
Publikationen des Projekts
Eichelberger, Holger; Palmer, Gregory; Niederee, Claudia Developing an AI-enabled Industry 4.0 platform - Performance experiences on deploying AI onto an industrial edge device Journal Article In: Softwaretechnik-Trends, 43 (1), pp. 35-37, 2023, ISSN: 0702-8928. Abstract | BibTeX | Schlagwörter: IIP-Ecosphere, Industrie 4.0, Industry 4.0, Platform Sauer, Christian Severin; Eichelberger, Holger Performance Evaluation of BaSyx based Asset Administration Shells for Industry 4.0 Applications Journal Article In: Softwaretechnik-Trends, 43 (1), pp. 47-49, 2023, ISSN: 0702-8928. Abstract | BibTeX | Schlagwörter: Asset Administration Shells, IIP-Ecosphere, Industrie 4.0, Industry 4.0, Platform, Verwaltungsschale Eichelberger, Holger; Palmer, Gregory; Niederée, Claudia Developing an AI-enabled Industry 4.0 platform – Performance experiences on deploying AI onto an industrial edge device Inproceedings In: 13th Symposium on Software Performance 2022, 2022. Abstract | Links | BibTeX | Schlagwörter: IIP-Ecosphere, Industrie 4.0, Platform Activities, Virtual Platform Alamoush, Ahmad; Eichelberger, Holger Adapting Kubernetes to IIoT and Industry 4.0 protocols - An initial performance analysis Inproceedings In: 13th Symposium on Software Performance 2022, 2022. Abstract | Links | BibTeX | Schlagwörter: IIoT, IIP-Ecosphere, Industrie 4.0, Industry 4.0, Virtual Platform Sauer, Christian; Eichelberger, Holger Performance Evaluation of BaSyx based Asset Administration Shells for Industry 4.0 Applications Inproceedings In: 13th Symposium on Software Performance 2022, 2022. Abstract | Links | BibTeX | Schlagwörter: Asset Administration Shells, IIP-Ecosphere, Industrie 4.0 Faubel, Leonhard; Schmid, Klaus; Eichelberger, Holger Is MLOps different in Industry 4.0? General and Specific Challenges Conference 3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL), 2022. Abstract | Links | BibTeX | Schlagwörter: Industrie 4.0, MLOps Eichelberger, Holger; Palmer, Gregory; Reimer, Svenja; Trong Vu, Tat; Do, Hieu; Laridi, Sofiane; Weber, Alexander; Niederée, Claudia; Hildebrandt, Thomas Developing an AI-Enabled IIoT Platform - Lessons Learned from Early Use Case Validation Inproceedings In: Batista, Thais; Burevs, Tom'avs; Raibulet, Claudia; Muccini, Henry (Ed.): Software Architecture. ECSA 2022 Tracks and Workshops, pp. 265-283, Springer International Publishing, Cham, 2022, ISBN: 978-3-031-36889-9. Abstract | BibTeX | Schlagwörter: IIoT, IIoT-Platform, IIP-Ecosphere, Industrie 4.0, Industry 4.0, KI in der Produktion, Virtual Platform Eichelberger, Holger; Ahmadian, Amir Shayan; Dewes, Andreas; Ehl, Marco; Alamoush, Ahmad; Staciwa, Monika; Casado, Miguel Gómez IIP-Ecosphere Platform Handbook v0.4.0 Whitepaper In: 2022. Links | BibTeX | Schlagwörter: Architecture, IIP-Ecosphere, Industrie 4.0, Manual, Virtual Platform Arnu, David; Klinkenberg, Ralf Industrial Data Science Platform and Applications in Electronics and Manufacturing Industries Presentation 14.12.2021. BibTeX | Schlagwörter: Artificial Intelligence, IIP-Ecosphere, Industrie 4.0, RapidMiner, Sennheiser Niederée, Claudia; Eichelberger, Holger; Schmees, Hans-Dieter; Broos, Alexander; Schreiber, Per KI in der Produktion – Quo vadis? Whitepaper In: 2021. Links | BibTeX | Schlagwörter: IIoT, IIP-Ecosphere, Industrie 4.0, KI in der Produktion, Produktion, Umfrage Niederée, Claudia; Eichelberger, Holger; Schmees, Hans-Dieter; Broos, Alexander; Schreiber, Per Management Summary zu Whitepaper "KI in der Produktion – Quo vadis?" Miscellaneous 2021. Links | BibTeX | Schlagwörter: IIoT, IIP-Ecosphere, Industrie 4.0, KI in der Produktion Casado, Miguel Gomez; Eichelberger, Holger Industry 4.0 Resource Monitoring - Experiences With Micrometer and Asset Administration Shells Inproceedings In: CEUR-WS Proceedings of Symposium on Software Performance 2021 (SSP'21), CEUR-WS.org, 2021. Links | BibTeX | Schlagwörter: Asset Administration Shells, IIP-Ecosphere, Industrie 4.0 Denkena, Berend; Bergmann, Benjamin; Reimer, Svenja; Schmidt, Alexander; Stiehl, Tobias; Witt, Matthias KI-gestützte Prozessüberwachung in der Zerspanung Journal Article In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 115 (5), pp. 295-298, 2020, ISBN: 0947–0085. Links | BibTeX | Schlagwörter: Industrie 4.0, Künstliche Intelligenz, Produktion, Prozessüberwachung2023
@article{nokey,
title = {Developing an AI-enabled Industry 4.0 platform - Performance experiences on deploying AI onto an industrial edge device},
author = {Holger Eichelberger and Gregory Palmer and Claudia Niederee},
issn = {0702-8928},
year = {2023},
date = {2023-02-01},
journal = {Softwaretechnik-Trends},
volume = {43},
number = {1},
pages = {35-37},
publisher = {GI},
abstract = {Maximizing the benefits of AI for Industry 4.0 is about more than just developing effective new AI methods. Of equal importance is the successful integration of AI into production environments. One open challenge is the dynamic deployment of AI on industrial edge devices within close proximity to manufacturing machines. Our IIP-Ecosphere platform was designed to overcome limitations of existing Industry 4.0 platforms. It supports flexible AI deployment through employing a highly configurable low-code based approach, where code for tailored platform components and applications is generated. In this paper, we measure the performance of our platform on an industrial demonstrator and discuss the impact of deploying AI from a central server to the edge. As result, AI inference automatically deployed on an industrial edge is possible, but in our case three times slower than on a desktop computer, requiring still more optimizations.},
keywords = {IIP-Ecosphere, Industrie 4.0, Industry 4.0, Platform},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Performance Evaluation of BaSyx based Asset Administration Shells for Industry 4.0 Applications},
author = {Christian Severin Sauer and Holger Eichelberger},
issn = {0702-8928},
year = {2023},
date = {2023-02-01},
journal = {Softwaretechnik-Trends},
volume = {43},
number = {1},
pages = {47-49},
publisher = {GI},
abstract = {The Asset Administration Shell (AAS) is an upcoming information model standard, which aims at interoperable modeling of “assets”, i.e., products, machines, services or digital twins in IIoT/Industry 4.0. Currently, a number of IIoT-platforms use proprietary information models similar to AAS, but not a common standard, which affects interoperability.A key question for a broad uptake is if AAS can be applied in a performant and scalable manner. In this paper, we examine this question for the open source Eclipse BaSyx middleware. To explore capabilities and possible performance limitations, we present four experiments measuring the performance of experimental AAS in BaSyx and, within the context set by our experiments, i.e., 10-1000 AAS instances, can conclude good scalability.},
keywords = {Asset Administration Shells, IIP-Ecosphere, Industrie 4.0, Industry 4.0, Platform, Verwaltungsschale},
pubstate = {published},
tppubtype = {article}
}
2022
@inproceedings{eichelberger2022ssp,
title = {Developing an AI-enabled Industry 4.0 platform – Performance experiences on deploying AI onto an industrial edge device},
author = {Holger Eichelberger and Gregory Palmer and Claudia Niederée},
url = {https://www.iip-ecosphere.de/ssp22/},
year = {2022},
date = {2022-11-09},
booktitle = {13th Symposium on Software Performance 2022},
