Title: Experimentation and prediction of material removal rate of electrical discharge micromachining of nickel-based super alloy thin sheet

Authors: Rajesh Kumar Porwal; Vinod Yadava

Addresses: Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad (U.P.)-211 004, India ' Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad (U.P.)-211 004, India

Abstract: Material removal rate (MRR) has been an important factor in predicting the performance measure of any micromachining process. Hole drilling electrical discharge micromachining (HD-EDMM) is an efficient technology for micromachining of electrically conductive engineering materials. A predictive artificial neural network (ANN) model for the MRR in HD-EDMM process has been proposed in the present paper. For this purpose, MATLAB with the neural networks toolbox (nntool) has been used. Training of the model has been performed with data from an extensive series of HD-EDMM experiments on invar thin sheet workpiece material. The proposed model uses the gap voltage, capacitance of capacitor, and the revolution per minute of tool electrode as input parameters. The reported results indicate that the proposed ANN model can satisfactorily predict the MRR in HD-EDMM with an average prediction error of 2.3%.

Keywords: hole drilling; electrical discharge micromachining; HD-EDMM; artificial neural networks; ANNs; EDMM; EDM; electrical discharge machining; electro-discharge micromachining; electro-discharge machining; back-propagation; material removal rate; MRR prediction; nickel based super alloys; thin sheets.

DOI: 10.1504/IJCAET.2014.058004

International Journal of Computer Aided Engineering and Technology, 2014 Vol.6 No.1, pp.62 - 73

Published online: 17 Jun 2014 *

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