Publikationen des Projekts
Eichelberger, Holger; Ahmadian, Amir Shayan; Dewes, Andreas; Ehl, Marco; Staciwa, Monika; Ahmadian, Miguel Gómez Casado; Dewes, Andreas; Ehl, Marco; Staciwa, Monika; Casado, Miguel Gómez IIP-Ecosphere Platform Handbook v0.30 Whitepaper In: 2022. Links | BibTeX | Schlagwörter: Architecture, IIP-Ecosphere, IIP-Ecosphere Manual, Manual, UML, Virtual Platform Holger Eichelberger, and Heiko Stichweh; Sauer, Christian Requirements for an AI-enabled Industry 4.0 Platform – Integrating Industrial and Scientific Views Conference 2022, ISBN: 978-1-61208-946-1 / 2519-8394. Abstract | Links | BibTeX | Schlagwörter: adaptation, AI, asset administration shell, Edge, Industry 4.0 platforms, intelligent production, Requirements Denkena, Berend; Bergmann, Benjamin; Becker, Jonas; Blech, Heiko Sensorlose Überwachung der Einzelteilfertigung Journal Article In: Wt Werkstattstechnik online, Jahrgang 111 (2021) (Heft 05), pp. 305-308, 2022, ISSN: 1436-4980. Abstract | Links | BibTeX | Schlagwörter: Einzelteilfertigung, Maschinelles Lernen, Überwachung Denkena, Berend; Bergmann, Benjamin; H., Tobias Transfer of Process References between Machine Tools for Online Tool Condition Monitoring Journal Article In: Machines, 9 (11), 2021. Abstract | Links | BibTeX | Schlagwörter: Knowledge Transfer, Machine Tools; Turning; Process Monitoring 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 Denkena, Berend; Bergmann, Benjamin; Becker, Jonas; Stiehl, Tobias Time Series Search and Similarity Identification Journal Article In: Production at the Leading Edge of Technology, 2022 , pp. 479-487, 2021, ISBN: 978-3-030-78424-9. Abstract | Links | BibTeX | Schlagwörter: Barycenter Averaging, Time Series Clustering Denkena, Berend; Dittrich, Marc-André; Fohlmeister, Silas; Kemp, Daniel; Palmer, Gregory 2021. Abstract | Links | BibTeX | Schlagwörter: Eichelberger, Holger; Ahmadian, Amir Shayan; Dewes, Andreas; Ehl, Marco; Staciwa, Monika; Ahmadian, Miguel Gómez Casado; Dewes, Andreas; Ehl, Marco; Staciwa, Monika; Casado, Miguel Gómez IIP-Ecosphere Platform Handbook v0.20 Whitepaper In: 2021. Links | BibTeX | Schlagwörter: Architecture, IIP-Ecosphere, Manual, Rationales, UML, Virtual Platform Bonhage, Malte; Wilkens, Rainer; Denkena, Berend; Kemp, Daniel Der digitale Zwilling als Basis für ein intelligentes und skalierbares Produktionssystem Magazine 2021. Abstract | Links | BibTeX | Schlagwörter: Asset-Administration-Shell, Digital Twin, Verwaltungsschale Jalowski, Max; Roth, Angela; Oks, Sascha J.; Wilga, Matthäus Innovation KI-basierter Dienstleistungen für die industrielle Wertschöpfung – Ein artefaktzentrierter Ansatz Book Chapter In: Bruhn, Martin; Hadwich, Karsten (Ed.): Künstliche Intelligenz im Dienstleistungsmanagement. Forum Dienstleistungsmanagement., pp. 158-183, 2021, ISBN: 978-3-658-34324-8. Abstract | Links | BibTeX | Schlagwörter: Dienstleistungsmanagement, Künstliche Intelligenz Wilga, Matthäus; Jalowski, Max; Kirschbaum, Julius; Roth, Angela 21st European Academy of Management (EURAM) Conference 2021, 2021. Links | BibTeX | Schlagwörter: Artificial Intelligence, Business Model Graf, Walter; Wilmsmeier, Sören Quantum Technology in Flexible Job Shop Scheduling? – A Field Report Using Digital Annealer Conference 2021. Abstract | Links | BibTeX | Schlagwörter: Digital annealer, Flexible job shop scheduling, Quantum Algorithms Denkena, Berend; Fritz Schinkel, Jonathan Pirnay; Wilmsmeier, Sören Quantum Algorithms for Process Parallel Flexible Job Shop Scheduling Journal Article In: CIRP Journal of Manufacturing Science and Technology, 33 , pp. 100-114, 2021. Abstract | Links | BibTeX | Schlagwörter: Digital annealer, Flexible job shop scheduling, Process parallel optimization, Production planning and control