An ANFIS-Based Approach for Predicting the Surface Roughness of Cold Work Tool Steel in WEDM

Abstract

Wire electrical discharge machining known as non-traditional machining processes, has a significant role in the manufacturing industry. Conductive materials, which can have intricate and complex forms, can be obtained regardless of hardness. In this study, the surface roughness of Sleipner cold work steel is evaluated under various machining process parameters in the WEDM process. In the experiments the feed rate, current, and pulse on time are used as independent variables. In order to predict the surface roughness, an Adaptive Neuro-Fuzzy Inference system was applied based on experimental data.