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Approach for Developing Digital Twins of Smart Products Based on Linked Lifecycle Information

Published online by Cambridge University Press:  26 May 2022

T. Eickhoff ,
S. Forte  and
J. C. Göbel
T. Eickhoff*
Affiliation:
Technische Universität Kaiserslautern, Germany
S. Forte
Affiliation:
Technische Universität Kaiserslautern, Germany
J. C. Göbel
Affiliation:
Technische Universität Kaiserslautern, Germany
*
eickhoff@mv.uni-kl.de

Abstract

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The ongoing digitization of engineering processes and the increasing prevalence of smart products create possibilities for new business models and services. Digital twins enable the collection of all required data about a smart product in order to make these possibilities a reality. This paper describes a flexible approach towards a digital product twin that is tightly integrated with existing product models while being lightweight and easy to integrate with existing IT solutions.

Keywords

digital twinproduct lifecycle management (PLM)digital threadprocess improvementdata-driven design
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1559 - 1568
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
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
The Author(s), 2022.

References

Abramovici, M., Göbel, J.C. and Dang, H.B. (2016), “Semantic data management for the development and continuous reconfiguration of smart products and systems”, CIRP annals, Vol. 65 No. 1, pp. 185–188. 10.1016/j.cirp.2016.04.051.CrossRefGoogle Scholar
Abramovici, M. and Herzog, O. (2016), Engineering im Umfeld von industrie 4.0: Einschätzungen und Handlungsbedarf, Herbert Utz Verlag.Google Scholar
Alt, O. (2012), Modellbasierte Systementwicklung mit SysML, Carl Hanser Verlag GmbH Co KG.Google Scholar
Amsden, J. and Speicher, S. (2021), OSLC Core Version 3.0. Part 1: Overview, OASIS. Project Specification Draft. URL: https://docs.oasis-open-projects …Google Scholar
Aurich, J.C., Koch, W., Kölsch, P. and Herder, C. (2019), Entwicklung datenbasierter Produkt-Service Systeme, Springer Berlin Heidelberg, Berlin, Heidelberg. 10.1007/978-3-662-59643-2.Google Scholar
Bergsjö, D., Malmqvist, J. and Ström, M. (2006), “Architectures for mechatronic product data integration in PLM systems”.Google Scholar
Bernard, Y. (2012), “Requirements management within a full model-based engineering approach”, Systems Engineering, Vol. 15 No. 2, pp. 119–139. 10.1002/sys.20198.CrossRefGoogle Scholar
Coppen, R., Banks, A., Briggs, E., Borgendale, K. and Gupta, R. (2019), MQTT Version 5.0, available at: https://docs.oasis-open.org/mqtt/mqtt/v5.0/os/mqtt-v5.0-os.html.Google Scholar
Corcho, O., Fernández-López, M. and Gómez-Pérez, A. (2006), “Ontological Engineering: Principles, Methods, Tools and Languages”, in Ontologies for software engineering and software technology, Springer, pp. 1–48. 10.1007/3-540-34518-3_1.Google Scholar
Eickhoff, T., Apostolov, C. and Göbel, J.C. (2020a), “Methodologically supported development of digital twins for smart product-service systems”, Digital Proceedings of TMCE 2020, pp. 155–164.Google Scholar
Eickhoff, T., Eiden, A., Göbel, J.C. and Eigner, M. (2020b), “A Metadata Repository for Semantic Product Lifecycle Management”, Procedia CIRP, Vol. 91, pp. 249–254. 10.1016/j.procir.2019.11.006.Google Scholar
Eickhoff, T., Eiden, A., Gries, J. and Göbel, J.C. (2021), “Data Model Canvas für die IT-Systemübergreifende Integration von Datenmodellen zur Unterstützung von Datenanalyse-Anwendungen im Produktlebenszyklus”, pp. 99–109. 10.25368/2021.14.Google Scholar
Eiden, A., Eickhoff, T., Gries, J., Göbel, J.C. and Psota, T. (2021), “Supporting semantic PLM by using a lightweight engineering metadata mapping engine”, Procedia CIRP, Vol. 100, pp. 690–695. 10.1016/j.procir.2021.05.146.Google Scholar
Forte, S., Göbel, J.C. and Dickopf, T. (2021), “SYSTEM OF SYSTEMS LIFECYCLE ENGINEERING APPROACH INTEGRATING SMART PRODUCT AND SERVICE ECOSYSTEMS”, Proceedings of the Design Society, Vol. 1, pp. 2911–2920. 10.1017/pds.2021.552.Google Scholar
Friedenthal, S., Moore, A. and Steiner, R. (2014), A practical guide to SysML: the systems modeling language, Morgan Kaufmann.Google Scholar
Göbel, J.C. and Eickhoff, T. (2020), “Konzeption von Digitalen Zwillingen smarter Produkte”, Zeitschrift für wirtschaftlichen Fabrikbetrieb, Vol. 115 No. s1, pp. 74–77.Google Scholar
Hildebrandt, C., Torsleff, S., Caesar, B. and Fay, A. (2018), “Ontology Building for Cyber-Physical Systems: A domain expert-centric approach”. 10.1109/COASE.2018.8560465.CrossRefGoogle Scholar
ISO 15288 (2015), Systems and software engineering — System life cycle processes No. 15288:2015.Google Scholar
Kellogg, G., Champin, P.-A. and Longley, D. (2019), “JSON-LD 1.1-A JSON-based Serialization for Linked Data”, W3C, 2019.Google Scholar
Kuhn, T. (2017), “Digitaler Zwilling”, Informatik-Spektrum, Vol. 40 No. 5, pp. 440–444. 10.1007/s00287-017-1061-2.Google Scholar
Lentes, J., Eckstein, H. and Zimmermann, N. (2012), “A Platform to Integrate Manufacturing Engineering and Product Lifecycle Management”, IFAC Proceedings Volumes, Vol. 45 No. 6, pp. 1071–1076. 10.3182/20120523-3-RO-2023.00425.CrossRefGoogle Scholar
Porter, M.E. and Heppelmann, J.E. (2014), “How smart, connected products are transforming competition”, Harvard business review, Vol. 92 No. 11, pp. 64–88.Google Scholar
Savarino, P. (2019), Dynamische Ermittlung echtzeitkompatibler internetbasierter Services für smarte kundenindividuelle Massenprodukte in deren Nutzungsphase, Shaker Verlag.Google Scholar
Schroeder, G.N., Steinmetz, C., Pereira, C.E. and Espindola, D.B. (2016), “Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange”, IFAC-PapersOnLine, Vol. 49 No. 30, pp. 12–17. 10.1016/j.ifacol.2016.11.115.Google Scholar
Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J. and Wang, L. (2012), “Modeling, simulation, information technology & processing roadmap”, National Aeronautics and Space Administration, Vol. 32, pp. 1–38.Google Scholar
Stark, R., Kind, S. and Neumeyer, S. (2017), “Innovations in digital modelling for next generation manufacturing system design”, CIRP annals, Vol. 66 No. 1, pp. 169–172. 10.1016/j.cirp.2017.04.045.Google Scholar
Zeimetz, T. and Schenkel, R. (2020), “Sample Driven Data Mapping for Linked Data and Web APIs”. 10.1145/3340531.3417438.Google Scholar