Abstract: The surface roughness is one of the most specified customer requirements in metal cutting industries and it plays a vital role in determining, how a real object interacts with its environment. Rough surface usually wear more quickly and have higher friction coefficients than smooth surfaces, since roughness is a good predictor of the performance of a mechanical components, its measurement carries vital importance. Since productivity is linked to Material Removal Rate (MRR), its investigation is also equally important. End milling is the most important milling operation and it is widely used in most of the manufacturing industries due to its capability of producing complex geometric surfaces with reasonable accuracy and surface finish. However, with the inventions of CNC milling machine, the flexibility has been adopted along with versatility in end milling process. Proper setting of cutting parameter is important to obtain better surface quality. Unfortunately, conventional trial and error method is time consuming as well as it incurs high cost. The purpose for this study is to determine the most significant parameter and its optimum range in CNC end milling process using various statistical tools such as Taguchiís grey relational method, Analysis of variance (ANOVA), and Regression analysis. It is also proposed to develop a mathematical model, which can be used for prediction. The spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict the multiple responses surface roughness and Material Removal Rate (MRR) simultaneously. For initial investigation of ANOVA, grey relational analysis and regression analysis may be employed to determine, which is most significant parameter among Spindle speed, feed rate, and depth of cut that influence surface roughness and MRR. With the optimum combination of levels from ANOVA, Grey relational analysis, and Regression analysis, confirmation test is proposed to be conducted. The experiment are planned to be conducted on YCM EV 1020A vertical CNC milling machine and the response will be measured by Mitutoyo SURFTEST SJ-210. In this work, Minitab 16 expert will be used for developing a regression mathematical model, which in turn can be used for prediction.
Keywords: Milling operation, Analysis of Variance (ANOVA), Signal to Noise Ratio (SN), Grey Relational Analysis, Regression Analysis.