A deep learning method for predicting microvoid growth in heterogeneous polycrystals. (1st April 2022) - Liu, Jianqiu; Huang, Minsheng; Li, Zhenhuan; Zhao, Lv; Zhu, Yaxin
Graphical abstract: Highlights: A novel deep-learning (DL) neural network is creatively designed to predict statistical microvoid growth in heterogeneous polycrystals. The heterogeneous microstructural information has been transferred to the DL network by 3-channel RGB image and CNN. The LSTM network has been specially employed to capture the history dependency of statistical microvoid growth. The correlation between microvoid growth and polycrystalline microstructures can be effectively excavated by the designed DL model. The present DL model has a huge potential to investigate the microstructure-dependent damage problems. Abstract: In heterogeneous polycrystals, microvoid growth presents inherent randomness and dispersion, which generally follows a statistical law. This statistical characteristic intrinsically arises from randomly distributed grain-orientations around microvoids. It remains a huge challenge to explicitly depict the inherent correlation between microvoid growth and grain-orientation distribution by conventional deterministic damage models. In recent years, deep learning has gained in popularity in materials science and has been demonstrated to exhibit excellent data-mining abilities. To our best knowledge, deep learning has not been applied to investigate the statistical damage-evolution issues hitherto. In this work, a novel microvoid growth model based on deep neural network is creatively designed, incorporating both convolutional and long short-term memory components. The former extracts the spatial grain-orientation information, and the latter captures the causal effect of strain history on the microvoid growth. Moreover, to train and test the deep learning-based model, a microvoid-growth database is generated through a large number of crystal plasticity-based finite element simulations, incorporating randomly-oriented grains and different void locations. All the sample data (i.e., the grain-orientation distributions, microvoid locations and microvoid-growth curves) are processed by specific methods (e.g., the pixel-based method) to be amenable for the training process. Our results show that this novel model well captures the statistical characteristic of the microvoid growth in heterogeneous polycrystals. It is expected that the deep learning-based method can provide a new way to predict the microvoid growth at the grain-level.