical Engineering, University of South Carolina, 300 Main Street, Columbia, SC 29208, USA, 2 Department of Pharmacology, Medical University of South Carolina, Charleston, SC

5-06 E. P. Furlani, B. G. Price, G. Hawkins, and A. G. Lopez, Thermally Induced Marangoni Instability of Liquid Microjets with Application to Continuous Inkjet Printing, Proceedings of NSTI Nanotech Conference 2006, Vol. 2, pp 534-537.

28-05 O B Fawehinmi, P H Gaskell, P K Jimack, N Kapur, and H M Thompson, A combined experimental and computational fluid dynamics analysis of the dynamics of drop formation, May 2005. DOI: 10.1243/095440605X31788

5-05 E. P. Furlani, Thermal Modulation and Instability of Newtonian Liquid Microjets, presented at Nanotech 2005, Anaheim, CA, May 8-12, 2005.

1-05 C.W. Hirt, Electro-Hydrodynamics of Semi-Conductive Fluids: With Application to Electro-Spraying, Flow Science Technical Note #70, FSI-05-TN70

19-04 G. F. Yao, Modeling of Electroosmosis Without Resolving Physics Inside a Electric Double Layer, Flow Science Technical Note (FSI-04-TN69)

12-04 Jun Zeng and Tom Korsmeyer, Principles of Droplet Electrohydrodynamics for Lab-on-a-Chip, Lab. Chip. Journal, 2004, 4(4), 265-277

9-04 Constantine N. Anagnostopoulos, James M. Chwalek, Christopher N. Delametter, Gilbert A. Hawkins, David L. Jeanmaire, John A. Lebens, Ali Lopez, and David P. Trauernicht, Micro-Jet Nozzle Array for Precise Droplet Metering and Steering Having Increased Droplet Deflection, Proceedings of the 12th International Conference on Solid State Sensors, Actuators and Microsystems, sponsored by IEEE, Boston, June 8-12, 2003, pp. 368-71

8-04 Christopher N. Delametter, David P. Trauernicht, James M. Chwalek, Novel Microfluidic Jet Deflection – Significant Modeling Challenge with Great Application Potential, Technical Proceedings of the 2002 International Conference on Modeling and Simulation of Microsystems sponsored by NSTI, San Juan, Puerto Rico, April 21-25, 2002, pp. 44-47

6-04 D. Vadillo*, G. Desie**, A Soucemarianadin*, Spreading Behavior of Single and Multiple Drops, *Laboratoire des Ecoulements Geophysiques et Industriels (LEGI), and **AGFA-Gevaert Group N.V., XXI ICTAM, 15-21 August 2004, Warsaw, Poland

2-04 Herman Wijshoff, Free Surface Flow and Acousto-Elastic Interaction in Piezo Inkjet, Nanotech 2004, sponsored by the Nano Science & Technology Institute, Boston, MA, March 2004

30-03 D Souders, I Khan and GF Yao, Alessandro Incognito, and Matteo Corrado, A Numerical Model for Simulation of Combined Electroosmotic and Pressure Driven Flow in Microdevices, 7th International Symposium on Fluid Control, Measurement and Visualization

27-03 Jun Zeng, Daniel Sobek and Tom Korsmeyer, Electro-Hydrodynamic Modeling of Electrospray Ionization – CAD for a µFluidic Device-Mass Spectrometer Interface, Agilent Technologies Inc, paper presented at Transducers 2003, June 03 Boston (note: Reference #10 is to FLOW-3D)

17-03 John Uebbing, Switching Fiber-optic Circuits with Microscopic Bubbles, Sensors Magazine, May 2003, Vol 20, No 5, p 36-42

16-03 CFD Speeds Development of MEMS-based Printing Technology, MicroNano Magazine, June 2003, Vol 8, No 6, p 16

3-03 Simulation Speeds Design of Microfluidic Medical Devices, R&D Magazine, March 2003, pp 18-19

1-03 Simulations Help Microscopic Bubbles Switch Fiber-Optic Circuits, Agilent Technologies, Fiberoptic Product News, January 2003, pp 22-23

27-02 Feng, James Q., A General Fluid Dynamic Analysis of Drop Ejection in Drop-on-Demand Ink Jet Devices, Journal of Imaging Science and Technology®, Volume 46, Number 5, September/October 2002

1-02 Feixia Pan, Joel Kubby, and Jingkuang Chen, Numerical Simulation of Fluid Structure Interaction in a MEMS Diaphragm Drop Ejector, Xerox Wilson Research Center, Institute of Physics Publishing, Journal of Micromechanics and Microengineering, 12 (2002), PII: SO960-1317(02)27439-2, pp. 70-76

48-01 Rainer Gruber, Radial Mass Transfer Enhancement in Bubble-Train Flow, PhD thesis in Engineering Sciences, Rheinisch- Westf alischen Technische Hochschule Aachen, December 2001.

