Influence of Electrode Material and Process Parameters on Surface Quality and MRR in EDM of AISI H13 using ANN

Abstract

Electrical Discharge Machining (EDM) is a non conventional machining process where electrically conductive materials are machined by using precisely controlled spark that occurs between an electrode and a work piece in the presence of a dielectric fluid. It has been a demanding research area to model and optimize the EDM process in the present scenario. In the present p aper Artificial Neural Network (ANN) model has been proposed for the prediction of Material Removal Rate (MRR), Surface Roughness (SR) and Tool Wear Rate (TWR) in Electrical Discharge Machining (ED M) of AISI H13 Steel. For this purpose Neural Network Toolbox (nntool) with Matlab 7.1 has been used. The neural network based on process model has been generated to establish relationship between input process conditions ( Gap Voltage, Peak Current, Pulse On Time, Pulse Off Time and Electrode M aterial ) an d process responses (MRR, SR and TWR ). The ANN model has been trained and tested using the d ata generated from a series of experiments on EDM machine. The trained neural network system has been used to predict MRR , SR and TWR for different input conditions. The ANN model has been found efficient to predict EDM process response s for selected process conditions.