Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-13T05:29:20.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Digital twins to increase sustainability throughout the system life cycle: a systematic literature review

Published online by Cambridge University Press:  16 May 2024

Malte Trienens ,
Rik Rasor ,
Aschot Kharatyan ,
Roman Dumitrescu  and
Harald Anacker
Malte Trienens*
Affiliation:
Fraunhofer IEM, Germany
Rik Rasor
Affiliation:
Fraunhofer IEM, Germany
Aschot Kharatyan
Affiliation:
Fraunhofer IEM, Germany
Roman Dumitrescu
Affiliation:
Heinz Nixdorf Institute, Paderborn University, Germany
Harald Anacker
Affiliation:
Fraunhofer IEM, Germany
*
malte.trienens@iem.fraunhofer.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Sustainability is not a new trend, but a mandatory measure for responsible and environmentally conscious use of resources. The digital transformation offers new potential in engineering and competitive advantages for companies through innovative technologies like the digital twin. Based on digital twins, products can be optimized, and new business models can be developed. Long-term added value is generated for manufacturing companies and customers. This paper explores the benefits of digital twins in the context of sustainability. Current challenges and use cases of digital twins are analysed.

Keywords

digital twinsustainabilitysystematic literature review
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2277 - 2286
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), 2024.

References

Argyroudis, S. A., Mitoulis, S. A., Chatzi, E., Baker, J. W., Brilakis, I., Gkoumas, K., Vousdoukas, M., Hynes, W., Carluccio, S., Keou, O., Frangopol, D. M., & Linkov, I. (2022). Digital technologies can enhance climate resilience of critical infrastructure. Climate Risk Management, 35, 100387. https://doi.org/10.1016/j.crm.2021.100387CrossRefGoogle Scholar
Azevedo, K., Bras, B., Doshi, S., & Guldberg, T. (2010). Modeling Sustainability of Complex Systems: A Multi-Scale Framework Using SysML, 1437–1448. https://doi.org/10.1115/DETC2009-87496CrossRefGoogle Scholar
Ball, P., & Badakhshan, E. Sustainable Manufacturing Digital Twins: A Review of Development and Application, 262, 159–168. https://doi.org/10.1007/978-981-16-6128-0_16CrossRefGoogle Scholar
Barni, A., Fontana, A., Menato, S., & Canetta, L. (2018). Exploiting the Digital Twin in the Assessment and Optimization of Sustainability Performances. IEEE. https://doi.org/ 10.1109/IS.2018.8710554CrossRefGoogle Scholar
Bauernhansl, T., Krüger, J., Reinhart, G., & Schuh, G. Wgp-Standpunkt Industrie 4.0. https://www.ipa.fraunhofer.de/content/dam/ipa/de/documents/Presse/Presseinformationen/2016/Juni/WGP_Standpunkt_Industrie_40.pdfGoogle Scholar
Becker, C., Chitchyan, R., Duboc, L., Easterbrook, S., Penzenstadler, B., Seyff, N., & Venters, C. C. (2015). Sustainability Design and Software: The Karlskrona Manifesto, 467–476. https://doi.org/10.1109/ICSE.2015.179CrossRefGoogle Scholar
Bellis, S., & Denil, J. (2022). Challenges and possible approaches for sustainable digital twinning, 643–648. https://doi.org/10.1145/3550356.3561551CrossRefGoogle Scholar
Carvalho, R., & Da Silva, A. R. (2021). Sustainability Requirements of Digital Twin-Based Systems: A Meta Systematic Literature Review. Applied Sciences, 11(12), 5519. https://doi.org/10.3390/app11125519CrossRefGoogle Scholar
Chakrabortty, R. K., Rahman, H. F., Mo, H., & Ryan, M. J. (2019). Digital Twin-based Cyber Physical System for Sustainable Project Scheduling, 820–824. https://doi.org/10.1109/IEEM44572.2019.8978712CrossRefGoogle Scholar
