Prediction of the surface roughness and wheel wear of modern ceramic material (Al₂O₃) during grinding using multiple regression analysis model

Abstract

Grinding process is used widely for producing industrial parts with high precision and high surface quality for modern ceramics. But only a few machining tests were carried out on grinding by using silicon carbide (SiC) grinding wheel with various parameters. In this paper, an analytical model is developed to determine the surface roughness (<em>R</em>_a) and wheel wear (<em>W</em>_w) of modern ceramic material (Al₂O₃) during grinding. The model is developed to fitting the relationships <em>R</em>_a, <em>W</em>_w versus three process parameters (depth of cut, feed and grain size) using multiple regression analysis method. The main objective of this paper is to develop a model for optimizing the <em>R</em>_a and <em>W</em>_w values of modern Al₂O₃ ceramic material and SiC grinding wheels during grinding. Besides, experimental results are used to establish the multiple regression analysis equations for <em>R</em>_a and Ww. The predicted values of <em>R</em>_a and <em>W</em>_w show linear relationships versus three parameters and have a good agreement with experiment results.