Optimization of Process Parameters for Better Surface Morphology of Electrical Discharge Machining-Processed Inconel 825 Using Hybrid Response Surface Methodology-Desirability Function and Multi-objective Genetic Algorithm Approaches

The present work focuses on the influence of spark current (Is), pulse on time (Ton) and duty cycle (DC) on the material removal rate (MRR) and surface roughness (SR) in electrical discharge machining of Inconel 825. The experimental runs have been decided using the Box-Behnken design, and further analysis of variance (ANOVA) has been employed to develop the relationship between process and performance parameters. Often, the findings based on experiments and their analysis to optimize the process parameters do not provide accurate results. Therefore, this work implemented a hybrid approach including response surface methodology-desirability function (RSM-DF) and multi-objective genetic algorithm (MOGA) to determine the optimal process parameters to obtain a high MRR and a low SR value. The ANOVA's outcomes indicate that Is contributes significantly to MRR and SR compared to Ton and DC. Essentially, this work inculcates process parameters optimization using RSM-DF and MOGA for better surface morphology of the machined surfaces. Based on the confirmation test, it was observed that MOGA results outperformed the RSM-DF results. Additionally, surface characterizations of the machined surfaces with atomic force microscopy, energy-dispersive x-ray spectroscopy and field emission scanning electron microscopy have been performed, which also revealed that the optimum process parameters obtained from MOGA have better surface morphological characteristics. Furthermore, microhardness testing revealed that optimized parameters have better surface properties than initial machined conditions.