An MBD-Enabled Digital Twin Modeling Method for Cognition Assistance in Human-Centric Smart Assembly

In line with the emerging Industry 5.0 paradigm's human-centricity characteristic, manual assembly has long been an indispensable element of small-batch and customized products. However, the complex assembly information brings a huge cognition burden for the worker, which restricts the development of the human-centric assembly. To take advantage of the valuable model-based definition (MBD) information in the intelligent manufacturing environment for cognition assistance, an MBD-enabled digital twin modeling method is studied. Firstly, the MBD information is divided into five layers to describe the information according to the assembly process. Based on the assembly layer information of the MBD model, an MBD-digital twin (MBD-DT) model framework is established to illustrate the connection between the MBD model and the digital twin assembly. The MBD-DT modeling problem is solved by an optimization method for assembly model mapping. Finally, the cognition needs in the manual assembly process are analyzed, and the cognition assistance method using the MBD-DT model is discussed. The research of this paper can be applied to digital manufacturing enterprises, extending MBD resources from the design end to the on-site assembly end, providing real-time assistance for manual assembly, and achieving sustainability in human-centric smart assembly, which meets the core value of Industry 5.0.

• Publication year

  2023

• Venue

  2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)

• Publication date

  2023-08-26

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/CASE56687.2023.10260573
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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