Predicting the mechanical response of oligocrystals with deep learning

TL;DR

This paper introduces a deep learning model that predicts the mechanical response of oligocrystals efficiently, capturing microstructural variations with accuracy comparable to detailed simulations.

Contribution

A novel neural network architecture combining convolutional and recursive components for fast, accurate prediction of oligocrystal stress-strain behavior.

Findings

Achieves simulation-level accuracy with minimal computational cost.

Effectively captures microstructural variations in stress response.

Outperforms traditional homogenization methods in sensitivity to local structures.

Abstract

In this work we employ data-driven homogenization approaches to predict the particular mechanical evolution of polycrystalline aggregates with tens of individual crystals. In these oligocrystals the differences in stress response due to microstructural variation is pronounced. Shell-like structures produced by metal-based additive manufacturing and the like make the prediction of the behavior of oligocrystals technologically relevant. The predictions of traditional homogenization theories based on grain volumes are not sensitive to variations in local grain neighborhoods. Direct simulation of the local response with crystal plasticity finite element methods is more detailed, but the computations are expensive. To represent the stress-strain response of a polycrystalline sample given its initial grain texture and morphology we have designed a novel neural network that incorporates a convolution component to observe and reduce the information in the crystal texture field and a recursive component to represent the causal nature of the history information. This model exhibits accuracy on par with crystal plasticity simulations at minimal computational cost per prediction.