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Cognitive manufacturing: definition and current trends

Author

Listed:
  • Fadi El Kalach

    (University of South Carolina)

  • Ibrahim Yousif

    (University of South Carolina)

  • Thorsten Wuest

    (West Virginia University)

  • Amit Sheth

    (University of South Carolina)

  • Ramy Harik

    (University of South Carolina)

Abstract

Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

Suggested Citation

  • Fadi El Kalach & Ibrahim Yousif & Thorsten Wuest & Amit Sheth & Ramy Harik, 2025. "Cognitive manufacturing: definition and current trends," Journal of Intelligent Manufacturing, Springer, vol. 36(6), pages 3695-3715, August.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:6:d:10.1007_s10845-024-02429-9
    DOI: 10.1007/s10845-024-02429-9
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    References listed on IDEAS

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    1. Baifan Zhou & Tim Pychynski & Markus Reischl & Evgeny Kharlamov & Ralf Mikut, 2022. "Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1139-1163, April.
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