Modeling and Optimization of Surface Roughness of AISI2312 Hot Worked Steel in EDM based on Mathematical Modeling and Genetic Algorithm

Abstract

In this study the effect of input EDM parameters on the surface quality of 2312 hot worked steel parts has been 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). The experimental data are gathered using Taguchi L36 design matrix. In order to establish the relations between input and output parameters, regression function has been fitted on the Signal to Noise ratios of the experimental data. The results of analysis of variance (ANOVA) revealed that pulse on time and peak currents significantly influence the surface quality. In the next stage, the developed model is embedded into a genetic algorithm to determine the optimal set of process parameters for any desired surface roughness (within feasible ranges). Using optimization results, a set of verification tests is performed to verify the accuracy of the optimization procedure in determining the optimal levels of machining parameters. Computational results indicate that the proposed modeling technique and genetic algorithm are quite efficient in modeling and optimization of EDM process parameters.