DEVELOPMENT OF HYBRID MODEL AND OPTIMIZATION OF METAL REMOVAL RATE IN ELECTRIC DISCHARGE MACHINING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM

The present work is aimed at optimizing the metal removal rate of die sinking electric discharge machining (EDM) by considering the simultaneous affect of various input parameters. The experiments are carried out on Ti6Al4V, HE15, 15CDV6 and M-250. Experiments were conducted by varying the peak current and voltage and the corresponding values of metal removal rate (MRR) were measured. Multiperceptron neural network models were developed using Neuro solutions package. Genetic algorithm concept is used to optimize the weighting factors of the network. It is observed that the developed model is within the limits of the agreeable error when experimental and network model results are compared for all performance measures considered. It is further observed that the maximum error when the network is optimized by genetic algorithm has been reduced considerably. Sensitivity analysis is also done to find the relative influence of factors on the performance measures. It is observed that type of material is having more influence on the performance measures