Artificial Neural Network-Based Prediction and Validation of Drill Flank Wear in GFRP Machining for Sustainable and Smart Manufacturing

Glass fiber-reinforced polymer composites (GFRPCs) are extensively utilized in the aerospace, automotive, and structural sectors; nevertheless, their heterogeneous and abrasive characteristics result in rapid tool wear during drilling. Drill flank wear among various wear mechanisms notably influences hole quality and dimensional accuracy. This research investigates the impact of spindle speed, feed rate, and drill diameter on flank wear during dry drilling of GFRPC laminates with high-speed steel (HSS) twist drills. A full-factorial design with 81 experiments is used to create a comprehensive dataset. ANOVA indicates that spindle speed is the dominant factor affecting wear changes, accounting for 74.43%, followed by feed rate (15.80%) and drill diameter (6.16%). A linear regression model demonstrates reasonable statistical sufficiency (R² = 0.964), but it falls short in reflecting nonlinear interactions. Hence, an artificial neural network (ANN) model is developed to improve prediction. The multilayer feed-forward ANN with a 3-10-6-1 architecture, trained using the Levenberg–Marquardt optimization algorithm, achieves excellent predictive accuracy, with high correlation and low root-mean-square error. Model validation was achieved through independent confirmation experiments, yielding a mean absolute percentage error of only 2.27%, with all predictions falling within the permissible wear range. The findings indicate that ANN-based modeling provides a reliable framework for capturing the complex nonlinear relationships governing tool wear in GFRPC drilling and serves as a viable soft sensor for tool condition monitoring, process optimization, and sustainable, data-driven manufacturing.