A Texture-Generalizable Deep Material Network via Orientation-Aware Interaction Learning for Polycrystal Modeling and Texture Evolution

TL;DR

This paper introduces a novel framework combining TACS and GNN to enable generalizable deep material networks for polycrystal modeling, accurately predicting nonlinear responses without retraining.

Contribution

It reformulates ODMN as a microstructure-to-parameter inference problem, allowing for the construction of fully parameterized models for unseen microstructures.

Findings

Accurately predicts nonlinear mechanical responses and texture evolution.

Close agreement with direct numerical simulations across diverse textures.

Enables microstructure-informed multiscale simulations without retraining.

Abstract

Machine learning surrogate models have emerged as a promising approach for accelerating multiscale materials simulations while preserving predictive fidelity. Among them, the Orientation-aware Interaction-based Deep Material Network (ODMN) provides a hierarchical homogenization framework in which material nodes encode crystallographic texture and interaction nodes enforce stress equilibrium under the Hill--Mandel condition. Trained solely on linear-elastic stiffness data, ODMN captures intrinsic microstructure--mechanics relationships, enabling accurate prediction of nonlinear mechanical responses and texture evolution. However, its applicability remains fundamentally limited by the absence of a parametric mapping from arbitrary microstructures to the ODMN parameter space. This limitation necessitates retraining for each new microstructure. To address this challenge, we reformulate ODMN generalization as a microstructure-to-parameter inference problem and propose the TACS--GNN--ODMN framework. The proposed framework combines a Texture-Adaptive Clustering and Sampling (TACS) scheme for texture representation with a Graph Neural Network (GNN) for inferring micromechanical equilibrium parameters. This strategy enables the construction of fully parameterized ODMNs for previously unseen microstructures without retraining. Numerical results demonstrate that the proposed framework accurately predicts nonlinear mechanical responses and texture evolution across diverse texture distributions. The predicted responses show close agreement with direct numerical simulations (DNS), highlighting the framework as a generalizable and physically interpretable surrogate model for microstructure-informed multiscale materials simulations.