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Module partition of complex mechanical products based on weighted complex networks

Author

Listed:
  • Na Zhang

    (Chongqing University)

  • Yu Yang

    (Chongqing University)

  • Yujie Zheng

    (Chongqing University)

  • Jiafu Su

    (Chongqing University)

Abstract

It is tough to build an effective mathematical model to describe the complicated relationships in complex mechanical products, which leads to the module partition of complex mechanical products and the guarantee of accurate results become more difficult. In addition, the module partition method cannot bring about a satisfactory module partition result if the scale of the products is huge and complicated. In this case, complex network theory is used to solve these problems in this paper. Firstly, a weighted complex network is established to systematically express the structure of complex mechanical products. In particular, customer demands are taken into account for module partition by introducing customer involvement. Secondly, the interval-valued intuitionistic fuzzy sets are used to calculate the relationships between parts for reducing the subjectivity of the calculation process. Afterwards, a modified GN algorithm (community detection algorithm) is proposed to achieve the module partition of complex mechanical products. Finally, the module partition of a wind turbine is carried out to verify the effectiveness of the proposed method in this paper. The result of the case study shows that the modified GN algorithm achieves better module partition performance than the classical GN algorithm and fuzzy clustering analysis method, which obtains a satisfactory result for applications.

Suggested Citation

  • Na Zhang & Yu Yang & Yujie Zheng & Jiafu Su, 2019. "Module partition of complex mechanical products based on weighted complex networks," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1973-1998, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1367-6
    DOI: 10.1007/s10845-017-1367-6
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    References listed on IDEAS

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    Cited by:

    1. Wang, Jianxin & Lim, Ming K. & Zhan, Yuanzhu & Wang, XiaoFeng, 2020. "An intelligent logistics service system for enhancing dispatching operations in an IoT environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    2. Yuming Guo, 2023. "Towards the efficient generation of variant design in product development networks: network nodes importance based product configuration evaluation approach," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 615-631, February.
    3. Omar Ezzat & Khaled Medini & Xavier Boucher & Xavier Delorme, 2022. "A clustering approach for modularizing service-oriented systems," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 719-734, March.

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