Investigating the Dominance of Depth of Cut on Surface Integrity in Al-Mg3 Turning with Large-Nose-Radius Tools

Abstract

This study presents a systematic investigation into the parametric optimization of surface roughness during computer numerical control (CNC) turning of aluminum-magnesium alloy (Al-3Mg), utilizing cutting inserts with a fixed nose radius of 1.2 mm. Recognizing the critical role of surface quality in advanced engineering applications, this research employs a robust experimental strategy grounded in the Taguchi philosophy. A triadic framework of control factors—cutting speed, feed rate, and depth of cut—was established, each examined across three distinct levels within an L9 orthogonal array to ensure statistical efficiency and analytical rigor. The methodological approach integrates Signal-to-Noise (S/N) ratio analysis with comprehensive Analysis of Variance (ANOVA) to delineate the optimal cutting regime and quantitatively apportion the contribution of each parameter to surface roughness. The findings reveal a distinct hierarchy of influence: depth of cut emerges as the dominant factor, accounting for a substantial 60.71% of the variance in surface roughness, closely followed by cutting speed with a significant contribution of 36.12%. In contrast, feed rate exhibits a marginal influence, contributing only 2.79% under the investigated conditions. The optimized parametric combination—achieved at a feed rate of 0.5 mm/rev, a cutting speed of 500 rpm, and a depth of cut of 0.5 mm—demonstrates a synergistic balance that minimizes surface irregularities. These insights offer a compelling contribution to precision manufacturing, providing a data-driven framework for enhancing surface integrity in the machining of light alloys while minimizing experimental overhead.