Prediction of steel nanohardness by using graph neural networks on surface polycrystallinity maps
Nanoscale hardness in polycrystalline metals is strongly dependent on microstructural features that are believed to be influenced from polycrystallinity — namely, grain orientations and neighboring grain properties. We train a graph neural networks (GNN) model, with grain centers as graph nodes, to...
Main Authors: | , , , , , , |
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Format: | Article |
Language: | English |
Published: |
Acta Materialia Inc
2023
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Subjects: | |
Online Access: | View Fulltext in Publisher |