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A Lightweight Intelligent Manufacturing System Based on Cloud Computing for Plate Production

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Abstract

In this paper, a flexible lightweight plate intelligent manufacturing system (LPIMS) based on cloud computing and assembly manufacturing process is proposed for industry 4.0. The system allows optimizing automated production manufacturing process to save raw materials and improve the production efficiency. A multi-layered LPIMS framework is introduced for assisting the system and the corresponding function module is analyzed. Moreover, the critical concept of optimal state is proposed in the optimal intelligent manufacturing to realize precise process control. Based on the optimal control requirements, we present an intelligent insulating glass production line based on LPIMS, we analyze key optimization problems and corresponding coupled relationships existed in the production line, and we develop an integrated method to achieve the optimal control effect to minimize the total trim loss and enhance productivity and mobility.

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Acknowledgements

This work was supported by the Natural Science Foundation of China under grant 51675108, and the Science and Technology Planning Project of Guangdong Province of China under grant 2015B010128007 and 2016A010106006.

We are thankful to all the anonymous reviewers for their valuable suggestion and comments, and all the referenced authors for their ideas and research.

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Authors and Affiliations

  1. Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, China

    Lei Wang, Xin Chen & Qiang Liu

  2. Department of Information Engineering, Guangdong Vocational College of Science and Trade, Guangzhou, China

    Lei Wang

Authors
  1. Lei Wang
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  2. Xin Chen
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  3. Qiang Liu
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Corresponding author

Correspondence to Qiang Liu.

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Wang, L., Chen, X. & Liu, Q. A Lightweight Intelligent Manufacturing System Based on Cloud Computing for Plate Production. Mobile Netw Appl 22, 1170–1181 (2017). https://doi.org/10.1007/s11036-017-0862-5

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  • DOI: https://doi.org/10.1007/s11036-017-0862-5

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