Dumitrescu, R [R.], Albers, A., Riedel, O., Stark, R., & Gausemeier, J. (2021). Advanced Systems Engineering – Value Creation in Transition.Google Scholar
Eigner, M., & Schäfer, P. (2014). Nachhaltigkeit aus Engineering Perspektive, 134–154. https://doi.org/10.5771/9783845255996_134CrossRefGoogle Scholar
European Commission. (2023). Circular economy action plan. [online] https://environment.ec.europa.eu/strategy/circular-economy-action-plan_en (accessed 12.11.2023).Google Scholar
European Parliament. (2022). Green Deal: key to a climate-neutral and sustainable EU. [online] https://www.europarl.europa.eu/news/en/headlines/society/20200618STO81513/green-deal-key-to-a-climate-neutral-and-sustainable-eu?&at_campaign=20234-Green&at_medium=Google_Ads&at_platform=Search&at_creation=RSA&at_goal=TR_G&at_audience=eu%20green%20deal&at_topic=Green_Deal&at_location=DE&gclid=EAIaIQobChMImYTTy7i8ggMVqZCDBx2ovALaEAAYAiAAEgIkI_D_BwE (accessed 12.11.2023).Google Scholar
Gausemeier, J., Dumitrescu, R [Roman], Echterfeld, J., Pfänder, T., Steffen, D., & Thielemann, F. (2019). Innovationen für die Märkte von morgen: Strategische Planung von Produkten, Dienstleistungen und Geschäftsmodellen. Hanser. ISBN: 978-3-446-42824-9.Google Scholar
Götz, T., Adisorn, T., & Tholen, L. (2021). Der Digitale Produktpass als Politik-Konzept: Kurzstudie im Rahmen der Umweltpolitischen Digitalagenda des Bundesministeriums für Umwelt, Naturschutz und nukleare Sicherheit (BMU). ISSN: 1862-1953.Google Scholar
Grieves, M. (2002). PLM Initiatives [Powerpoint Slides]: Product Lifecycle Management Special Meeting, 2002.Google Scholar
Hassan, M., Svadling, M., & Björsell, N. (2023). Experience from implementing digital twins for maintenance in industrial processes. Journal of Intelligent Manufacturing. Advance online publication. https://doi.org/10.1007/s10845-023-02078-4CrossRefGoogle Scholar
Hassani, H., Huang, X., & MacFeely, S. (2022). Enabling Digital Twins to Support the UN SDGs. Big Data and Cognitive Computing, 6(4), 115. https://doi.org/10.3390/bdcc6040115CrossRefGoogle Scholar
Helu, M., & Hedberg, T. (2015). Enabling Smart Manufacturing Research and Development using a Product Lifecycle Test Bed. Procedia Manufacturing, 1, 86–97. https://doi.org/10.1016/j.promfg.2015.09.066CrossRefGoogle ScholarPubMed
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics. Sensors (Basel, Switzerland), 21(19). https://doi.org/10.3390/s21196340CrossRefGoogle ScholarPubMed
Industrial Digital Twin Association e.V. (2023). [online] Digitaler Zwilling. https://industrialdigitaltwin.org/glossar/digitaler-zwilling (accessed 12.11.2023).Google Scholar
Industrial Internet Consortium. (2023). IIC vocabulary. [online] https://hub.iiconsortium.org/vocabulary (accessed 12.11.2023).Google Scholar
Kamble, S. S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2022). Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework. Technological Forecasting and Social Change, 176, 121448. https://doi.org/10.1016/j.techfore.2021.121448CrossRefGoogle Scholar
Kies, A. D., Krauß, J., Schmetz, A., Schmitt, R. H., & Brecher, C. (2022). Interaction of Digital Twins in a Sustainable Battery Cell Production. Procedia CIRP, 107, 1216–1220. https://doi.org/10.1016/j.procir.2022.05.134CrossRefGoogle Scholar
Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. https://www.researchgate.net/publication/302924724Google Scholar
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474CrossRefGoogle Scholar
Lélé, S. M. (1991). Sustainable development: A critical review. World Development, 19(6), 607–621. https://doi.org/10.1016/0305-750X(91)90197-PCrossRefGoogle Scholar
