Experimental analysis and optimization of input control variables on milling of SS316L steel using hybrid MCDM method (AHP-TOPSIS)

Abstract

Micromilling of the biomaterial SS316L is a precision manufacturing technique employed to fabricate micro-scale features. This study utilized a hybrid TOPSIS-AHP (Technique for Order Preference by Similarity to Ideal Solution-Analytical Hierarchy Process) optimization method to simultaneously evaluate and optimize multiple response variables during the micromilling of SS316L stainless steel. The impact of critical process parameters—spindle speed (SS), feed rate (FR), and depth of cut (DC)—on material subtraction rate (MSR) and surface roughness (SR) was investigated using a Taguchi L27 orthogonal array design. Statistical analyses, including analysis of variance (ANOVA), F-tests, and significance testing at a 95% confidence level, unveiled that FR was the most influential factor affecting MSR, facilitating 63.02% to its variation, whereas SS predominantly influenced SR, accounting for 84.45% of its variation. Regression models were developed to predict MSR and SR, exhibiting strong predictive accuracy with R² values of 91.28% (MSR) and 89.51% (SR), alongside low average prediction errors of 8.72% and 10.49%, respectively. The optimal parameter settings—SS at 4500 rpm, FR at 0.05 mm/rev, and DC at 0.5 mm—achieved the highest MSR and the lowest SR, thereby validating the efficacy of the proposed multi-response optimization strategy.