Enabling federated learning services using OPC UA, linked data and GAIA-X in cognitive production

• Autorzy: Friedrich Christian , Vogt Stefan , Rudolph Franziska , Patolla Paul , Grützmann Jossy Milagros , Hohmeier Orlando , Richter Martin , Wenzel Ken , Reichelt Dirk , Ihlenfeldt Steffen

Identyfikatory

DOI

10.36897/jme/188618

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

Industry 4.0; digital manufacturing; data space; cognitive production

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2024
• Tom: Vol. 24, No. 2
• Strony: 18--33
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Friedrich Christian

• Department of IIoT Controls and Technical Cybernetics, Fraunhofer Institute for Machine Tools and Forming Technology, Dresden, Germany

• https://orcid.org/0000-0002-1882-5524

autor

Vogt Stefan

• Chair of Information Management, HTWD University of Applied Sciences, Dresden, Germany

autor

Rudolph Franziska

• Department Digitalization in Production, Fraunhofer Institute for Machine Tools and Forming Technology, Chemnitz, Germany

autor

Patolla Paul

• Chair of Information Management, HTWD University of Applied Sciences, Dresden, Germany

autor

Grützmann Jossy Milagros

• Department Digitalization in Production, Fraunhofer Institute for Machine Tools and Forming Technology, Chemnitz, Germany

autor

Hohmeier Orlando

• Katulu GmbH, Hamburg, Germany

autor

Richter Martin

• Department of IIoT Controls and Technical Cybernetics, Fraunhofer Institute for Machine Tools and Forming Technology, Dresden, Germany

autor

Wenzel Ken

• Department Digitalization in Production, Fraunhofer Institute for Machine Tools and Forming Technology, Chemnitz, Germany

autor

Reichelt Dirk

• Chair of Information Management, HTWD University of Applied Sciences, Dresden, Germany

autor

Ihlenfeldt Steffen

• Department of IIoT Controls and Technical Cybernetics, Fraunhofer Institute for Machine Tools and Forming Technology, Dresden, Germany

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