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Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing

Author

Listed:
  • Chengjun Xu

    (Wuhan University)

  • Guobin Zhu

    (Wuhan University)

Abstract

Due to the improvement of network infrastructure and the application of Internet of Things equipment, a large number of sensors are deployed in the industrial pipeline production, and the large size of data is generated. The most typical case in the production line is product inspection, that is, defect inspection. To implement an efficient and robust detection system, in this study, we propose a classification computing model based on Lie Group Machine Learning, which can find the possible defective products in production. Usually, a workshop has a lot of assembly lines. How to process large data on so many production lines in real-time and accurately is a difficult problem. To solve this problem, we use the concept of fog computing to design the system. By offloading the computation burden from the cloud server center to the fog nodes, the system obtains the ability to deal with extremely data. Our system has two obvious advantages. The first one is to apply Lie Group Machine Learning to fog computing environment to improve the computational efficiency and robustness of the system. The other is that without increasing any production costs, it can quickly detect products, reduce network latency, and reduce the load on bandwidth. The simulations prove that, compared with the existing methods, the proposed method has an average running efficiency increase of 52.57%, an average delay reduction of 42.13%, and an average accuracy increase of 27.86%.

Suggested Citation

  • Chengjun Xu & Guobin Zhu, 2021. "Intelligent manufacturing Lie Group Machine Learning: real-time and efficient inspection system based on fog computing," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 237-249, January.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:1:d:10.1007_s10845-020-01570-5
    DOI: 10.1007/s10845-020-01570-5
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    References listed on IDEAS

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    1. Cheng Hao Jin & Hyun-Jin Kim & Yongjun Piao & Meijing Li & Minghao Piao, 2020. "Wafer map defect pattern classification based on convolutional neural network features and error-correcting output codes," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1861-1875, December.
    2. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
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    Cited by:

    1. Jinfeng Liu & Qiukai Ji & Xiaohu Zhang & Yu Chen & Yiming Zhang & Xiaojun Liu & Mingming Tang, 2024. "Digital twin model-driven capacity evaluation and scheduling optimization for ship welding production line," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3353-3375, October.

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