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Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic

Author

Listed:
  • Mohamed Arezki Mellal

    (M’Hamed Bougara University)

  • Edward J. Williams

    (University of Michigan
    University of Michigan)

Abstract

Unconventional machining processes (communally named advanced or modern machining processes) are widely used by manufacturing industries. These advanced machining processes allow producing complex profiles and high quality-products. However, several process parameters should be optimized to achieve this end. In this paper, the optimization of process parameters of two conventional and four advanced machining processes is investigated: drilling process, grinding process, abrasive jet machining, abrasive water jet machining, ultrasonic machining, and water jet machining, respectively. This research employed two bio-inspired algorithms called the cuckoo optimization algorithm and the hoopoe heuristic to optimize the machining control parameters of these processes. The obtained results are compared with other optimization algorithms described and applied in the literature.

Suggested Citation

  • Mohamed Arezki Mellal & Edward J. Williams, 2016. "Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 927-942, October.
    • Handle: RePEc:spr:joinma:v:27:y:2016:i:5:d:10.1007_s10845-014-0925-4
    DOI: 10.1007/s10845-014-0925-4
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    Citations

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    Cited by:

    1. Neeraj Kumar Bhoi & Harpreet Singh & Saurabh Pratap & Pramod K. Jain, 2022. "Chemical reaction optimization algorithm for machining parameter of abrasive water jet cutting," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 350-363, March.
    2. 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.
    3. Mellal, Mohamed Arezki & Zio, Enrico, 2020. "System reliability-redundancy optimization with cold-standby strategy by an enhanced nest cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. Byeongwoo Jeon & Joo-Sung Yoon & Jumyung Um & Suk-Hwan Suh, 2020. "The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1837-1859, December.
    5. R. Venkata Rao & Dhiraj P. Rai & J. Balic, 2019. "Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2101-2127, June.
    6. N. A. Fountas & R. Benhadj-Djilali & C. I. Stergiou & N. M. Vaxevanidis, 2019. "An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1581-1599, April.
    7. Mohamed Arezki Mellal & Enrico Zio, 2019. "An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies," Journal of Risk and Reliability, , vol. 233(6), pages 1099-1105, December.

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