Optimization of CNC Milling Parameters for Improved Surface Finish and Material Removal Rate

Abstract

CNC milling performance depends strongly on the selection of cutting parameters because surface quality and productivity are often conflicting objectives. This methodology paper proposes a structured multi-response optimization procedure for simultaneously reducing average surface roughness (Ra) and increasing material removal rate (MRR) during end milling. A three-factor, three-level Taguchi L9 design is adopted using spindle speed, feed rate, and axial depth of cut as the principal control variables. Aluminium 6061-T6 is selected as a representative work material and a carbide end mill is used under controlled dry or minimum-lubrication conditions. Surface roughness is measured with a contact profilometer, while MRR is calculated from the product of feed rate, radial width of cut, and axial depth of cut. Signal-to-noise ratios, analysis of variance (ANOVA), and a normalized desirability index are proposed to identify statistically important factors and obtain a compromise solution. The paper also provides an illustrative response matrix to demonstrate the analysis sequence; these values are not presented as new experimental measurements. The framework is designed to be directly reusable in a laboratory study and is consistent with published CNC milling optimization research available through 2019.