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 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.