IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i7d10.1007_s10845-024-02481-5.html

A systematic multi-layer cognitive model for intelligent machine tool

Author

Listed:
  • Tengyuan Jiang

    (The Cognitive Manufacturing Laboratory (Northwestern Polytechnical University)
    Ministry of Industry and Information Technology)

  • Jingtao Zhou

    (The Cognitive Manufacturing Laboratory (Northwestern Polytechnical University)
    Ministry of Industry and Information Technology)

  • Xiang Luo

    (China CNTC International Tendering Corporation)

  • Mingwei Wang

    (The Cognitive Manufacturing Laboratory (Northwestern Polytechnical University)
    Ministry of Industry and Information Technology)

  • Shusheng Zhang

    (The Cognitive Manufacturing Laboratory (Northwestern Polytechnical University)
    Ministry of Industry and Information Technology)

Abstract

As the basic manufacturing capabilities provide unit of the production system, the intelligent level of the CNC machine tool will affect the realization of intelligent manufacturing. Academia has carried out a lot of intelligent research on CNC machine tool from technical perspective, but there still needs a systematic cognitive model to promote the construction of cognitive abilities, to support the intelligent realization and continuous improvement of CNC machine tool. Therefore, this paper proposes a three-part, seven-layer cognitive model based on cognitive informatics to promote the construction of cognitive abilities and the intelligent transformation of CNC machine tool. Firstly, a systematic multi-layer cognitive model is proposed, and each cognitive layer is introduced to promote the different cognitive abilities construction of CNC machine tool. Then, this paper introduces the cognitive analysis loop and the cognitive learning loop contained in the multi-layer cognitive model, which can promote the construction of the adaptive and continuous learning abilities of CNC machine tool. The evaluation indicators of the intelligence machine tool are given, which is used to evaluate machine tool intelligence model. Furthermore, the cognitive enabling technologies of the multi-layer cognitive model for intelligent machine tool is presented, which supports the realization of cognitive abilities such as analysis, decision making, and learning. Finally, the feasibility of the proposed systematic multi-layer cognitive model is verified by the developed computable digital twin platform and comparison before and after implementation for intelligent machine tool.

Suggested Citation

  • Tengyuan Jiang & Jingtao Zhou & Xiang Luo & Mingwei Wang & Shusheng Zhang, 2025. "A systematic multi-layer cognitive model for intelligent machine tool," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4915-4939, October.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02481-5
    DOI: 10.1007/s10845-024-02481-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02481-5
    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/s10845-024-02481-5?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    2. Changyi Deng & Ruifeng Guo & Chao Liu & Ray Y. Zhong & Xun Xu, 2018. "Data cleansing for energy-saving: a case of Cyber-Physical Machine Tools health monitoring system," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 1000-1015, January.
    3. Julian Germann, 2023. "Global rivalries, corporate interests and Germany’s ‘National Industrial Strategy 2030’," Review of International Political Economy, Taylor & Francis Journals, vol. 30(5), pages 1749-1775, September.
    Full references (including those not matched with items on IDEAS)

    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. Arsenyan, Jbid & Mirowska, Agata & Piepenbrink, Anke, 2023. "Close encounters with the virtual kind: Defining a human-virtual agent coexistence framework," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Maschke, Andreas, 2024. "Talking exports: The representation of Germany's current account in newspaper media," MPIfG Discussion Paper 24/1, Max Planck Institute for the Study of Societies.
    3. Shimin Liu & Pai Zheng & Jinsong Bao, 2024. "Digital Twin-based manufacturing system: a survey based on a novel reference model," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2517-2546, August.
    4. Lehdonvirta, Vili & Wu, Boxi & Hawkins, Zoe, 2025. "Weaponized interdependence in a bipolar world: How economic forces and security interests shape the global reach of U.S. and Chinese cloud data centres," SocArXiv 6s7dn, Center for Open Science.
    5. Lijun Liu & Huisong Meng & Wei Yang & Xiaoyu Wang & Yuxuan Li & Xinyu Li, 2025. "A Novel Collaborative Method to Integrate Carbon Efficiency into Multi-Equipment Operational Coupling for Smart Manufacturing System," Sustainability, MDPI, vol. 17(18), pages 1-34, September.
    6. Krebs, Tom & Weber, Isabella, 2025. "The Green Transformation and the Costs of Market Fundamentalism," IZA Discussion Papers 17834, IZA Network @ LISER.
    7. Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
    8. Gaurav Garg & Vladimir Kuts & Gholamreza Anbarjafari, 2021. "Digital Twin for FANUC Robots: Industrial Robot Programming and Simulation Using Virtual Reality," Sustainability, MDPI, vol. 13(18), pages 1-22, September.
    9. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    10. repec:bcp:journl:v:8:y:2024:i:10:p:1836-1852 is not listed on IDEAS
    11. Li, Lixu & Hou, Yan & Chen, Lujie & Liu, Yaoqi & Trindade, Maria A.M., 2025. "Unlocking sustainable performance with blockchain technology: Insights from organizational learning theory," International Journal of Production Economics, Elsevier, vol. 283(C).
    12. Ali Keshvarparast & Daria Battini & Olga Battaia & Amir Pirayesh, 2024. "Collaborative robots in manufacturing and assembly systems: literature review and future research agenda," Journal of Intelligent Manufacturing, Springer, vol. 35(5), pages 2065-2118, June.
    13. Huanrong Ren & Pingyu Jiang & Qingzong Li, 2025. "Machine as a smart service: a hybrid knowledge graph approach," Flexible Services and Manufacturing Journal, Springer, vol. 37(3), pages 750-775, September.
    14. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
    15. repec:osf:socarx:6s7dn_v1 is not listed on IDEAS
    16. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    17. Hazrathosseini, Arman & Moradi Afrapoli, Ali, 2023. "The advent of digital twins in surface mining: Its time has finally arrived," Resources Policy, Elsevier, vol. 80(C).
    18. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    19. Kamble, Sachin S & Gunasekaran, Angappa & Parekh, Harsh & Mani, Venkatesh & Belhadi, Amine & Sharma, Rohit, 2022. "Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    20. Claire Y. T. Chen & Edward W. Sun & Yi-Bing Lin, 2025. "Reconciling spatiotemporal conjunction with digital twin for sequential travel time prediction and intelligent routing," Annals of Operations Research, Springer, vol. 348(1), pages 671-716, May.
    21. Neto, Anis Assad & Ribeiro da Silva, Elias & Deschamps, Fernando & do Nascimento Junior, Laercio Alves & Pinheiro de Lima, Edson, 2023. "Modeling production disorder: Procedures for digital twins of flexibility-driven manufacturing systems," International Journal of Production Economics, Elsevier, vol. 260(C).
    22. Jiang, Mingdong & Yu, Xinxin, 2025. "Enhancing the resilience of urban energy systems: The role of artificial intelligence," Energy Economics, Elsevier, vol. 144(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:36:y:2025:i:7:d:10.1007_s10845-024-02481-5. 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.