Prediction and Optimization of Material Removal Rate in Wire Electrical Discharge Machining of Ti-6Al-4V Using Taguchi Design and Machine Learning Approach

Ti-6Al-4V is widely employed for use in aerospace, biomedicine and marine engineering applications. Material removal rate (MRR) in Wire Electrical Discharge Machining (WEDM) is strongly dependent on the wire electrode and discharge settings but has been rarely studied using statistical and Machine Learning (ML) methods together. For this purpose, a Taguchi L27 orthogonal array design was used to study the influence of peak current, pulse-on time, pulse-off time and wire electrode (brass, zinc-coated brass, molybdenum) on the MRR. The very same 27 experimental data points were analyzed using the signal-to-noise ratio method and analysis of variance, as well as using five ML algorithms for regression (polynomial ridge regression, support vector regression, random forest, extreme gradient boosting and artificial neural network) assessed through leave-one-out cross-validation. Peak current, pulse-on time and wire electrode had the strongest influence, whereas pulse-off time was statistically insignificant. The support vector regression approach performed better with an R2 of 0.81 in cross-validation and root mean squared error of 0.31 mm3/min in predicting the MRR. Both optimization methods predicted identical optimal conditions (zinc-coated brass wire, pulse-on time of 111 µs, pulse-off time of 61 µs, and peak current of 220 A). The only exception was an insignificant pulse-off time. Since both analysis methods use the same non-replicated data set (n = 27), the agreement is just a consistency test, not a validation, and the results are valid only for the examined range.