Multi-Objective Optimization and Empirical Modeling of Centerless Grinding Parameters
This paper deals with the optimization and analysis of grinding parameters in external centerless grinding process. Taguchi’s technique is used to analyze effects of grinding parameters on surface roughness of the workpiece, specific energy consumption, a
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ocess is dissimilar to other machining processes such as turning and milling, as the multipoint cutting edges of the grinding wheel don’t have uniformity, which act differently on the work piece at each grinding. These complexities and N. Senthil Kumar (*) · C. K. Dhinakarraj Adhiparasakthi Engineering College, Melmaruvathur 603319, India e-mail: [email protected] C. K. Dhinakarraj e-mail: [email protected] B. Deepanraj National Institute of Technology, Calicut 673601, India e-mail: [email protected] G. Sankaranarayanan Sri Muthukumaran Institute of Technology, Chennai 600069, India e-mail: [email protected] S. Sathiyamoorthy et al. (eds.), Emerging Trends in Science, Engineering and Technology, Lecture Notes in Mechanical Engineering, DOI: 10.1007/978-81-322-1007-8_25, © Springer India 2012
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difficulties of illustrating the grinding process also raise obstacles to the optimization of the grinding process and to the verification of the interrelationship between grinding parameters and outcomes of the process. High quality output on the centerless grinding machine is achieved through proper selection of grinding parameters. Improper selection of input parameters gives rise to out of roundness, poor surface finish etc. Quantification of surface roughness value, specific energy consumption and roundness error of the workpiece is necessary to determine the quality of the component undergoing grinding. The centerless grinding parameters have to be optimized in order to obtain best quality of machined component and to achieve less production cost. Figure 1 shows the schematic diagram of centerless grinding process [1]. Dhavlikar et al. [2] have minimized the roundness error of workpiece by applying both Taguchi and dual response methodology and has carried out regression analysis to model an equation to average out roundness error. Garitaonandia et al. [3] have predicted the setup conditions to analyze the dynamic and geometrical instabilities using root locus perspective, making it possible to study the influence of different machine variables in stability of the process. Asilturk and Cunkas [4] has developed a surface roughness model using regression analysis for turning and predicted it using ANN and has concluded that the feed rate is the dominant factor affecting the surface roughness, followed by depth of cut and cutting speed. Kwak [5] has evaluated the effect of grinding parameters on geometric error and has shown that depth of cut is the dominant parameter followed by grain size and has developed a second-order response model to predict it. Kwak et al. [6] have analyzed the grinding power and surface roughness of the workpiece by RSM and have developed a model to predict them and increasing the depth of cut affects the grinding power more than increasing the transverse speed. Krajnik et al. [7] have minimized the surface roughness by optimizing the grinding process by RSM and by developing an empirical model for it. Nalbant et al. [8] have identified an op
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