abstract = {Bei der Maximierung des Nutzens von KI für die Industrie 4.0 geht es um mehr als nur die Entwicklung effektiver neuer KI-Methoden. Ebenso wichtig ist die erfolgreiche Integration von KI in Produktionsumgebungen. Eine offene Herausforderung ist der dynamische Einsatz von KI auf industriellen Edge-Geräten in unmittelbarer Nähe von Produktionsmaschinen. Die IIP-Ecosphere-Plattform wurde entwickelt, um die Einschränkungen bestehender Industrie 4.0-Plattformen zu überwinden. Sie unterstützt den flexiblen Einsatz von KI durch einen hochgradig konfigurierbaren Low-Code-basierten Ansatz, bei dem Code für maßgeschneiderte Plattformkomponenten und Anwendungen generiert wird. In diesem Papier messen wir die Leistung unserer Plattform an einem industriellen Demonstrator und diskutieren die Auswirkungen des Einsatzes von KI, ausgehend von einem zentralen Server, auf der Edge.},
keywords = {IIP-Ecosphere, Industrie 4.0, Platform Activities, Virtual Platform},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{alamoush2022ssp,
title = {Adapting Kubernetes to IIoT and Industry 4.0 protocols - An initial performance analysis},
author = {Ahmad Alamoush and Holger Eichelberger},
url = {https://www.iip-ecosphere.de/ssp-kubernetes-cr/},
year = {2022},
date = {2022-11-08},
urldate = {2022-11-08},
booktitle = {13th Symposium on Software Performance 2022},
abstract = {Kubernetes ist eines der am häufigsten verwendeten Container-Orchestrierungstools, denn es bietet
reichhaltige Funktionen zur Verwaltung von containerisierten Anwendungen, ist anpassbar und erweiterbar. Die Virtualisierung von Containern von Anwendungen und deren Orchestrierung auf heterogenen Ressourcen einschließlich Edge-Geräten ist ein neuer Trend im industriellen Internet der Dinge (IIoT)/Industrie 4.0 in der auch verstärkt Kubernetes eingesetzt wird. Allerdings sind, IIoT/Industrie 4.0 Domänen mit hohen Anforderungen an derzeitigen und künftigen Standardisierungsanforderungen. Neben Gerätenormen, z.B. für elektrische Schaltschränke, gibt es auch
Anforderungen an die Standardisierung von Netzwerkprotokollen, Daten Datenformaten oder Informationsmodellen. Hier ist das proprietäre Kommunikationsprotokoll von Kubernetes gegebenenfalls ein Hindernis für die Akzeptanz von Kubernetes. Um diese Situation unter dem Gesichtspunkt der Interoperabilität und Integration zu untersuchen, stellen wir in diesem Papier einen Ansatz zum Austausch des Kommunikationsprotokolls von Kubernetes vor, ohne dessen Codebasis zu verändern. Wir zeigen die Anwendung unseres Ansatzes der Verwendung von Kubernetes mit drei aktuellen Formen der IIoT-Kommunikation: Message Queuing Telemetry Transport (MQTT), Advanced Advanced Message Queuing Protocol (AMQP) und Asset Administration Shell (AAS).},
keywords = {IIoT, IIP-Ecosphere, Industrie 4.0, Industry 4.0, Virtual Platform},
pubstate = {published},
tppubtype = {inproceedings}
}
reichhaltige Funktionen zur Verwaltung von containerisierten Anwendungen, ist anpassbar und erweiterbar. Die Virtualisierung von Containern von Anwendungen und deren Orchestrierung auf heterogenen Ressourcen einschließlich Edge-Geräten ist ein neuer Trend im industriellen Internet der Dinge (IIoT)/Industrie 4.0 in der auch verstärkt Kubernetes eingesetzt wird. Allerdings sind, IIoT/Industrie 4.0 Domänen mit hohen Anforderungen an derzeitigen und künftigen Standardisierungsanforderungen. Neben Gerätenormen, z.B. für elektrische Schaltschränke, gibt es auch
