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The emergence of cognitive digital twin: vision, challenges and opportunities

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  • Xiaochen Zheng
  • Jinzhi Lu
  • Dimitris Kiritsis

Abstract

As a key enabling technology of Industry 4.0, Digital Twin (DT) has been widely applied to various industrial domains covering different lifecycle phases of products and systems. To fully realize the Industry 4.0 vision, it is necessary to integrate multiple relevant DTs of a system according to a specific mission. This requires integrating all available data, information and knowledge related to the system across its entire lifecycle. It is a challenging task due to the high complexity of modern industrial systems. Semantic technologies such as ontology and knowledge graphs provide potential solutions by empowering DTs with augmented cognitive capabilities. The Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current DT concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. This paper reviews existing studies relevant to the CDT concept, and further explores its definitions and key features. To facilitate CDT development, a reference architecture is proposed based on the RAMI4.0 and some other existing architectures. Moreover, some key enabling technologies and several application scenarios of CDT are introduced. The challenges and opportunities are discussed in the end to boost future studies.

Suggested Citation

  • Xiaochen Zheng & Jinzhi Lu & Dimitris Kiritsis, 2022. "The emergence of cognitive digital twin: vision, challenges and opportunities," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7610-7632, December.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:24:p:7610-7632
    DOI: 10.1080/00207543.2021.2014591
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    Cited by:

    1. Rantala, Tero & Ukko, Juhani & Nasiri, Mina & Saunila, Minna, 2023. "Shifting focus of value creation through industrial digital twins—From internal application to ecosystem-level utilization," Technovation, Elsevier, vol. 125(C).
    2. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).

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