ANN MODELING OF WIRE EDM AND OPTIMIZATION OF CUTTING PARAMETERS BY GA

Abstract

Wire electrical discharge machining (WEDM) technology has been widely used in conductive material machining especially when intricate shapes and profiles have to be cut. Manufacturers and users of this process always want to achieve higher machining productivity with a desired accuracy and surface finish. The WEDM process's performance, in terms of surface fmish and machining productivity is however affected by; many factors such as applied machining voltage, ignition pulse current, pulse duration, time between two pulses, servo-speed variation, servo-control reference voltage, wire speed, wire tension, conductivity of dielectric and injection pressure for dielectric. The material of the work piece and its height also influence the process. If the setting of one of the above parameters changes, it affects the process in a complex way. Because of the many variables and the complex and stochastic nature of the process, achieving the optimal performance, even for a highly skilled operator with a state-of-the-art WEDM machine is rarely possible. An effective way to solve this problem is to discover the relationship between the performance of the process and its controllable input parameters i.e., model the process through suitable mathematical techniques. However, the complex and stochastic nature of the WEDM process has made it difficult to establish a conclusive analytical model; therefore, an empirical method can be adopted. The present study is amid at exploiting the strong capabilities of both ANN and GA, which are suitable for solving manufacturing problems that are amenable for modeling using traditional methods. A feed-forward back-propagation neural network based on Taguchi L18 experimental design is developed to model the machining process. GA is then employed to find the optimal operating conditions so that the productivity of wire EDM is improved for a given surface finish requirement. The set of Pareto-optimal solutions is searched for the processing of titanium alloy. The model was tested with experimental data and good correlation was obtained between the expected and experimental results.