Dept. of Production Engineering and Metallurgy, University of Technology, Baghdad, Iraq.


In this paper, Artificial Neural Network was adopted to predict the effect of current, the concentration of aluminum oxide (Al2O3) and graphite Nanopowders in dielectric fluid for the machining of Carbon steel 304 using Electrical Discharge Machining (EDM). The process variables were utilized to find their effect on Material Removal Rate (MRR), Surface Roughness (SR), and Tool Wear Rate (TWR). It was revealed from the experimental work that the addition of aluminum oxide and graphite Nanopowders into dielectric fluid maximizing MRR, minimized the SR and TWR at various variables. Minitab software was used in the design of experiments. Analysis of the process outputs of EDM indicates that graphite powder concentration greatly influencing SR also the discharge current whereas the current and Nanopowders concentration has more percentage of influence on the TWR and MRR.


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