Grain-level micromechanical modeling and assessment of fatigue-critical pores using graph neural networks

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, w ^P , a fatigue damage indicator linked to crack initiation. Although prediction accuracy decreases for extreme and plasticity-driven quantities, the predicted grain-maximum w ^P enables rapid assessment of pore criticality by determining whether fatigue hotspots occur in pore-adjacent grains. This approach advances GNN-based surrogate modeling of polycrystalline response by extending predictions to plasticity-driven localized fatigue metrics and enabling fast screening of fatigue-critical pores in AM components.