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Building a cloud-based digital twin for remote monitoring and control of a robotic assembly system

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Abstract

The potential of digital twin (DT) technology to revolutionize industry by enabling virtual simulations of physical systems in real-time has garnered significant attention in recent years. DTs have been widely applied in the manufacturing field to solve various problems, such as shopfloor resource optimization, layout design, commissioning, monitoring, and supervisory control. Cloud-based DT (CBDT) is an emerging concept and shows promise in achieving enhanced remote accessibility, data processing and analysis capabilities, and scalability. However, current CBDT research is still very limited and mainly focuses on theoretical framework that leverages cloud computing advantages in data processing aspects. Yet, practical implementation with technical details for creating a CBDT of a complex manufacturing system is rarely reported, and the interactions between cloud infrastructure and DT modeling and visualization are scarcely investigated. To fill the gaps, this paper first proposes a general CBDT framework for supporting smart manufacturing services. This framework features the integration of modularized cloud intelligence, DT modeling, and DT visualization to achieve enhanced remote accessibility. Moreover, a prototyping system that entails the CBDT-enabled remote monitoring and control services is implemented for a legacy robotic assembly system to partially showcase the process of the proposed framework. The usefulness and remote accessibility of the developed CBDT-based prototype system is further demonstrated with web-based functionalities such as assembly job status update, real-time 3-dimensional DT visualization and simulation of assembly tasks, and remote feedback control over the physical system. Lastly, the prototype system is built upon open-source toolkits (e.g., WebGL) and low-cost commercial software platforms (e.g., Unity and Google Cloud Platform), which could potentially open new opportunities for aiding small-to-medium companies for digital transformation. Future works and limitations are also discussed in the end.

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Acknowledgements

The financial sponsorship of Aleo Canada Inc. is highly appreciated. The authors would like to acknowledge the invaluable contributions of the editorial board and reviewers who provided valuable insights, constructive feedback, and discerning suggestions.

Funding

The study was partially supported by the funding from the Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2022-03448).

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Authors and Affiliations

  1. School of Engineering, University of Guelph, Guelph, Canada

    Md Tahmid Bin Touhid, Mrunal Marne, Theodore Oskroba, Sayed Amirhossein Mirahmadi, Enshen Zhu, Fantahun Defersha & Sheng Yang

  2. Aléo Canada Inc., Montreal, QC, H3C 2M7, Canada

    Alireza Mehrabian

Authors
  1. Md Tahmid Bin Touhid
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  2. Mrunal Marne
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  3. Theodore Oskroba
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  4. Sayed Amirhossein Mirahmadi
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  5. Enshen Zhu
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  6. Alireza Mehrabian
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  7. Fantahun Defersha
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  8. Sheng Yang
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Contributions

Conceptualization—M.T and S.Y. Literature review and methodology—M.T and S.Y. Prototyping and testing—M.T, M.M, T.O, S.M. Original draft writing and review—M.T, E.Z, and S.Y. Manuscript revision and review—M.T, E.Z, and S.Y. Project management and supervision—A.M, F.D, S.Y. Funding acquisition—S.Y and F.D. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Sheng Yang.

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Touhid, M.T.B., Marne, M., Oskroba, T. et al. Building a cloud-based digital twin for remote monitoring and control of a robotic assembly system. Int J Adv Manuf Technol 129, 4045–4057 (2023). https://doi.org/10.1007/s00170-023-12611-7

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  • DOI: https://doi.org/10.1007/s00170-023-12611-7

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