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Interaction with Industrial Digital Twin Using Neuro-Symbolic Reasoning.

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Neuro-symbolic reasoning (NSR) enables natural language interaction with 3D digital twins for manufacturing and maintenance. This AI approach achieves 96.2% accuracy in executing commands, enhancing virtual machinery interaction.

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Area of Science:

  • Artificial Intelligence
  • Manufacturing Technology
  • Human-Computer Interaction

Background:

  • Digital twins offer advanced simulations for manufacturing and maintenance.
  • Current digital twins lack natural language communication capabilities, limiting user interaction.
  • Conventional natural language processing methods have limitations in complex task execution.

Purpose of the Study:

  • To introduce neuro-symbolic reasoning (NSR) for natural language interaction with 3D digital twins.
  • To enable autonomous execution of maintenance procedures and information retrieval from manuals.
  • To enhance the versatility and robustness of digital twin interactions.

Main Methods:

  • Developed a neuro-symbolic reasoning (NSR) approach for natural language interaction with 3D digital twins.
  • Created a neuro-symbolic dataset comprising machine-understandable manuals, 3D models, and user queries.
  • Trained the NSR interaction mechanism on the collected dataset.

Main Results:

  • NSR accurately interprets user requests and context to manipulate 3D digital twin components.
  • The system autonomously performs installations and removal procedures based on manual instructions.
  • Achieved 96.2% accuracy in executing user commands on test data.

Conclusions:

  • NSR provides a robust method for natural language interaction with 3D digital twins.
  • The technology facilitates autonomous maintenance procedures and information access from manuals.
  • NSR significantly enhances interaction with complex virtual machinery in industrial settings.