Optimization of EDM process parameters for P20 tool steel using grey relational analysis
Authors :- S Kumari, A Yadav, SK Gupta, K Abhishek
Publication :- Journal of Umm Al-Qura University for Engineering and Architecture, IEEE, 2025.
This study employs Grey Relational Analysis (GRA) to optimize the Electric Discharge Machining (EDM) process parameters for AISI P20 tool steel, focusing on the influence of different electrode materials copper, brass, and graphite on key performance metrics such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). The objective was to identify optimal parameter settings that simultaneously enhance machining efficiency, extend tool life, and improve surface quality for P20 tool steel, which is widely used in injection molding applications. Experiments were designed using a Taguchi L9 orthogonal array by varying discharge current, pulse-on time, pulse-off time, and gap voltage. The results indicate that copper electrodes delivered high productivity with controlled tool wear and acceptable surface finish. Brass electrodes achieved superior surface quality, while graphite electrodes showed minimal tool wear but compromised surface integrity. The novelty of this work lies in the application of GRA for simultaneous multi-response optimization, offering a robust framework for selecting electrode-specific parameters to balance productivity and quality in EDM of P20 tool steel.