|Title:||RSM and ANN modeling approaches for predicting average cutting speed during WEDM of SiCp/6061 Al MMC|
Jain, P. K.
|Published in:||Proceedings of Procedia Engineering|
|Abstract:||This paper describes the response surface methodology (RSM) and artificial neural network (ANN) based mathematical modeling for average cutting speed of SiCp/6061 Al metal matrix composite (MMC) during wire electric discharge machining (WEDM). Four WEDM parameters namely servo voltage (SV), pulse-on time (ToN), pulse-off time (TOFF) and wire feed rate (WF) were chosen as machining process parameters. A back propagation neural network was developed to establish the process model. The performance of the developed ANN models were compared with the RSM mathematical models of average cutting speed. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. Combined effect of input process parameters on average cutting speed shows that voltage is more significant parameter on avergae cutting speed than pulse-off time and wire feed rate. Â© 2013 The Authors. Published by Elsevier Ltd.|
|Citation:||Proceedings of Procedia Engineering, (2013), 767- 774. Chennai|
|Keywords:||Artificial Neural Network|
Metal Matrix Composite
Response Surface Methodology
Wire Electric Discharge Machining
|Author Scopus IDs:||54785113700|
|Author Affiliations:||Shandilya, P., Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, 211004, India|
Jain, P.K., Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, 247667, Uttarakhand, India
Jain, N.K., Department of Mechanical Engineering, Indian Institute of Technology, Indore, 452017, India
|Corresponding Author:||Shandilya, P.; Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, 211004, India; email: email@example.com|
|Appears in Collections:||Conference Publications [ME]|
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