OPTIMIZATION AND PREDICTIVE MODELING OF SURFACE ROUGHNESS IN CNC MILLING OF 40Cr ALLOY STEEL USING TAGUCHI-ANN APPROACH

Abstract

This study presents an experimental and predictive modeling investigation on surface roughness in CNC milling of 40Cr alloy steel. A Taguchi L9 design, ANOVA, cutting force estimation, tool wear consideration, and ANN modeling were employed. Surface morphology interpretation using SEM illustrations and force–roughness relationships were integrated. The results show that feed per tooth is the dominant factor affecting surface roughness. The proposed hybrid statistical-AI framework improves machining parameter optimization and prediction accuracy.