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

Tsopanidis, Stylianos; Osovski, S.

doi:10.1016/j.matdes.2021.110272

Cited by 4 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al 16 use GNNs to predict atom-level properties, including stress fields and energy distributions, by modeling a few grains along with contained structural defects. Instances, where the microstructure topology was captured in graphs were presented to predict the stored elastic energy functional 17 , grain-scale toughness 18 , effective magnetostriction 19 , and deformation twinning 20 . Graph-based approaches and the notion of message passing between node entities provide an inductive bias that might be suitable to learn aspects such as interactions within grain ensembles.…”

Section: Introductionmentioning

confidence: 99%

Materials fatigue prediction using graph neural networks on microstructure representations

Thomas

Durmaz

Alam

et al. 2023

Sci Rep

View full text Add to dashboard Cite

The local prediction of fatigue damage within polycrystals in a high-cycle fatigue setting is a long-lasting and challenging task. It requires identifying grains tending to accumulate plastic deformation under cyclic loading. We address this task by transcribing ferritic steel microtexture and damage maps from experiments into a microstructure graph. Here, grains constitute graph nodes connected by edges whenever grains share a common boundary. Fatigue loading causes some grains to develop slip markings, which can evolve into microcracks and lead to failure. This data set enables applying graph neural network variants on the task of binary grain-wise damage classification. The objective is to identify suitable data representations and models with an appropriate inductive bias to learn the underlying damage formation causes. Here, graph convolutional networks yielded the best performance with a balanced accuracy of 0.72 and a F1-score of 0.34, outperforming phenomenological crystal plasticity (+ 68%) and conventional machine learning (+ 17%) models by large margins. Further, we present an interpretability analysis that highlights the grains along with features that are considered important by the graph model for the prediction of fatigue damage initiation, thus demonstrating the potential of such techniques to reveal underlying mechanisms and microstructural driving forces in critical grain ensembles.

show abstract