Multi-Response Performance Optimization in the Turning Process of Low-Carbon Steel Alloys Using Response Surface Methodology and Desirability Function Approach

Abstract

In the present investigation, Response Surface Methodology (RSM) in conjunction with the Desirability Function Approach (DFA) was utilized to ascertain the optimal values for cutting parameters—namely, cutting speed, feed rate, and depth of cut—with the objective of minimizing surface roughness and maximizing the Material Removal Rate (MRR). A Face-Centered Central Composite Design (FCCD) was implemented to systematically and effectively facilitate the experimental procedures. Second-order mathematical models for both MRR and surface roughness (Ra) were formulated based on the empirical data obtained from the experiments. The validity of these mathematical constructs was corroborated through (F-tests), and their adequacy was meticulously assessed utilizing Analysis of Variance (ANOVA) pertinent to the specified responses. Subsequently, the cutting parameters were optimized employing the predictive models generated through RSM, while a comprehensive multi-response optimization was executed via the desirability-based DFA. The findings indicated that the optimal values for the cutting parameters—resulting in the minimal surface roughness (1.8934 µm) and the maximal material removal rate (11,459 mm³/min)—were attained at a spindle speed of 1120 rpm, a feed rate of 0.20 mm/rev, and a depth of cut of 0.6788 mm. These results substantiate the efficacy of the proposed methodology in augmenting the machining performance of low-carbon steel alloys.