Anforderungen an die Standardisierung von Netzwerkprotokollen, Daten Datenformaten oder Informationsmodellen. Hier ist das proprietäre Kommunikationsprotokoll von Kubernetes gegebenenfalls ein Hindernis für die Akzeptanz von Kubernetes. Um diese Situation unter dem Gesichtspunkt der Interoperabilität und Integration zu untersuchen, stellen wir in diesem Papier einen Ansatz zum Austausch des Kommunikationsprotokolls von Kubernetes vor, ohne dessen Codebasis zu verändern. Wir zeigen die Anwendung unseres Ansatzes der Verwendung von Kubernetes mit drei aktuellen Formen der IIoT-Kommunikation: Message Queuing Telemetry Transport (MQTT), Advanced Advanced Message Queuing Protocol (AMQP) und Asset Administration Shell (AAS).@inproceedings{sauer2022ssp,
title = {Performance Evaluation of BaSyx based Asset Administration Shells for Industry 4.0 Applications},
author = {Christian Sauer and Holger Eichelberger},
url = {https://www.iip-ecosphere.de/ssp22_aas_paper-crc/},
year = {2022},
date = {2022-11-08},
booktitle = {13th Symposium on Software Performance 2022},
abstract = {Die Asset Administration Shell (AAS) ist ein neuer Informationsmodell-Standard, der auf die interoperable Modellierung von "Assets", d.h. von Produkten, Maschinen, Dienstleistungen oder digitalen Zwillingen im IIoT/Industrie 4.0. Derzeit, verwenden eine Reihe von IIoT-Plattformen proprietäre Informationsmodelle ähnlich dem AAS, aber keinen gemeinsamen Standard, was die Interoperabilität beeinträchtigt. Eine Schlüsselfrage für eine breite Akzeptanz der AAS ist, ob AAS leistungsfähig und skalierbar eingesetzt werden können. In diesem Papier untersuchen wir diese Frage für AAS, die mit der quelloffene Eclipse BaSyx Middleware erstellt werden.},
keywords = {Asset Administration Shells, IIP-Ecosphere, Industrie 4.0},
pubstate = {published},
tppubtype = {inproceedings}
}
@conference{nokey,
title = {Is MLOps different in Industry 4.0? General and Specific Challenges},
author = {Leonhard Faubel and Klaus Schmid and Holger Eichelberger},
doi = {10.5220/0011589600003329},
year = {2022},
date = {2022-11-04},
urldate = {2022-11-04},
booktitle = {3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL)},
pages = {161-167},
abstract = {An important part of the Industry 4.0 vision is the use of machine learning (ML) techniques to create novel capabilities and flexibility in industrial production processes. Currently, there is a strong emphasis on MLOps as an enabling collection of practices, techniques, and tools to integrate ML into industrial practice. However, while MLOps is often discussed in the context of pure software systems, Industry 4.0 systems received much less attention. So far, there is no specialized research for Industry 4.0 in this regard. In this position paper, we discuss whether MLOps in Industry 4.0 leads to significantly different challenges compared to typical Internet systems. We identify both context-independent MLOps challenges (general challenges) as well as challenges particular to Industry 4.0 (specific challenges) and conclude that MLOps works very similarly in Industry 4.0 systems to pure software systems. This indicates that existing tools and approaches are also mostly suited for the Industry 4.0 context.},
keywords = {Industrie 4.0, MLOps},
pubstate = {published},
tppubtype = {conference}
}
@inproceedings{10.1007/978-3-031-36889-9_19,
title = {Developing an AI-Enabled IIoT Platform - Lessons Learned from Early Use Case Validation},
author = {Eichelberger, Holger
and Palmer, Gregory
and Reimer, Svenja
and Trong Vu, Tat
and Do, Hieu
and Laridi, Sofiane
and Weber, Alexander
and Niederée, Claudia
and Hildebrandt, Thomas},
editor = {Batista, Thais
and Bure{v{s}}, Tom{'a}{v{s}}
and Raibulet, Claudia
and Muccini, Henry},
isbn = {978-3-031-36889-9},
year = {2022},
date = {2022-09-19},
booktitle = {Software Architecture. ECSA 2022 Tracks and Workshops},
