Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy

Modelling of Surface Roughness and Change in Out-of-Roundness of Tool during Electrical Discharge Machining with Cermet Tool Tip Using Machine Learning

Surface roughness of the finished part and profile of the tool electrode are significant factors to assess the functionality of electrical discharge machining process. In this study, EDM was utilized for the machining of hardened EN31 steel. A sintered cermet tool tip with 75% copper–25% titanium carbide was fabricated and used as tool electrode. A data set of 262 such samples was developed with machining variables including discharge current (Ip), gap voltage (Vg), pulse on time (Ton), pulse off time (Toff) and flushing pressure (P). By correlating the machining variables, a machine learning-based regression model was developed for the prediction of surface roughness of the machined surface and change in out-of-roundness of tool during the EDM process. With the help of heat maps and a probability table, it was found that Ip, Ton, Toff and P had significant effect on SR, and Ip, Ton and Toff affected OOR. The machine learning-based regression equation predicted SR with average error of 1.6% and OOR with average error of 0.48%. It was found that machine learning-based regression equation had better accuracy as compared to a DOE-based regression equation.