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

一种基于人工智能的方法，用于解释微观结构梯度和残余应力对增材制造铝疲劳性能的影响

基本信息

批准号：
566664-2021
负责人：
Inal, Kaan
金额：
$ 2.19万
依托单位：
University of Waterloo
依托单位国家：
加拿大
项目类别：
Alliance Grants
财政年份：
2021
资助国家：
加拿大
起止时间：
2021-01-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=723698
关键词：
artificial intelligence based approach account

项目摘要

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.

该联盟项目的重点是开发一种新的数值框架，将基于晶体塑性的有限元法（CPFEM）与基于人工智能（AI）的机器学习（ML）方法相结合，以研究微结构梯度和残余应力对增材制造（AM）部件疲劳性能的影响。AM构建部件中的一个主要问题是激光粉末床熔融（LPBF）处理的部件中经常产生的过度和各向异性残余应力。这些材料的机械性能（例如疲劳寿命）强烈地受残余应力支配，残余应力通常可以与材料的屈服强度一样高。此外，这些应力由于局部微观结构效应（例如晶粒形态、织构和滑移行为的局部差异）而形成，并且由于固有的复杂和异质微观结构而成为增材制造合金的关注原因。由于CPFEM可以结合宏观和微观水平的残余应力，微观结构属性（即，晶粒形态、微结构梯度、织构），并且可以模拟在滑移系水平上诱导的塑性变形的累积，因此它是目前微结构敏感应用的理想候选者。然而，CPFEM建模是计算昂贵的，其应用程序的疲劳寿命预测进一步复杂化的问题，由于疲劳加载的重复性和耗时的性质。该项目结合了晶体塑性和人工智能的最新进展，以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。新的数值框架将使用人工智能算法来显着加速CPFEM模拟，从而允许疲劳的数值模拟，可以解释增材制造获得的微观结构的全部复杂性。