Rosemeyer, Jannik ; Beck, Laurenz ; Metternich, Joachim
Hrsg.: Herberger, David ; Hübner, Marco ; Stich, Volker (2023)
A cognitive assistance system to support the implementation of machine learning applications in manufacturing.
4th Conference on Production Systems and Logistics. Santiago de Querétaro, Mexiko (14.11.2023-17.11.2023)
doi: 10.15488/13453
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Despite the increasing spread of digitalisation in manufacturing, humans will still play an important role in future production environments. Evidently, their role will change from physical to rather cognitive tasks, such as decision-making or control and monitoring of processes. A suitable medium that can support employees in interpreting the data generated are machine learning (ML) applications. Nevertheless, recent studies show that the knowledge required to implement an ML solution is not available in a large number of companies. In order to close the knowledge gap and subsequently prepare human operators for the implementation and use of ML applications, it is highly relevant to provide proper assistance. For this reason, the present publication aims to develop a cognitive assistance system that supports shop floor managers in implementing ML use cases in manufacturing (referred to as CAS-ML). The CAS-ML concretizes a previously published procedure model with additional steps as well as learning material and is realized as a software thereupon. Finally, the CAS-ML is evaluated by operative employees and tested on an open-source data set.
Typ des Eintrags: | Konferenzveröffentlichung |
---|---|
Erschienen: | 2023 |
Herausgeber: | Herberger, David ; Hübner, Marco ; Stich, Volker |
Autor(en): | Rosemeyer, Jannik ; Beck, Laurenz ; Metternich, Joachim |
Art des Eintrags: | Bibliographie |
Titel: | A cognitive assistance system to support the implementation of machine learning applications in manufacturing |
Sprache: | Englisch |
Publikationsjahr: | 2023 |
Ort: | Hannover |
Verlag: | publish-Ing |
Buchtitel: | Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 1 |
Veranstaltungstitel: | 4th Conference on Production Systems and Logistics |
Veranstaltungsort: | Santiago de Querétaro, Mexiko |
Veranstaltungsdatum: | 14.11.2023-17.11.2023 |
DOI: | 10.15488/13453 |
URL / URN: | https://www.repo.uni-hannover.de/handle/123456789/13563 |
Kurzbeschreibung (Abstract): | Despite the increasing spread of digitalisation in manufacturing, humans will still play an important role in future production environments. Evidently, their role will change from physical to rather cognitive tasks, such as decision-making or control and monitoring of processes. A suitable medium that can support employees in interpreting the data generated are machine learning (ML) applications. Nevertheless, recent studies show that the knowledge required to implement an ML solution is not available in a large number of companies. In order to close the knowledge gap and subsequently prepare human operators for the implementation and use of ML applications, it is highly relevant to provide proper assistance. For this reason, the present publication aims to develop a cognitive assistance system that supports shop floor managers in implementing ML use cases in manufacturing (referred to as CAS-ML). The CAS-ML concretizes a previously published procedure model with additional steps as well as learning material and is realized as a software thereupon. Finally, the CAS-ML is evaluated by operative employees and tested on an open-source data set. |
Freie Schlagworte: | cognitive assistance system, shop floor manager, machine learning, operator 4.0, vocational education |
Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > CiP Center für industrielle Produktivität |
Hinterlegungsdatum: | 24 Apr 2023 05:41 |
Letzte Änderung: | 27 Jun 2024 07:56 |
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