An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm

• Published in: Engineering science and technology, an international journal Vol. 20; no. 2; pp. 552 - 562
• Main Authors: Mohanty, Chinmaya P., Mahapatra, Siba Sankar, Singh, Manas Ranjan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2017; Elsevier
• Subjects: Electrical discharge machining; Quantum behaved particle swarm optimization; Radial over cut; Utility concept; Radial over cut; Quantum behaved particle swarm optimization; Electrical discharge machining; Utility concept
• ISSN: 2215-0986; 2215-0986
• DOI: 10.1016/j.jestch.2016.07.003

Although significant research has gone into the field of electrical discharge machining (EDM), analysis related to the machining efficiency of the process with different electrodes has not been adequately made. Copper and brass are frequently used as electrode materials but graphite can be used as a potential electrode material due to its high melting point temperature and good electrical conductivity. In view of this, the present work attempts to compare the machinability of copper, graphite and brass electrodes while machining Inconel 718 super alloy. Taguchi’s L27 orthogonal array has been employed to collect data for the study and analyze effect of machining parameters on performance measures. The important performance measures selected for this study are material removal rate, tool wear rate, surface roughness and radial overcut. Machining parameters considered for analysis are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and electrode material. From the experimental analysis, it is observed that electrode material, discharge current and pulse-on-time are the important parameters for all the performance measures. Utility concept has been implemented to transform a multiple performance characteristics into an equivalent performance characteristic. Non-linear regression analysis is carried out to develop a model relating process parameters and overall utility index. Finally, the quantum behaved particle swarm optimization (QPSO) and particle swarm optimization (PSO) algorithms have been used to compare the optimal level of cutting parameters. Results demonstrate the elegance of QPSO in terms of convergence and computational effort. The optimal parametric setting obtained through both the approaches is validated by conducting confirmation experiments.