34-01 Furlani, E.P., Delametter, C.N., Chwalek, J.M., and Trauernicht, D., Surface Tension Induced Instability of Viscous Liquid Jets, Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

12-01 C. N. Delametter, Eastman Kodak Company, Micro Resolution, Mechanical Engineering, Col 123/No 7, July 2001, pp 70-72

11-01 C. N. Delametter, Eastman Kodak Company, Surface Tension Induced Instability of Viscous Liquid Jets, Technical Proceeding of the Fourth International Conference on Modeling and Simulation of Microsystems, April 2001

9-01 Aman Khan, Unipath Limited Research and Development, Effects of Reynolds Number on Surface Rolling in Small Drops, PVP-Col 431, Emerging Technologies for Fluids, Structures and Fluids, Structures and Fluid Structure Interaction — 2001

2-00 Narayan V. Deshpande, Significance of Inertance and Resistance in Fluidics of Thermal Ink-Jet Transducers, Journal of Imaging Science and Technology, Volume 40, Number 5, Sept./Oct. 1996, pp.457-461

4-98 D. Deitz, Connecting the Dots with CFD, Mechanical Engineering Magazine, pp. 90-91, March 1998

14-94 M. P. O’Hare, N. V. Deshpande, and D. J. Drake, Drop Generation Processes in TIJ Printheads, Xerox Corporation, Adv. Imaging Business Unit, IS&T’s Tenth International Congress on Advances in Non-Impact Printing, Tech. 1994

14-92 Asai, A.,Three-Dimensional Calculation of Bubble Growth and Drop Ejection in a Bubble Jet Printer, Journal of Fluids Engineering Vol. 114 December 1992:638-641

Automotive Bibliography

다음은 자동차 관련 기술 문서 모음입니다.
이 모든 논문은 FLOW-3D 결과를 포함하고 있습니다. FLOW-3D 를 사용하여 자동차 산업의 어플리케이션을 성공적으로 시뮬레이션 하는 방법에 대해 자세히 알아보십시오.

Below is a collection of technical papers in our Automotive Bibliography. All of these papers feature FLOW-3D results. Learn more about how FLOW-3D can be used to successfully simulate applications for the Automotive Industry.

2024년 8월 12일 Update

159-22 Shihao Li, Yan Yan, Wei Wei, Zhao Wang, Zhonghua Ni, Numerical simulation on the thermal dynamic behavior of liquid hydrogen in a storage tank for trailers, Case Studies in Thermal Engineering, 40; 102520, 2022. doi.org/10.1016/j.csite.2022.102520

53-22 Ilias Papadimitriou, Michael Just, Manufacturing of structural components for internal combustion engine, electric motor and battery using casting and 3D printing, Advances in Engine and Powertrain Research and Technology, Mechanisms and Machine Science 114, Ed. Tigran Parikyan, 2022. doi.org/10.1007/978-3-030-91869-9_15

33-21 Xiaozhou Hu Ao Wang Pingping Li Jianing Wang, Influence of dynamic attitudes on oil supply for bearings and churning power losses in a splash lubricated spiral bevel gearbox, Tribology International, 159; 106951, 2021. doi.org/10.1016/j.triboint.2021.106951

64-18 Vasilios Fourlakidis, Ilia Belov and Attila Diószegi, Strength prediction for pearlitic lamellar graphite iron: Model validation, Metals, Vol. 8, No. 9, 2018. doi.org/10.3390/met8090684

43-18 R.A. Ibrahim and B. Singh, Assessment of ground vehicle tankers interacting with liquid sloshing dynamics, International Journal of Heavy Vehicle Systems, vol. 25, no. 1, 2018. doi.org/10.1504/IJHVS.2018.089894

34-17 Hidenori Arisawa, Yuji Shinoda, Mitsuaki Tanaka, Tatsuhiko Goi, Hirofumi Akahori and Mamoru Yoshitomi, Classification of Fluid Dynamic Loss in Aeroengine Transmission Gears – Experimental Analysis and CFD Validation, Proceedings of ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, GT2017, June 26-30, 2017, Charlotte, NC, USA.

26-15 Herbert Obame Mve, Romuald Rullière and Philippe Haberschill, Modeling and Parametric Study of the Heat Transfer Inside a Lithium Bromide Flow Confined by Wire Screen, Journal of Energy and Power Engineering 9 (2015) 417-425, doi: 10.17265/1934-8975/2015.05.001

17-15 Peyman Jafarian, Gear interlocking effect study using CFD, published online April 2015.

