Optimisation of surface roughness when CNC turning of Al-6061: Application of Taguchi design of experiments and genetic algorithm

Abstract

Surface roughness is often used as a measure to identify surface integrity of machined parts. The objective of this study was to optimise part surface roughness by investigating the effects of cutting speed, feed rate, depth of cut and tool nose radius on the surface roughness of Aluminium 6061. A five-level L25 Taguchi orthogonal array was modified to accommodate a four-level process parameter. The optimization was conducted on the prediction model generated by use of Response Surface Methodology (RSM) together with Analysis of Variance (ANOVA), and confirmation test validated the predicted values obtained from the Genetic Algorithm (GA). The best combination of parameters for minimum surface roughness was found to be a cutting speed of 250 m/min, feed rate of 0.03 mm/rev, depth of cut of 0.2 mm and tool nose radius of 0.503 mm. The study proves the efficacy of the GA approach in optimisation of machining parameters for improved surface roughness.