The Performance Prediction of Electrical Discharge Machining of AISI D6 Tool Steel Using ANN and ANFIS Techniques: A Comparative Study

AISI-D6 steel is widely used in the creation of dies and molds. In present paper, first electrical discharge machining (EDM) aforementioned material performed with a testing plan 32 trials. Then, artificial neural networks (ANN) adaptive neuro-fuzzy inference system (ANFIS) were applied to predict outputs. The effects some significant operational parameters—specifically pulse on-time (Ton), current (I), voltage (V)—on performance measures EDM processes such as removal rate (MRR), tool wear ratio (TWR), average surface roughness (Ra) are extracted. To lead process operators, plans (i.e., parameter–effect correlations) created. outcomes exposed upper values caused by higher amounts MRR Ra, likewise lower volumes TWR. Furthermore, growing resulted rate, ratio, roughness. Besides, input amount MRR, TWR, Ra. estimation models developed using experimental data recounting root means square error was determine training models. estimated based on have been proven an unseen validation set experiments. They found be decent agreement issues. investigation shows powerful learning capability ANFIS model its advantage terms modeling complex linear processes.