Investigation and optimization of electro discharge face grinding of monel 400 alloy using response surface methodology and genetic algorithm

This study investigates and optimizes electro discharge face grinding (EDFG) for processing Monel 400 alloy. The EDFG process is complex, and choosing the appropriate input process parameters is challenging. In this work, the central composite design (CCD) of response surface methodology (RSM) was used to plan the experiments, and analysis of variance (ANOVA) was used to identify the significance of process parameters. This work aimed to assess the influence of grinding wheel speed (GWS) along with other process parameters on response parameters and to optimize the process parameters for the EDFG process. The ANOVA confirmed that GWS significantly affects the response parameters. Finally, the Desirability approach and Genetic algorithm (GA) optimizations were employed and compared to determine the best optimal process parameter settings for the process, with the three different objective function weights related to material removal rate (MRR) and average surface roughness (ASR). The optimum conditions were identified and confirmed through validation experiments. The machined surfaces were also analyzed using scanning electron microscope (SEM) images to assess the effects of the suggested parameter combinations. The study concluded that the GA approach was more effective in optimizing the EDFG process.