IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i6d10.1007_s10845-020-01648-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01648-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01648-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Damien McParland & Szymon Baron & Sarah O’Rourke & Denis Dowling & Eamonn Ahearne & Andrew Parnell, 2019. "Prediction of tool-wear in turning of medical grade cobalt chromium molybdenum alloy (ASTM F75) using non-parametric Bayesian models," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1259-1270, March.
    2. Diethard Klatte & Hans-Jakob Lüthi & Karl Schmedders (ed.), 2012. "Operations Research Proceedings 2011," Operations Research Proceedings, Springer, edition 127, number 978-3-642-29210-1, May.
    3. De, Arijit & Mogale, D.G. & Zhang, Mengdi & Pratap, Saurabh & Kumar, Sri Krishna & Huang, George Q., 2020. "Multi-period multi-echelon inventory transportation problem considering stakeholders behavioural tendencies," International Journal of Production Economics, Elsevier, vol. 225(C).
    4. Zoran Jurkovic & Goran Cukor & Miran Brezocnik & Tomislav Brajkovic, 2018. "A comparison of machine learning methods for cutting parameters prediction in high speed turning process," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1683-1693, December.
    5. Emel Savku & Gerhard-Wilhelm Weber, 2018. "A Stochastic Maximum Principle for a Markov Regime-Switching Jump-Diffusion Model with Delay and an Application to Finance," Journal of Optimization Theory and Applications, Springer, vol. 179(2), pages 696-721, November.
    6. 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.
    7. Soheyl Khalilpourazari & Shima Soltanzadeh & Gerhard-Wilhelm Weber & Sankar Kumar Roy, 2020. "Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study," Annals of Operations Research, Springer, vol. 289(1), pages 123-152, June.
    8. Tsao, Yu-Chung & Thanh, Vo-Van, 2019. "A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 124(C), pages 13-39.
    9. Morteza Lalmazloumian & Kuan Yew Wong & Kannan Govindan & Devika Kannan, 2016. "A robust optimization model for agile and build-to-order supply chain planning under uncertainties," Annals of Operations Research, Springer, vol. 240(2), pages 435-470, May.
    10. Khishtandar, Soheila, 2019. "Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design," Applied Energy, Elsevier, vol. 236(C), pages 183-195.
    11. D. G. Mogale & Naoufel Cheikhrouhou & Manoj Kumar Tiwari, 2020. "Modelling of sustainable food grain supply chain distribution system: a bi-objective approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(18), pages 5521-5544, September.
    12. Hossein Hashemi Doulabi & Gilles Pesant & Louis-Martin Rousseau, 2020. "Vehicle Routing Problems with Synchronized Visits and Stochastic Travel and Service Times: Applications in Healthcare," Transportation Science, INFORMS, vol. 54(4), pages 1053-1072, July.
    13. Zhao, G.Y. & Liu, Z.Y. & He, Y. & Cao, H.J. & Guo, Y.B., 2017. "Energy consumption in machining: Classification, prediction, and reduction strategy," Energy, Elsevier, vol. 133(C), pages 142-157.
    14. Miray Hanım Yıldırım & Ayşe Özmen & Özlem Türker Bayrak & Gerhard Wilhelm Weber, 2012. "Electricity Price Modelling for Turkey," Operations Research Proceedings, in: Diethard Klatte & Hans-Jakob Lüthi & Karl Schmedders (ed.), Operations Research Proceedings 2011, edition 127, pages 39-44, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Keng-Yu Lin & Kuei-Hu Chang, 2023. "Artificial Intelligence and Information Processing: A Systematic Literature Review," Mathematics, MDPI, vol. 11(11), pages 1-20, May.
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    2. Soheyl Khalilpourazari & Hossein Hashemi Doulabi, 2022. "Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec," Annals of Operations Research, Springer, vol. 312(2), pages 1261-1305, May.
    3. Li, Zhuyue & Zhao, Peixin & Han, Xue, 2022. "Agri-food supply chain network disruption propagation and recovery based on cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    4. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    5. Chen, Jianxin & Zheng, Junhao & Zhang, Tonghua & Hou, Rui & Zhou, Yong-wu, 2022. "Dynamical complexity of pricing and green level for a dyadic supply chain with capital constraint," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 195(C), pages 1-21.
    6. Ghazale Kordi & Parsa Hasanzadeh-Moghimi & Mohammad Mahdi Paydar & Ebrahim Asadi-Gangraj, 2023. "A multi-objective location-routing model for dental waste considering environmental factors," Annals of Operations Research, Springer, vol. 328(1), pages 755-792, September.
    7. Kayse Lee Maass & Vera Mann Hey Lo & Anna Weiss & Mark S. Daskin, 2015. "Maximizing Diversity in the Engineering Global Leadership Cultural Families," Interfaces, INFORMS, vol. 45(4), pages 293-304, August.
    8. M. Rezaei Kallaj & M. Hasannia Kolaee & S. M. J. Mirzapour Al-e-hashem, 2023. "Integrating bloodmobiles and drones in a post-disaster blood collection problem considering blood groups," Annals of Operations Research, Springer, vol. 321(1), pages 783-811, February.
    9. Gabriel Frahm, 2018. "An Intersection–Union Test for the Sharpe Ratio," Risks, MDPI, vol. 6(2), pages 1-13, April.
    10. Qi, Yue & Liao, Kezhi & Liu, Tongyang & Zhang, Yu, 2022. "Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths," Operations Research Perspectives, Elsevier, vol. 9(C).
    11. Yiran Sun & Yuqian Wang & Jingci Xie, 2022. "The co-evolution of seaports and dry ports in Shandong province in China under the Belt and Road Initiative," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-27, December.
    12. Na Liu & Pui-Sze Chow & Hongshan Zhao, 2020. "Challenges and critical successful factors for apparel mass customization operations: recent development and case study," Annals of Operations Research, Springer, vol. 291(1), pages 531-563, August.
    13. Reza Eslamipoor & Abbas Sepehriyar, 2024. "Promoting green supply chain under carbon tax, carbon cap and carbon trading policies," Business Strategy and the Environment, Wiley Blackwell, vol. 33(5), pages 4901-4912, July.
    14. Shailendra Pawanr & Kapil Gupta, 2024. "A Review on Recent Advances in the Energy Efficiency of Machining Processes for Sustainability," Energies, MDPI, vol. 17(15), pages 1-21, July.
    15. Sinha, Priyank & Kumar, Sameer & Chandra, Charu, 2023. "Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 304(1), pages 339-352.
    16. Giri, Binoy Krishna & Roy, Sankar Kumar, 2024. "Fuzzy-random robust flexible programming on sustainable closed-loop renewable energy supply chain," Applied Energy, Elsevier, vol. 363(C).
    17. Yan Li & Xiao Xu & Fuyu Wang, 2023. "Research on Home Health Care Scheduling Considering Synchronous Access of Caregivers and Vehicles," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    18. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    19. Filipe Rodrigues & Agostinho Agra & Lars Magnus Hvattum & Cristina Requejo, 2021. "Weighted proximity search," Journal of Heuristics, Springer, vol. 27(3), pages 459-496, June.
    20. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-020-01648-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.