Mathematical and artificial neural network model in composite electrode assisted electrical discharge coating

Abstract The present paper is studied the mathematical and artificial neural network (ANN) model in electrical discharge deposition of magnesium alloy. Surface coating is covering the workpiece surface with desired coating materials to improve the surface properties. Electrical discharge coating (EDC) is an electro thermal process, used for creating hard coating over the workpiece. In this present study, magnesium alloy is deposited using WC-Cu composite electrode by EDC. RSM is used to develop design matrix for carrying out EDC experiments. Compaction load, discharge current and pulse on time are controlled, whereas material deposition rate (MDR) and surface roughness (SR) are measured as response. The objective of this investigation is to predict the MDR and SR using neural network technique. ANN model developed by back propagation algorithm is proposed in this study for predicting the responses. ANOVA is conducted to identify the dominating parameter, which significantly affects the responses. Correlation coefficient between the ANN and RSM is 0.99, which is close to the unity for ANN. It was revealed that the prediction of proposed ANN was found to be excellent to the RSM model. MDR increased with increasing discharge current and pulse on time. SR decreased with increasing compaction load.