Taguchi Design of Experiments for Optimization and Modeling of Surface Roughness When Dry Turning X210Cr12 Steel

Abstract

The surface roughness is a widely used index of product quality in terms of precision fit of mating surfaces, fatigue life improvement, corrosion resistance, aesthetics, etc. This paper presents an approach for determining the optimum machining parameters leading to minimum surface roughness by Taguchi method. The turning operations were performed based on the Taguchi design of experiment method using L₁₈(2¹ × 3⁴) a mixed orthogonal array. The signal to noise ratio (S/N) based on the “smaller-is-the better” approach was calculated to determine the optimum levels of the machining parameters. The results of optimization showed that the best surface roughness is obtained by using small feed rate and large nose radius. The application of the analysis of variance (ANOVA) is used to study the effects of the machining parameters on the surface roughness. The results of this study indicate that the feed rate (f) and the nose radius (r) have the most significant effect followed by the interaction (f × ap) on surface roughness. Mathematical models in function of machining parameters and these interactions were developed by regression analysis for the prediction of surface roughness. The results obtained have shown that the Taguchi method is very reliable in optimizing the machining parameters for improved surface roughness.