Stress Field Prediction and DIC-Informed Spatial Regularization for AlSi10Mg Alloy Tensile Specimens Based on Graph Neural Networks.

High-fidelity finite-element (FE) simulation of nonlinear full-field responses in AlSi10Mg tensile specimens is computationally expensive, while the available digital image correlation (DIC) data in this study consist only of engineering-strain screenshots rather than exportable numerical fields. This work therefore develops a simulation-to-experiment framework for rapid full-field stress prediction and weakly supervised DIC-informed spatial regularization. Two hundred independent Abaqus/Explicit loading cases generated 5600 graph samples, which were used to train an eight-layer FiLM-conditioned graph neural network for nodal von Mises stress and three-component displacement prediction, together with a decoupled structured force-displacement branch. On independent FE tests, von Mises stress prediction achieved R2 = 0.9984 with an RMSE of 4.60 MPa, while the dense force-displacement curve achieved R2 = 0.9957 and RMSE = 59.70 N. Five machine tensile tests gave mean pre-fracture curve NRMSEs of 4.40% for FE versus experiment and 5.31% for GNN versus experiment. On independent specimens L-7 and L-8, the constrained adapter reduced DIC spatial shape loss by 3.21% with a mean absolute stress correction of 0.232 MPa. The framework therefore provides rapid FE-surrogate prediction with conservative experiment-informed spatial regularization rather than experimental stress inversion.