Improved grain structure prediction in metal additive manufacturing using a Dynamic Kinetic Monte Carlo framework
Improved grain structure prediction in metal additive manufacturing using a Dynamic Kinetic Monte Carlo framework
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DOI：
10.1016/j.addma.2020.101649
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发表时间：
2020-10
影响因子：
11
通讯作者：
Sumair Sunny;Haoliang Yu;Ritin Mathews;A. Malik;Wei Li
中科院分区：
文献类型：
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作者：
Sumair Sunny;Haoliang Yu;Ritin Mathews;A. Malik;Wei Li
本工作描述了一种动态动力学蒙特卡罗数值模拟框架，该框架可以预测粉末床熔化(PBF)和定向能量沉积(DED)添加剂制造(AM)过程中的金属微观结构，同时考虑到构建过程中发生的热历史和热积累的显著变化。尽管传统的动力学蒙特卡罗(KMC)方法是成熟的，但它不能适应熔池(MP)和热影响区(HAZ)的空间域随时间的变化。因此，即使在大的AM构建域上，预测的微观结构也保持相对相似。虽然现有的KMC方法可能在MP和HAZ保持相对不变的空间区域上足够，但这种情况在很大程度上与实验者最近在PBF和DED AM构建中成像不同区域时发现的情况相反，从而引发了该方法的可扩展性和通用性的问题。本文提出的动态KMC框架通过在晶粒结构预测过程中的每个时间增量实现离散的、空间变化的MP和HAZ来解决这些问题。新框架分两个阶段运行；第一阶段使用热有限元(FE)模拟或通过实验3D热成像建立3D空间MP和HAZ维度；第二阶段随后在构建过程中的每个时间增量将这些随时间变化的MP和HAZ维度整合到KMC算法中。因此，动态的KMC框架捕捉到了快速热循环和热积累对颗粒形核和生长的影响。通过对采用选择性激光熔化(SLM)型激光粉床熔化(PBF-LB)制造的Inconel625薄壁结构的案例研究，验证了该方法的有效性。数值预测的不同区域和扫描层的微结构与文献中报道的实验观察的趋势显示出很强的一致性。根据特定的热历史，动态KMC框架预测的颗粒形态的显著变化可以为研究人员提供新的能力，以评估AM部件不同区域的机械性能变化。
This work describes a Dynamic Kinetic Monte Carlo numerical modeling framework that can predict the microstructure of metals during powder bed fusion (PBF) and directed energy deposition (DED) additive manufacturing (AM) while considering significant variations in thermal history and heat accumulation that occur during the build. Although the conventional Kinetic Monte Carlo (KMC) method is well-established, it does not accommodate variation in the spatial domains of the melt pool (MP) and heat affected zone (HAZ) with time. Thus, the predicted microstructure remains relatively similar even over large AM build domains. While the existing KMC approach may suffice over spatial regions in which the MP and HAZ remain relatively unchanged, this circumstance is largely contrary to what experimentalists have recently found when imaging different regions in PBF and DED AM builds, thus raising issues with scalability and versatility of the method. The Dynamic KMC framework proposed in this work addresses these concerns by implementing discretized, spatially-varying MP and HAZ at every time increment during the grain structure prediction. The new framework operates in two stages; stage one establishes the 3D spatial MP and HAZ dimensions using either thermal finite element (FE) simulation or through experimental 3D thermal imaging; stage two subsequently integrates these time-varying MP and HAZ dimensions into the KMC algorithm at every time increment during the build. Thus, the Dynamic KMC framework captures the effects that rapid thermal cycles and heat accumulation have on grain nucleation and growth. The method is demonstrated through a case study involving a thin-walled Inconel 625 structure made by the selective laser melting (SLM) type of laser-based powder bed fusion (PBF-LB). The numerically predicted microstructures at various regions and scan layers within the build show strong agreement with experimentally observed trends reported in literature. Significant variations in grain morphology predicted by the Dynamic KMC framework can, according to specific thermal histories, provide investigators with new capabilities in assessing mechanical property variations across different regions of AM parts.