Fuzzy logic-based forward modeling of Electro Chemical Machining process

Electro Chemical Machining (ECM) is one of the most widely used advanced machining processes to produce complicated shapes from electrically conductive but difficult to machine materials. This paper presents a Fuzzy Logic (FL) - based modeling of ECM process and optimization of its rule base, data base and consequent part utilizing a Genetic Algorithm (GA). A binary coded GA has been used for the said purpose. While modeling with FL, the output parameters, namely Material Removal rate (MRR) and Surface finish (Ra) have been predicted for different combinations of process parameters, such as current, voltage, flow rate and gap between work piece and the tool. A batch mode of training has been provided to the FL with the help of two hundred training data generated artificially using the conventional statistical regression equations. The results of this study on a number of test scenarios show that the proposed genetic-fuzzy approach can predict in a near optimal manner.