Articlenpj computational materials2026
Grain-level micromechanical modeling and assessment of fatigue-critical pores using graph neural networks.
Article in npj computational materials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Fatigue assessment is essential for the insertion of new materials and manufacturing processes into engineering applications, yet experimental testing is costly and time-consuming, and while crystal plasticity (CP) captures microstructure-sensitive fatigue mechanisms, it remains computationally expensive. This work presents GFF-MAP (Grain-level Fatigue Failure - Micromechanical Assessment of Pores), a framework that leverages graph neural network (GNN) surrogate models trained on CP data to predict grain-level micromechanical fields in additively manufactured (AM) IN718 microstructures containing representative pore defects. The models predict grain-average and grain-maximum stress and accumulated plastic strain energy density,
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