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.

这项工作描述了一个动态动力学蒙特卡洛数值建模框架，该框架可以预测粉末床熔合（PBF）和定向能量沉积（DED）增材制造（AM）期间的金属微观结构，同时考虑到热历史和热量积累的显着变化。构建。尽管已经建立了常规的动力学蒙特卡洛（KMC）方法，但是它不能适应熔池（MP）和热影响区（HAZ）的空间域随时间的变化。因此，即使在较大的AM构建域上，预测的微观结构也保持相对相似。尽管现有的KMC方法可以满足MP和HAZ相对保持不变的空间区域的要求，这种情况与实验人员最近在对PBF和DED AM的不同区域成像时发现的结果大相径庭，从而引发了该方法的可扩展性和多功能性问题。在这项工作中提出的动态KMC框架通过在晶粒结构预测期间的每个时间增量实施离散化的，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）模拟或通过实验性3D热成像来建立3D空间MP和HAZ尺寸；第二阶段随后在构建期间的每个时间增量处将这些随时间变化的MP和HAZ维度集成到KMC算法中。从而，动态KMC框架捕获了快速热循环和热量积累对晶粒成核和生长的影响。通过案例研究证明了该方法，该案例涉及通过基于激光的粉末床熔融（PBF-LB）的选择性激光熔化（SLM）类型制成的Inconel 625薄壁结构。在建筑物内各个区域和扫描层的数值预测微观结构与文献报道的实验观察到的趋势非常吻合。根据特定的热历史，通过动态KMC框架预测的晶粒形态的显着变化可以为研究人员提供评估AM零件不同区域的机械性能变化的新功能。通过案例研究证明了该方法，该案例涉及通过基于激光的粉末床熔融（PBF-LB）的选择性激光熔化（SLM）类型制成的Inconel 625薄壁结构。在建筑物内各个区域和扫描层的数值预测微观结构与文献报道的实验观察到的趋势非常吻合。根据特定的热历史，通过动态KMC框架预测的晶粒形态的显着变化可以为研究人员提供评估AM零件不同区域的机械性能变化的新功能。通过案例研究证明了该方法，该案例涉及通过基于激光的粉末床熔融（PBF-LB）的选择性激光熔化（SLM）类型制成的Inconel 625薄壁结构。在建筑物内各个区域和扫描层的数值预测微观结构与文献报道的实验观察到的趋势非常吻合。根据特定的热历史，通过动态KMC框架预测的晶粒形态的显着变化可以为研究人员提供评估AM零件不同区域的机械性能变化的新功能。在建筑物内各个区域和扫描层的数值预测微观结构与文献报道的实验观察到的趋势非常吻合。根据特定的热历史，通过动态KMC框架预测的晶粒形态的显着变化可以为研究人员提供评估AM零件不同区域的机械性能变化的新功能。在建筑物内各个区域和扫描层的数值预测微观结构与文献报道的实验观察到的趋势非常吻合。根据特定的热历史，通过动态KMC框架预测的晶粒形态的显着变化可以为研究人员提供评估AM零件不同区域的机械性能变化的新功能。