Application of Taguchi Grey Approach and Sustainable Development of Hybrid Machine Learning Model for Wire Electrical Discharge Machining of Low Carbon Steel

SAE 1010 steel is widely used in the automotive industry for fasteners and in aerospace and other engineering applications. Traditional machining of complicated forms is difficult. Wire Electrical Discharge Machining (WEDM), a specialized form of EDM, is effective for complex machining operations. This study optimises WEDM of SAE 1010 steel while considering environmental impact. A naturally available dielectric medium promotes sustainability without reducing machining efficiency. Pulse on time, pulse off time, and peak current affect material removal rate (MRR), surface roughness (Ra), and tolerance errors. ANOVA is used to determine the significance of the parameters. A hybrid Grey-ANFIS (Adaptive Neuro-Fuzzy Inference System) model improves performance predictions. The results show that Grey-ANFIS provides accurate forecasts, improving machining parameter optimization. The prediction model was highly accurate, with a MAPE of 0.0432, RMSE of 0.00029, and MAE of 0.000432. The model also correlated well with actual values, with a Correlation Coefficient of 0.9997.