Deep learning-based prediction of high-strain-rate shock response in metastable high-entropy alloys

Metastable high-entropy alloys (HEA) have attracted considerable attention due to their exceptional mechanical properties at high strain rates. However, their engineering applications under high strain rates are limited, which stems from an inadequate understanding of the relationship between microstructure and impact response. An end-to-end deep learning framework has been implemented, combining the crystal plasticity finite element (CPFE) method with a convolutional neural network (CNN) to elucidate the mapping between microstructure and shock response. A crystal plasticity constitutive model, which couples dislocation slip and martensitic transformation mechanisms, has been developed and validated against experimental results, confirming the model's effectiveness. Based on this constitutive model, a dataset for training the deep learning model is generated, including the complete stress-strain response and martensite volume fraction evolution of metastable HEA with corresponding textures and loading conditions at high strain rates. The two-branch CNN model is used to extract microstructural features. Its input is microstructural information in image format and loading conditions, and its output consists of two branches corresponding to stress-strain curves and the evolution of martensite volume fraction. The collected dataset was used to train the CNN model. The results show that the model can accurately predict the shock response of metastable HEA under high strain rate conditions. This study demonstrates that the deep learning framework, while maintaining predictive accuracy, offers a significant computational efficiency advantage over CPFE simulations. It provides a novel approach for efficiently assessing the mechanical behavior of metastable high-entropy alloys under high strain rates.