Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model

Understand

Crystal plasticity theory is often employed to predict the mesoscopic states of polycrystalline metals, and is well-known to be costly to simulate.

• Using a neural network with convolutional layers encoding correlations in time and space, we were able to predict the evolution of the stress field given only the initial microstructure and external loading.
• In comparison to our recent work we were able to predict not only the spatial average of the stress response but the field itself.
• We show that the stress fields and their rates are in high fidelity with the crystal plasticity data and have no visible artifacts.