Eichelberger, Holger; Sauer, Christian; Ahmadian, Amir Shayan; Schicktanz, Michael; Dewes, Andreas; Palmer, Gregory; Niederée, Claudia IIP-Ecosphere Plattform – Anforderungen (Funktionale und Qualitäts-Sicht) Whitepaper In: 2021. Abstract | Links | BibTeX | Schlagwörter: Functional, Quality, Requirements, Virtual Platform Stichweh, Heiko; Sauer, Christian; Eichelberger, Holger IIP-Ecosphere Platform Requirements (Usage View) Whitepaper In: 2021. Abstract | Links | BibTeX | Schlagwörter: AI Services, Application Building, Artificial Intelligence, IIoT, IIoT-Platform, IIP-Ecosphere, Platform Activities, Platform Requirements, Usage View Fabian Bruckner,; Jahnke, Nils Datenschutz und Datensicherheit in Datenökosystemen Whitepaper In: 2021. Abstract | Links | BibTeX | Schlagwörter: Data Ecosystems, Datenökosysteme, Datenschutz, Datensicherheit, IIP-Ecosphere, International Data Spaces, Usage Control Wilmsmeier, Sören Taktzeitoptimierung mithilfe von künstlicher Intelligenz Booklet 2021. Abstract | Links | BibTeX | Schlagwörter: Optimierung, Taktzeit, Ursache-Wirkungs-Analyse Sauer, Christian; Eichelberger, Holger; Ahmadian, Amir Shayan; Dewes, Andreas; Jürjens, Jan Aktuelle Industrie 4.0 Plattformen – Eine Übersicht Whitepaper In: (DE: IIP-2020/001, EN: IIP-2020/001-en), 2021. Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Customizability, Ecosystem, Edge, Industry 4.0, platforms, Protocols Jomaa, Hadi S.; Schmidt-Thieme, Lars; Grabocka, Josif Dataset2Vec: Learning Dataset Meta-Features Journal Article In: Data Mining and Knowlege Discovery, 10618 (0737), pp. 22, 2021. Abstract | Links | BibTeX | Schlagwörter: Hyperparameter Optimization, Meta-feature Learning, Meta-learning Jalowski, Max; Schymanietz, Martin; Möslein, Kathrin M. 2020. Links | BibTeX | Schlagwörter: Creative Process, Creativity, Participant Support, Persuasive Technology, User Behavior 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, Prozessüberwachung2022
@whitepaper{Eichelbergerd,
title = {IIP-Ecosphere Platform Handbook v0.30},
author = {Holger Eichelberger and Amir Shayan Ahmadian and Andreas Dewes and Marco Ehl and Monika Staciwa and Miguel Gómez Casado Ahmadian and Andreas Dewes and Marco Ehl and Monika Staciwa and Miguel Gómez Casado},
url = {https://www.iip-ecosphere.de/wp-content/uploads/2022/06/PlatformHandbook-final-V0.3.pdf},
doi = {https://doi.org/10.5281/zenodo.6620882},
year = {2022},
date = {2022-06-04},
urldate = {2022-06-04},
keywords = {Architecture, IIP-Ecosphere, IIP-Ecosphere Manual, Manual, UML, Virtual Platform},
pubstate = {published},
tppubtype = {whitepaper}
}
@conference{Eichelberger2022,
title = {Requirements for an AI-enabled Industry 4.0 Platform – Integrating Industrial and Scientific Views},
author = {Holger Eichelberger,and Heiko Stichweh and Christian Sauer },
editor = {Luigi Lavazza, University of Insubria - Varese, Italy},
url = {https://www.thinkmind.org/index.php?view=article&articleid=softeng_2022_1_20_90004},
isbn = {978-1-61208-946-1 / 2519-8394},
year = {2022},
date = {2022-04-24},
journal = {SOFTENG 2022 The Eighth International Conference on Advances and Trends in Software Engineering Engineering The Eighth International Conference on Advances and Trends in Software Engineering},
pages = {7-14},