23-14 Daiki Saegusa and Shinji Kawai, CFD Analysis of Lubricant Fluid Flow in Automotive Transmission, SAE Technical Paper 2014-01-1772, 2014, doi:10.4271/2014-01-1772, Copyright © 2014 SAE International.

15-14 Hidenori Arisawa, Motohiko Nishimura, Hideyuki Imai and Tatsuhiko Goi, CFD Simulations and Experiments for Reduction of Oil Churning Loss and Windage Loss in Aeroengine Transmission Gears, Journal of Engineering for Gas Turbines and Power, ASME, doi:10.1115/1.4026952, 2014.

87-13 Daiki Saegusa and Shinji Kawai, Technique for Prediction of Automotive Transmission Lubrication Performance, Honda R&D Technical Review, October 2013.

26-13 Hidenori Arisawa, Motohiko Nishimura, Hideyuki Imai, Kenichiro Tanaka, and Tatsuhiko Goi, CFD Simulations and Experiments for Reduction of Oil Churning Loss and Windage Loss on Aeroengine Transmission Gears, No. 2012-JCT-0705, © 2013 The Japan Society of Mechanical Engineers. In Japanese.

53-09 Hidenori Arisawa, Motohiko Nishimura, Hideyuki Imai and Tatsuhiko Goi, CFD Simulation for Reduction of Oil Churning Loss and Windage Loss on Aeroengine Transmission Gears, ASME Turbo Expo 2009: Power for Land, Sea and Air, Orlando, Florida, USA, June 8-12, 2009.

45-09 Chih-Chung Chang, Sy-Chi Kuo, Chen-Kang Huang, and Sih-Li Chen, The Investigation of Motor Cooling Performance, International Journal of Mechanical, Industrial and Aerospace Engineering, 3:1, 2009.

37-04 U, H., Cleghorn, W. and Mills, J., Design and Analysis of Fuel Tank Baffles to Reduce the Noise Generated From Fuel Sloshing, SAE Technical Paper 2004-01-0403, 2004, doi:10.4271/2004-01-0403.

38-03 Hidenori Arisawa, Katsuya Umemoto, Atsushi Ueshima and Yuichi Kawamoto, CFD Simulation of the Lubricating Oil Flow in Motorcycle Oilpan, 2003 SAE/JSAE Small Engine Technology, Conference & Exhibition, Madison, Wisconsin, USA, September 15-18, 2003.

제품 소개 요청

FLOW-3D 소개 요청

산업 분야별 해석 사례

FLOW-3D 를 이용한 각각의 산업분야 적용 가능성을 살펴보십시오.
경험이 풍부한 당사 FLOW-3D Engineer가 귀하의 궁금하신 사항에 대해 언제든지 답변해 드립니다.

주조분야
Gravity Pour 중력 주조 High Pressure Die Casting 고압 다이캐스팅 Tilt Casting 경동 주조 Centrifugal Casting 원심 주조 Investment Casting 정밀 주조 Vacuum Casting 진공 주조 Continuous Casting 연속 주조 Lost Foam Casting 소실 모형 주조 Fill and Defects Tracking 용탕 주입 및 결함 추적 Solidification and Shrinkage 응고 및 수축 해석 Thermal Stress Evolution and Deformation 열응력 및 변형 해석
물 및 환경 응용 분야
Wastewater Treatment and Recovery 폐수 처리 및 복구 Pump Stations 펌프장 Dams, Weirs, Spillways 댐, 위어, 여수로 River Hydraulics 강 유역 Inundation & Flooding 침수 및 범람 Open Channel Flow 개수로 흐름 Sediment and Scour 퇴적 및 세굴(쇄굴) Plumes, Hydraulic Zones of Influence 기둥, 수리 영향 구역 Coastal and Critical Infrastructure Wave Run-Up 연안 및 핵심 인프라 웨이브 런업
에너지 분야
Fuel/cargo sloshing in oceangoing containers 해양 컨테이너 용 연료 /화물 슬로싱 Offshore platform wave effects 근해 플랫폼 파 영향 Separation devices undergoing 6 DOF motion 6 자유도 운동을하는 분리 장치 Wave energy converters 파동 에너지 변환기
미세유체
Continuous-Flow 연속 흐름 Droplet, Digital 물방울, 디지털 Molecular Biology 분자 생물학 Opto-Microfluidics 광 마이크로 유체 Cell Behavior 세포 행동 Fuel Cells 연료 전지들
용접 제조
Laser Welding 레이저 용접 Laser Metal Deposition 레이저 금속 증착 Additive Manufacturing 첨가제 제조 Multi-Layer Build 다중 레이어 빌드 Polymer 3D Printing 폴리머 3D 프린팅
코팅 분야
Curtain Coating 커튼 코팅 Dip Coating 딥 코팅 Gravure Printing 그라비아 코팅 Roll Coating 롤 코팅 Slide Coating 슬라이드 코팅 Slot Coating 슬롯 코팅 Contact Insights 접촉면 분석
연안 / 해양분야
Breakwater Structures 방파제 구조물 Offshore Structures 항만 연안 구조물 Ship Hydrodynamics 선박 유체 역학 Sloshing & Slamming 슬로싱 & 슬래 밍 Tsunamis 쓰나미 해석
생명공학 분야
Active Mixing 액티브 믹싱 Chemical Reactions 화학 반응 Dissolution 용해 Drug Delivery 약물 전달 Drug Particles 마약 입자 Microdispensers 마이크로 디스펜서 Passive Mixing 패시브 믹싱 Piezo Driven Pumps 피에조 구동 펌프
자동차 분야
Fuel Tanks 연료 탱크 Early Fuel Shut-Off 초기 연료 차단 Gear Interaction 기어 상호 작용 Filters 필터 Degas Bottles 병의 가스제거	Fuel Tank Simulation
우주 항공 분야
Sloshing Dynamics 슬로싱 동역학 Electric Charge Distribution 전기 충전 배분 PMDs PMD	aerospace-sloshing-simulation


