Meso-scale modeling of multiple-layer fabrication process in Selective Electron Beam Melting: Inter-layer/track voids formation

Yan, Wentao; Qian, Ya; Ge, Wenjun; Lin, Stephen; Liu, Wing Kam; Feng, Lin; Wagner, Gregory J.

doi:10.1016/j.matdes.2017.12.031

Cited by 174 publications

(54 citation statements)

References 27 publications

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“…A study has shown that as metal powders reach their melting and solidification temperatures by heating or cooling during SLM, phase change (i.e., melting) occurs [3,4]. However, the physical and chemical metallurgical processes in SLM are complex and involve heat transfer, heat convection, and radiation [5,6,7,8]. Due to the particularity of SLM, it is difficult to observe the thermal behavior, phase transitions, and melt-pool behavior directly because they strongly depend on the laser process parameters, such as the hatch spacing, scanning speed, and laser power etc.…”

Section: Introductionmentioning

confidence: 99%

Effect of Hatch Spacing on Melt Pool and As-built Quality During Selective Laser Melting of Stainless Steel: Modeling and Experimental Approaches

et al. 2018

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In this study, a combined simulation and experimental approach is utilized to investigate the influence of hatch spacing on the microstructure and as-built quality of 316L stainless steel (SS) samples fabricated by selective laser melting (SLM). A three-dimensional finite element model (FEM) is employed to investigate heat transfer and melt pool during the SLM of 316L SS. The phase transformation and variation of the thermo-physical properties of the materials are considered in this model. The effects of hatch spacing (H) on the temperature field, microstructure and melt pool size, overlap rate, surface quality, and relative density during the SLM of 316L SS are investigated. The simulated results indicate that, as the hatch spacing increases, the depth increases and the width of the melt pool decreases. Meanwhile, with the increase of hatch spacing, the simulated temperature of the subsequent tracks falls below the melting temperature of the first track. Moreover, the microstructures were found to coarsen with the increasing hatch spacing due to the reduced cooling rate. The optimized hatch spacing and overlap rate between adjacent tracks were obtained from numerical simulations. Simulation results illustrate that, when the optimized hatch spacing of 100 μm is adopted, fully dense parts with a smooth surface can be fabricated by SLM, thus experimentally validating the simulation results.

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Section: Introductionmentioning

confidence: 99%

Effect of Hatch Spacing on Melt Pool and As-built Quality During Selective Laser Melting of Stainless Steel: Modeling and Experimental Approaches

et al. 2018

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“…图5 (网络版彩图)基于离散元方法的铺粉过程模型模拟重力、摩擦、碰撞等作用下的铺粉过程, 基于FVM法的高保真热分析模拟粉末的加热、熔化、流动、凝固等传质传热过程, 两个模型间交互传递基于STL格式的三维几何数据 [38] (图片来自文献, 已获得授权) Figure 5 (Color online) The powder spreading process model based on the discrete element method simulates the powder spreading process subject to gravity, friction, collision, etc., and the high-fidelity thermal analysis based on the finite volume method for the heat transfer and mass transfer process of the powder heating, melting, flow, solidification, etc. The 3D geometry data transferred between two models is based on the STL format [38]. Reprinted with permission from ref.…”

Section: 热源模型mentioning

confidence: 99%

Numerical simulation on metallic additive manufacturing

Chen

Xiong

Huang

et al. 2020

Sci. Sin.-Phys. Mech. Astron.

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“…We developed a comprehensive modeling framework to model the EBSM process at the meso-scale, from spreading powder layers to selectively melting powder along the designated scan path [18]. It consists of a powder spreading model using the discrete element method (DEM) and a hydrodynamic model using the finite volume method Fig.…”

Section: Meso-scale Modelingmentioning

confidence: 99%

Modeling process-structure-property relationships for additive manufacturing

et al. 2018

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This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the processstructure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a highefficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.

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Meso-scale modeling of multiple-layer fabrication process in Selective Electron Beam Melting: Inter-layer/track voids formation

Cited by 174 publications

References 27 publications

Effect of Hatch Spacing on Melt Pool and As-built Quality During Selective Laser Melting of Stainless Steel: Modeling and Experimental Approaches

Effect of Hatch Spacing on Melt Pool and As-built Quality During Selective Laser Melting of Stainless Steel: Modeling and Experimental Approaches

Numerical simulation on metallic additive manufacturing

Modeling process-structure-property relationships for additive manufacturing

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