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

点击翻译按钮获取中文摘要

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.