Modeling and multi-response optimization of cutting parameters in turning of AISI 316L using RSM and desirability function approach

In this study, the effects of different machining parameters such as cutting speed (Vc), feed rate (f), and cutting depth (ap) on surface roughness and tool flank wear in turning of AISI 316L material are presented. The machining experiments were performed under dry conditions on a conventional lathe using coated carbide insert TP2501. Experimental tests were planned according to an L27 Taguchi design. Both the surfaces of response (RSM) and analysis of variance (ANOVA) methods were applied to determine and classify the cutting parameters affecting the surface roughness and tool flank wear and for deriving the mathematical models to be used in the optimization stage when implementing the desirability function (DF). Moreover, in order to localize the surface defects in the machined profiles, 3D topographic analysis based on the peak and valley bar chart (Abbott-Firestone curve) was employed. The results revealed that the surface roughness is largely influenced by the feed rate (with a contribution of 79.61%), while the feed rate is the factor that most influenced the tool flank wear followed by the cutting speed (with a contribution of 30.47% and 26.369%, respectively).