EVOLUTIONARY OPTIMIZATION OF WIRE EDM PROCESS FOR THE SURFACE FINISH ON A MAGNESIUM AZ91D ALLOY USING AN ANN AND A GENETIC ALGORITHM

In this research the optimizations of the wire EDM process parameters to achieve a minimal surface roughness on a magnesium AZ91D alloy have been carried out. The experiments were conducted with three machining factors, i.e., the pulse-on time, the pulse-off time, and the wire feed, using a Box-Behnken design of experiment. The effects of the Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) models were compared and studied, and it has been found that the ANN approach predicts the perfect output response. The genetic algorithm (GA) was then utilized to determine the best machining parameters to provide a better surface finish using the projected ANN outcomes, which were then used to build a quadratic equation. Furthermore, the optimum machining parameters were identified for a better surface finish through the integration of the ANN and GA approach. Based on the aforementioned findings, this study showed that the suggested methods are capable of predicting the optimum machining parameters, which would be beneficial in the low-cost manufacturing sector.