Intelligent Modeling and Optimization of ECM Process Parameters

  • T. M. Chenthil Jegan
  • D. Ravindran
  • M. Dev Anand
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)

Abstract

Electrochemical machining (ECM) is an unconventional process used for the machining of hard materials and metal matrix composites. In the present work, the artificial neural network trained with back-propagation algorithm is used for correlating the interactive and high-order influences of various machining parameters on the predominant machining factors. The operators’ requirements cannot be satisfied by the machining parameters provided by ECM machine tool builders. The process parameters are then optimized using weighted sum particle swarm optimization. The fitness function for optimization is obtained from the developed model.

Keywords

Electrochemical machining Artificial neural network Weighted sum particle swarm optimization 

References

  1. 1.
    H. Ganesan, G. Mohankumar, K. Ganesan, K. Ramesh Kumar, Optimization of machining parameters in turning process using genetic algorithm and particle swarm optimization with experimental veri cation. Int. J. Eng. Sci. Technol. 3 (2011)Google Scholar
  2. 2.
    H.H. Abuzied, M.A. Awad, H.A. Senbel, Prediction of electrochemical machining process parameters using artificial neural networks. Int. J. Comp. Sci. Eng. 4(1), 125–132 (2012)Google Scholar
  3. 3.
    M. Sen, H.S. Shan, Electro jet drilling using hybrid NNGA approach, Robot. Comp.-Integr. Manuf. 23, 17–24 (2007)Google Scholar
  4. 4.
    H. Soleimanimehr, M. J. Nategh, S. Amini, Modeling of surface roughness in vibration cutting by artificial neural network. Proc. World Acad. Sci, Eng. Technol. 40 (2009)Google Scholar
  5. 5.
    T. Navalertporn, N.V. Afzulpurkar. Optimization of tile manufacturing process using particle swarm optimization. Swarm Evol. Comput. 1, 97–109 (2011)Google Scholar
  6. 6.
    K.E. Parsopoulos, M.N. Vrahatis, Particle swarm optimization method for constrained optimization problems. Intell. Technol. Theor. Appl. 76, 214–220 (2002)Google Scholar
  7. 7.
    Y. Karpat, T. Zel, Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization. Int. J. Adv. Manuf. Technol. 35, 234–247 (2007)CrossRefGoogle Scholar
  8. 8.
    B. Rubenstein-Montano, R.A. Malaga, Weighted sum genetic algorithm to support multiple-party multiple-objective negotiations. IEEE Trans. Evol. Comput. 6(4), 366–377 (2002)Google Scholar
  9. 9.
    A. Konak, D.W. Coit, A.E. Smith, Multi-objective optimization using genetic algorithms: A tutorial. J. Dalian Univ. Technol. 91, 992–1007 (2006)Google Scholar
  10. 10.
    X. Hu, R. Eberhart, Multi objective optimization using dynamic neighbor-hood particle swarm optimization, in Proceedings of IEEE Swarm Intelligence Symposium (2002), pp. 1404–1411Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • T. M. Chenthil Jegan
    • 1
  • D. Ravindran
    • 2
  • M. Dev Anand
    • 3
  1. 1.Department of Mechanical EngineeringSt. Xaviers Catholic College of EngineeringKanyakumariIndia
  2. 2.Department of Mechanical EngineeringNational Engineering CollegeThoothukudiIndia
  3. 3.Department of Mechanical EngineeringNoorul Islam Centre for Higher EducationKanyakumariIndia

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