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CN-MgMP: a multi-granularity module partition approach for complex mechanical products based on complex network

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Abstract

Module partitioning is beneficial for engineers to gain a better understanding of the structure and function of complex mechanical products (CMPs). It plays an important role in the entire life cycle of the CMPs. However, owing to the large number of mechanical parts and the complex relationships, existing module partition approaches cannot obtain multi-level information. Consequently, the in-depth analysis of CMPs is hindered. Therefore, a novel multi-granularity module partition approach for CMPs based on a complex network (CN-MgMP) is proposed in this paper. Using this approach, a weighted complex network for mechanical parts (MP_WCN) was constructed. The multi-granularity module partition algorithm is presented based on MP_WCN. It consists of three phases. First, based on the concept of modularity increment, the MP_WCN is decomposed into several sub-networks, and fine-grained modules are thus obtained. Next, the topic semantic vectors of these fine-grained modules are extracted using the termfrequency–inversedocumentfrequency (TF-IDF) text analysis method. Finally, taking the global module distance (Glo-MD) as the optimization goal, the fine-grained modules are merged hierarchically to obtain optimal coarse-grained modules. The experimental results for the vertical elevator demonstrate the effectiveness and superiority of the proposed approach.

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Acknowledgements

We acknowledge financial support from NSFC (62103121), National key R&D project (2022YFE0210700), Zhejiang Province Outstanding Youth Fund (LR21F030001), Zhejiang Province Public Welfare Technology Application Research Project (LGG22F020023, LGF21F020013), Zhejiang Province Key R&D projects (2021C03015, 2021C03142), NSFC (52171352).

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Authors and Affiliations

  1. China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing, Hangzhou Dianzi University, Hangzhou, Zhejiang, China

    Zhenjie Zhang, Botao Lu, Xiaobin Xu & Jing Feng

  2. School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou, Zhejiang, China

    Zhenjie Zhang, Xiaobin Xu & Jing Feng

  3. Hangzhou Yinuo Technology Co., Ltd, Hangzhou, Zhejiang, China

    Xufeng Shen

  4. Hangzhou Qianhang Ship Repair and Construction Co., Ltd, Hangzhou, Zhejiang, China

    Xufeng Shen

  5. Institute for Energy Systems und Thermodynamics, TU Wien, Getreidemarkt 9, 1060, Vienna, Austria

    Georg Brunauer

  6. Salzburg University of Applied Sciences, Campus Kuchl, Markt 136a, 5431, Kuchl, Austria

    Georg Brunauer

  7. NOVAPECC GmbH, Hildebrandgasse 28, 1180, Vienna, Austria

    Georg Brunauer

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  1. Zhenjie Zhang
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  2. Botao Lu
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  3. Xiaobin Xu
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  4. Xufeng Shen
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  5. Jing Feng
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  6. Georg Brunauer
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Correspondence to Xiaobin Xu.

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Zhang, Z., Lu, B., Xu, X. et al. CN-MgMP: a multi-granularity module partition approach for complex mechanical products based on complex network. Appl Intell 53, 17679–17692 (2023). https://doi.org/10.1007/s10489-022-04430-2

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