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A digital twin-driven perception method of manufacturing service correlation based on frequent itemsets

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Abstract

Manufacturing service composition is a key technology in service-oriented manufacturing systems. Service correlation is a mix-order correlation, which is supposed to be defined as adjacent-order correlation (AO-C) and non-adjacent-order correlation (NAO-C). The existing works mainly focus on AO-C without considering NAO-C, and constantly lead to the failure of composite service execution path (CSEP). In this paper, with the support of digital twin, firstly the non-uniform transitivity of correlation from AO-C to NAO-C is analyzed. Then, the basic model of AO-C, multi-order model of NAO-C, and its relevancy degree formula are proposed based on workflow and modular design. Meanwhile, a perception method based on improved Apriori algorithm is designed and the relevant supporting data is collected by digital twin technology, so as to percept AO-C relevancy degree and calculate the relevancy degree of mix-order correlation in CSEP in the proposed AO-C and NAO-C models. Finally, a case study of magnetic bearing manufacturing service composition is conducted to verify the effectiveness of proposed method.

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Acknowledgements

Sincere appreciation is extended to the reviewers of this paper for their helpful comments.

Funding

This work is sponsored by National Natural Science Foundation of China (No. 51975431 and 51805020), and Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering (Wuhan University of Science and Technology) (No. 2017A07).

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Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, People’s Republic of China

    Feng Xiang & Jie Fan

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, People’s Republic of China

    Feng Xiang & Jie Fan

  3. Wuhan Iron & Steel Co., Ltd, Wuhan, 430080, People’s Republic of China

    Xuerong Zhang

  4. Research Institute for Frontier Science, Beihang University, Beijing, 100191, People’s Republic of China

    Ying Zuo

  5. Shaoguan Cigarette Factory of Guangdong Zhongyan Industry Co., Ltd, Shaoguan, 512026, People’s Republic of China

    Sheng Liu

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  1. Feng Xiang
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  4. Ying Zuo
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All the authors have contributed to the creation of this manuscript for important intellectual content and read and approved the final manuscript.

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Correspondence to Ying Zuo.

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Xiang, F., Fan, J., Zhang, X. et al. A digital twin-driven perception method of manufacturing service correlation based on frequent itemsets. Int J Adv Manuf Technol 131, 5661–5677 (2024). https://doi.org/10.1007/s00170-022-08762-8

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