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A digital twin ecosystem for additive manufacturing using a real-time development platform.

Minas Pantelidakis1, Konstantinos Mykoniatis1, Jia Liu1

  • 1Industrial and Systems Engineering, Auburn University, 357-359 W Magnolia Ave, Auburn, AL 36832 USA.

The International Journal, Advanced Manufacturing Technology
|April 19, 2022
PubMed
Summary

This study introduces a cost-efficient digital twin for additive manufacturing, enabling real-time monitoring and remote management of 3D printers. The system ensures accurate synchronization between physical machines and virtual models.

Keywords:
Additive manufacturingDigital twinFused deposition modelingSimulationUnity 3D

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Area of Science:

  • Engineering
  • Computer Science
  • Materials Science

Background:

  • Additive manufacturing (AM) enables complex designs difficult with traditional methods.
  • Rapid prototyping and manufacturing benefit from AM's capabilities.
  • Digital twins offer virtual environments for testing and monitoring.

Purpose of the Study:

  • Implement a novel digital twin ecosystem for additive manufacturing (AM) fused deposition modeling (FDM) machines.
  • Enable testing, process monitoring, and remote management in a virtual environment.
  • Evaluate sensor-data-driven vs. web controller-driven approaches for synchronization.

Main Methods:

  • Developed a digital twin ecosystem with two data-driven approaches.
  • Utilized an open-source web controller for 3D printer status and parameters.
  • Employed externally mounted sensors to approximate physical printer behavior for synchronization.

Main Results:

  • Achieved near-real-time synchronization between physical and virtual 3D printers.
  • Validated the digital twin for position, temperature, and run duration accuracy.
  • Demonstrated the sensor-data-driven approach against the web controller (ground truth).

Conclusions:

  • The digital twin ecosystem is cost-efficient, reliable, and replicable.
  • Enables digital twin capabilities for legacy equipment.
  • Facilitates historical data collection and analytics generation for AM processes.