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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2074/1044
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Full metadata record
| DC Field | Value | Language |
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| contributor.author | Suresh, P V S | - |
| contributor.author | Rao, P Venkateswara | - |
| contributor.author | Deshmukh, S G | - |
| date.accessioned | 2005-12-26T07:15:10Z | - |
| date.available | 2005-12-26T07:15:10Z | - |
| date.issued | 2002 | - |
| identifier.citation | International Journal of Machine Tools and Manufacture, 42(6), 675-680 | en |
| identifier.uri | http://eprint.iitd.ac.in/dspace/handle/2074/1044 | - |
| description.abstract | Due 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 |
| format.extent | 146806 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | en |
| subject | automated machine tools | en |
| subject | dimensional accuracy | en |
| subject | roughness prediction model | en |
| subject | response surface methodology | en |
| subject | TiN-coated tungsten carbide | en |
| subject | machining parameters | en |
| subject | genetic algorithms | en |
| subject | optimal machining | en |
| title | A genetic algorithmic approach for optimization of surface roughness prediction model | en |
| type | Article | en |
| Appears in Collections: | Mechanical Engineering
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Description |
Size | Format |
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| sureshgen2002.pdf | | 143Kb | Adobe PDF | View/Open |
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