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Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence

Author

Listed:
  • Soheyl Khalilpourazari

    (Concordia University
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

  • Saman Khalilpourazary

    (Urmia University of Technology)

  • Aybike Özyüksel Çiftçioğlu

    (Manisa Celal Bayar University)

  • Gerhard-Wilhelm Weber

    (Poznan University of Technology
    Middle East Technical University)

Abstract

This paper suggests a novel robust formulation designed for optimizing the parameters of the turning process in an uncertain environment for the first time. The aim is to achieve the lowest energy consumption and highest precision. With this aim, the current paper considers uncertain parameters, objective functions, and constraints in the offered mathematical model. We proposed several uncertain models and validated the results in real-world case studies. In addition, several artificial intelligence-based solution techniques are designed to solve the complex nonlinear problem. We determined the most efficient solution approach by solving various test problems. Then, simulated several scenarios to demonstrate the robustness of our results. The results showed that the solutions provided by the offered model significantly reduce energy consumption in different setups. To ensure the reliability of the results, we carried out worst-case sensitivity analyses and found the most critical parameters. The results of the worst-case analyses indicated that the offered robust model is efficient and saves a significant amount of energy comparing to traditional models. It is shown that the provided solution by the presented robust formulation is reliable in all situations and results in the lowest energy and the best machining precision.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-020-01648-0
    DOI: 10.1007/s10845-020-01648-0
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    References listed on IDEAS

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    Cited by:

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    2. Han, Bing & Zhang, Ying & Wang, Song & Park, Yongshin, 2023. "The efficient and stable planning for interrupted supply chain with dual‐sourcing strategy: a robust optimization approach considering decision maker's risk attitude," Omega, Elsevier, vol. 115(C).
    3. Zhao, Junhua & Li, Li & Li, Lingling & Zhang, Yunfeng & Lin, Jiang & Cai, Wei & Sutherland, John W., 2023. "A multi-dimension coupling model for energy-efficiency of a machining process," Energy, Elsevier, vol. 274(C).

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