OPTIMIZATION OF CUTTING PARAMETERS IN CNC MILLING USING THE TAGUCHI METHOD

Abstract

In this paper, an experimental optimization of cutting parameters in computer numerical control (CNC) milling of mild steel with the Taguchi method of robust design is presented. Three control factors, spindle speed, feed rate and depth of cut, were selected and tested at three levels each, and arranged in Taguchi L9(3³) orthogonal array. The quality responses used were surface roughness (Ra) (smaller-the-better) and flank tool wear (VB) (smaller-the-better) with each trial repeated three times to get the mean and the signal-to-noise (S/N) ratio. The surface roughness and tool wear were found to be dependent mainly upon feed rate for surface roughness and spindle speed for tool wear, with 82.81% and 63.40% of their variation, respectively, attributable to feed rate and spindle speed, with depth of cut being a statistically significant secondary variable for both responses. The individually optimal settings were a spindle speed of 2000 rpm, feed of 0.10 mm/rev and depth of cut of 0.5 mm for minimum surface roughness (predicted Ra = 1.38 µm), and a spindle speed of 1000 rpm, feed of 0.10 mm/rev and depth of cut of 0.5 mm for minimum tool wear (predicted VB = 0.173 mm). The grey relational analysis (GRA) was used to find a compromise condition, for which 1000 rpm, 0.10 mm/rev feed, and 0.5 mm depth of cut were determined and a grey relational grade of 0.918 was predicted (Ra = 1.66 µm and VB = 0.171 mm), and the predicted grey relational grade within a 95% confidence interval was verified for both responses. The results obtain a validated set of recommended cutting parameters for the machine–tool–material combination considered and illustrate how Taguchi–GRA analysis can solve the conflicting optima problem for two quality characteristics.