PARAMETRIC OPTIMIZATION AND SURFACE CHARACTERIZATION OF WIRE ELECTRICAL-DISCHARGE MACHINING PROCESS

Wire electrical discharge machining (WEDM) technology has been widely used in conductive material machining. The WEDM process, which is a co mbination of electrodynamic, electromagnetic, thermaldynamic, and hydr odynamic actions, exhibits a complex and stochastic nature. Its perfor mance, in terms of surface finish and machining productivity, is affec ted by many factors. This paper presents an attempt at optimization of the process parametric combinations by modeling the process using art ificial neural networks (ANN) and characterizes the WEDMed surface thr ough time series techniques. A feed-forward back-propagation neural ne twork based on a central composite rotatable experimental design is de veloped to model the machining process. Optimal parametric combination s are selected for the process. The periodic component of the surface texture is identified, and an autoregressive AR(3) model is used to de scribe its stochastic component. (C) Elsevier Science Inc., 1997.