Multi-objective optimization of EN19 steel milling parameters using Taguchi, ANOVA, and TOPSIS approaches

This study focuses on optimizing milling parameters for EN19 steel through the Taguchi method. Utilizing an L9 orthogonal array with three parameters each set at three levels, the study evaluated four quality characteristics in every run. The optimization process involved the use of Signal-to-Noise Ratio (SNR), Analysis of Variance (ANOVA), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine optimal conditions and identify critical parameters influencing surface roughness, temperature, cutting force, and material removal rate.

ANOVA results underscore the significant impact of spindle speed, cutting fluids, and depth of cut (DOC). The TOPSIS analysis identified the optimal conditions as a spindle speed of 710 rpm, a DOC of 0.5 mm, and the use of Neem oil combined with graphene as the cutting fluid. Among these factors, the depth of cut was found to be the most influential, followed by spindle speed and cutting fluid. Confirmation tests corroborated the effectiveness of the optimization approach, affirming its potential to improve milling outcomes. This research offers valuable insights into enhancing machining efficiency and product quality in the milling of EN19 steel.