Multi-Objective Electric Discharge Machining Process ParametersOptimization of Inconel 718 by using Full Factorial Design of Experiments.
DOI:
https://doi.org/10.37255/jme.v20i2pp057-060Keywords:
Inconel 718, EDM, MRR, Ra, Machine Learning predictive model, full factorial, pulse on and off, peak current.Abstract
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 superalloys.
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