Predicting Material Removal Rate of Electrical Discharge Machining (EDM) using Artificial Neural Network for High Igap current

Author

Andromeda, Trias ; Yahya, Azli ; Hisham, Nor ; Khalil, Kamal ; Erawan, Ade

Author_Institution

Fac. of Electr. Eng., UTM, Skudai, Malaysia

fYear

2011

fDate

21-22 June 2011

Firstpage

259

Lastpage

262

Abstract

This article presents a prediction of Material Removal Rate (MRR) in Electrical Discharge Machining (EDM) using Artificial Neural Network (ANN). Experimental data were gathered from Die sinking EDM process for copper-electrode and steel-workpiece. It is aimed to develop a behavioral model using input-output pattern of raw data from EDM process experiment. The behavioral model is used to predict MRR and than the predicted MRR is compared to actual MRR value. The results show good agreement of predicting MRR between them.

Keywords

electrical discharge machining; neural nets; production engineering computing; ANN; I_gap current; artificial neural network; copper-electrode; die sinking EDM process; electrical discharge machining; material removal rate prediction; steel-workpiece; Artificial neural networks; Discharges; Electrodes; Machining; Materials; Predictive models; Training; Artificial Neural Network(ANN); Electrical Discharge Machining(EDM); Material Removal Rate(MRR); predicting;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical, Control and Computer Engineering (INECCE), 2011 International Conference on

Conference_Location

Pahang

Print_ISBN

978-1-61284-229-5

Type

conf

DOI

10.1109/INECCE.2011.5953887

Filename

5953887