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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/440

Title: Parametric Analysis and Optimization of Cutting Parameters for Turning arametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Method
Authors: Mahapatra, S S
Patnaik, A
Patnaik, Prabina Ku
Keywords: Machining Operation
Surface Roughness
Mathematical Model
Taguchi Method
Genetic Algorithm
Issue Date: 2006
Citation: Proceedings of the International Conference on Global Manufacturing and Innovation - July 27-29, 2006
Abstract: urface quality is one of the specified customer requirements for machined parts. There are many parameters that have an effect on surface roughness, but those are difficult to quantify adequately. In finish turning operation many parameters such as cutting speed, feed rate, and depth of cut are known to have a large impact on surface quality. In order to enable manufacturers to maximize their gains from utilizing hard turning, an accurate model of the process must be constructed. Several statistical modeling techniques have been used to generate models including regression and Taguchi methods. In this study, an attempt has been made to generate a surface roughness prediction model and optimize the process parameters Genetic algorithms (GA). Future directions and implications for manufacturers in regard to generation of an robust and efficient machining process model is discussed
Description: Copyright for the paper belongs to Proceedings Publisher
URI: http://hdl.handle.net/2080/440
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