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Closed-Loop Engineering Approach for Data-Driven Product Planning
Published online by Cambridge University Press: 26 May 2022
Abstract
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This contribution introduces an approach for data-driven optimization of products and their product generations through a Closed-Loop Engineering approach resulting from the German research project DizRuPt. The approach focuses on data-driven product planning by ensuring data consistency and traceability between product planning, product development, and product operation by combining aspects and functions from Product Lifecycle Management (PLM) and the Internet of Things (IoT). The presented approach is illustrated and validated by pilot applications from the research project.
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
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References
Abramovici, M. (2007), Future Trends in Product Lifecycle Management (PLM). In: Krause, FL. (eds) The Future of Product Development. Springer, Berlin, Heidelberg. 10.1007/978-3-540-69820-3_64.Google Scholar
acatech (2011), Cyber-Physical Systems Driving force for innovation in mobility, health, energy and production [online]. acatech POSITION PAPER Dezember 2011, Springer Verlag, Berlin. Available at: https://www.acatech.de/publikation/cyber-physical-systems/ (accessed 15.11.2021). 10.1007/978-3-642-27567-8Google Scholar
Albers, A., Bursac, N., Wintergerst, E. (2015), Product Generation Development - Importance and Challenges from a Design Research Perspective. In: Mastorakis, N.E. (Ed), New developments in mechanics and mechanical engineering: proceedings of the International Conference on Mechanical Engineering (ME 2015), 15-17 March, Vienna, Austria, pp. 16–21.Google Scholar
Broy, M. (2010), Cyber-Physical Systems – Innovation Durch Software-Intensive Eingebettete Systeme. Springer-Verlag Berlin Heidelberg. acatech DISKUTIERT, Berlin, Heidelberg. 10.1007/978-3-642-14901-6Google Scholar
Dickopf, T. (2020), A holistic Methodology for the Development of Cybertronic Systems in the Context of the Internet of Things. Shaker Verlag, Düren. 10.2370/9783844073690Google Scholar
Dickopf, T., Apostolov, H., Müller, P., Göbel, J.C., Forte, S. (2019), A Holistic System Lifecycle Engineering Approach – Closing the Loop between System Architecture and Digital Twins. 29th CIRP Design Conference 2019, 08-10 May 2019, Póvoa de Varzim, Portgal. Procedia CIRP, (84), pp. 538–544. 10.1016/j.procir.2019.04.257Google Scholar
Dickopf, T., Forte, S., Apostolov, C., Göbel, J.C. (2021), Closed-Loop Systems Engineering—Supporting Smart System Design Adaption by Integrating MBSE and IoT. In: Krob, D. et al. (Eds), Complex Systems Design & Management, Proceedings of the 4th International Conference on Complex Systems Design & Management Asia and of the 12th Conference on Complex Systems Design & Management CSD&M 2021, 12-13 April 2021, Beijing, China, pp. 203–214. 10.1007/978-3-030-73539-5_16Google Scholar
Dienst, S. (2014), Analyse von Maschinendaten zur Entscheidungsunterstützung bei der Produktverbesserung durch die Anwendung eines Feedback Assistenz Systems [online]. Dissertation, Naturwissenschaftlich-Technische Fakultät, Universität Siegen, Universitätsbibliothek Universität Siegen, Siegen. Available at: https://dspace.ub.uni-siegen.de/handle/ubsi/817 (accessed 15.11.2021).Google Scholar
Eigner, M. (2021), System Lifecycle Management - Engineering Digitalization (Engineering 4.0). Springer Vieweg, Wiesbaden. 10.1007/978-3-658-33874-9Google Scholar
