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Cyber physical process monitoring systems

Author

Listed:
  • Jeff Morgan

    (Trinity College Dublin)

  • Garret E. O’Donnell

    (Trinity College Dublin)

Abstract

Manufacturing process monitoring systems is evolving from centralised bespoke applications to decentralised reconfigurable collectives. The resulting cyber-physical systems are made possible through the integration of high power computation, collaborative communication, and advanced analytics. This digital age of manufacturing is aimed at yielding the next generation of innovative intelligent machines. The focus of this research is to present the design and development of a cyber-physical process monitoring system; the components of which consist of an advanced signal processing chain for the semi-autonomous process characterisation of a CNC turning machine tool. The novelty of this decentralised system is its modularity, reconfigurability, openness, scalability, and unique functionality. The function of the decentralised system is to produce performance criteria via spindle vibration monitoring, which is correlated to the occurrence of sequential process events via motor current monitoring. Performance criteria enables the establishment of normal operating response of machining operations, and more importantly the identification of abnormalities or trends in the sensor data that can provide insight into the quality of the process ongoing. The function of each component in the signal processing chain is reviewed and investigated in an industrial case study.

Suggested Citation

  • Jeff Morgan & Garret E. O’Donnell, 2018. "Cyber physical process monitoring systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1317-1328, August.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1180-z
    DOI: 10.1007/s10845-015-1180-z
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    References listed on IDEAS

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    1. Ki-Young Song & Gerald T. G. Seniuk & Janusz A. Kozinski & Wen-Jun Zhang & Madan M. Gupta, 2015. "An Innovative Fuzzy-Neural Decision Analyzer for Qualitative Group Decision Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 659-696.
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    Cited by:

    1. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
    2. Shashi Bhushan Jha & Radu F. Babiceanu & Remzi Seker, 2020. "Formal modeling of cyber-physical resource scheduling in IIoT cloud environments," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1149-1164, June.
    3. Zhiwen Huang & Jianmin Zhu & Jingtao Lei & Xiaoru Li & Fengqing Tian, 2020. "Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 953-966, April.
    4. Guodong Huang & Jie Chen & Yacob Khojasteh, 2021. "A cyber-physical system deployment based on pull strategies for one-of-a-kind production with limited resources," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 579-596, February.
    5. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    6. Rishi Kumar & Kuldip Singh Sangwan & Christoph Herrmann & Rishi Ghosh, 2024. "Development of a cyber physical production system framework for smart tool health management," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3037-3066, October.

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