slugA Review: Evaluating the Parametric Optimization of Electrical Discharge Machining (EDM) by Using & Comparing Artificial Neural Network (ANN) and Genetic Algorithm (GA)
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 1
Abstract
Artificial neural networks (ANN) and Genetic algorithms (GA) in a wide sense both belong to the class of evolutionary computing algorithms that try to mimic natural evolution or information handling with respect to everyday problems. Both methods have gained more ground in recent years, especially with respect to prediction based problems. But owing to the dynamics inherent in their evolution, they belong to somewhat disjunct development communities that interact seldom. Hence comparisons between the different methods are rare. Despite their obvious design differences, they also have several features in common that are sufficiently interesting for the innovation-oriented to follow up and so to understand these commonalities and differences. Here, there is an demonstration of how these two methodologies tackle the problem of optimize the EDM process. Electrical Discharge Machining (EDM) is a non conventional machining process, where electrically conductive materials are machined by using a precisely controlled spark that occurs between an electrode and a work piece in the presence of a dielectric fluid. It has been a demanding research area to model and optimize the EDM process in the present scenario. Lots of efforts have been exercised to model and optimize the performance and process parameters of EDM process using ANN & GA.
Authors and Affiliations
Dharmendra A. Gholetar, Kamlesh V. Dave
Keywords
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