abstract = {Intelligent manufacturing is one goal of smart industry/ Industry 4.0 that could be achieved through Artificial Intelligence (AI). Flexibly combining AI methods and platform capabilities, such as dynamic offloading of code close to production machines, security or interoperability mechanisms are major demands in this context. However, recent Industry 4.0 software platforms fall short in various of these demands, in particular in upcoming ecosystem scenarios, e.g., when data or services shall be shared across platforms or companies without vendor lock-ins. The aim of the funded Intelligent Industrial Production (IIP) IIP-Ecosphere project is to research concepts and solutions for ‘easy-to-use’ AI in Industry 4.0 and to demonstrate the results in a prototypical software platform. Core questions are which demands shall drive the development of such a platform and how a feasible set of requirements can be determined that balances scientific and industrial interests. In this paper, we discuss our approach on eliciting requirements in this context for two interlinked requirements perspectives, a usage and a functional view. In summary, we collected 67 usage view activities / scenarios and 141 top-level requirements with 179 detailing sub-requirements. About 35% of the requirements have so far been realized in a prototype and some of the identified concepts are currently being taken up by a standardization initiative for edge devices in Industry 4.0.},
keywords = {adaptation, AI, asset administration shell, Edge, Industry 4.0 platforms, intelligent production, Requirements},
pubstate = {published},
tppubtype = {conference}
}
@article{Denkena2022,
title = {Sensorlose Überwachung der Einzelteilfertigung},
author = {Berend Denkena and Benjamin Bergmann and Jonas Becker and Heiko Blech},
url = {https://elibrary.vdi-verlag.de/10.37544/1436-4980-2021-05-39/sensorlose-ueberwachung-der-einzelteilfertigung-spindle-current-based-process-monitoring-using-artificial-intelligence-jahrgang-111-2021-heft-05},
doi = {10.37544/1436-4980-2021-05-39},
issn = {1436-4980},
year = {2022},
date = {2022-03-31},
journal = {Wt Werkstattstechnik online},
volume = {Jahrgang 111 (2021)},
number = {Heft 05},
pages = {305-308},
abstract = {Durch die Messung von Spindelströmen lassen sich Informationen aus spanenden Fertigungsprozessen ohne zusätzliche Sensorik erfassen.},
keywords = {Einzelteilfertigung, Maschinelles Lernen, Überwachung},
pubstate = {published},
tppubtype = {article}
}
2021
@article{Denkena2021c,
title = {Transfer of Process References between Machine Tools for Online Tool Condition Monitoring},
author = {Berend Denkena and Benjamin Bergmann and Tobias H.},
url = {https://www.mdpi.com/2075-1702/9/11/282},
doi = {https://doi.org/10.3390/machines9110282},
year = {2021},
date = {2021-11-10},
journal = {Machines},
volume = {9},
number = {11},
abstract = {Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools.},
keywords = {Knowledge Transfer, Machine Tools; Turning; Process Monitoring},
pubstate = {published},
tppubtype = {article}
}
@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}
}
@article{Denkena2021d,
title = {Time Series Search and Similarity Identification},
author = {Berend Denkena and Benjamin Bergmann and Jonas Becker and Tobias Stiehl},
url = {https://link.springer.com/chapter/10.1007/978-3-030-78424-9_53},
doi = {10.1007/978-3-030-78424-9_53},
isbn = {978-3-030-78424-9},
year = {2021},
date = {2021-09-05},
journal = {Production at the Leading Edge of Technology},
volume = {2022},
pages = {479-487},
abstract = {Monitoring process errors and tool condition in single item production is challenging, as a teach-in is not possible due to a missing reference process. An approach to this problem is anomaly detection, e.g. based on motor currents or axis position signals from metal cutting processes. However, with no references anomaly detection struggles to detect failures from signals, because failure patterns are often too similar to regular process dynamics. While single items inherently constitute an anomaly by themselves, they do contain repetitive elements, like boreholes or milled pockets. These elements are similar, what provides an anomaly detection with additional information on regular processes.
