Orientation-aware interaction-based deep material network in polycrystalline materials modeling

TL;DR

This paper introduces ODMN, a novel deep learning model that efficiently predicts mechanical behavior and texture evolution in polycrystalline materials, overcoming previous limitations of surrogate models.

Contribution

The paper presents ODMN, an orientation-aware interaction-based deep material network that captures texture evolution and complex responses using only linear elastic training data.

Findings

Accurately predicts mechanical responses under complex deformation.

Effectively captures texture evolution in polycrystalline materials.

Requires only linear elastic data for training.

Abstract

Multiscale simulations are indispensable for connecting microstructural features to the macroscopic behavior of polycrystalline materials, but their high computational demands limit their practicality. Deep material networks (DMNs) have been proposed as efficient surrogate models, yet they fall short of capturing texture evolution. To address this limitation, we propose the orientation-aware interaction-based deep material network (ODMN), which incorporates an orientation-aware mechanism and an interaction mechanism grounded in the Hill-Mandel principle. The orientation-aware mechanism learns the crystallographic textures, while the interaction mechanism captures stress-equilibrium directions among representative volume element (RVE) subregions, offering insight into internal microstructural mechanics. Notably, ODMN requires only linear elastic data for training yet generalizes effectively to complex nonlinear and anisotropic responses. Our results show that ODMN accurately predicts both mechanical responses and texture evolution under complex plastic deformation, thus expanding the applicability of DMNs to polycrystalline materials. By balancing computational efficiency with predictive fidelity, ODMN provides a robust framework for multiscale simulations of polycrystalline materials.