Digital Twinning in Product Development

Development and use of experimental digital twins

Heiko Matheis ORCID Icon, Guido Grau, Florian Mews, Lukas Schüller
JournalIndustrie 4.0 Management
Issue Volume 39, 2023, Edition 5, Pages 37-41
Open Accesshttps://doi.org/10.30844/IM_23-5_37-41
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Abstract

The development of textile products is associated with high material, time, personnel and cost expenditure. The paper describes the digital twinning for materials and processes and their application in a digital product development process, which can accelerate the ramp-up phase and thus reduce development costs by up to 60%.

Article

The development of textile products is associated with high material, time, personnel and cost expenditure. Up to now, the development process follows an empirical approach based on the know- how of long-standing experts. Yet this expert knowledge is hardly documented, and given the shortage of skilled workers in the textile industry the long-term transfer of knowledge is at risk. The paper describes the digital twinning for materials and processes and their application in a digital product development process, which can accelerate the ramp-up phase and thus reduce development costs by up to 60%.

Bibliography

[1] SozioTex/RWTH Aachen: SozioTex – Neue Soziotechnische Systeme in der Textilbranche. Film. Aachen 2019. URL: www.soziotex.de/, Abrufdatum 10.05.2019.
[2] Ashino, T.: Materials Ontology: An Infrastructure for Exchanging Materials Information and Knowledge. In: Data Science Journal 9 (2010).
[3] Rossmann, J.; Schluse, M.: Virtual Robotic Testbeds: A foundation for e-Robotics in Space, in Industry – and in the Woods. In: The 4th International Conference on Developments in eSystems Engineering (DeSE). Dubai 2011.
[4] Rast, M.: Domänenübergreifende Modellierung und Simulation als Grundlage für virtuelle Testbeds. Dissertation. Aachen 2015.
[5] Matheis, H.; Wüstner, C.; Weinert, M.; Weiß, M.; Strangfeld, M.; Gaußer, T.: DITF Forschungsberichte, Virtualisierung im Innovationsprozess für designbasierte Konsumgüter aus formflexiblen Materialien. Abgeschlossenes IGF 19120 BG, DITF, FILK, IHD. Denkendorf 2019.
[6] Grau, G.: Digitaler Zwilling und digitaler Schatten im Herstellungsprozess von Implantaten aus Vliesstoffen. Vortrag auf den 34. Hofer Vliesstofftagen 2019. URL: www.hofer-vliesstofftage.de/geschuetzter/vortraege/2019-22-P.pdf, Abrufdatum 16.12.2020.
[7] Goetz, C.; Hanuschik, D.; Schröder, E.: Erweiterung des Anwendungsgebietes der Tuftingtechnik durch den Einsatz einer elektronisch gesteuerten Jerkerbarre (AiF 16678) TFI-Schriftenreihe 2013/98, Aachen, 2013
[8] Goetz, C.: Entwicklung eines Wärmetauschers mit neuem Reinigungskonzept, kompakter Bauform, flexibler Formgebung und verbesserter Akustik auf Basis getufteter Strukturen. Schlussbericht IGF 20540. Aachen 2022.
[9] Goetz, C.: Textiles Bogensieb – Filtersystem zur Feststoffabscheidung aus Abwasserströmen mit wartungsarmem Reinigungskonzept auf Basis strukturierter Poltextilien mit definiertem Abscheideverhalten. In: TFI-Schriftenreihe 121 (2022).
[10] Seifert, I.; Bürger, M.; Wangler, L.; Christmann-Budian, S.; Rohde, M.; Gabriel, P.; Zinke, G.: Potenziale der Künstlichen Intelligenz im produzierenden Gewerbe in Deutschland. 2018.

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