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Towards scalable and reusable predictive models for cyber twins in manufacturing systems

Author

Listed:
  • Cinzia Giannetti

    (Bay Campus, Swansea University)

  • Aniekan Essien

    (University of Sussex Business School)

Abstract

Smart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories.

Suggested Citation

  • Cinzia Giannetti & Aniekan Essien, 2022. "Towards scalable and reusable predictive models for cyber twins in manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 441-455, February.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:2:d:10.1007_s10845-021-01804-0
    DOI: 10.1007/s10845-021-01804-0
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    References listed on IDEAS

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    1. Werner Zellinger & Thomas Grubinger & Michael Zwick & Edwin Lughofer & Holger Schöner & Thomas Natschläger & Susanne Saminger-Platz, 2020. "Multi-source transfer learning of time series in cyclical manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 777-787, March.
    2. Juncheng Wang & Bin Zou & Mingfang Liu & Yishang Li & Hongjian Ding & Kai Xue, 2021. "Milling force prediction model based on transfer learning and neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 947-956, April.
    Full references (including those not matched with items on IDEAS)

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