Modelling electrode wear in an EDM process using data transformation-based polynomial and GLM

Modelling Electrical Discharge Machining (EDM) processes based on physical laws has been the subject of much debate in the literature. Therefore empirical regression modelling is frequently used in practice. In this study, a polynomial regression model was developed to correlate the electrode wear with four EDM process parameters (current, pulse on-time, pulse off-time and capacitance) while machining the cobalt-bonded tungsten carbide ceramic. As many factor interactions were found significant, lambda plot was used to find an appropriate response transformation that can simplify the model and improve its interpretability. This was attained when adopting the inverse transformation as only three parameters (current, pulse on-time and pulse off-time) were significant with an adjusted-R2 of 0.939. Due to the possibility of rendering illogical predicted values when detransforming the results of the fitted model, an alternative Generalized Linear Model (GLM) with gamma distribution and a reciprocal link function was developed. Being associated with shorter mean response confidence intervals, the GLM was more reliable for estimating and predicting the response. This paper is the first to report the use of GLM in the context of modelling EDM processes.