Optimization of CNC Turning Parameters for Improved Surface Roughness and Material Removal Rate

Abstract

This methodology paper presents a systematic multi-response framework for optimizing computer numerical control (CNC) turning parameters to obtain low surface roughness and high material removal rate (MRR). The proposed procedure combines a Taguchi L9 orthogonal array with signal-to-noise analysis, Grey Relational Analysis (GRA), analysis of variance (ANOVA), and a confirmation experiment. Cutting speed, feed rate, and depth of cut are treated as controllable factors, while average surface roughness (Ra) and MRR are selected as performance responses. The method is intended for medium-carbon-steel turning with a carbide insert under dry cutting, but it can be transferred to other workpiece-tool combinations by redefining factor ranges. Literature up to 2016 shows that feed rate commonly exerts a strong influence on surface finish, whereas MRR increases with speed, feed, and depth of cut; therefore, simultaneous optimization is necessary. A representative L9 dataset is used to demonstrate the calculations because raw laboratory measurements were not supplied. The Grey grade identifies the best compromise parameter combination, and ANOVA quantifies the relative influence of factors. The proposed workflow reduces the number of trials while providing a statistically interpretable basis for balancing machining quality and productivity.