Multi-Objective Optimization of MRR, TWR and Radial Overcut of EDMed AISI D2 Tool Steel Using Response Surface Methodology, Grey Relational Analysis And Entropy Measurement

This article illustrates an application of a hybrid optimization approach for the determination of the optimum machining parameters to achieve better productivity without negotiating the qualities and accuracy of the EDMed components. A synergy of Response Surface Methodology (RSM), Grey Relational Analysis (GRA) coupled with Energy measurement method has been applied that maximises Material Removal Rate (MRR) and simultaneously minimises Tool Wear Rate (TWR) & Radial overcut or Gap(G) during Electrical Discharge Machining (EDM) of AISI D2 Tool steel. The input process parameters considered are pulse current (Ip), pulse duration (Ton), duty cycle (Tau) and discharge voltage (V). A face centered Central Composite Design (CCD) has been adopted for conducting the experiments. The designed experimental results were used in grey relational analysis, and the weights of the quality characteristics were decided by utilizing the entropy measurement method. The significant parameters are obtained by accomplishing Analysis of Variance (ANOVA). Based on the RSM results, it is found that the grey relational grades are considerably influenced by the machining parameters and some of their interactions. Ip is found to be most influencing parameter with 35.02%contribution followed by interaction of Ip×Ton and Tau with 21.74%and 17.73%contribution respectively. The coefficient of determination (R2) is found to be 91.1%which is quite satisfactory. These results furnish useful information to control the responses and ensure the high productivity and accuracy of the component. This method is simple with easy operability, and moreover the results have also been confirmed by running the confirmation tests.