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dc.contributor.authorMahapatra, S S-
dc.contributor.authorPatnaik, A-
dc.contributor.authorPatnaik, Prabina Ku-
dc.identifier.citationProceedings of the International Conference on Global Manufacturing and Innovation - July 27-29, 2006en
dc.descriptionCopyright for the paper belongs to Proceedings Publisheren
dc.description.abstracturface 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 discusseden
dc.format.extent138319 bytes-
dc.subjectMachining Operationen
dc.subjectSurface Roughnessen
dc.subjectMathematical Modelen
dc.subjectTaguchi Methoden
dc.subjectGenetic Algorithmen
dc.titleParametric Analysis and Optimization of Cutting Parameters for Turning arametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Methoden
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