Prediction and optimization of influential kerf width parameters for machining of aluminum hybrid ceramic composite material

Abstract

In this study, the effectiveness of different wire feed rates, pulsed current, spark gap voltage, pulse on time, and pulse off time was investigated to determine their impact on kerf width. The ideal parameters for wire-EDM machining of aluminum hybrid ceramic composite were determined through experimental investigation and an adaptive neuro-fuzzy inference system (ANFIS). Additionally, five distinct predictive models for influential kerf width parameters were developed as an innovative approach. A hybrid learning methodology combining back propagation and least square estimations was employed to create these predictive models. The prediction ranked the machining parameters affecting kerf width dimensions as wire feed rate, pulsed current, pulse on time, pulse off time, and spark gap voltage. Experimental findings showed that the kerf width of the machined workpiece significantly increased as the wire feed rate increased. This exploratory study suggested a wire feed rate setting of 3 mm/min with a current of 2 A and a pulse on time of 0.6 μs to achieve the best quality machined surface for the aluminum hybrid ceramic composite. Similarly, the proposed optimization model results proved that the experimental findings were near-optimal solutions.