A Physics-Informed Intelligent Constitutive Approach for Predicting Creep Deformation Considering Polycrystalline Microstructure Evolution

Crystal plasticity finite element (CPFE) modeling has emerged as a leading mesoscopic modeling approach by integrating fully resolved microstructures and physics-based microscale deformation mechanisms into the constitutive modeling of crystal materials. However, this approach demands substantial computational resources, which limits its application for complex polycrystalline microstructures subjected to extreme loadings such as creep. In this study, a physics-informed intelligent constitutive model is proposed to accelerate the simulation of the creep response and life of polycrystalline microstructures and applied to nickel alloy Inconel 617. The model is trained with physical constraints and creep data generated by CPFE simulations that explicitly consider the interaction between dislocation glide and climb, grain boundary sliding and opening, and various grain orientations. To address the computational challenges and data redundancy issues associated with polycrystalline representative volume elements, two dimensionality reduction methods, namely principal component analysis and homogenized fabric tensor condensation, are proposed and studied. An autoregressive physics-informed neural network model is then developed using initial state and loading conditions as input, while creep time, evolution of creep strain, and grain orientation are the outputs. The model is trained using CPFE modeling data to predict the high-temperature creep behavior of Inconel 617. The latter demonstrates better predictive performance and delivers six orders of higher efficiency compared to direct numerical simulation using CPFE. The developed model is further used to study the rapid construction of intelligent constitutive and texture description, which shows improved efficiency and accuracy in predicting the creep behavior. The effect of texture description is further studied by using the proposed model. The fabric tensor is demonstrated to be an effective microstructural indicator.