An artificial intelligence based approach to account for the effects of microstructure gradients and residual stresses on fatigue performance of additively manufactured aluminum

该联盟项目专注于开发一种新的数值框架，将基于晶体塑性的有限元方法（CPFEM）与基于人工智能（AI）的机器学习（ML）方法结合起来，研究微观结构梯度和残余应力对增材制造（AM）部件疲劳性能的影响。增材制造零件的一个主要问题是激光粉末床熔合（LPBF）加工零件中经常出现的过量和各向异性残余应力。这些材料的机械性能，如疲劳寿命，很大程度上受残余应力的影响，残余应力通常与材料的屈服强度一样高。此外，这些应力的形成是由于局部微观组织的影响，如晶粒形态、织构和滑移行为的局部差异，并且由于固有的复杂和异质的微观组织而引起增材制造合金的关注。由于CPFEM可以结合宏观和微观水平的残余应力、微观组织属性（即晶粒形态、微观组织梯度、织构），并且可以在滑移系统水平上模拟诱导塑性变形的积累，因此它是当前微观结构敏感应用的理想候选者。然而，CPFEM建模的计算成本很高，并且由于疲劳加载的重复性和耗时性，将其应用于疲劳寿命预测进一步使问题复杂化。该项目结合了晶体塑性和人工智能的最新进展，使研究微观结构梯度和残余应力对增材制造铝合金疲劳性能的影响成为可能。新的数值框架将使用人工智能算法来显著加速CPFEM模拟，从而允许疲劳的数值模拟，可以解释通过增材制造获得的微结构的全部复杂性。

This Alliance project focuses on developing a new numerical framework to couple crystal plasticity-based finite element method (CPFEM) with artificial intelligence (AI) based machine learning (ML) approaches to investigate the effects of microstructural gradients and residual stresses on the fatigue performance of additively manufactured (AM) components. One major concern in AM build parts is the excessive and anisotropic residual stresses that frequently develop in laser powder bed fusion (LPBF) processed components. The mechanical properties such as the fatigue life of these materials are strongly governed by residual stresses that can often be as high as the material's yield strength. Furthermore, these stresses form due to local microstructural effects, such as local differences in grain morphologies, texture, and slip behavior, and are a cause of concern for additively manufactured alloys due to the inherent complex and heterogeneous microstructures. Since CPFEM can incorporate macro and micro-level residual stresses, microstructural attributes (i.e., grain morphologies, microstructural gradient, texture), and can model the accumulation of the induced plastic deformation at the slip system level, it is an ideal candidate for the present microstructure sensitive application. However, CPFEM modeling is computationally expensive, and its application to fatigue life predictions further complicates the problem due to the repetitive and time-consuming nature of fatigue loading. This project combines the latest advancements in crystal plasticity and artificial intelligence to enable the investigation of the effects of microstructural gradients and residual stresses on the fatigue performance of additively manufactured aluminum alloys. The new numerical framework will use artificial intelligence algorithms to significantly accelerate CPFEM simulations and thus permit numerical simulations of fatigue that can account for the full complexity of microstructures obtained by additive manufacturing.