A new ANN based crystal plasticity model for FCC materials and its application to non-monotonic strain paths

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. •ML framework is proposed to predict stress–strain response and texture evolution.•Detailed feature engineering and dataset construction.•Detailed the training and validation of neural networks.•Successful prediction of stress tensor and texture evolution for polycrystals.•Huge computational savings compared to conventional crystal plasticity models.