Prediction of steel nanohardness by using graph neural networks on surface polycrystallinity maps
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-09
Department
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Mcode
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Language
en
Pages
5
1-5
1-5
Series
Scripta Materialia, Volume 234
Abstract
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 assess the predictability of micromechanical responses of nano-indented 310S steel surfaces, based on surface polycrystallinity, captured by electron backscatter diffraction maps. The grain size distribution ranges between 1–100 μm, with mean size at 18μm. The GNN model is trained on nanomechanical load-displacement curves to make predictions of nano-hardness, with sole input being the grain locations and orientations. We explore model performance and its dependence on various structural/topological grain-level descriptors (e.g. grain size and number of neighbors). Analogous GNN-based frameworks may be utilized for quick, inexpensive hardness estimates, for guidance to detailed nanoindentation experiments, akin to cartography tool developments in the world exploration era.Description
Funding Information: This research was funded by the European Union Horizon 2020 research and innovation program under grant agreement no. 857470 and from the European Regional Development Fund via Foundation for Polish Science International Research Agenda PLUS program grant no. MAB PLUS/2018/8 . We wish to acknowledge fruitful discussions with Daniel Cieslinski. | openaire: EC/H2020/857470/EU//NOMATEN
Keywords
Graph Neural Network, Hall–Petch effect, Hardness, Misorientation, Modeling
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Citation
Karimi, K, Salmenjoki, H, Mulewska, K, Kurpaska, L, Kosińska, A, Alava, M J & Papanikolaou, S 2023, ' Prediction of steel nanohardness by using graph neural networks on surface polycrystallinity maps ', Scripta Materialia, vol. 234, 115559, pp. 1-5 . https://doi.org/10.1016/j.scriptamat.2023.115559
