OPTIMIZATION OF TOOL PATH STRATEGIES IN POCKET MILLING OF AA7075-T6 ALLOY BY STATISTICAL AND MULTI-CRITERIA APPROACHES

This study investigated the optimal tool path strategy by analysing surface roughness and cutting force using statistical and multi-criteria approaches. Pocket milling of AA7075-T6 was carried out under different tool path levels, feed rates, and spindle speeds within a computer-aided manufacturing (CAM) setup, and their effects on cutting force and surface roughness were evaluated. The optimal parameter combination for minimising both outputs was identified through Taguchi S/N analysis and multi-criteria decision-making techniques (Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA)). Furthermore, the contribution ratios of machining parameters were determined using ANOVA, and 3D topography images visually supported the results. Findings indicated that toolpath was the most influential factor on both responses. According to ANOVA, the tool path accounted for 91.05% and 76.32% of the variation in cutting force under roughing and finishing conditions, respectively, and 91.75% of the variation in surface roughness. The statistical results were consistent with the 3D topography observations. One-way tool path, 1200 mm/min feed rate, and 4800 rpm cutting speed were determined as the optimal parameters for minimum cutting forces (Fx: 55.11 N, Fy: 81.76 N, and Fz: 74.83 N). Minimum surface roughness (0.299 μm) was obtained with the parameters V-zigzag tool path, 220 mm/min feed rate and 4800 rpm cutting speed. This study contributes to the literature by demonstrating that tool path is a critical parameter in multi-response optimisation and by simultaneously addressing both rough and finish milling conditions, which have not been comprehensively analysed together in earlier works.