Hierarchical K-Means clustering combined with Dynamic Time Warping (DTW) and Barycenter Averaging (DBA) enables the identification of similar process elements. The algorithm allows ordering similar process segments by similarity in a tree structure. The introduced method supports querying subsequences from any given cutting process, for which it returns the closest cluster in the tree. This allows to (a) improve the data basis for anomaly detection and (b) to transfer labels with errors between processes. The article demonstrates the transfer of labels (for errors) from a turning process, to a single item milling process.},
keywords = {Barycenter Averaging, Time Series Clustering},
pubstate = {published},
tppubtype = {article}
}
Hierarchical K-Means clustering combined with Dynamic Time Warping (DTW) and Barycenter Averaging (DBA) enables the identification of similar process elements. The algorithm allows ordering similar process segments by similarity in a tree structure. The introduced method supports querying subsequences from any given cutting process, for which it returns the closest cluster in the tree. This allows to (a) improve the data basis for anomaly detection and (b) to transfer labels with errors between processes. The article demonstrates the transfer of labels (for errors) from a turning process, to a single item milling process.@conference{Denkena2021b,
title = {Scalable cooperative multi-agentreinforcement-learning for order-controlled on schedule manufacturing in flexible manufacturing systems},
author = {Berend Denkena and Marc-André Dittrich and Silas Fohlmeister and Daniel Kemp and Gregory Palmer },
url = {http://www.asim-fachtagung-spl.de/asim2021/papers/Proof_108.pdf},
year = {2021},
date = {2021-08-26},
abstract = { To operate flexible manufacturing systems efficiently, a robust and optimal
production control is crucial. With an increasing number of workpieces being
processed in parallel, ensuring guaranteed lead times represents a complex
optimization tasks, better known as the flexible scheduling problem. Cooperative
multi-agent reinforcement learning approaches have recently shown their potential in
production control. However, ensuring guaranteed lead times in flexible
manufacturing systems with these approaches remains an open problem. In this work,
an existing cooperative multi-agent framework for flexible job-shop scheduling is
transferred and modified to optimize production control in flexible manufacturing
systems. Using a centralized training for decentralized execution multi-agent deep
reinforcement learning approach, the goal is to optimize order agents to ensure
guaranteed lead times. Furthermore, a comprehensive simulation study investigates
the effect of common knowledge on facilitating cooperation, and empirically evaluate
the frameworks scalability to a range of challenging scenarios. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
production control is crucial. With an increasing number of workpieces being
processed in parallel, ensuring guaranteed lead times represents a complex
optimization tasks, better known as the flexible scheduling problem. Cooperative
multi-agent reinforcement learning approaches have recently shown their potential in
production control. However, ensuring guaranteed lead times in flexible
manufacturing systems with these approaches remains an open problem. In this work,
an existing cooperative multi-agent framework for flexible job-shop scheduling is
transferred and modified to optimize production control in flexible manufacturing
systems. Using a centralized training for decentralized execution multi-agent deep
reinforcement learning approach, the goal is to optimize order agents to ensure
guaranteed lead times. Furthermore, a comprehensive simulation study investigates
the effect of common knowledge on facilitating cooperation, and empirically evaluate
the frameworks scalability to a range of challenging scenarios. @whitepaper{Eichelberger,
title = {IIP-Ecosphere Platform Handbook v0.20},
author = {Holger Eichelberger and Amir Shayan Ahmadian and Andreas Dewes and Marco Ehl and Monika Staciwa and Miguel Gómez Casado Ahmadian and Andreas Dewes and Marco Ehl and Monika Staciwa and Miguel Gómez Casado},
url = {https://www.iip-ecosphere.eu/wp-content/uploads/2021/08/PlatformHandbook-final-V0.2.pdf},
doi = {10.5281/zenodo.5168946},
