Weyand, Astrid ; Seyfried, Stefan ; Grosch, Benedikt ; Fuhrländer-Völker, Daniel ; Panten, Niklas ; Abele, Eberhard (2020)
Method for Transforming Energy and Production Systems Into Mobile Demonstrators.
In: Procedia Manufacturing, 45
doi: 10.1016/j.promfg.2020.04.084
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Learning factories represent realistic learning environments for academia and industry in order to develop competencies. The design of learning factories can be facilitated by a configuration system. The configuration of a learning factory describes the selection of factory elements and products. Mathematically this selection can be described by an optimization problem based on a utility function and restrictions. Optimization algorithms facilitate the planning process by selecting the feasible combination of factory elements with the highest utility. This paper describes the methodology to develop a configuration system for learning factories. Customer needs for the configuration system were identified by an explorative stakeholder study: Learning factory developers, operators, and trainers were interviewed with open and partially open questions. These customer needs were then evaluated regarding their importance. The relationship between customer needs and functional requirements is described in a House of quality. To determine the system design systematically, the decomposition principle and the zig-zag process of the Axiomatic Design were applied. Axiomatic Design is a method to design engineering systems including complex systems and software. Consequently, the functional requirements were transformed into design parameters. Design parameters characterize the design of the configuration system.
Typ des Eintrags: | Artikel |
---|---|
Erschienen: | 2020 |
Autor(en): | Weyand, Astrid ; Seyfried, Stefan ; Grosch, Benedikt ; Fuhrländer-Völker, Daniel ; Panten, Niklas ; Abele, Eberhard |
Art des Eintrags: | Bibliographie |
Titel: | Method for Transforming Energy and Production Systems Into Mobile Demonstrators |
Sprache: | Englisch |
Publikationsjahr: | 2020 |
Verlag: | Elsevier B.V. |
Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia Manufacturing |
Jahrgang/Volume einer Zeitschrift: | 45 |
DOI: | 10.1016/j.promfg.2020.04.084 |
Kurzbeschreibung (Abstract): | Learning factories represent realistic learning environments for academia and industry in order to develop competencies. The design of learning factories can be facilitated by a configuration system. The configuration of a learning factory describes the selection of factory elements and products. Mathematically this selection can be described by an optimization problem based on a utility function and restrictions. Optimization algorithms facilitate the planning process by selecting the feasible combination of factory elements with the highest utility. This paper describes the methodology to develop a configuration system for learning factories. Customer needs for the configuration system were identified by an explorative stakeholder study: Learning factory developers, operators, and trainers were interviewed with open and partially open questions. These customer needs were then evaluated regarding their importance. The relationship between customer needs and functional requirements is described in a House of quality. To determine the system design systematically, the decomposition principle and the zig-zag process of the Axiomatic Design were applied. Axiomatic Design is a method to design engineering systems including complex systems and software. Consequently, the functional requirements were transformed into design parameters. Design parameters characterize the design of the configuration system. |
Freie Schlagworte: | Learning Factory, Factory Design, Axiomic Design, System Development, House of Quality |
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: | 07 Mai 2020 05:34 |
Letzte Änderung: | 04 Okt 2024 09:21 |
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