Indian Journal of Engineering & Materials Sciences Vol. 24, June 2017, pp. 182-186 Prediction of the surface roughness and wheel wear of modern ceramic material (Al 2 O 3 ) during grinding using multiple regression analysis model P Kanakarajan a *, S. Sundaram b , A Kumaravel c , R Rajasekar d & R Venkatachalam a a Department of Automobile Engineering, K S R College of Engineering, Tiruchengode 637 215, India b Department of Mechanical Engineering, Muthayammal Engineering College, Rasipuram 637 408, India c Department of Mechanical Engineering, K S Rangasamy College of Technology, Tiruchengode 637 215, India d Department of Mechanical Engineering, Kongu Engineering College, Erode 638 052, India Received 18 January 2016; accepted 8 February 2017 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 (R a ) and wheel wear (W w ) of modern ceramic material (Al 2 O 3 ) during grinding. The model is developed to fitting the relationships R a , W 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 R a and W w values of modern Al 2 O 3 ceramic material and SiC grinding wheels during grinding. Besides, experimental results are used to establish the multiple regression analysis equations for R a and Ww. The predicted values of R a and W w show linear relationships versus three parameters and have a good agreement with experiment results. Keywords: Multiple regression analysis, Al 2 O 3 , SiC, Surface roughness, Wheel wear In recent years, modern ceramics are utilized widely in various fields, such as medical, aerospace, marine, nuclear power plant, chemical industry and automobile etc. They are generally used to produce clay, stone elements, powders, water and preferred forms. It will be sintered at high temperature when the modern ceramics have been shaped, and the shrinkage phenomenon will occur. Therefore, machining of sintered ceramics is necessary to obtain the final parts with shape and accuracy requirement. However, the hardness and brittleness nature of modern ceramic materials make machining difficult. In machining, surface quality (i.e., surface roughness (R a )) is one of the most important qualities for machined components that customers require. Hence, grinding process is used widely for machining surface finishing in modern ceramic industry. The performance of machining is measured in terms of R a and grinding wheel wear (W w ). The R a and W w values are influenced by various process parameters, such as depth of cut, feed and grain size. Recently, a number of researches have been focused on the tool wear mechanism and material surface roughness of advanced ceramic materials. Xhou and Xi 1 proposed a new model for R a prediction in ceramic during grinding by calculating random distribution of the grain protrusion heights through Gaussian distribution model. Agarwal and Rao 2 developed an analytical model for R a prediction of ground ceramics based on analysis of the grooves left by the grains that intersect with the work-piece. Therefore, The described experimental design was used to develop a R a prediction model for a turning operation. A single cutting parameter and vibration subsequently to three axes were used to construct a multiple regression model for an in-process R a prediction system. A physically effective linear correlation amid the parameters (feed rate and vibration measured in three axes) and the response like R a were found using multiple regressions and ANOVA analysis by Daniel Kirby et al. 3 Gopalsamy et al. 4 reported that Taguchi method was applied to find optimum process parameters for end milling during hard machining of hardened steel. AL18 array, signal-to-noise ratio and ANOVA had been implemented to apprehend the performance characteristics of machining parameters (cutting speed, feed, depth of cut and width of cut) by considering surface finish and tool life. However, —————— *Corresponding author (E-mail:kanagu.dhana@gmail.com)