Gartner Inc, . (2014), The Internet of Things and Related Definitions. Gartner Inc. Available at: https://www.gartner.com/doc/2884417/internet-things-related-definitions (accessed 15.11.2021).Google Scholar
Gilz, T. (2014), PLM-Integrated Interdisciplinary System Models in the Conceptual Design Phase Based on Model-Based Systems Engineering. Ph.D. Thesis, Schriftenreihe VPE, Vol. 13, University of Kaiserslautern, 2014.Google Scholar
Göbel, J.C., Eickhoff, T (2020), Konzeption von Digitalen Zwillingen smarter Produkte. In: Eigner, M. (ed.): Zeitung für wirtschaftlichen Fabrikbetrieb – Digitaler Zwilling. Hanser, Band 115.Google Scholar
Göckel, N., Müller, P (2020), Entwicklung und Betrieb Digitaler Zwillinge. In: Eigner, M. (ed.): Zeitung für wirtschaftlichen Fabrikbetrieb – Digitaler Zwilling. Hanser, Band 115.CrossRefGoogle Scholar
Institute, Heinz Nixdorf (2018), Outline plan for the research project DizRuPt - Data-driven Retrofit and Generation Planning in Mechanical and Plant Engineering (unpublished)Google Scholar
Kiritsis, D. (2011), Closed-loop PLM for intelligent products in the era of the internet of things. Computer-Aided Design, (43), pp. 479–501. http://doi.org/10.1016/j.cad.2010.03.002CrossRefGoogle Scholar
Kruk, R. (2011), Retrofit - ein Beispiel für die Modernisierung von Werkzeugmaschinen. In: Lohrengel, A., Müller, N. (Eds.): Mitteilungen aus dem Institut für Maschinenwesen der Technischen Universität Clausthal, no. 36, p. 105Google Scholar
Lueth, K., Patsioura, C., Williams, Z., Kermani, Z. (2016), Industrial Analytics 2016/2017 – The current state of data analytics usage in industrial companies [online]. IoT Analytics, Hamburg. Available at: https://digital-analytics-association.de/wp-content/uploads/2016/03/Industrial-Analytics-Report-2016-2017-vp-singlepage.pdf (accessed 15.11.2021).Google Scholar
Massmann, M., Meyer, M., Frank, M., von Enzberg, S., Kühn, A., Dumitrescu, R. (2020), Framework for Data Analytics in Data-Driven Product Planning. Procedia Manufacturing.CrossRefGoogle Scholar
Meyer, M., Frank, M., Massmann, M., Dumitrescu (2020a), “Research and Consulting in Data-Driven Strategic Product Planning”, Proceedings of The 11th International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC 2020).Google Scholar
Meyer, M., Frank, M., Massmann, M., Wendt, N., Dumitrescu, R. (2020b), Data-Driven Product Generation and Retrofit Planning. Procedia CIRP, (93), pp. 965–970CrossRefGoogle Scholar
Müller, P. (2013), Integrated Engineering of Products and Services – Layer-based Development Methodology for Product-Service Systems, Fraunhofer Verlag, Berlin.Google Scholar
Negri, E., Fumagalli, L., Macchi, M. (2017), A Review of the Roles of Digital Twin in CPS-based Production Systems. In Procedia Manufacturing 11, pages 939–948.Google Scholar
Reichel, J., Müller, G., Mandelartz, J. (2009), Betriebliche Instandhaltung, Springer Verlag, Berlin. 10.1007/978-3-642-00502-2Google Scholar
Stark, J. (2016), Product Lifecycle Management (Volume 2) - The Devil is in the Details, Springer, Cham. 10.1007/978-3-319-24436-5Google Scholar
Stark, R., Damerau, T. (2019), Digital Twin. In: Chatti, S, Laperrière, L, Reinhart, G, Tolio, T (eds.): The International Academy for Production Engineering, CIRP Encyclopedia of Production Engineering. Springer-Verlag, Berlin, Heidelberg.Google Scholar
VDMA (2016), Machine Learning 2030 – Zukunftsbilder für den Maschinen- und Anlagenbau. VDMA Future Business, Band 1, Frankfurt am Main.Google Scholar
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