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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
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    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. 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.
    4. Jason Xinghang Dai & Nada Matta & Guillaume Ducellier, 2014. "Knowledge discovery in collaborative design projects," Post-Print hal-02920349, HAL.
    5. 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.
    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.
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