pages = {265-283},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {For a broader adoption of AI in industrial production, adequate infrastructure capabilities and ecosystems are crucial. This includes easing the integration of AI with industrial devices, support for distributed deployment, monitoring, and consistent system configuration. IIoT platforms can play a major role here by providing a unified layer for the heterogeneous Industry 4.0/IIoT context.},
keywords = {IIoT, IIoT-Platform, IIP-Ecosphere, Industrie 4.0, Industry 4.0, KI in der Produktion, Virtual Platform},
pubstate = {published},
tppubtype = {inproceedings}
}
@whitepaper{Eichelbergerdb,
title = {IIP-Ecosphere Platform Handbook v0.4.0},
author = {Holger Eichelberger and Amir Shayan Ahmadian and Andreas Dewes and Marco Ehl and Ahmad Alamoush and Monika Staciwa and Miguel Gómez Casado},
url = {https://www.iip-ecosphere.de/wp-content/uploads/2022/09/PlatformHandbook-final-V0.4.pdf},
doi = {https://doi.org/10.5281/zenodo.7047640},
year = {2022},
date = {2022-08-10},
urldate = {2022-08-10},
keywords = {Architecture, IIP-Ecosphere, Industrie 4.0, Manual, Virtual Platform},
pubstate = {published},
tppubtype = {whitepaper}
}
2021
@misc{nokey,
title = {Industrial Data Science Platform and Applications in Electronics and Manufacturing Industries},
author = {David Arnu and Ralf Klinkenberg},
year = {2021},
date = {2021-12-14},
booktitle = {AI in Manufacturing },
publisher = {Finnish-German Collaboration Initiatives},
keywords = {Artificial Intelligence, IIP-Ecosphere, Industrie 4.0, RapidMiner, Sennheiser},
pubstate = {published},
tppubtype = {presentation}
}
@whitepaper{Niederée2021,
title = {KI in der Produktion – Quo vadis?},
author = {Claudia Niederée and Holger Eichelberger and Hans-Dieter Schmees and Alexander Broos and Per Schreiber},
url = {https://www.iip-ecosphere.de/wp-content/uploads/2021/11/IIP-Ecosphere-Whitepaper-zur-Umfrage-KI-in-der-Produktion.pdf},
year = {2021},
date = {2021-11-03},
keywords = {IIoT, IIP-Ecosphere, Industrie 4.0, KI in der Produktion, Produktion, Umfrage},
pubstate = {published},
tppubtype = {whitepaper}
}
@misc{nokey,
title = {Management Summary zu Whitepaper "KI in der Produktion – Quo vadis?"},
author = {Claudia Niederée and Holger Eichelberger and Hans-Dieter Schmees and Alexander Broos and Per Schreiber},
url = {https://www.iip-ecosphere.de/wp-content/uploads/2021/10/Management-Summary_IIP-Ecosphere-Umfrage_KI-Produktion.pdf},
year = {2021},
date = {2021-11-03},
keywords = {IIoT, IIP-Ecosphere, Industrie 4.0, KI in der Produktion},
pubstate = {published},
tppubtype = {misc}
}
@inproceedings{41924,
title = {Industry 4.0 Resource Monitoring - Experiences With Micrometer and Asset Administration Shells},
author = {Miguel Gomez Casado and Holger Eichelberger},
url = {http://ceur-ws.org/Vol-3043/short8.pdf},
year = {2021},
date = {2021-08-13},
urldate = {2021-08-13},
booktitle = {CEUR-WS Proceedings of Symposium on Software Performance 2021 (SSP'21)},
publisher = {CEUR-WS.org},
keywords = {Asset Administration Shells, IIP-Ecosphere, Industrie 4.0},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
@article{Denkena2020,
title = {KI-gestützte Prozessüberwachung in der Zerspanung},
author = {Berend Denkena and Benjamin Bergmann and Svenja Reimer and Alexander Schmidt and Tobias Stiehl and Matthias Witt},
url = {https://www.degruyter.com/document/doi/10.3139/104.112282/html},
doi = {https://doi.org/10.3139/104.112282},
isbn = {0947–0085},
year = {2020},
date = {2020-05-05},
journal = {ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb},
volume = {115},
number = {5},
pages = {295-298},
keywords = {Industrie 4.0, Künstliche Intelligenz, Produktion, Prozessüberwachung},
pubstate = {published},
tppubtype = {article}
}