Nowadays, electrical discharge machining (EDM) has become one of the most extensively used non-traditional material removal process. Its unique feature of using thermal energy to machine hard to machine electrically conductive materials is its distinctive advantage in the manufacturing of moulds, dies and aerospace components. However, EDM is a costly process and hence proper selection of its process parameters is essential to reduce production cost and improve product quality. In this study the effect of input EDM process parameters on AISI2312 hot worked steel, widely used in mold manufacturing, is modeled and optimized. The proposed approach is based on statistical analysis on the experimental data. The input parameters are peak current (I), pulse on time (Ton), pulse off time (Toff), duty factor () and voltage (V). Material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) are the most important performance characteristics of the EDM process. The experimental data are gathered using Taguchi L36 design matrix. Taguchi robust design technique was applied to obtain the signal to noise ratio (S/N ratio) for the quality characteristics being investigated. In order to establish the relations between the input and the output parameters, various regression functions have been fitted on the evaluated S/Ns data based on output characteristics. The significance of the process parameters on the quality characteristics of the EDM process was also evaluated quantitatively using the analysis of variance (ANOVA) method. Then, statistical analyses and validation experiments have been carried out to select the best and most fitted models. In the last section of this research, simulated annealing (SA) algorithm has been employed for optimizations of process parameters. Using the proposed optimization procedure, proper levels of input parameters for any desirable group of process outputs can be identified. A set of verification tests is also performed to verify the accuracy of optimization procedure in determining the optimal levels of machining parameters. The results indicate that the proposed modeling technique and simulated annealing algorithm are quite efficient in modeling and optimization of EDM process parameters.

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