Machine learning based surrogate modeling approach for mapping crystal deformation in three dimensions

Abstract

We present a machine learning based surrogate modeling method for predicting spatially resolved 3D crystal orientation evolution of polycrystalline materials under uniaxial tensile loading. Our approach is orders of magnitude faster than the existing crystal plasticity methods enabling the simulation of large volumes that would be otherwise computationally prohibitive. This work is a major step beyond existing ML-based modeling results, which have been limited to either 2D structures or only providing average, rather than local 3D full-field predictions. We demonstrate the speed and accuracy of our surrogate model approach on experimentally collected data from a face-centered cubic copper sample undergoing tensile deformation.