The machining characteristics of LM25 Al/20%SiCP composite using electrochemical machining was investigated in the present study. The present paper highlights the features of the development of a comprehensive mathematical model for correlating the interactive and higher order influences of various machining parameters on the dominant machining criteria, i.e. the metal removal rate and the surface roughness Ra phenomena, through response surface methodology, utilising relevant experimental data as obtained through experimentation. Experimental plan is performed by a standard response surface methodology design called a central composite design. The results of analysis of variance (ANOVA) indicate that the proposed mathematical model obtained can adequately describes the performance within the limits of the factors being studied. Optimal combination of these parameters can be used in order to achieve maximum metal removal rate and minimum Ra.

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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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