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Journal of Mechanical Science and Technology

, Volume 26, Issue 6, pp 1875–1883 | Cite as

Multi-objective optimization of electric-discharge machining process using controlled elitist NSGA-II

  • Pushpendra S. BhartiEmail author
  • S. Maheshwari
  • C. Sharma
Article

Abstract

Parametric optimization of electric discharge machining (EDM) process is a multi-objective optimization task. In general, no single combination of input parameters can provide the best cutting speed and the best surface finish simultaneously. Genetic algorithm has been proven as one of the most popular multi-objective optimization techniques for the parametric optimization of EDM process. In this work, controlled elitist non-dominated sorting genetic algorithm has been used to optimize the process. Experiments have been carried out on die-sinking EDM by taking Inconel 718 as work piece and copper as tool electrode. Artificial neural network (ANN) with back propagation algorithm has been used to model EDM process. ANN has been trained with the experimental data set. Controlled elitist non-dominated sorting genetic algorithm has been employed in the trained network and a set of pareto-optimal solutions is obtained.

Keywords

Artificial neural networks Electric discharge machining Genetic algorithm Material removal rate Optimization Pareto-optimal solutions Surface roughness 

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Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pushpendra S. Bharti
    • 1
    Email author
  • S. Maheshwari
    • 2
  • C. Sharma
    • 3
  1. 1.U.S.E.T.Guru Gobind Singh Indraprastha UniversityDelhiIndia
  2. 2.MPAE DivisionNetaji Subhas Institute of TechnologyDelhiIndia
  3. 3.Department of MAEIndira Gandhi Institute of TechnologyDelhiIndia

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