Ma, S., Ding, W., Liu, Y., Ren, S., & Yang, H. (2022). Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries. Applied Energy, 326, 119986. https://doi.org/10.1016/j.apenergy.2022.119986CrossRefGoogle Scholar
Monteiro, J., Barata, J., Veloso, M., Veloso, L., & Nunes, J. (2023). A scalable digital twin for vertical farming. Journal of Ambient Intelligence and Humanized Computing, 14(10), 13981–13996. https://doi.org/10.1007/s12652-022-04106-2CrossRefGoogle Scholar
Mügge, J., Hahn, I. R., Riedelsheimer, T., Chatzis, J., & Boes, J. (2023). End-of-life decision support to enable circular economy in the automotive industry based on digital twin data. Procedia CIRP, 119, 1071–1077. https://doi.org/10.1016/j.procir.2023.03.150CrossRefGoogle Scholar
Ramesohl, S., Berg, H., & Wirtz, J. (2022). The Circular Economy and Digitalisation: Strategies for a digital-ecological industry transformation.Google Scholar
Rocca, R., Rosa, P., Sassanelli, C., Fumagalli, L., & Terzi, S. (2020). Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case. Sustainability, 12(6), 2286. https://doi.org/10.3390/su12062286CrossRefGoogle Scholar
Rojek, I., Mikołajewski, D., & Dostatni, E. (2021). Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance. Applied Sciences, 11(1), 31. https://doi.org/10.3390/app11010031CrossRefGoogle Scholar
Rückert, M., Merkelbach, S., Alt, R., & Schmitz, K. Online Life Cycle Assessment for Fluid Power Manufacturing Systems – Challenges and Opportunities, 536, 128–135. https://doi.org/10.1007/978-3-319-99707-0_17CrossRefGoogle Scholar
Scholz, U., Pastoors, S., Becker, J. H., Hofmann, D., & van Dun, R. (2018). Praxishandbuch nachhaltige Produktentwicklung: Ein Leitfaden mit Tipps zur Entwicklung und Vermarktung nachhaltiger Produkte. Springer Gabler. https://doi.org/10.1007/978-3-662-57320-4CrossRefGoogle Scholar
Senna, P. P., Almeida, A. H., Barros, A. C., Bessa, R. J., & Azevedo, A. L. (2020). Architecture Model for a Holistic and Interoperable Digital Energy Management Platform. Procedia Manufacturing, 51, 1117–1124. https://doi.org/10.1016/j.promfg.2020.10.157CrossRefGoogle Scholar
Shafto, M., Conroy, M., Doyle, R., Glaessgn, E., Kemp, C., LeMoigne, J., & Wang, L. (2010). Draft Modeling, Simulation, Information Technology & Processing Roadmap: Technology area 11. https://www.researchgate.net/publication/280310295Google Scholar
Sulewski, P., Kłoczko-Gajewska, A., & Sroka, W. (2018). Relations between Agri-Environmental, Economic and Social Dimensions of Farms’ Sustainability. Sustainability, 10(12), 4629. https://doi.org/10.3390/su10124629CrossRefGoogle Scholar
United Nations. (2015). The 17 Goals. [online] https://sdgs.un.org/goals (accessed 12.11.2023).Google Scholar
Wang, G., Zhang, G., Guo, X., & Zhang, Y. (2021). Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing. Journal of Manufacturing Systems, 59, 165–179. https://doi.org/10.1016/j.jmsy.2021.02.008CrossRefGoogle Scholar
Wang, Y., Wang, S., Yang, B., Zhu, L., & Liu, F. (2020). Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits. Journal of Cleaner Production, 248, 119299. https://doi.org/10.1016/j.jclepro.2019.119299CrossRefGoogle Scholar
Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review.Google Scholar
Wellsandt, S., Ahlers, R., Terzi, S., & Corti, D. (2017). Model-Supported Lifecycle Analysis. IEEE.Google Scholar
Xiang, F., Zhang, Z., Zuo, Y., & Tao, F. (2019). Digital Twin Driven Green Material Optimal-Selection towards Sustainable Manufacturing. Procedia CIRP, 81, 1290–1294. https://doi.org/10.1016/j.procir.2019.04.015CrossRefGoogle Scholar