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Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer

Author

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  • Hasan Tercan

    (University of Wuppertal)

  • Philipp Deibert

    (University of Wuppertal)

  • Tobias Meisen

    (University of Wuppertal)

Abstract

Deep learning-based predictive quality enables manufacturing companies to make data-driven predictions of the quality of a produced product based on process data. A central challenge is that production processes are subject to continuous changes such as the manufacturing of new products, with the result that previously trained models may no longer perform well in the process. In this paper, we address this problem and propose a method for continual learning in such predictive quality scenarios. We therefore adapt and extend the memory-aware synapses approach to train an artificial neural network across different product variations. Our evaluation in a real-world regression problem in injection molding shows that the approach successfully prevents the neural network from forgetting of previous tasks and improves the training efficiency for new tasks. Moreover, by extending the approach with the transfer of network weights from similar previous tasks, we significantly improve its data efficiency and performance on sparse data. Our code is publicly available to reproduce our results and build upon them.

Suggested Citation

  • Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.
  • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-021-01793-0
    DOI: 10.1007/s10845-021-01793-0
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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. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    3. Michael D. T. McDonnell & Daniel Arnaldo & Etienne Pelletier & James A. Grant-Jacob & Matthew Praeger & Dimitris Karnakis & Robert W. Eason & Ben Mills, 2021. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1471-1483, June.
    4. Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
    5. Huixin Tian & Daixu Ren & Kun Li & Zhen Zhao, 2021. "An adaptive update model based on improved Long Short Term Memory for online prediction of vibration signal," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 37-49, January.
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