Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data

TL;DR

This paper explores the use of graph neural networks to predict grain-scale elastic responses in polycrystalline alloys, combining simulated and experimental data for improved modeling of anisotropic behavior.

Contribution

It introduces a transfer learning GNN framework trained on simulations to predict real microstructure responses, incorporating various microstructural descriptors and analyzing anisotropy effects.

Findings

GNN models accurately predict grain stresses in alloys.

Transfer learning improves predictions on experimental data.

Elastic anisotropy impacts GNN performance.

Abstract

Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al (Ti-7Al), as example face centered cubic and hexagonal closed packed alloys, are predicted. A transfer learning approach is taken in which GNN surrogate models are trained using crystal elasticity finite element method (CEFEM) simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured using high-energy X-ray diffraction microscopy (HEDM). The performance of using various microstructural and micromechanical descriptors for input nodal features to the GNNs is explored through comparisons to traditional mean-field theory predictions, reserved full-field CEFEM data, and measured far-field HEDM data. The effects of elastic anisotropy on GNN model performance and outlooks for extension of the framework are discussed.