Drilling Process of GFRP Composites: Modeling and Optimization Using Hybrid ANN

This paper aims to optimize the machining parameters of drilling process woven-glass-fiber reinforced epoxy (WGFRE) composites. It will focus on modeling and optimizing drill spindle speed feed with different laminate thicknesses, respect torque delamination factor. The response surface analysis artificial neural networks are utilized model evaluate effect control their interaction outcomes. particle swarm optimization algorithm is used improve ANN training, increase its performance in prediction. method desirability, based RSM, applied validate optimal combination factors, space study. influences outcomes discussed detail. were 0.025 mm/r 1600 rpm for speed, respectively, a GFRE 5.4 mm thickness. RSM ANN–PSO models predict drilling-process showed very high agreement experimental data.