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A framework for shopfloor material delivery based on real-time manufacturing big data

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Abstract

Although auto-identification devices such as radio frequency identification and smart sensors have been widely used in shopfloor management and control, major challenges still hinder the vision of real-time and multi-source data-driven material delivery in a manufacturing big data environment. For instance, how to collect manufacturing big data in a timely and accurate manner, and how to discover the hidden pattern from the manufacturing big data rapidly to improve the efficiency of material delivery. To address these challenges, in this paper, a framework for shopfloor material delivery based on real-time manufacturing big data is proposed. Key technologies of the proposed framework are investigated. Firstly, a solution of data sensing and acquisition is designed. Secondly, the methods of manufacturing big data preprocessing and storage are developed to integrate and share the manufacturing data in a unified data format, and to ensure the reusability of the data. Thirdly, a graphic model for the manufacturing big data mining is presented. An improved Apriori-based association analysis model is exploited to identify the frequency trajectories of material delivery. In order to demonstrate the implementation of the proposed framework, a proof-of-concept scenario is designed. The key findings and insights from the experimental results are summarized as managerial implications, which can guide manufacturers to make more informed decisions for the shopfloor management.

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Acknowledgements

The authors would like to acknowledge the financial supports of National Science Foundation of China (51675441), 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, Northwestern Polytechnical University, Shaanxi, People’s Republic of China

    Shan Ren, Binbin Huang & Zhe Wang

  2. School of Software, Key Laboratory for Information System Security, Ministry of Education, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, People’s Republic of China

    Xibin Zhao

  3. Department of Electrical and Computer Engineering, Portland State University, Portland, OR, 97207, USA

    Xiaoyu Song

Authors
  1. Shan Ren
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  2. Xibin Zhao
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  3. Binbin Huang
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  4. Zhe Wang
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  5. Xiaoyu Song
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Correspondence to Shan Ren or Xibin Zhao.

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Ren, S., Zhao, X., Huang, B. et al. A framework for shopfloor material delivery based on real-time manufacturing big data. J Ambient Intell Human Comput 10, 1093–1108 (2019). https://doi.org/10.1007/s12652-018-1017-7

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  • DOI: https://doi.org/10.1007/s12652-018-1017-7

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