GREY-BASED MULTI-OBJECTIVE OPTIMIZATION OF MACHINING FACTORS AFFECTING SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN STAINLESS STEEL

Currently, surface roughness and material removal rate (MRR) are two key indicators that directly impact both the service life of machine components and overall machining productivity. Achieving an optimal balance between surface quality and material removal efficiency through the selection of appropriate cutting parameters has become a crucial objective for manufacturers. This study investigates the effects of cutting parameters, including cutting speed (V), feed rate (S), and depth of cut (t), on surface roughness and material removal rate (MRR) using the Taguchi design of experiments method in the machining of 304 stainless steel. The analytical results show that among the three parameters, feed rate (S) exerts the greatest influence on surface roughness, followed by depth of cut (t), while cutting speed (V) contributes the least. The optimal cutting parameter set for achieving the lowest surface roughness is V = 100 m/min, S = 0.1 mm/rev, and t = 0.5 mm. In contrast, for maximizing the material removal rate (MRR), cutting speed (V) has the strongest effect, followed by feed rate (S) and depth of cut (t). The optimal conditions for achieving the highest MRR are V = 100 m/min, S = 0.3 mm/rev, and t = 1.3 mm. Additionally, Grey Relational Analysis (GRA) is applied as a multi-objective optimization method to simultaneously minimize surface roughness and maximize MRR. The results indicate that the optimal cutting parameter combination is V = 100 m/min, S = 0.1 mm/rev, and t = 0.5 mm.