OPTIMIZATION OF OPERATING PARAMETERS FOR EDM PROCESS BASED ON ANN – A HYBRID APPROACH
Abstract
A suitable selection of machining parameters for the electrical discharge machining process relies heavily on the operators’ technologies and experience because of their numerous and diverse range. Machining parameters tables provided by the machine tool builder can not meet the operators’ requirements, since for an arbitrary desired machining time for a particular job, they do not provide the optimal machining conditions. An approach to determine parameters setting is proposed. Based on the Taguchi parameter design method and the analysis of variance, the significant factors affecting the machining performance such as total machining time, oversize and taper for a hole machined by EDM process, are determined.Artificial neural networks are highly flexible modeling tools with an ability to learn the mapping between input variables and output feature spaces. The superiority of using artificial neural networks in modeling machining processes make easier to model the EDM process with dimensional input and output spaces. On the basis of the developed neural network model, for a required total machining time, oversize and taper the corresponding process parameters to be set in EDM by using the developed and trained ANN are determined.
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