Multi-objective optimization of HS-WEDM for hole cutting in thin-walled CFRP composites using COCOSO and genetic algorithms

This study aimed to enhance the cutting process parameters in molybdenum HS-WEDM of thin-walled carbon fiber composite (CFRP) products. Various hole quality characteristics were examined, including out-of- roundness, delamination factor, hole dimensional deviation, and cutting speed. The study utilized input parameters, including pulse-on duration, pulse-off duration, input current, and CFRP thickness. Optimization techniques, involving a second-order regression model and a genetic algorithm were used to individually optimize each hole characteristic. The combined compromise solution (COCOSO) method was employed to concurrently adjust the hole quality characteristics into a unified metric called the multiple performance criteria index (MPCI), with weight criteria for each response established using an entropy principle. The MPCI was then optimized using a genetic search algorithm. The findings reveal that the entropy, COCOSO, and genetic algorithm approaches efficiently optimize the HS-WEDM process parameters at various CFRP thicknesses to obtain precise hole quality characteristics. The implementation of these methods is expected to significantly improve hole quality, with enhancements of 29.57% at 0.5 mm, 1.35% at 1.0 mm, 9.65% at 1.5 mm, and 6.05% at 2.0 mm CFRP thicknesses.