An Integrated ANN - GA Approach to Maximise the Material Removal Rate and Surface Roughness of Wire Cut EDM on Titanium Alloy

This investigation was planned to get the optimized material removal rate and surface roughness of Wire Cut Electric Discharge Machining (WCEDM) on Ti6A4 V by taking into consideration of four input factors such as pulse on, pulse off, voltage and input power. Taguchi supported L9 orthogonal array was used to determine the total number of experimental conditions and its values of material removal rate (MRR) and surface roughness (SR) were calculated. Instead of trying the traditional regression model, in this investigation ANN model was constructed; as ANN is more effective when the number of experiments is restricted. To optimize the material removal rate and surface roughness, a feed forward artificial neural network model was developed and genetic algorithm was used by optimizing the weighing factors of the network in the neural power software. Finally, the model was achieved with the root mean square error of 0.0059 and 0.0033 for MRR and SR respectively. In turn the optimized value of MRR and SR were found 7429 mm(3)/min and 2.1068 mu m.