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http://hdl.handle.net/2080/1018
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DC Field | Value | Language |
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dc.contributor.author | Pradhan, M K | - |
dc.contributor.author | Das, R | - |
dc.contributor.author | Biswas, C K | - |
dc.date.accessioned | 2009-08-28T05:11:54Z | - |
dc.date.available | 2009-08-28T05:11:54Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture Volume 223, Issue 7, 1 July 2009, Pages 801-808 | en |
dc.identifier.uri | http://dx.doi.org/10.1243/09544054JEM1367 | - |
dc.identifier.uri | http://hdl.handle.net/2080/1018 | - |
dc.description.abstract | In this work, two different artificial neural networks (ANNs) models: Back propagation neural network (BPN) and radial basis function neural network (RBFN) are presented for the prediction of surface roughness in die sinking Electrical Discharge Machining (EDM). The pulse current (Ip), the pulse duration (Ton) and duty cycle (τ) are chosen as input variable with a constant voltage 50 volt, surface roughness is the output parameters of the model. A widespread series of EDM experiments was conducted on AISI D2 steel to acquire the data for training and testing and it was found that the neural models could predict the process performance with reasonable accuracy, under varying machining conditions. However, RBFN is faster than the BPNs and the BPN is reasonably more accurate. Moreover, they can be considered as valuable tools for EDM, by giving reliable predictions and provide a possible way to avoid time and money consuming experiments. | en |
dc.format.extent | 567453 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | Professional Engineering Publishing | en |
dc.subject | Back propagation neural network | en |
dc.subject | Electrical discharge machining | en |
dc.subject | Radial basis function neural network | en |
dc.subject | Surface roughness | en |
dc.title | Comparisons of Neural Network Models on Surface Roughness in Electrical Discharge Machining | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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comparison.pdf | 554.15 kB | Adobe PDF | View/Open |
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