Deep Learning and Crystal Plasticity: A Preconditioning Approach for Accurate Orientation Evolution Prediction

TL;DR

This paper presents a novel unsupervised data preparation method that significantly enhances the accuracy and efficiency of neural network models in predicting crystal orientation evolution during deformation, bridging experimental and simulated data.

Contribution

Introduces an unsupervised preconditioning approach that improves neural network training for crystal plasticity data, enabling more accurate and efficient orientation evolution predictions.

Findings

Test score improved from 0.831 to 0.999 with preconditioning.

Training iterations reduced by an order of magnitude.

Good agreement between surrogate model predictions and EBSD measurements.

Abstract

Efficient and precise prediction of plasticity by data-driven models relies on appropriate data preparation and a well-designed model. Here we introduce an unsupervised machine learning-based data preparation method to maximize the trainability of crystal orientation evolution data during deformation. For Taylor model crystal plasticity data, the preconditioning procedure improves the test score of an artificial neural network from 0.831 to 0.999, while decreasing the training iterations by an order of magnitude. The efficacy of the approach was further improved with a recurrent neural network. Electron backscattered (EBSD) lab measurements of crystal rotation during rolling were compared with the results of the surrogate model, and despite error introduced by Taylor model simplifying assumptions, very reasonable agreement between the surrogate model and experiment was observed. Our method is foundational for further data-driven studies, enabling the efficient and precise prediction of texture evolution from experimental and simulated crystal plasticity results.