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| Content Provider | Springer Nature Link |
|---|---|
| Author | Prete, A. Vitis, A. A. Anglani, A. |
| Copyright Year | 2010 |
| Abstract | Due to the widespread use of highly automated machine tools, 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 Al7075-T6, using Response Surface Methodology (RSM). Machining operations of work pieces made by Al7075-T6 covering a wide range of machining conditions have been carried out with by flat end mill with four teeth made by High Speed Steel. A RS model, in terms of machining parameters, was developed for surface roughness prediction using the Radial Basis Functions (RBF) technique. This model gives the process response sensitivity to the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA). |
| Starting Page | 467 |
| Ending Page | 470 |
| Page Count | 4 |
| File Format | |
| ISSN | 19606206 |
| Journal | International Journal of Material Forming |
| Volume Number | 3 |
| Issue Number | 1 |
| e-ISSN | 19606214 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2010-06-12 |
| Publisher Place | Paris |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Milling Cutting conditions Surface Roughness Response Surfaces Methodology Genetic Algorithm Computer-Aided Engineering (CAD, CAE) and Design Computational Intelligence Mechanical Engineering Manufacturing, Machines, Tools Materials Science Operating Procedures, Materials Treatment |
| Content Type | Text |
| Resource Type | Article |
| Subject | Materials Science |
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