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Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine

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Abstract

This paper aims to introduce the concept of cloud manufacturing (CMfg) in the injection molding industry. The CMfg platform for injection molding enterprises is built to improve the sharing, circulation and integration of the injection molding resources. With the implementation of the Internet of Things technologies in the traditional injection molding shop, the real-time manufacturing information of resources can be accurately captured and the entire molding process becomes more visible and traceable. The virtual machining service of the injection molding machine is encapsulated as a cloud service that published into the platform for on-demand use. When task orders are published, through the presented task-driven proactive service discovery method, competent services can be quickly found. The custom-oriented evaluation method based on technique for order preference by similarity to ideal solution is designed to help the demanders to find satisfying services according to their customized criteria. Since the task orders arrive dynamically, after these orders are assigned to the specified machine, a real-time order dispatching mechanism is developed to provide an optimal scheduling plan for the cloud service. Finally, the proposed framework and methods are illustrated by a numerical simulation.

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Acknowledgements

The authors would like to acknowledge financial supports of National Science Foundation of China (51675441) and the 111 Project Grant (B13044).

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Authors and Affiliations

  1. Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi, People’s Republic of China

    Yingfeng Zhang, Dong Xi & Zhe Wang

  2. Research and Development Institute in Shenzhen, Northwestern Polytechnical University, Shenzhen, 518057, People’s Republic of China

    Yingfeng Zhang

  3. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China

    Haidong Yang

  4. School of Mechanical Engineering, Guangdong University of Technology, Guangzhou, People’s Republic of China

    Haidong Yang

  5. School of Automation Science and Electrical Engineering, Beihang University, Beijing, People’s Republic of China

    Fei Tao

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  1. Yingfeng Zhang
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Correspondence to Yingfeng Zhang.

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Zhang, Y., Xi, D., Yang, H. et al. Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine. J Intell Manuf 30, 2681–2699 (2019). https://doi.org/10.1007/s10845-017-1322-6

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