In the present study artificial neural network (ANN) along with particle swarm optimization (PSO) and simulated annealing (SA) algorithms have been employed for modeling and optimization of electrical discharge machining (EDM) process of AISI2312 hot worked steel parts. The process input parameters considered here include voltage (V), peak current (I), pulse off time (Toff), pulse on time (Ton) and duty factor (η). The process quality measures are surface roughness (SR), tool wear rate (TWR) and material removal rate (MRR). The objective is to determine a combination of process parameters to minimize TWR and SR and maximize MRR independently (as single objective) and also simultaneously as multi-criteria optimization. The experimental data are gathered based on Taguchi L36 orthogonal array design of experiments. The three performance characteristics (MRR, TWR and SR) obtained from experimental tests. Then, the outputs are used to develop the artificial neural network (ANN) model. Next, in order to determine the best set of process parameters values for a desired set of process quality measures the developed ANN model is embedded into heuristic algorithms (SA and PSO) and their derived results have been compared. Validation of the results has been carried out through a series of experimental test run under the optimal machining conditions. It is evident that the proposed optimization procedure is quite efficient in modeling and optimization of EDM process parameters

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