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

Abstract

Surface coating is covering the surface of workpiece with desired materials in order to improve the surface properties. Electrical discharge coating (EDC) is an electro thermal process, used for creating hard coating over the workpiece. EDM parameters play important role to improve the surface properties. 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. During 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 (Artificial neural network) model developed by back propagation algorithm is proposed in this study for predicting 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 of discharge current and on time. SR decreased with increasing of applied load of the compaction, whereas when increase the current and pulse on time, SR increased.

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Research on self-healing characteristic and state prediction method of the copper based powder metallurgy materials on friction interface

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