Multiresponse optimization of micro-wire electrical discharge machining process

In recent years, micro-wire electric discharge machining (micro-WEDM) has become one of the most popular micromachining processes used for creating complex microfeatures on electrically conductive materials. However, it suffers from few limitations such as low machining efficiency and poor surface finish. To overcome these limitations, an attempt has been made to model the performance of micro-WEDM process. The effect of various process parameters such as gap voltage, capacitance, feed rate, and wire tension on the performance characteristics of micro-WEDM is studied on titanium alloy (Ti-6AL-4V). Analysis of variance (ANOVA) is performed to identify the significant factors. From the study, it is revealed that capacitance is the predominant factor that influences the machining characteristics. In order to correlate relationship between process parameters and responses, a fuzzy logic model has been employed to predict the process characteristics based on experimental observations. Traditional fuzzy logic system is unable to directly handle uncertainties and its simple information processing method will lead to low accuracy and poor dynamic quality of the system. The feasible way for solving this is to use particle swarm optimization (PSO) algorithm to enhance the performance of micro-WEDM process. Experimental results confirm the feasibility of proposed fuzzy logic strategy and the developed particle swarm optimization (PSO) algorithm. The adequacy of the model is tested and validates the results by conducting the experiments with optimum parameter settings.