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Selection of optimal conditions in the surface grinding process using the quantum based optimisation method

Author

Listed:
  • Mahdi S. Alajmi

    (Public Authority for Applied Education and Training)

  • Fawzan S. Alfares

    (Public Authority for Applied Education and Training)

  • Mohamed S. Alfares

    (Public Authority for Applied Education and Training)

Abstract

A novel optimisation technique based on quantum computing principles, namely the quantum based optimisation method (QBOM), is proposed to solve the surface grinding process problem optimisation. In grinding process there is a trade-off between faster material removal rates, with a reduction in cutting time and its associated cost and shorter tool life or higher tool cost. The objective of the surface grinding optimisation problem is to determine the optimal machining conditions, which will minimize the unit production cost and unit production time with the finest possible surface finish but without violating any imposed constraints. The performance of QBOM is investigated against two test cases, one of a rough grinding process and the other of a finished grinding process and the computational results show that the proposed optimisation technique obtained better results than most of the methods presented in the literatures.

Suggested Citation

  • Mahdi S. Alajmi & Fawzan S. Alfares & Mohamed S. Alfares, 2019. "Selection of optimal conditions in the surface grinding process using the quantum based optimisation method," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1469-1481, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1326-2
    DOI: 10.1007/s10845-017-1326-2
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    Cited by:

    1. Lenin Nagarajan & Siva Kumar Mahalingam & Jayakrishna Kandasamy & Selvakumar Gurusamy, 2022. "A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1337-1354, June.
    2. Hengyuan Ma & Wei Liu & Xionghui Zhou & Qiang Niu & Chuipin Kong, 2020. "An effective and automatic approach for parameters optimization of complex end milling process based on virtual machining," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 967-984, April.

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