International Journal of Scientific and Research Publications, Volume 3, Issue 6, June 2013 1 ISSN 2250-3153 www.ijsrp.org Optimization of Process Parameter in Turning of Copper by Combination of Taguchi and Principal Component Analysis Method Chintan Kayastha*, Jaivesh Gandhi** *Mechanical Department, Sad Vidhyamandal Institute of Engineering and Technology, India **Mechanical Department, Sad Vidhyamandal Institute of Engineering and Technology, India Abstract- The aim of this article is to optimize the process parameters for turning operation by use of Taguchi and Principal component analysis method. The aim of the present work is to investigate the effects of process parameters on surface finish and material removal rate (MRR) to obtain the optimal setting of these process parameters as product with good finishing surface is desirable in turning process with minimum machining cost. Index Terms- Optimization, PCA, Surface roughness, Taguchi and Principal Component Analysis Method I. INTRODUCTION urface roughness plays important role in heat transfer application and electrical transformation application and copper is used as material for it. In addition copper is widely used for so many applications of electrical, industrial application and medical science. Turning operation is widely used in manufacturing company. For optimization of MRR and Surface roughness PCA method coupled with grey based Taguchi method is implemented as multi response optimization problem cannot solved by other optimization methods In this method orthogonal array and signal to noise ratio is calculated as Taguchi method and correlation, eigenvalue, principal component and Grey relational coefficient is calculated by PCA methods. II. LITERATURE REVIEW In 2012, Yadav and narang had conclude from ANOVA analysis and Taguchi method for medium carbon steel, parameters making significant effect on surface roughness are feed rate and cutting speed. He shown that with the increase in feed rate the surface roughness also increases & as the cutting speed decreases the surface roughness increases. [1] In 2008, H.S Lu et al. had used PCA method for milling of steel and shown that contribution of milling type, spindle speed and feed is totally 79% and radial and axial depth of cut has comparatively less contribution. [2] In 2010, Tejender pal sing et al. had used aluminum bar for turning and by mathematical model shown that surface roughness decrease with increase in rack angle. [3] In 2008, Mustafa gunay has shown that negative rack angle produces poor surface finish and positive rack angle produce good surface finish with less surface roughness using anova. [4] In 2010, Mehmat et al. had used multiple regression and artificial neural network approaches to predict the surface roughness in AISI 1040 steel. The parameters such as cutting speed, feed, and cutting of depth were measured by means of full factorial experimental design. He shown that the feed rate is the dominant factor affecting the surface roughness, followed by depth of cut and cutting speed. The proposed models can be used effectively to predict the surface roughness in turning process. [5] S