Title :
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
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; Igap 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;
Conference_Titel :
Electrical, Control and Computer Engineering (INECCE), 2011 International Conference on
Conference_Location :
Pahang
Print_ISBN :
978-1-61284-229-5
DOI :
10.1109/INECCE.2011.5953887
