An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials

作     者：Khorrami, Mohammad S. Mianroodi, Jaber Rezaei Siboni, Nima H. Goyal, Pawan Svendsen, Bob Benner, Peter Raabe, Dierk 

作者机构：Microstructure Physics and Alloy Design Max-Planck-Institut für Eisenforschung Düsseldorf Germany Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Material Mechanics RWTH Aachen University Aachen Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Deep learning 

摘      要：The purpose of this work is the development of an artificial neural network (ANN) for surrogate modeling of the mechanical response of viscoplastic grain microstructures. To this end, a U-Net-based convolutional neural network (CNN) is trained to account for the history dependence of the material behavior. The training data take the form of numerical simulation results for the von Mises stress field under quasi-static tensile loading. The trained CNN (tCNN) can accurately reproduce both the average response as well as the local von Mises stress field. The tCNN calculates the von Mises stress field of grain microstructures not included in the training dataset about 500 times faster than its calculation based on the numerical solution with a spectral solver of the corresponding initial-boundary-value problem. The tCNN is also successfully applied to other types of microstructure morphologies (e.g., matrix-inclusion type topologies) and loading levels not contained in the training dataset. Copyright © 2022, The Authors. All rights reserved.