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Title: Prediction of the surface roughness and wheel wear of modern ceramic material (Al2O3) during grinding using multiple regression analysis model
Authors: Kanakarajan, P
Sundaram, S.
Kumaravel, A
Rajasekar, R
Venkatachalam, R
Keywords: Multiple regression analysis;Al2O3;SiC;Surface roughness;Wheel wear
Issue Date: Jun-2017
Publisher: NISCAIR-CSIR, India
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 (Ra) and wheel wear (Ww) of modern ceramic material (Al2O3) during grinding. The model is developed to fitting the relationships Ra, Ww 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 Ra and Ww 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 Ra and Ww. The predicted values of Ra and Ww show linear relationships versus three parameters and have a good agreement with experiment results.
Page(s): 182-186
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Appears in Collections:IJEMS Vol.24(3) [June 2017]

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