Deep learning and crystal plasticity: A preconditioning approach for accurate orientation evolution prediction

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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Efficient and precise plasticity prediction relies on appropriate data preparation and a well-designed model. This study introduces an unsupervised machine learning-based data preparation method to maximize the trainability of crystal orientation evolution data during deformation, achieving improved test scores and reduced training iterations. The approach shows reasonable agreement between the surrogate model and experimental data, laying a foundation for further data-driven studies in texture evolution prediction.