Comparative Neural Network Models on Material Removal Rate and surface Roughness in Electrical Discharge Machining

Electro-discharge machining (EDM) is increasingly being used in many industries for producing molds and dies, and machining complex shapes with material such as steel, cemented carbide, and engineering ceramics. The stochastic nature of EDM process has frustrated number of attempts to model it physically. Artificial neural networks (ANNs), as one of the most attractive branches in Artificial Intelligence (AI), has the potentiality to handle problems such as prediction of design and manufacturing cost, material removal rate (MRR), diagnosis, modeling, and adaptive control in a complex design and manufacturing systems. This paper uses Back Propagation Neural Network (BP) and Radial Basis Function (RBF) approach for prediction of material removal rate and surface roughness and presents the results of the experimental investigation. Charmilles Technology (EDM-ROBOFORM200) in he mechanical engineering department is used for machining parts. The networks have four inputs of current (I), voltage (V), Period of pulse on (Ton) and period of pulse off (Toff) as the input processes variables. Two outputs results of material removal rate (MRR) and surface roughness (Ra) as performance characteristics. In order to train the network, and capabilities of the models in predicting material removal rate and surface roughness, experimental data are employed. Then the output of MRR and Ra obtained from neural net compare with experimental results, and amount of relative error is calculated.