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Knowledge-embedded machine learning and its applications in smart manufacturing

Author

Listed:
  • Farzam Farbiz

    (Institute of High Performance Computing)

  • Mohd Salahuddin Habibullah

    (Institute of High Performance Computing)

  • Brahim Hamadicharef

    (Institute of High Performance Computing)

  • Tomasz Maszczyk

    (Institute of High Performance Computing)

  • Saurabh Aggarwal

    (Institute of High Performance Computing)

Abstract

Demands for more accurate machine learning models have given rise to rethinking current modeling approaches that were deemed unsuitable, primarily due to their computational complexity and the lack of availability and accessibility to representative data. In Industry 4.0, rapid advancements in Digital Twin (DT) technologies and the pervasiveness of cost-effective sensor technologies have pushed the incorporation of artificial intelligence, particularly data-driven machine learning models, for use in smart manufacturing. However, the persistent issue with such models is their high sensitivity to the training data and the lack of interpretability in the outcomes, at times generating unrealistic results. The incorporation of knowledge into the machine learning pipeline has been earmarked as the most promising approach to address such issues. This paper aims to answer this call through a Knowledge-embedded Machine Learning (KML) framework for smart manufacturing, which embeds knowledge from experience and, or physics information into the machine learning pipeline, thus making the outcomes from these models more representative of real applications. The merits of KML were then presented through comparative studies showing its capability to outperform knowledge-based and data-driven models. This promising outcome led to the development of frameworks that can potentially incorporate KML for smart manufacturing applications such as Prognostics and Health Management (PHM) and DT, further supporting the usefulness of the proposed KML framework.

Suggested Citation

  • Farzam Farbiz & Mohd Salahuddin Habibullah & Brahim Hamadicharef & Tomasz Maszczyk & Saurabh Aggarwal, 2023. "Knowledge-embedded machine learning and its applications in smart manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2889-2906, October.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:7:d:10.1007_s10845-022-01973-6
    DOI: 10.1007/s10845-022-01973-6
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    References listed on IDEAS

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    1. Kwok L. Tsui & Nan Chen & Qiang Zhou & Yizhen Hai & Wenbin Wang, 2015. "Prognostics and Health Management: A Review on Data Driven Approaches," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-17, May.
    2. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
    3. Andrew Whitmore & Anurag Agarwal & Li Xu, 2015. "The Internet of Things—A survey of topics and trends," Information Systems Frontiers, Springer, vol. 17(2), pages 261-274, April.
    4. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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