IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i4d10.1007_s10845-021-01889-7.html
   My bibliography  Save this article

A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design

Author

Listed:
  • Yongjun Ji

    (Shanghai Jiao Tong University)

  • Zuhua Jiang

    (Shanghai Jiao Tong University)

  • Xinyu Li

    (Donghua University)

  • Yongwen Huang

    (Shanghai Waigaoqiao Shipbuilding Company)

  • Fuhua Wang

    (Shanghai Jiao Tong University)

Abstract

To proactively assist engineers in finding and reusing massive design lesson-learned knowledge (DLK), knowledge recommendation has become a key technology of knowledge management. However, in collaborative product design, complex multitask context information disrupts the perception of engineers’ knowledge needs for every single task. In this situation, traditional knowledge recommendation approach is prone to provide a mixed DLK recommendation list, thus resulting in a lack of pertinence and low accuracy. Facing these challenges, scarcely any reports on context-aware knowledge recommendation in the multitask environment of collaborative product design. Aiming to fill this gap, a multitask context-aware DLK recommendation approach is proposed to assist collaborative product design in a smarter manner. The mutual interference of context information from different tasks is addressed by preprocessing works, multitask knowledge need awareness, DLK recommendation engine, respectively. Therefore, the proposed approach not only effectively acquires engineers’ knowledge needs from different task contexts and pertinently provides the corresponding DLK recommendation list for each task but also guarantees the accuracy of DLK recommendation in multitask context of collaborative product design. To validate the proposed approach, a DLK recommendation system is implemented in a shipbuilding scenario, and some comparative experiments are carried out. Experimental results show that the proposed approach outperforms conventional approaches in the aspects of effectiveness and performance. Therefore, it opens up a promising way to help engineers reuse needed DLK in collaborative product design.

Suggested Citation

  • Yongjun Ji & Zuhua Jiang & Xinyu Li & Yongwen Huang & Fuhua Wang, 2023. "A multitask context-aware approach for design lesson-learned knowledge recommendation in collaborative product design," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1615-1637, April.
  • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01889-7
    DOI: 10.1007/s10845-021-01889-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01889-7
    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/s10845-021-01889-7?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. Peter Chhim & Ratna Babu Chinnam & Noureddin Sadawi, 2019. "Product design and manufacturing process based ontology for manufacturing knowledge reuse," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 905-916, February.
    2. Zuoxu Wang & Chun-Hsien Chen & Pai Zheng & Xinyu Li & Li Pheng Khoo, 2021. "A graph-based context-aware requirement elicitation approach in smart product-service systems," International Journal of Production Research, Taylor & Francis Journals, vol. 59(2), pages 635-651, January.
    3. Jason Xinghang Dai & Nada Matta & Guillaume Ducellier, 2014. "Knowledge discovery in collaborative design projects," Post-Print hal-02920349, HAL.
    4. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    5. Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
    6. Xinyu Li & Zuhua Jiang & Lijun Liu & Bo Song, 2018. "A novel approach for analysing evolutional motivation of empirical engineering knowledge," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2897-2923, April.
    7. Yongwen Huang & Zuhua Jiang & Chengneng He & Jianfeng Liu & Bo Song & Lijun Liu, 2015. "A semantic-based visualised wiki system (SVWkS) for lesson-learned knowledge reuse situated in product design," International Journal of Production Research, Taylor & Francis Journals, vol. 53(8), pages 2524-2541, April.
    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. Rui Wang & Xiangyu Guo & Shisheng Zhong & Gaolei Peng & Lin Wang, 2022. "Decision rule mining for machining method chains based on rough set theory," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 799-807, March.
    2. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.
    3. Chen-Fu Chien & Hsin-Jung Wu, 2024. "Integrated circuit probe card troubleshooting based on rough set theory for advanced quality control and an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 275-287, January.
    4. Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
    5. Pulin Li & Kai Cheng & Pingyu Jiang & Kanet Katchasuwanmanee, 2022. "Investigation on industrial dataspace for advanced machining workshops: enabling machining operations control with domain knowledge and application case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 103-119, January.
    6. Gautam Dutta & Ravinder Kumar & Rahul Sindhwani & Rajesh Kr. Singh, 2021. "Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1679-1698, August.
    7. Zhenyong Wu & Lina He & Yuan Wang & Mark Goh & Xinguo Ming, 2020. "Knowledge recommendation for product development using integrated rough set-information entropy correction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1559-1578, August.
    8. Xiaochen Zheng & Xiaodu Hu & Rebeca Arista & Jinzhi Lu & Jyri Sorvari & Joachim Lentes & Fernando Ubis & Dimitris Kiritsis, 2024. "A semantic-driven tradespace framework to accelerate aircraft manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 175-198, January.
    9. Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.

    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:34:y:2023:i:4:d:10.1007_s10845-021-01889-7. 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.