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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1018

Title: Comparisons of Neural Network Models on Surface Roughness in Electrical Discharge Machining
Authors: Pradhan, M K
Das, R
Biswas, C K
Keywords: Back propagation neural network
Electrical discharge machining
Radial basis function neural network
Surface roughness
Issue Date: 2009
Publisher: Professional Engineering Publishing
Citation: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture Volume 223, Issue 7, 1 July 2009, Pages 801-808
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.
URI: http://dx.doi.org/10.1243/09544054JEM1367
http://hdl.handle.net/2080/1018
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