Experimentalanalysis and Predictive Neural Networks for Optimization of Edm Process

Abstract

ABSTRACT newlineModelling and optimization of machining process is recognized to be an newlineextremely challenging research area in current scenario. This study illustrates newlinework suggestion, an intellectual approach in solving multi-response newlineoptimization problem involving Electrical Discharge Machining (EDM) of newlineLM25 Al composite and AISI1020 steel using Response Surface newlineMethodology (RSM) combined with optimization techniques. newlineEDM is one in every of the foremost widespread electrical based nontraditional newlinemachining process for difficult to machine conducting materials newlineand in industry, it s sort of comprehensively and fruitfully utilized in newlineprecision manufacturing with no difficulty and error free owing to its newlinecomplimentary features and advantages that it offers. It proves its utilization newlinein yield of biomedical science, aerospace and automobile industries. It is newlinecapable of producing terribly exact contours showing even micro level burrs newlinecomparatively lesser than drilling and energy-beam processing products. It is newlinevital for materials used in micro capsules, micro robots, fuel nozzles, micro newlinesensors, micro turbine, micro motors, micro surgical instruments, and micromoulds newlinein resisting not only wear but also high temperature. In EDM newlinetechnology, parts manufactured involving high pressure is extensively utilized newlineexpecting high performance. With incomparable ease and accuracy it works newlineeven on hardest known substances. It prefers moreover the growing newlineattractiveness as a novel replacement technique to formulate micro structures newlinebecause it s economical in setting up giving elevated accuracy and great newlinedesign freedom. In concept of providing high aspect ratio, it fabricates three newlinedimensional configurations showing immense easiness that is weighed against newlinenot only with but also with other methods. In the current examination newlineoptimization of EDM is performed by preferring input process parameters like newlinedischarge voltage, current, pulse-on time, pulse-off time, oil pressure and newlinespark gap, and also output responses as Material Removal Rate (MRR