A graph based workflow for extracting grain-scale toughness from meso-scale experiments

TL;DR

This paper presents a graph neural network-based framework that accurately predicts micro-scale material toughness from limited meso-scale experimental data, enabling efficient analysis of microstructure effects on toughness.

Contribution

The paper introduces a novel graph neural network framework capable of predicting micro-scale toughness with limited training data from meso-scale experiments.

Findings

High accuracy in predicting crack growth resistance at micro-scale

Effective with small datasets of 200-300 grains

Framework adaptable to different material systems

Abstract

We introduce a novel machine learning computational framework that aims to compute the material toughness, after subjected to a short training process on a limited meso-scale experimental dataset. The three part computational framework relies on the ability of a graph neural network to perform high accuracy predictions of the micro-scale material toughness, utilizing a limited size dataset that can be obtained from meso-scale fracture experiments. We analyze the functionality of the different components of the framework, but the focus is on the capabilities of the neural network. The minimum size of the dataset required for the network training is investigated. The results demonstrate the high efficiency of the algorithm in predicting the crack growth resistance in micro-scale level, using a crack path trajectory limited to 200-300 grains for the network training. The merit of the proposed framework arises from the capacity to enhance its performance in different material systems with a limited additional training on data obtained from experiments that do not require complex or cumbersome measurements. The main objective is the development of an efficient computational tool that enables the study of a wide range of material microstructure properties and the investigation of their influence on the material toughness.