Abstract
Wire electrical discharge machining (WEDM) technology has been widely used in conductive material machining. The WEDM process, which is a combination of electrodynamic, electromagnetic, thermaldynamic, and hydrodynamic actions, exhibits a complex and stochastic nature. Its performance, in terms of surface finish and machining productivity, is affected by many factors. This paper presents an attempt at optimization of the process parametric combinations by modeling the process using artificial neural networks (ANN) and characterizes the WEDMed surface through time series techniques. A feed-forward back-propagation neural network based on a central composite rotatable experimental design is developed to model the machining process. Optimal parametric combinations are selected for the process. The periodic component of the surface texture is identified, and an autoregressive AR(3) model is used to describe its stochastic component.
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