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The development of a hybrid firefly algorithm for multi-pass grinding process optimization

Author

Listed:
  • Zhonglei Liu

    (Tsinghua University)

  • Xuekun Li

    (Tsinghua University
    Tsinghua University)

  • Dingzhu Wu

    (China Academy of Engineering Physics)

  • Zhiqiang Qian

    (China Academy of Engineering Physics)

  • Pingfa Feng

    (Tsinghua University)

  • Yiming Rong

    (Tsinghua University
    Southern University of Science and Technology)

Abstract

Industrial grinding processes always involve multiple sequential passes, such as rough, semi-rough, semi-finish, finish, and spark-out, for the surface and geometrical accuracy generation. The design of multi-pass grinding process parameters usually requires in-depth heuristic knowledge or complex process modeling. However, when multiple process output objectives must be achieved, either heuristic knowledge or analytical modeling is incapable in dealing with the large number of process parameters or decoupling the dependency of individual pass with its neighboring passes. In this paper, a hybrid Non-dominated Sorting Firefly Algorithm (NSFA) is proposed by incorporating the non-dominated sorting algorithm with the firefly algorithm. The developed NSFA is capable in searching the optimal whole set of grinding process parameters at improved convergence speed and with less iterations, which is proved by comparing with Non-dominated Genetic Algorithm (NSGA-II) and Non-dominated Particle Swarm Optimization (NSPSO). Finally, a variable-pass internal grinding for engineering ceramics is carried out to verify the efficacy of the proposed NSFA. With the process output objectives of minimal grinding time and geometric error, the optimized process by the NSFA can realize the achievement of all process quality objectives simultaneously with the grinding efficiency increased by 32.4%.

Suggested Citation

  • Zhonglei Liu & Xuekun Li & Dingzhu Wu & Zhiqiang Qian & Pingfa Feng & Yiming Rong, 2019. "The development of a hybrid firefly algorithm for multi-pass grinding process optimization," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2457-2472, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1405-z
    DOI: 10.1007/s10845-018-1405-z
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    References listed on IDEAS

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    1. Vladimir Stojanovic & Novak Nedic, 2016. "A Nature Inspired Parameter Tuning Approach to Cascade Control for Hydraulically Driven Parallel Robot Platform," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 332-347, January.
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    Cited by:

    1. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
    2. 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.
    3. Daniele Marini & Jonathan R. Corney, 2021. "Concurrent optimization of process parameters and product design variables for near net shape manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 611-631, February.

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