Effect of machining parameters on the machinability of 15‐5 precipitation hardened stainless steel during dry turning

Abstract This research presents the studies on the effect of machining variables and the empirical modeling of machinability output responses during dry turning of 15–5 precipitation‐hardened stainless steel (PHSS) using the taguchi meta‐heuristic algorithm. L 9 orthogonal array (OA) robust experimental design was selected for conducting the dry turning operations. The input variables included cutting velocity, depth of cut, and feed rate, while the output responses measured were surface roughness (R a ) and cutting force (F c ). The influence of these process variables was determined through analysis of variance (ANOVA). ANOVA results revealed that cutting speed, feed rate and depth of cut were impacting the average surface roughness by 36 %, 29 %, and 31 %, respectively and influencing the cutting force by 2 %, 16 %, and 72 %, respectively, during turning operation. Empirical models for predicting cutting force and surface roughness were developed using the Taguchi meta‐heuristic algorithm. The optimization process resulted in a significant reduction in surface roughness, with R a decreasing by 17 % and a notable decrease in cutting force, F c , by 8 %. These numerical improvements indicate that the proposed optimization approach substantially enhances machining performance, validating its effectiveness.