Predicting plastic anisotropy using crystal plasticity and Bayesian neural network surrogate models

This work presents an efficient data-driven protocol to accurately predict plastic anisotropy from initial crystallographic texture. In this work, we integrated feed forward neural networks with Variational Bayesian Inference techniques to establish an accurate low-computational cost surrogate model capable of predicting the anisotropic constants based on the texture of the polycrystalline material with quantifiable uncertainty. The developed model was trained on the results of 54,480 crystal plasticity simulations. The performed simulations parametrized Hill's anisotropic yield model for single crystals and polycrystalline textures, which were robustly represented using generalized spherical harmonics (GSH). Subsequently, the GSH-based representation of the different textures was linked to its corresponding Hill's anisotropic coefficients using a variational Bayesian neural network. The efficacy and accuracy of the developed surrogate model were critically validated with the results of 20,000 new textures. The predictions from the Bayesian neural network model showed excellent agreement with results obtained from experiments and high-fidelity crystal plasticity finite element simulations.

Automated summary

This work presents an efficient data-driven protocol to accurately predict plastic anisotropy from initial crystallographic texture. By integrating feed forward neural networks with Variational Bayesian Inference techniques, an accurate and low-computational cost surrogate model was established to predict anisotropic constants of polycrystalline materials with quantifiable uncertainty.