Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Christian Friedrich - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)
  • Stefan Vogt - , Dresden University of Applied Sciences (HTW) (Author)
  • Franziska Rudolph - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)
  • Paul Patolla - , Dresden University of Applied Sciences (HTW) (Author)
  • Jossy Milagros Grützmann - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)
  • Orlando Hohmeier - , Katulu GmbH (Author)
  • Martin Richter - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)
  • Ken Wenzel - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)
  • Dirk Reichelt - , Dresden University of Applied Sciences (HTW) (Author)
  • Steffen Ihlenfeldt - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)

Abstract

Value creation in production is based on collaboration of different stakeholders and requires the secure and sovereign exchange of knowledge. Today, knowledge has mostly been built up individually and is only exchanged in a proprietary manner. This paper presents an exemplary pipeline for federated services in cross-domain and cross-company value creation networks for cognitive production. On the example of collaboratively training of a federated machine learning model, machine tool lifetime is predicted in industrial manufacturing for high-end operating resources (high-quality cutting tools). From the shop floor to the cloud, all service relevant information is structured using existing digital twin standards and a linked data approach. In particular, the Industry 4.0 Asset Administration Shell (AAS) and OPC UA are used for collecting and referencing operational and engineering data. GAIA-X connectors transfer the service relevant data through a shared data space. The solution enables intelligent analysis and decision-making under the prioritization of data sovereignty and transparency and, therefore, acts as an enabler for future collaborative, data-driven manufacturing applications.

Details

Original languageEnglish
Pages (from-to)18-33
Number of pages16
JournalJournal of Machine Engineering
Volume24
Issue number2
Publication statusPublished - 2024
Peer-reviewedYes
Externally publishedYes

Keywords

Keywords

  • cognitive production, Data Spaces, Digital Manufacturing, Industry 4.0