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Research on services encapsulation and virtualization access model of machine for cloud manufacturing

Author

Listed:
  • Yingfeng Zhang

    (Northwestern Polytechnical University
    Northwestern Polytechnical University)

  • Geng Zhang

    (Northwestern Polytechnical University)

  • Yang Liu

    (University of Vaasa)

  • Di Hu

    (Northwestern Polytechnical University)

Abstract

Considering the new requirements of the services encapsulation and virtualization access of manufacturing resources for cloud manufacturing (CMfg), this paper presents a services encapsulation and virtualization access model for manufacturing machine by combining the Internet of Things techniques and cloud computing. Based on this model, some key enabling technologies, such as configuration of sensors, active sensing of real-time manufacturing information, services encapsulation, registration and publishing method are designed. By implementing the proposed services encapsulation and virtualization access model to manufacturing machine, the capability of the machine could be actively perceived, the production process is transparent and can be timely visited, and the virtualized machine could be accessed to CMfg platform through a loose coupling, ‘plug and play’ manner. The proposed model and methods will provide the real-time, accurate, value-added and useful manufacturing information for optimal configuration and scheduling of large-scale manufacturing resources in a CMfg environment.

Suggested Citation

  • Yingfeng Zhang & Geng Zhang & Yang Liu & Di Hu, 2017. "Research on services encapsulation and virtualization access model of machine for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1109-1123, June.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:5:d:10.1007_s10845-015-1064-2
    DOI: 10.1007/s10845-015-1064-2
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    References listed on IDEAS

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    1. Zhang, Yingfeng & Zhang, Geng & Du, Wei & Wang, Junqiang & Ali, Ebad & Sun, Shudong, 2015. "An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 282-292.
    2. Ferrer, Geraldo & Heath, Susan K. & Dew, Nicholas, 2011. "An RFID application in large job shop remanufacturing operations," International Journal of Production Economics, Elsevier, vol. 133(2), pages 612-621, October.
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    5. Kim, Jindae & Ok, Chang-Soo & Kumara, Soundar & Yee, Shang-Tae, 2010. "A market-based approach for dynamic vehicle deployment planning using radio frequency identification (RFID) information," International Journal of Production Economics, Elsevier, vol. 128(1), pages 235-247, November.
    6. Wong, W.K. & Guo, Z.X. & Leung, S.Y.S, 2014. "Intelligent multi-objective decision-making model with RFID technology for production planning," International Journal of Production Economics, Elsevier, vol. 147(PC), pages 647-658.
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    Cited by:

    1. Wei He & Guozhu Jia & Hengshan Zong & Jili Kong, 2019. "Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    2. Haibo Yi, 2021. "A post-quantum secure communication system for cloud manufacturing safety," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 679-688, March.
    3. Yu Feng & Biqing Huang, 2020. "Cloud manufacturing service QoS prediction based on neighbourhood enhanced matrix factorization," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1649-1660, October.
    4. Wattana Viriyasitavat & Li Xu & Zhuming Bi & Assadaporn Sapsomboon, 2020. "Blockchain-based business process management (BPM) framework for service composition in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1737-1748, October.
    5. Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.
    6. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.

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