PREDICTION OF MATERIAL REMOVAL RATE USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORK OF ECDM PROCESS

The combination of two non conventional machining processes is known as Hybrid machining. It has become one of the most important machining techniques for glass and other brittle materials. In this paper, an attempt has been made to combine Electro Chemical machining(ECM) and Electrical Discharge Machining (EDM) to form a hybrid machining known as Electro Chemical Discharge Machining (ECDM). It can be applied for micro machining and micro finishing on the glass and its composites. The empirical models have been developed for grooving process of ECDM using Regression Analysis (RA) and the Artificial Neural Network (ANN) to predict the Material Removal Rate (MRR) . According to results, the prediction of ANN model for MRR is better as compared to the Regression model. The two models of ECDM process can be recommended to predict the MRR for grooving of ECDM for the given range of process parameters. The process parameters of the ECDM process are optimized. ANOVA is used to identify the significant affect of process parameters.