ARTIFICIAL INTELLIGENT PREDICTION METHODS OF MATERIAL CONSTITUTIVE BEHAVIOR BASED ON CRYSTAL PLASTICITY FRAMEWORK

Artificial Neural Networks (ANNs) have become a powerful and indispensable tool for multiscale constitutive modeling of nonlinear materials. This paper develops an intelligent prediction method for the constitutive behavior of nickel-based single-crystal alloys widely used in aerospace industries, based on the framework of crystal plasticity. The proposed approach integrates data-driven techniques with a conventional crystal plasticity constitutive model, resulting in a hybrid framework that enhances predictive capabilities while retaining the fundamental physical principles governing crystal slip systems. The solution framework preserves the conventional formulation for resolving slip systems, where state variables associated with these slip systems are used as inputs to the neural network. This enables the establishment of a physical relationship between the state variables and shear strain increments of the slip systems. A physics-informed loss function is employed to achieve the implicit stress integration, allowing the neural network to accurately predict the mechanical responses of single-crystal materials under both monotonic and cyclic loading conditions. The study also investigates the effects of different loss functions on the model training process, revealing that the combination of data-driven learning and physical constraints significantly improves the model’s performance. Integrating physical information within the loss function enhances the model's ability to extrapolate predictions beyond the range of training data, providing more accurate predictions for unseen scenarios. However, challenges remain in regions where training data is sparse, leading to less precise predictions. To address the limitations in sparse data regions, an innovative online training scheme is introduced on top of conventional offline learning strategies. This scheme enables the neural network to adaptively improve its performance by minimizing residual errors during predictions, essentially allowing the model to self-learn and refine its accuracy. As a result, the online-trained model achieves prediction accuracy comparable to that of conventional constitutive models, bridging the gap between data-driven approaches and established material modeling techniques. The neural network-based crystal plasticity framework proposed in this work offers a novel and effective approach for studying material constitutive relationships, with significant potential to further advance the development of multiscale constitutive models for complex material systems.