Taguchi-based optimization of milling parameters for en24 steel: experimental analysis of material removal rate and surface roughness

Abstract

EN24 alloy steel is widely used in high-strength engineering components such as shafts, gears and spindles, where machining performance and surface integrity are critical for reliable service. The present work investigates the influence of spindle speed, feed rate and depth of cut on material removal rate (MRR) and surface roughness (Ra) during milling of EN24 steel. A Taguchi L27 orthogonal array is employed to design the experiments, and three replications are carried out for each condition. Signal-to-noise ratio analysis and analysis of variance (ANOVA) are used to identify the most significant cutting parameters for both responses. The results indicate that depth of cut is the dominant factor for MRR, contributing more than 80% of the total variation, whereas feed rate is the most influential parameter for Ra. Regression models are developed to predict MRR and Ra as functions of the cutting parameters, and confirmation experiments are performed at the optimal settings. For MRR, the optimum combination of 600 rpm spindle speed, 118 mm/min feed rate and 0.5 mm depth of cut yields a prediction error below 1%, while for Ra the optimal condition of 600 rpm, 102 mm/min and 0.1 mm results in a prediction error of about 2%. The study demonstrates that the Taguchi approach provides an effective and economical framework for optimising metal machining of EN24 steel, enabling higher productivity with controlled surface roughness, and contributes to improved, more sustainable manufacturing practice..