This study investigates the optimization of Electrical Discharge Machining (EDM) parameters for Inconel 718 using a copper electrode, focusing on improving material removal rate (MRR) and minimizing surface roughness (Ra). A full factorial design of experiments (DOE) was employed with three key input parameters: pulse on time, pulse off time, and peak current. Regression models and machine learning algorithms were applied to predict response outcomes, with statistical validation through ANOVA and multi-objective optimization using desirability functions. The optimal parameters achieved an MRR of 54.12 mm³/min and Ra of 3.38 µm. The findings demonstrate the effectiveness of full factorial design of the exprements in enhancing EDM performance, supporting its adoption for precision machining of nickel-based super alloys.
Kebebew Alemu Tufa (Sun,) studied this question.
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