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

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...

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Main Authors: Christian Friedrich, Stefan Vogt, Franziska Rudolph, Paul Patolla, Jossy Milagros Grützmann, Orlando Hohmeier, Martin Richter, Ken Wenzel, Dirk Reichelt, Steffen Ihlenfeldt
Format: Article
Language:English
Published: Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT 2024-05-01
Series:Journal of Machine Engineering
Subjects:
Online Access:http://jmacheng.not.pl/Enabling-Federated-Learning-Services-Using-OPC-UA-Linked-Data-and-GAIA-X-in-Cognitive,188618,0,2.html
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author Christian Friedrich
Stefan Vogt
Franziska Rudolph
Paul Patolla
Jossy Milagros Grützmann
Orlando Hohmeier
Martin Richter
Ken Wenzel
Dirk Reichelt
Steffen Ihlenfeldt
author_facet Christian Friedrich
Stefan Vogt
Franziska Rudolph
Paul Patolla
Jossy Milagros Grützmann
Orlando Hohmeier
Martin Richter
Ken Wenzel
Dirk Reichelt
Steffen Ihlenfeldt
author_sort Christian Friedrich
collection DOAJ
description 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.
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issn 1895-7595
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publishDate 2024-05-01
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spelling doaj-art-d17d02a4089b4e38b446fb88579d74be2025-08-20T02:56:27ZengPublishing House of Wrocław Board of Scientific Technical Societies Federation NOTJournal of Machine Engineering1895-75952391-80712024-05-01242183310.36897/jme/188618188618Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive ProductionChristian Friedrich0https://orcid.org/0000-0002-1882-5524Stefan Vogt1Franziska Rudolph2Paul Patolla3Jossy Milagros Grützmann4Orlando Hohmeier5Martin Richter6Ken Wenzel7Dirk Reichelt8Steffen Ihlenfeldt9IIoT Controls, Fraunhofer IWU, GermanyFaculty of Informatic/Mathematic, HTW Dresden, GermanyDigital Production, Fraunhofer IWU, GermanyFaculty of Informatic/Mathematic, HTW Dresden, GermanyDigital Production, Fraunhofer IWU, GermanyEngineering, Katulu GmbH, GermanyIIoT Controls, Fraunhofer IWU, GermanyDigital Production, Fraunhofer IWU, GermanyFaculty of Informatic/Mathematic, HTW Dresden, GermanyIIoT Controls, Fraunhofer IWU, GermanyValue 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.http://jmacheng.not.pl/Enabling-Federated-Learning-Services-Using-OPC-UA-Linked-Data-and-GAIA-X-in-Cognitive,188618,0,2.htmlindustry 4.0digital manufacturingdata spacescognitive production
spellingShingle Christian Friedrich
Stefan Vogt
Franziska Rudolph
Paul Patolla
Jossy Milagros Grützmann
Orlando Hohmeier
Martin Richter
Ken Wenzel
Dirk Reichelt
Steffen Ihlenfeldt
Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
Journal of Machine Engineering
industry 4.0
digital manufacturing
data spaces
cognitive production
title Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
title_full Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
title_fullStr Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
title_full_unstemmed Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
title_short Enabling Federated Learning Services Using OPC UA, Linked Data and GAIA-X in Cognitive Production
title_sort enabling federated learning services using opc ua linked data and gaia x in cognitive production
topic industry 4.0
digital manufacturing
data spaces
cognitive production
url http://jmacheng.not.pl/Enabling-Federated-Learning-Services-Using-OPC-UA-Linked-Data-and-GAIA-X-in-Cognitive,188618,0,2.html
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