year = {2021},
date = {2021-08-21},
urldate = {2021-08-21},
keywords = {Architecture, IIP-Ecosphere, Manual, Rationales, UML, Virtual Platform},
pubstate = {published},
tppubtype = {whitepaper}
}
@magazine{Bonhage2021,
title = {Der digitale Zwilling als Basis für ein intelligentes und skalierbares Produktionssystem},
author = {Malte Bonhage and Rainer Wilkens and Berend Denkena and Daniel Kemp},
url = {https://cdn.tedo.be/tedo-ecms/4/SPS-MAGAZIN_6_(Juli)_2021.pdf},
year = {2021},
date = {2021-07-12},
journal = {SPS Magazin},
number = {6},
pages = {61-63},
abstract = {Der digitale Zwilling ist der interdisziplinäre Kern zahlreicher I4.0-Anwendungen. Implementierungsansätze sind allerdings oft noch individuell und kostenintensiv. Abhilfe verspricht an dieser Stelle die Verwaltungsschale, als standardisierter digitaler Zwilling. Im Rahmen von IIP-E entsteht eine Implementierung der Verwaltungsschale bei Sennheiser electronic},
keywords = {Asset-Administration-Shell, Digital Twin, Verwaltungsschale},
pubstate = {published},
tppubtype = {magazine}
}
@inbook{nokey,
title = {Innovation KI-basierter Dienstleistungen für die industrielle Wertschöpfung – Ein artefaktzentrierter Ansatz},
author = {Max Jalowski and Angela Roth and Sascha J. Oks and Matthäus Wilga},
editor = {Martin Bruhn and Karsten Hadwich},
url = {https://link.springer.com/chapter/10.1007/978-3-658-34324-8_7},
doi = {https://doi.org/10.1007/978-3-658-34324-8_7},
isbn = {978-3-658-34324-8},
year = {2021},
date = {2021-06-29},
urldate = {2021-06-29},
booktitle = {Künstliche Intelligenz im Dienstleistungsmanagement. Forum Dienstleistungsmanagement.},
pages = {158-183},
abstract = {Dieser Beitrag zeigt auf, wie insbesondere für kleine und mittlere Unternehmen die Gestaltung und Innovation von KI-basierten Dienstleistungen in der industriellen Wertschöpfung unterstützt werden kann. Dazu werden Herausforderungen bei der Entwicklung KI-basierter Dienstleistungen erhoben und anschließend Artefakte präsentiert, die zur Innovation von Dienstleistungen zum Einsatz kommen. Diese werden basierend auf den zuvor identifizierten Herausforderungen adaptiert, um die Innovation von KI-basierten Dienstleistungen zu ermöglichen.},
keywords = {Dienstleistungsmanagement, Künstliche Intelligenz},
pubstate = {published},
tppubtype = {inbook}
}
@conference{nokey,
title = {A Systematic Characterization of Artificial Intelligence Business Models as a Fundament for Business Model Innovation and Strategic Decision-Making},
author = {Matthäus Wilga and Max Jalowski and Julius Kirschbaum and Angela Roth},
url = {https://cris.fau.de/converis/portal/publication/260644612?lang=en_GB},
year = {2021},
date = {2021-06-17},
urldate = {2021-06-17},
booktitle = {21st European Academy of Management (EURAM) Conference 2021},
keywords = {Artificial Intelligence, Business Model},
pubstate = {published},
tppubtype = {conference}
}
@conference{Graf2021,
title = {Quantum Technology in Flexible Job Shop Scheduling? – A Field Report Using Digital Annealer},
author = {Walter Graf and Sören Wilmsmeier},
url = {https://youtu.be/doylgwUXy-I
https://www.iip-ecosphere.eu/wp-content/uploads/2021/06/Wil21_Quantum-Technology-in-Flexible-Job-Shop-Scheduling.pdf},
year = {2021},
date = {2021-05-26},
journal = {Quantum Summit 2021 (26. - 27.05.2021)},
abstract = {One area of current research is focusing on the further development of quantum computers in order to achieve the so called "Quantum Advantage". Another activity stream is concerned with the algorithms or solution scenarios required for this. A major challenge here is that the algorithms that can run on today's quantum computers are of a very rudimentary nature and thus have not yet a real solution character, or they are too complex for existing systems and therefore provide only limited insights. In the meantime, Fujitsu has created a bridging technology in the form of the Digital Annealer, which makes it possible even today to process problems of relevant size that would normally be expected to run on a quantum computer, or more precisely here, on a quantum annealer. Together with the Institute of Production Engineering and Machine Tools of the Leibniz University of Hannover, a job shop scheduling scenario was implemented based on a real production environment for a tape dispenser. In this environment, the total production time could be reduced from about 300 hours to 200 hours in less than 1 minute of computing time on the Digital Annealer.},
