Physics-informed neural network framework for predicting drilling-induced delamination in GFRP composites.

Drilling-induced delamination is one of the most critical defects that affect the structural integrity, dimensional accuracy, and assembly performance of Glass Fiber Reinforced Polymer (GFRP) composites. Accurately predicting delamination is challenging due to the complex nonlinear interactions between machining parameters and interlaminar damage mechanisms. In this study, we developed a Physics-Informed Neural Network (PINN)-based predictive framework to estimate drilling-induced delamination in GFRP laminates by integrating experimental data with fundamental principles of fracture mechanics. A full-factorial experimental design with 243 drilling trials have been conducted by varying drill point angle, drill diameter, laminate thickness, spindle speed, and feed rate. The delamination factor was evaluated experimentally using image-based measurement techniques. The physics governing thrust-force-induced delamination has been incorporated into the neural network's loss function to ensure consistent and accurate learning. The proposed PINN architecture effectively captured the nonlinear relationship between drilling parameters and delamination behavior, achieving a coefficient of determination (R2) of 0.9769 and a low root mean square error (RMSE) of 0.0048. Sensitivity analysis revealed that the feed rate was the most influential parameter affecting delamination, followed by laminate thickness and drill diameter. Notably, higher spindle speed reduced damage formation due to improved cutting efficiency and decreased thrust force. Further interaction analysis demonstrated significant coupling effects between feed rate and spindle speed, as well as between drill diameter and laminate thickness. The integration of physics-based constraints significantly enhanced the robustness of predictions and the generalization capability compared to conventional data-driven approaches. Our findings confirm that the proposed PINN framework serves as a reliable and physically interpretable tool for predictive modeling, intelligent process optimization, and damage minimization in composite drilling applications.