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A particle swarm approach for multi-objective optimization of electrical discharge machining process

Author

Listed:
  • Chinmaya P. Mohanty

    (National Institute of Technology Rourkela)

  • Siba Sankar Mahapatra

    (National Institute of Technology Rourkela)

  • Manas Ranjan Singh

    (National Institute of Technology Rourkela)

Abstract

This paper proposes an experimental investigation and optimization of various machining parameters for the die-sinking electrical discharge machining (EDM) process using a multi-objective particle swarm (MOPSO) algorithm. A Box–Behnken design of response surface methodology has been adopted to estimate the effect of machining parameters on the responses. The responses used in the analysis are material removal rate, electrode wear ratio, surface roughness and radial overcut. The machining parameters considered in the study are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Fifty four experimental runs are conducted using Inconel 718 super alloy as work piece material and the influence of parameters on each response is analysed. It is observed that tool material, discharge current and pulse-on-time have significant effect on machinability characteristics of Inconel 718. Finally, a novel MOPSO algorithm has been proposed for simultaneous optimization of multiple responses. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence. The Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory to avoid subjectiveness and impreciseness in the decision making. The analysis offers useful information for controlling the machining parameters to improve the accuracy of the EDMed components.

Suggested Citation

  • Chinmaya P. Mohanty & Siba Sankar Mahapatra & Manas Ranjan Singh, 2016. "A particle swarm approach for multi-objective optimization of electrical discharge machining process," Journal of Intelligent Manufacturing, Springer, vol. 27(6), pages 1171-1190, December.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:6:d:10.1007_s10845-014-0942-3
    DOI: 10.1007/s10845-014-0942-3
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    References listed on IDEAS

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    1. P. Kuppan & S. Narayanan & A. Rajadurai, 2011. "Effect of process parameters on material removal rate and surface roughness in electric discharge drilling of Inconel 718 using graphite electrode," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 23(3/4), pages 214-233.
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    Cited by:

    1. Elango Natarajan & Varadaraju Kaviarasan & Wei Hong Lim & Sew Sun Tiang & S. Parasuraman & Sangeetha Elango, 2020. "Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 911-935, April.
    2. Raghav Prasad Parouha & Pooja Verma, 2022. "An innovative hybrid algorithm for bound-unconstrained optimization problems and applications," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1273-1336, June.
    3. Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.
    4. Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.

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