keywords = {Digital annealer, Flexible job shop scheduling, Quantum Algorithms},
pubstate = {published},
tppubtype = {conference}
}
@article{Denkena2021,
title = {Quantum Algorithms for Process Parallel Flexible Job Shop Scheduling},
author = {Berend Denkena and Fritz Schinkel, Jonathan Pirnay and Sören Wilmsmeier},
url = {https://www.sciencedirect.com/science/article/pii/S1755581721000432},
doi = {10.1016/j.cirpj.2021.03.006},
year = {2021},
date = {2021-03-23},
journal = {CIRP Journal of Manufacturing Science and Technology},
volume = {33},
pages = {100-114},
abstract = {Flexible Job Shop Scheduling is one of the most difficult optimization problems known. In addition, modern production planning and control strategies require continuous and process-parallel optimization of machine allocation and processing sequences. Therefore, this paper presents a new method for process parallel Flexible Job Shop Scheduling using the concept of quantum computing based optimization. A scientific benchmark and the application to a realistic use-case demonstrates the good performance and practicability of this new approach. A managerial insight shows how the approach for process parallel flexible job shop scheduling can be integrated in existing production planning and control IT-infrastructure.},
keywords = {Digital annealer, Flexible job shop scheduling, Process parallel optimization, Production planning and control},
pubstate = {published},
tppubtype = {article}
}
@whitepaper{Eichelberger2021,
title = {IIP-Ecosphere Plattform – Anforderungen (Funktionale und Qualitäts-Sicht)},
author = {Holger Eichelberger and Christian Sauer and Amir Shayan Ahmadian and Michael Schicktanz and Andreas Dewes and Gregory Palmer and Claudia Niederée},
url = {https://www.iip-ecosphere.eu/wp-content/uploads/2021/03/IIP-2021_002.pdf
https://www.iip-ecosphere.eu/wp-content/uploads/2021/03/IIP-2021_002-eng.pdf
},
doi = {10.5281/zenodo.4485774},
year = {2021},
date = {2021-03-15},
abstract = {Dieses Dokument beschreibt die Anforderungen an die IIP-Ecosphere Plattform. Die Anforderungen basieren auf Diskussionen mit den Partnern und den Arbeitspaketen bzw. Teilprojekten (bzw. deren Repräsentanten) von IIP-Ecosphere, wie etwa den Demonstratoren. Als Grundlagen wurden eine im Projekt erstellte Übersicht aktueller Industrie 4.0 Plattformen als Grundlage sowie eine Anforderungserhebung auf Benutzersicht einbezogen. },
keywords = {Functional, Quality, Requirements, Virtual Platform},
pubstate = {published},
tppubtype = {whitepaper}
}
@whitepaper{Stichweh2021,
title = {IIP-Ecosphere Platform Requirements (Usage View)},
author = {Heiko Stichweh and Christian Sauer and Holger Eichelberger},
url = {https://www.iip-ecosphere.eu/wp-content/uploads/2021/03/IIP-2021_001_IIP-Ecosphere_Platform_Requirements_Usage_View.pdf},
doi = {10.5281/zenodo.4485801},
year = {2021},
date = {2021-03-11},
abstract = {This Whitepaper describes a shared view on the IIP-Ecosphere platform, which was developed as a core technical contribution of the IIP-Ecosphere Think Thank “Platforms”, to foster and complement the requirements collection of the platform, based on this shared view on envisioned platform functionality. Following the Industrial Internet Reference Architecture (IIRA), this Whitepaper describes the IIP-Ecosphere platform from the Usage Viewpoint. The Usage View on the IIP-Ecosphere platform that we discuss in this document represents the common view of all partners involved in the design, the subsequent implementation and, finally, the operations of the platform based on the voice of the prospective users of the platform in the IIP-Ecosphere community. The shared Usage View was collected in terms of a series of workshops with all interested project partners. The shared Usage View established in this document therefore provides a basis for deriving/validating the functional and quality requirements of the overall platform and, thus, enables the subsequent work on the development of the concepts and solutions established in the shared Usage View. For this current version of the Usage View, we jointly decided to focus on application building, distribution and AI services, as these topics strongly correlate with the technical foundations of the platform to be developed. For this focus, we describe a System under Consideration with 18 entities, 19 roles and 67 activities in this Whitepaper.},
