Development of defect localization method for perforated carbon-fiber-reinforced plastic specimens using finite element method and graph neural network

In this study, we propose a novel defect localization method that integrates the graph neural network (GNN) with the finite element method (FEM) to estimate the three-dimensional location of defects in perforated carbon-fiber-reinforced plastic (CFRP) interstage structures. Specifically, the model uses distributions of the sum of principal stresses on the surface (DSPSS) to predict the three-dimensional location of defects. FEM is employed to simulate tensile loading conditions and generate stress distribution data using Teflon sheets to represent predefined delaminations. These distributions serve as inputs to the graph attention network (GAT), which classifies defect positions into 19 categories. The proposed method achieved a macro-averaged F1-score of 61% and accurately predicted both the insertion layers and planar positions of defects.