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Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes.

Georgia Stavropoulou1, Konstantinos Tsitseklis1, Lydia Mavraidi1

  • 1School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Athens, Greece.

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Summary
This summary is machine-generated.

This study integrates knowledge graphs (KG) with digital twins (DT) to create more autonomous and flexible manufacturing processes. This synergy enhances self-awareness and optimizes operations for improved quality and efficiency.

Keywords:
digital twinsknowledge graphslaser glass bendingmaterial manufacturingmaterial ontologies

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Area of Science:

  • Manufacturing Engineering
  • Computer Science
  • Materials Science

Background:

  • Material manufacturing demands higher product quality, reduced operational costs, and timely process completion.
  • Digital Twin (DT) technologies are key enablers for developing and managing manufacturing processes.
  • Knowledge Graphs (KG) offer structured data representation and advanced analytics for industrial applications.

Purpose of the Study:

  • To propose and develop an integrated solution combining Knowledge Graphs (KG) and Digital Twins (DT).
  • To create highly autonomous and flexible DTs leveraging semantic knowledge from a KG.
  • To enhance DT self-awareness and support advanced functionalities in manufacturing.

Main Methods:

  • Developed a domain-agnostic KG storing material properties and process information.
  • Designed DTs composed of Virtual Objects (VOs), each abstracting a step in an Industrial Business Process (IBP).
  • Utilized KG queries to orchestrate VO operations and configure parameters, enhancing DT self-awareness.

Main Results:

  • Demonstrated the architecture of the integrated KG and DT solution.
  • Showcased the application of the system in a real-world laser glass bending process.
  • The integration enables DTs to dynamically adapt and optimize manufacturing operations.

Conclusions:

  • The synergy between KG and DTs significantly enhances the autonomy and flexibility of manufacturing processes.
  • This approach provides a robust framework for intelligent manufacturing and process optimization.
  • The proposed solution effectively increases the self-awareness and adaptability of Digital Twins.