keywords = {AI Services, Application Building, Artificial Intelligence, IIoT, IIoT-Platform, IIP-Ecosphere, Platform Activities, Platform Requirements, Usage View},
pubstate = {published},
tppubtype = {whitepaper}
}
@whitepaper{Bruckner2021,
title = {Datenschutz und Datensicherheit in Datenökosystemen },
author = {Fabian Bruckner, and Nils Jahnke},
url = {https://www.iip-ecosphere.eu/wp-content/uploads/2021/03/IIP-2021-003-Whitepaper-Datenschutz_Datensicherheit.pdf},
doi = {10.5281/zenodo.4588330},
year = {2021},
date = {2021-03-08},
abstract = {Dieses Whitepaper stellt Probleme und Lösungsansätze im Bereich des Schutzes und der Sicherheit von Daten in Datenökosystemen dar. Dabei wird insbesondere die Perspektive von IIP-Ecosphere betrachtet. Im Rahmen des Whitepapers werden durch eine umfassende Literaturanalyse identifizierte unmittelbare und mittelbare Probleme aus den Bereichen Datenschutz und –sicherheit in Datenökosystemen und dazugehörige Lösungsansätze präsentiert. Insbesondere im Fokus der Betrachtungen steht dabei der in IIP-Ecosphere zu gestaltende Datenmarktplatz. Die ermittelten theoretischen Erkenntnisse werden durch die Befragung von Partnern und Assoziierten des Projekts auf ihre Praxisrelevanz geprüft und konsolidiert sowie mögliche Lösungsansätze für Datenschutz und -sicherheit eingeordnet.},
keywords = {Data Ecosystems, Datenökosysteme, Datenschutz, Datensicherheit, IIP-Ecosphere, International Data Spaces, Usage Control},
pubstate = {published},
tppubtype = {whitepaper}
}
@booklet{Wilmsmeier2021,
title = {Taktzeitoptimierung mithilfe von künstlicher Intelligenz},
author = {Sören Wilmsmeier},
url = {https://www.phi-hannover.de/forschung/artikel/detail/taktzeitoptimierung-mithilfe-von-kuenstlicher-intelligenz/},
year = {2021},
date = {2021-03-04},
journal = {phi – Produktionstechnik Hannover informiert},
abstract = {Die Ursache von Taktzeitschwankungen in verketteten Fertigungslinien zu ermitteln und die Auswirkung von Verzögerungen vorherzusagen ist eine komplexe Aufgabe. Wie sich künstliche Intelligenz dafür nutzen lässt, untersucht das IFW im Verbundprojekt IIP-Ecosphere.},
month = {03},
keywords = {Optimierung, Taktzeit, Ursache-Wirkungs-Analyse},
pubstate = {published},
tppubtype = {booklet}
}
@whitepaper{Sauer2020,
title = {Aktuelle Industrie 4.0 Plattformen – Eine Übersicht},
author = {Christian Sauer and Holger Eichelberger and Amir Shayan Ahmadian and Andreas Dewes and Jan Jürjens},
url = {https://www.iip-ecosphere.eu/wp-content/uploads/2021/02/IIP-2020_001.pdf
https://www.iip-ecosphere.eu/wp-content/uploads/2021/02/IIP-2020_001-en.pdf
https://zenodo.org/record/4485756
},
doi = {10.5281/zenodo.4485756 },
year = {2021},
date = {2021-02-15},
number = {DE: IIP-2020/001, EN: IIP-2020/001-en},
abstract = {Dieses Whitepaper gibt eine Übersicht über aktuelle Industrie 4.0 Plattformen, insbesondere aus dem Blickwinkel des IIP-Ecosphere-Projekts, das im KI-Innovationswettbewerb vom Bundesministerium für Wirtschaft und Energie (BMWi) gefördert wird. Dabei stehen Themen wir Interkonnektivität, digitale Zwillinge, Offenheit, Sicherheit und die Nutzung von Künstlicher Intelligenz im Kontext der intelligenten Produktion im Mittelpunkt. Das Dokument beschreibt sowohl die Vorgehensweise der Datenermittlung, die Detailergebnisse für einzelne industrielle Plattformen als auch eine zusammenfassende Übersicht. Es werden insgesamt 21 industrielle Plattformen basierend auf öffentlich verfügbaren Dokumenten anhand von 16 Themenfeldern analysiert. Sowohl Plattformen als auch Analysethemen entstammen intensiver Diskussionen der Projektpartner in IIP-Ecosphere.
Die untersuchten Plattformen decken insbesondere die benötigten Grundfunktionen ab. Beispielsweise wird oft eine Vielzahl an Kommunikationsprotokollen bereitgestellt und verschiedenste Cloud-Dienste integriert. Selbst neuere Trends wie Künstliche Intelligenz sind inzwischen in den Plattformbeschreibungen zu finden. Allerdings ist der Funktionsumfang zwischen den Plattformen auch sehr unterschiedlich. Neuere Standards wie OPC-UA, UMATI oder die Industrie 4.0 Verwaltungsschale werden oft nur zurückhaltend, wenn überhaupt eingesetzt, was teilweise der Entwicklungshistorie aber auch strategischen Erwägungen geschuldet sein mag.
