Genetic Algorithm-Based Optimization of Artificial Neural Network of Process Parameters and Characterization of Machining Errors in Graphene Mixed Dielectric

The increasing demands for precision and efficiency in machining processes, driven by advancements in aerospace, biomedical, and automotive industries, have led to a growing need for highly accurate machined parts. Stainless steel 316, a commonly used material in these sectors, presents specific challenges due to its intended applications, particularly in biomedical and aerospace fields. Electric discharge machining (EDM) is a favored method for working with this alloy. However, EDM has inherent challenges, such as dimensional overcuts, which have limited its applicability. To address these issues, the potential of three different electrode materials, namely, copper (Cu), brass, and aluminum (Al), has been extensively explored. Additionally, the choice of the optimal dielectric is crucial as it directly affects the heat input to the electrode, influencing the melting and vaporization of the tool wear. In this context, the inclusion of graphene as an additive has been explored to minimize radial overcuts. It is worth noting that these concerns have not been comprehensively addressed before. The experimental design by Taguchi has been employed for the research, and the results indicate that the performance of the Cu electrode surpasses that of other dielectrics. The artificial intelligence-based artificial neural network (ANN) was constructed to predict the values of OC. It was found that in the given dataset, the R² has a value greater than 0.99 for all possible training and validation. Based on the ANN, a multi-objective optimization through genetic algorithm (MOGA) was performed. It was found that the dimensional accuracy achieved by Cu electrode was 70.51% better than the highest OC value given by same electrode. Moreover, the OC suggested by the MOGA for brass and Al electrodes was 40.21% and 34.37% better compared to the highest values of radial overcut obtained during the actual experimentation.