Industrial Metaverse: Revolutionizing Industry 5.0 with Digital Twins and Extended Reality

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School of Engineering | Doctoral thesis (article-based) | Defence date: 2024-09-13
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Author
Date
2024
Department
Konetekniikan laitos
Department of Mechanical Engineering
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Mcode
Degree programme
Language
en
Pages
90 + app. 56
Series
Aalto University publication series DOCTORAL THESES, 171/2024
Abstract
The industrial sector is experiencing a paradigm shift to Industry 5.0, which emphasizes the integration of human ingenuity and advanced technologies. Central to this transformation is the convergence of digital twins and extended reality (XR), which together foster a more humancentric, technology-augmented industrial ecosystem. The dissertation looks into these transformative technologies within the context of the industrial metaverse, addressing the challenges that have impeded the convergence of digital and physical spaces in industrial settings. Foremost among these challenges is the limitation of current industrial XR solutions, which lack dynamic data interaction with digital twins and robust evaluation of control accuracy. This work addresses this gap by developing a mixed reality interface that actively interacts with a digital twinbased industrial crane, accompanied by measurement protocols for accessing the application's control accuracy. This application serves as a practical entry point into the industrial metaverse, illustrating how digital and physical elements can be synchronized for enhanced operation. Expanding on this foundation, the thesis tackles the scarcity of systematic integration of XR and digital twins across varied industrial machinery and environmental settings. By introducing the TwinXR method and implementing it on two case studies, the work enables scalable, efficient XR application development that leverages digital twin descriptions for enhanced information management and system interoperation across diverse industrial settings. Finally, the research proposes a comprehensive architecture of the industrial metaverse that extends beyond prevailing consumer-centric architectures and their narrow focus on XR. This architecture integrates physical factories with the metaverse through data flow and knowledge synchronization facilitated by the interplay of digital twins and semantic models. A case study on in-plant material flow tracking illustrates the practical application and benefits of this architecture in meeting the complex demands of industrial systems. Overall, the dissertation provides a thorough exploration of the industrial metaverse, traversing from focused applications to broader integration methodology, culminating in an expansive architectural design. The findings highlight the transformative impact of the industrial metaverse in the Industry 5.0 context, where digital twins and XR consolidate to reshape industrial processes and enhancing human-machine collaboration.
Description
Supervising professor
Tammi, Kari, Prof., Aalto University, School of Engineering, Finland
Thesis advisor
Ala-Laurinaho, Riku, Dr., Aalto University, Department of Mechanical Engineering, Finland
Keywords
industrial metaverse, digital twins, extended reality, human-machine interaction, cyber-manufacturing, industry 5.0
Other note
Parts
  • [Publication 1]: Tu, Xinyi and Autiosalo, Juuso and Jadid, Adnane and Tammi, Kari and Klinker, Gudrun. A Mixed Reality Interface for a Digital Twin Based Crane. Applied Sciences, Vol. 11, no. 20, Special Issue "Smart Manufacturing Systems in Industry 4.0", pp. 9480, October 2021.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202111019893
    DOI: 10.3390/app11209480 View at publisher
  • [Publication 2]: Tu, Xinyi and Autiosalo, Juuso and Ala-Laurinaho, Riku and Yang, Chao and Salminen, Pauli and Tammi, Kari. TwinXR: Method for using digital twin descriptions in industrial eXtended reality applications. Frontiers in Virtual Reality, Vol. 4, Research Topic "Exploring Synergies between the Digital Twin Paradigm and eXtended Reality", pp. 1019080, January 2023.
    Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202302082018
    DOI: 10.3389/frvir.2023.1019080 View at publisher
  • [Publication 3]: Tu, Xinyi and Ala-Laurinaho, Riku and Yang, Chao and Autiosalo, Juuso and Tammi, Kari. Architecture for data-centric and semantic enhanced industrial metaverse: Bridging physical factories and virtual landscape. Journal of Manufacturing Systems, Vol. 74, pp. 965–979, June 2024.
    DOI: 10.1016/j.jmsy.2024.05.016 View at publisher
Citation
Permanent link to this item
https://urn.fi/URN:ISBN:978-952-64-1979-4
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[diss] Insinööritieteiden korkeakoulu / ENG