Optimization of Electrical Discharge Machining ( E D M ) Process Parameters Using Soft Computing Techniques : a Review

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ABSTRACT
Electric Discharge Machining (EDM) is a thermo-electric non-traditional machining process in which material removal takes place through the process of controlled spark generation between a pair of electrodes which are submerged in a dielectric medium. Due to the difficulty of EDM, it is very complicated to determine optimal process parameters for improving machining performance. It relies on heuristics, which are not easy to model, and based on the experiences of specialists. A proper selection of machining parameters for the EDM process as per the operator’s requirement is very much difficult because of their numerous and diverse range. So, in order to approximate the EDM performances, -------------------------------------------------

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KEYWORDS
optimization,
electrical discharge machining,
soft computing,
artificial neural network,
fuzzy logic,
evolutionary algorithms,
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KEYWORDS
optimization,
electrical discharge machining,
soft computing,
artificial neural network,
fuzzy logic,
evolutionary algorithms,
estimator methods such as soft computing techniques are being used extensively to forecast the EDM process. With the increasing popularity of artificial intelligence (AI) tools in general, a considerable amount of research has been done in this area. In this paper, the use of soft computing techniques to deal with the optimization of EDM process parameters with single or multiple performance characteristics is reviewed.

1. INTRODUCTION
Electric Discharge Machining (EDM) is one of the most extensively used non-conventional material removal process [1] particularly used in precise machining for complex shaped work pieces, as an alternative to more traditional approaches. It is a thermal erosion process in which an electrically generated spark produces a very small plasma channel having a high energy density and high temperature (8,0000 C–12,0000 C) that melts and evaporates...
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