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Real-time task processing for spinning cyber-physical production systems based on edge computing

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Abstract

With a high-speed, dynamic and continuous yarn manufacturing process, spinning production suffers from different problems of dynamic disturbances such as yarn breakage, machine breakdown, and yarn quality. Processing real-time tasks is critical for tackling these problems, except for satisfying the requirements of mass production. The existing spinning cyber-physical production systems (CPPS), however, rely on a cloud center for centralized processing of real-time tasks. Thus, it becomes increasingly difficult for them to meet real-time requirements. As such, this paper proposes a novel real-time task processing method for spinning CPPS based on edge computing. First, a new hybrid structure of edge computing nodes (ECN) that consists of both 1-1 and N-1 modes is introduced for different types of tasks in spinning CPPS such as fixed tasks, decision-intensive tasks, and data-intensive tasks. Second, a collaboration mechanism is developed for collaborations between ECNs. The mathematical model and algorithms for real-time task processing are provided for a single ECN. Finally, a case study on a real spinning production is conducted. The results of this case study have demonstrated that the proposed method can significantly reduce the processing time of real-time tasks, as well as improve the production flexibility and production efficiency in spinning CPPS. The proposed method could be applied to continuous and batch manufacturing fields with high real-time requirements, such as weaving, chemical fiber production, and the pharmaceutical industry.

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Acknowledgements

This work was supported in part by Fundamental Research Funds for the Central University and Graduate Student Innovation Fund of Donghua University (Grant No. CUSF-DH-D-2019096), National Key Research and Development Plan of China (Grant No. 2017YFB1304000), and the Fundamental Research Funds for the Central Universities, China (2232017A-03).

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Authors and Affiliations

  1. College of Mechanical Engineering, Donghua University, Shanghai, 201620, China

    Shiyong Yin, Jinsong Bao, Jie Zhang, Jie Li & Junliang Wang

  2. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, 2640, Australia

    Xiaodi Huang

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  1. Shiyong Yin
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  2. Jinsong Bao
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  4. Jie Li
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Correspondence to Jinsong Bao.

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Yin, S., Bao, J., Zhang, J. et al. Real-time task processing for spinning cyber-physical production systems based on edge computing. J Intell Manuf 31, 2069–2087 (2020). https://doi.org/10.1007/s10845-020-01553-6

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