Full-field elastic strain tensor evolution of 3D polycrystals with recurrent neural networks and transfer learning

Abstract

High energy X-ray diffraction microscopy (HEDM) is a non-destructive characterization technique that enables the study of material evolution under in situ thermo-mechanical conditions. While HEDM provides valuable insights, successful experiments require extensive planning, data collection, and data reduction, making them time-intensive and expensive. Crystal plasticity simulations could improve experimental planning and reduce the time required for experimental data reconstruction, but they are too computationally intensive for real-time experimental feedback. Deep learning models offer the speed needed for real-time feedback that could optimize data collection and data reconstruction while expanding the experimental design space. However, these models are currently limited by the small size of available training datasets. This work develops a surrogate crystal plasticity model using a U-Net architecture with recurrent and recursive connections to predict the evolution of full-field elastic strain tensors in 3D polycrystalline materials—properties directly measured during HEDM experiments. Using a Cu polycrystal as the baseline material, the trained network can make predictions instantaneously, representing a significant step towards real-time crystal plasticity predictions for HEDM experiments and potentially enabling more efficient and adaptive experimental designs. However, training such a 3D network for different materials system is computationally expensive due to its numerous trainable parameters and the cost of generating training data. To address this challenge, we investigate transfer learning techniques that enable the network to predict the evolution of different materials without training from scratch, while using the Cu-trained network as a foundation for expanding the model’s capabilities. The transfer learning approach successfully reduced training time and data requirements while maintaining prediction accuracy for materials with similar microstructures, demonstrating the potential for rapid adaptation to new material systems.