Improvement of Surface Finish when EDM AISI 2312 Hot Worked Steel using Taguchi Approach and Genetic Algorithm

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. Howevere, 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 parameters on 2312 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 (h) and voltage (V). Surface roughness is the most important performance characteristic in EDM. The experimental data are gathered using Taguchi L36 design matrix. In order to establish the relations between input and output parameters, various regression functions have been fitted on the experimental data. In the last section of this research, genetic algorithm has been employed for optimization of process parameters. Using the proposed optimization procedure, proper levels of input parameters for any desirable output can be identified. A verification test 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 genetic algorithm are quite efficient in modeling and optimization of EDM process parameters.