Autonomous Development of a Machine-Learning Model for the Plastic Response of Two-Phase Composites from Micromechanical Finite Element Models

Abstract

We demonstrate a novel strategy for the autonomous development of a machine-learning model for predicting the equivalent stress–equivalent plastic strain response of a two-phase composite calibrated to micromechanical finite element models. A unique feature of the model is that it takes a user-defined three-dimensional, two-phase microstructure along with user-defined hardening laws for each constituent phase, and outputs the equivalent stress–plastic strain response of the microstructure modeled using J2-based isotropic plasticity theory for each constituent phase. Previously, this task was addressed using linear regression approaches on a large training dataset. In this work, it is demonstrated that the use of Gaussian process regression together with a Bayesian sequential design of experiments can lead to autonomous protocols for optimal generation of the training dataset and the development of the model. It is shown that this strategy dramatically reduces the time and effort expended in generating the training set.