Prediction and parametric effect of electrical discharge layering of AZ31B magnesium alloy using response surface methodology-assisted artificial neural network

Abstract

Electro-discharge layering (EDL) is a coating technique, used for fabricating wear and corrosion resistance layer on the parts used in automobile, biomedical and structural applications. Eventhough, selecting the suitable parameters and levels in EDL process is difficult to attain better coating characteristics. We have studied the prediction and effect of process parameters on EDL of magnesium alloy using response surface methodology (RSM)-assisted feed forward back propagation artificial neural network (ANN). In this work, WC–Cu composite coating was deposited on AZ31B magnesium alloy using EDL viz. compaction pressure (CP), discharge current (DC) and pulse on time (PT). Influence of process parameters on electrode deposition rate (EDR) and coating roughness (CR) during EDL of magnesium alloys is studied. It was revealed that the correlation between the experimental values of RSM and predicted values of ANN was 0.991, which is closely to the working limit. Therefore, it was agreed that the established ANN model is suitable for predicting the EDR and CR. Furthermore, effect of parameters on CR and EDR are studied with support of mean effect plots generated by RSM tool. It was studied that the CR and EDR will increase, as increase in DC and PT at processing with low compaction pressured electrode. Conversely, it decreases with increase in CP of the electrode. Mechanism of coating, such as craters and globules were identified in the surface coated with higher DC and PT, resulting in higher surface roughness values.