Casting 분야 적용사례 다운로드	Hydraulics 분야 적용사례 다운로드	WaterTreatments 분야 적용사례 다운로드	MEMS 분야 적용사례 다운로드

Maritime 분야 적용사례 다운로드	Laser Welding 분야 적용사례 다운로드	Metal 3D printing 분야 적용사례 다운로드

밀도	고상율

Tag: 3D Printing

바인더 제트 3D 프린팅 중 계면 유체-입자 상호 작용에 대한 CFD-DEM 결합 시뮬레이션

Abstract

Introduction

Section snippets

Mathematical description of interfacial fluid–particle interaction

CFD solver for incompressible flow with multifluid interfaces

DEM solver for solid particle dynamics

Unified CFD-DEM solver

Binder jet process modeling and validation experiments

Conclusions

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgments

References (155)

적층 가공된 니켈 기반 초합금의 가공 최적화를 안내하는 통합 멀티스케일 시뮬레이션

ABSTRACT

Abstract

References

Abstract

Graphical abstract

Keywords

1. Introduction

2. Methods

2.1. Laser-beam irradiation experiments

2.2. CtFD simulation

2.3. MPF simulation

3. Results

3.1. Experimental observation of melt pool

3.2. CtFD simulation

3.3. MPF simulations coupled with CtFD simulation

3.4. Remelting and resolidification simulation

4. Discussion

4.1. Effect of segregation of solute elements on liquation cracking susceptibility

4.2. Applicability of the conventional MPF simulation to microstructure formation under LPBF

5. Conclusions

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

Appendix A. Supplementary material

Data availability

References

다중물리 메조 규모 시뮬레이션을 통해 레이저 분말층 융합에서 공정 종료의 함몰 형성 메커니즘 공개

ABSTRACT

References

I. INTRODUCTION

II. MODELING

A. 3D powder bed modeling

1. DEM

2. Model building

B. Modeling of fluid mechanics simulation

1. VOF

2. Control equations and boundary conditions

3. Assumptions

4. Initial conditions

5. Material parameters

III. RESULTS AND DISCUSSION

A. Single-track simulation

B. Double-track simulation

C. Simulation analysis of molten pool under different process parameters

1. Laser power

2. Scanning speed

3. Hatch spacing

D. Simulation analysis of molten pool with powder particle size and laser focal spot diameter

IV. CONCLUSIONS

REFERENCES

용융 풀 구조, 선택적 온도 및 속도 흐름 레이저 용융 AlCu5MnCdVA 합금

Keywords

Abstract

References

CMSX -4 단결정 초합금의 레이저 용융에서 등축 응고와 에피택셜 응고 정량화

Abstract

Introduction

재료 및 방법

단일 트랙 실험

성격 묘사

응고 모델링

결과 및 논의

용융 풀 형태

레이저 흡수율 평가

세포가 함유된 마이크로겔의 온칩 제작 및 인-플로우 3D 프린팅
구성:하나의 통합 프로세스에서 칩에서 스캐폴드 재료까지