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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/1044

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dc.contributor.authorSuresh, P V S-
dc.contributor.authorRao, P Venkateswara-
dc.contributor.authorDeshmukh, S G-
dc.date.accessioned2005-12-26T07:15:10Z-
dc.date.available2005-12-26T07:15:10Z-
dc.date.issued2002-
dc.identifier.citationInternational Journal of Machine Tools and Manufacture, 42(6), 675-680en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/1044-
dc.description.abstractDue to the widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining mild steel, using Response Surface Methodology (RSM). The experimentation was carried out with TiN-coated tungsten carbide (CNMG) cutting tools, for machining mild steel work-pieces covering a wide range of machining conditions. A second order mathematical model, in terms of machining parameters, was developed for surface roughness prediction using RSM. This model gives the factor effects of the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA) to optimize the objective function. The GA program gives minimum and maximum values of surface roughness and their respective optimal machining conditions.en
dc.format.extent146806 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectautomated machine toolsen
dc.subjectdimensional accuracyen
dc.subjectroughness prediction modelen
dc.subjectresponse surface methodologyen
dc.subjectTiN-coated tungsten carbideen
dc.subjectmachining parametersen
dc.subjectgenetic algorithmsen
dc.subjectoptimal machiningen
dc.titleA genetic algorithmic approach for optimization of surface roughness prediction modelen
dc.typeArticleen
Appears in Collections:Mechanical Engineering

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