Regression Modeling of Surface Roughness in Grinding

Abstract

Grinding is a widely used manufacturing method in state of art industry. By realizing needs of manufacturers, grinding parameters must be carefully selected in order to maintain an optimum point for sustainable process. Surface roughness is generally accepted as an important indicator for grinding parameters. In this study, effects of grinding parameters to surface roughness were experimentally and statistically investigated. A complete factorial experimental flow was designed for three level and three variable. 62 HRC AISI 8620 cementation steel was used in grinding process with 95-96% Al2O3 grinding wheel. Surface roughness values (Ra, Rz) were measured at the end of process by using depth of cut, feed rate and workpiece speed as input parameters. Experimental results were used for modeling surface roughness values with linear, quadric and logarithmic regressions by the help of MINITAB 14 and SPSS 16 software. The best results according to comparison of models considering determination coefficient were achieved with quadric regression model (84.6% for Ra and 89% for Rz). As a result, a reliable model was developed in grinding process which is a highly complex machining operation and depth of cut was determined as the most effective parameter on grinding by variance analysis (ANOVA). Obtained theoretical and practical acquisitions can be used in various areas of manufacturing sector in the future.