Basierend auf der Plattform-übergreifenden Analyse der 16 Themenfelder leiten wir Herausforderungen für zukünftige Plattformen und insbesondere für unsere Arbeit in IIP-Ecosphere ab. Diese umfassen Themen wie offene Ökosysteme, erweiterbare Architekturen mit standardisierten Schnittstellenbeschreibungen, flexible und dynamische Unterstützung für KI-Verfahren, sicherer und vereinheitlichter Datenaustausch (für Data Sharing, Ressource Sharing und Data Usage Control) wie auch durchgängige und konsistente Konfigurierbarkeit, die das Vertrauen des Nutzers in die jeweilige Plattform stärkt. Eine Standardisierung von (einigen) dieser Themen wäre wünschenswert um den Austausch und die Interoperabilität zwischen Plattformen und Plattformökosystemen zu verbessern und Lock-ins zu vermeiden.},
keywords = {Artificial Intelligence, Customizability, Ecosystem, Edge, Industry 4.0, platforms, Protocols},
pubstate = {published},
tppubtype = {whitepaper}
}
Die untersuchten Plattformen decken insbesondere die benötigten Grundfunktionen ab. Beispielsweise wird oft eine Vielzahl an Kommunikationsprotokollen bereitgestellt und verschiedenste Cloud-Dienste integriert. Selbst neuere Trends wie Künstliche Intelligenz sind inzwischen in den Plattformbeschreibungen zu finden. Allerdings ist der Funktionsumfang zwischen den Plattformen auch sehr unterschiedlich. Neuere Standards wie OPC-UA, UMATI oder die Industrie 4.0 Verwaltungsschale werden oft nur zurückhaltend, wenn überhaupt eingesetzt, was teilweise der Entwicklungshistorie aber auch strategischen Erwägungen geschuldet sein mag.
Basierend auf der Plattform-übergreifenden Analyse der 16 Themenfelder leiten wir Herausforderungen für zukünftige Plattformen und insbesondere für unsere Arbeit in IIP-Ecosphere ab. Diese umfassen Themen wie offene Ökosysteme, erweiterbare Architekturen mit standardisierten Schnittstellenbeschreibungen, flexible und dynamische Unterstützung für KI-Verfahren, sicherer und vereinheitlichter Datenaustausch (für Data Sharing, Ressource Sharing und Data Usage Control) wie auch durchgängige und konsistente Konfigurierbarkeit, die das Vertrauen des Nutzers in die jeweilige Plattform stärkt. Eine Standardisierung von (einigen) dieser Themen wäre wünschenswert um den Austausch und die Interoperabilität zwischen Plattformen und Plattformökosystemen zu verbessern und Lock-ins zu vermeiden.@article{HadiS.Jomaa,
title = {Dataset2Vec: Learning Dataset Meta-Features},
author = {Hadi S. Jomaa and Lars Schmidt-Thieme and Josif Grabocka},
url = {https://arxiv.org/abs/1905.11063},
doi = {10.1007/s10618-021-00737-9},
year = {2021},
date = {2021-01-01},
journal = {Data Mining and Knowlege Discovery},
volume = {10618},
number = {0737},
pages = {22},
abstract = {Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditionally as engineered dataset statistics that require expert domain knowledge tailored for every meta-task. In this paper, first, we propose a meta- feature extractor called Dataset2Vec that combines the versatility of engineered dataset meta-features with the expressivity of meta-features learned by deep neural networks. Primary learning tasks or datasets are represented as hierarchical sets, i.e., as a set of sets, esp. as a set of predictor/target pairs, and then a DeepSet architecture is employed to regress meta-features on them. Second, we propose a novel auxiliary meta-learning task with abundant data called dataset similarity learning that aims to predict if two batches stem from the same dataset or different ones. In an experiment on a large-scale hyperparameter optimization task for 120 UCI datasets with varying schemas as a meta-learning task, we show that the meta-features of Dataset2Vec outperform the expert engineered meta-features and thus demonstrate the usefulness of learned meta-features for datasets with varying schemas for the first time.},
keywords = {Hyperparameter Optimization, Meta-feature Learning, Meta-learning},
pubstate = {published},
tppubtype = {article}
}
2020
@conference{MaxJalowski,
title = {Supporting Participants in Creative Processes: Opportunities for Persuasive Technology in Participatory Design},
author = {Jalowski, Max and Schymanietz, Martin and Möslein, Kathrin M.},
url = {https://aisel.aisnet.org/icis2020/user_behaviors/user_behaviors/4/},
year = {2020},
date = {2020-12-13},
journal = {Proceedings of the Forty-First International Conference on Information Systems, India 2020},
keywords = {Creative Process, Creativity, Participant Support, Persuasive Technology, User Behavior},
pubstate = {published},
tppubtype = {conference}
}
@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}
}