| Abstract | Wire cutting electrical discharge machining (WEDM) is a non-traditional technique by which the required profile is acquired using spark energy. Concerning wire cutting, precision machining is necessary to achieve high product quality. White layer thickness (WLT) is one of the most important factors for evaluating surface quality. Furthermore, WLT is among the most critical constraints in cutting parameters selection in WEDM. In this research, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the WLT in WEDM using a coated wire electrode. Experimental runs were conducted to validate the ANFIS model. The predicted data were compared with measured values, and the average prediction error for WLT was 2.61%. Based on the ANFIS model, minimum WLT is achieved at the lowest levels of peak current and pulse on-time with high level of pulse off-time. |