Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors
Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors
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DOI：
10.1016/j.ijplas.2023.103532
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发表时间：
2023-01
影响因子：
9.8
通讯作者：
S. Hashemi;S. Kalidindi
中科院分区：
文献类型：
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作者：
S. Hashemi;S. Kalidindi
晶体塑性有限元模型（CPFEM）在模拟大塑性应变下多晶聚集体的微观结构演化路径方面显示出巨大的潜力。然而，它们的高计算成本阻碍了它们在需要探索大型工艺设计空间的设计工作中的更广泛部署。在这项工作中，开发了一种新的机器学习框架，以建立低计算成本的降阶模型，用于预测任意拉伸张量的面心立方（FCC）多晶微结构中的微结构演化的细节。在此框架内，以前建立的多晶材料的材料知识系统（MKS）框架内的功能工程扩展，使其适用于大变形过程中获得的高度变形的微观结构。高斯过程自回归（GPAR）方法结合贝叶斯实验设计策略，用于建立所需的代理模型，以优化计算昂贵的训练数据（使用CPFEM产生）的生成。它表明，一个相对较小的训练集的1400个数据点是足够的，以产生一个高保真度的降阶模型预测的细节的微观结构演变在一个非常广泛的FCC多晶聚集体进行任意宏观施加拉伸张量。
Crystal plasticity finite element models (CPFEM) have shown tremendous potential for simulating the microstructure evolution paths in polycrystalline aggregates subjected to large plastic strains. However, their high computational cost has hindered their broader deployment in design efforts where large process design spaces need to be explored. In this work, a novel machine learning framework is developed to establish low-computational cost reduced-order models for predicting the details of microstructure evolution in face-centered cubic (FCC) polycrystalline microstructures subjected to arbitrary stretching tensors. Within this framework, the previously established feature engineering of polycrystalline materials within the material knowledge system (MKS) framework is extended such that it is applicable to the highly deformed microstructures obtained during large deformations. Gaussian process autoregression (GPAR) approaches combined with Bayesian design of experiment strategies are employed for building the desired surrogate models to optimize the generation of the computationally expensive training data (produced using CPFEM). It is demonstrated that a relatively small training set of 1400 datapoints is adequate to produce a high-fidelity reduced-order model for predicting the details of the microstructure evolution in a very broad set of FCC polycrystalline aggregates subjected to arbitrary macroscopically imposed stretching tensors.