选择性激光熔化316L钢晶粒形貌和织构的数值分析

O. Zinovieva, A. Zinoviev
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引用次数: 3

摘要

为了定制增材制造零件的力学性能,我们首先需要了解和预测增材制造的微观组织。本文旨在深入分析基于粉末床增材制造(AM)的316L奥氏体不锈钢的三维(3D)晶粒组织和织构。为此,我们采用了一种结合有限差分(FD)热模型和修正元胞自动机(CA)方法的三维框架来进行颗粒结构预测。首先对316L钢的选择性激光熔化实验结果进行了三维CAFD模型的验证。采用双向扫描策略制备的试样显示出强烈的{110}< 001 >高斯织构。通过至少一次前后扫描对块状材料进行多次重熔,应该会导致大柱状颗粒倾向于与构建方向对齐。织构强度随织构高度的增加呈指数增长趋势,横向和扫描方向的晶粒尺寸也呈指数增长趋势。为了定制增材制造零件的力学性能,我们首先需要了解和预测增材制造的微观组织。本文旨在深入分析基于粉末床增材制造(AM)的316L奥氏体不锈钢的三维(3D)晶粒组织和织构。为此,我们采用了一种结合有限差分(FD)热模型和修正元胞自动机(CA)方法的三维框架来进行颗粒结构预测。首先对316L钢的选择性激光熔化实验结果进行了三维CAFD模型的验证。采用双向扫描策略制备的试样显示出强烈的{110}< 001 >高斯织构。通过至少一次前后扫描对块状材料进行多次重熔,应该会导致大柱状颗粒倾向于与构建方向对齐。织构强度随着织构的增加而增加。
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Numerical analysis of the grain morphology and texture in 316L steel produced by selective laser melting
For tailoring the mechanical properties of additively manufactured parts, we first need to understand and predict additively manufactured microstructures. The present paper aims at in-depth analysis of a three-dimensional (3D) grain structure and texture of 316L austenitic stainless steel produced by powder bed based additive manufacturing (AM). For this purpose, we adopt a 3D framework combining the finite difference (FD) thermal model and modified cellular automata (CA) approach for grain structure prediction. The 3D CAFD model is first validated against the experimental findings on 316L steel produced by selective laser melting. The specimen manufactured by applying a bi-directional scanning strategy is shown to exhibit a strong {110}〈001〉 Goss texture. Multiple re-melting of the bulk material with scanning backward and forward at least once is supposed to result in a tendency of large columnar grains to align with the build direction. The texture strength is shown to increase with the increasing build height, following the exponential trend, as well as the grain size in the transverse and scanning directions.For tailoring the mechanical properties of additively manufactured parts, we first need to understand and predict additively manufactured microstructures. The present paper aims at in-depth analysis of a three-dimensional (3D) grain structure and texture of 316L austenitic stainless steel produced by powder bed based additive manufacturing (AM). For this purpose, we adopt a 3D framework combining the finite difference (FD) thermal model and modified cellular automata (CA) approach for grain structure prediction. The 3D CAFD model is first validated against the experimental findings on 316L steel produced by selective laser melting. The specimen manufactured by applying a bi-directional scanning strategy is shown to exhibit a strong {110}〈001〉 Goss texture. Multiple re-melting of the bulk material with scanning backward and forward at least once is supposed to result in a tendency of large columnar grains to align with the build direction. The texture strength is shown to increase with the increasing build...
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