Hybrid PSO-GA optimization and surface morphology analysis of abrasive water jet machining process for Ti-6Al-4V alloy

Abrasive Water Jet Machining (AWJM) is a precise non-traditional cutting process suitable for difficult-to-machine materials like titanium alloys. This study presents a multi-response optimization of AWJM parameters for Ti-6Al-4V alloy by integrating response surface methodology (RSM) with a hybrid Particle Swarm Optimization-Genetic Algorithm (PSO-GA). The influence of four key process parameters—water pressure (150–250 MPa), abrasive mesh size (#80–#120), traverse speed (50–150 mm/min), and abrasive flow rate (220–420 g/min)—on material removal rate (MRR) and kerf taper angle (KTA) was investigated. A Box-Behnken design (BBD) was employed to conduct 29 experiments, and the resulting data were used to develop predictive models. Parametric analysis revealed that abrasive mesh size is the most dominant factor influencing both MRR and KTA. The hybrid PSO-GA algorithm was developed and its performance was benchmarked against standalone PSO and GA. Algorithm performance metrics: convergence in 4 iterations, computational time of 0.3594 s, MAPE of 1.1398%, success rate of 89%. The optimal machining conditions: pressure 150 MPa, abrasive mesh size #120, traverse speed 150 mm/min, abrasive flow rate 380 g/min. Improvement in combined objective function: 7.43% compared to the best experimental run. Scanning electron microscopy (SEM) analysis of the machined surfaces under these conditions confirmed ductile erosion mechanisms characterized by parallel grooves, plastically deformed layers, and embedded abrasive particles. The results demonstrate that the hybrid PSO-GA approach is an effective and robust tool for optimizing complex manufacturing processes like AWJM.