可塑性
材料科学
压缩(物理)
纹理(宇宙学)
人工智能
单调函数
晶体塑性
机器学习
张力(地质)
人工神经网络
Crystal(编程语言)
算法
计算机科学
机械工程
复合材料
数学
数学分析
工程类
程序设计语言
图像(数学)
作者
Olga Ibragimova,Abhijit Brahme,Waqas Muhammad,J. Lévesque,Kaan Inal
标识
DOI:10.1016/j.ijplas.2021.103059
摘要
Machine learning (ML) methods are commonly used for pattern recognition in almost any field one could imagine. ML techniques can also offer a substantial improvement in computational time when compared to conventional numerical methods. In this research, a machine learning- and crystal plasticity-based framework is presented to predict stress–strain behaviour and texture evolution for a wide variety of materials within the face-centred cubic family (FCC). Firstly, the process of the framework design is described in detail. The proposed framework was designed to be built of ensemble of artificial neural networks (ANN) and a crystal-plasticity based algorithm. Next, the dataset constituent of crystal plasticity simulations was collected. The dataset consisted of examples of monotonic deformation cases, was prepared for training using mathematical transformations, and finally used to train ANNs used in the framework. Then, the ML framework was demonstrated to predict full stress–strain and texture evolution of different FCC single crystals under uniaxial tension, compression, simple shear, equibiaxial tension, tension–compression–tension, compression–tension–compression, and, finally, for some arbitrary non-monotonic loading cases. The proposed framework predicts the stress–strain response and texture evolution with a high degree of accuracy. The results demonstrated in this research show that the proposed machine learning- and crystal plasticity-based framework exhibits a tremendous computational advantage over conventional crystal plasticity model. Finally, one of the most important contributions of this work is to show the framework’s feasibility. The work demonstrates that machine learning methods can help predict complex strain paths without having to train machine learning models on the infinite set of possible non-monotonic loading scenarios.
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