Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-25T14:44:13.702Z Has data issue: false hasContentIssue false
  • English
  • Français

Data- and simulation-based material behaviour prediction

Published online by Cambridge University Press:  16 May 2024

Anton Dybov ,
Carina Fresemann  and
Rainer Stark
Anton Dybov*
Affiliation:
Technische Universität Berlin, Germany
Carina Fresemann
Affiliation:
Technische Universität Berlin, Germany
Rainer Stark
Affiliation:
Technische Universität Berlin, Germany
*
[email protected]

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.

In research environments and laboratories e.g. for material sciences the in- and output of simulation data is manually managed. Therefore, physical experiments as well as simulations might be carried out several times, learnings are not systematically gathered, and experiments do not systematically build on learnings from data. This paper proposes to engage an ontology in conjunction with a simulation to use data from already carried out experiments and on that basis predict material behaviour under certain condition and plan further physical experiments.

Keywords

ontologysimulationbehavioural design
Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 563 - 572
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

Alam, M., Birkholz, H., Dessì, D., Eberl, C., Fliegl, H., Gumbsch, P., & Thomas, …, A. (2021). Ontology modelling for materials science experiments. In Poster&Demo track and Workshop on Ontology-Driven Conceptual Modelling of Digital Twins co-located with Semantics 2021, Amsterdam and Online, September 6-9, 2021 (Vol. 2941, p. 11). Aachen, Germany: RWTH Aachen. https://doi.org/10.34657/7992Google Scholar
Benjamin, P. C., Menzel, C. P., Mayer, R. J., & Padmanaban, N. (1995). Toward a method for acquiring CIM ontologies. International Journal of Computer Integrated Manufacturing, 8(3), 225-234. https://doi.org/10.1080/09511929508944648CrossRefGoogle Scholar
Benjamin, P., Patki, M., & Mayer, R. (2006, December). Using ontologies for simulation modeling. In Proceedings of the 2006 winter simulation conference (pp. 1151-1159). IEEE. https://doi.org/10.1109/WSC.2006.323206CrossRefGoogle Scholar
Eichenseer, F., Heinkel, H. M., Benedikt, M., Ahmann, M., Holzner, M., & Stadler, C. (2023, July). Modeling & Simulation SPICE: Assessing the Capability of Credible Simulation Processes. In INCOSE International Symposium, Vol. 33, No. 1, (pp. 399-415). https://doi.org/10.1002/iis2.13029CrossRefGoogle Scholar
Elnagar, S., Yoon, V., & Thomas, M. A. (2022). An automatic ontology generation framework with an organizational perspective. arXiv preprint arXiv:2201.05910. https://doi.org/10.48550/arXiv.2201.05910CrossRefGoogle Scholar
Guarino, N., Oberle, D., Staab, S. (2009). What Is an Ontology?. In: Staab, S., Studer, R. (eds) Handbook on Ontologies. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92673-3_0Google Scholar
Jepsen, T. C. (2009). Just what is an ontology, anyway?. IT Professional Magazine, 11(5), 22.CrossRefGoogle Scholar
Lucassen, G., Dalpiaz, F.,van der Werf, J.M.E.M. et al. Improving agile requirements: the Quality User Story framework and tool. Requirements Eng 21, 383–403 (2016). https://doi.org/10.1007/s00766-016-0250-xCrossRefGoogle Scholar
Röhm, B., Emich, B., Anderl, R. (2021) Approach of simulation data management for the application of the digital simulation twin, Procedia CIRP, Volume 100, 2021 (pp. 421-426). https://doi.org/10.1016/j.procir.2021.05.098.Google Scholar
Skoogh, A., and Johansson, B. (2008). A methodology for input data management in discrete event simulation projects. In: 2008 Winter Simulation Conference (pp. 1727-1735). IEEE. https://doi.org/10.1109/WSC.2008.4736259CrossRefGoogle Scholar
Skoogh, A, Terrence, P., and Johansson, B. (2012). Input data management in simulation–Industrial practices and future trends." Simulation Modelling Practice and Theory 29, 2012 (pp 181-192). https://doi.org/10.1016/j.simpat.2012.07.009CrossRefGoogle Scholar
Stark, R. Fresemann, C., and BenHassine, S. (2021). Ontologien als Datenquelle für Prädiktive Simulation. In: Wigep News 2022/2Google Scholar
Wu, Y., & Tian, L. (2015, September). Simulation data management based on ontology tagging. In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 396-400). IEEE. https://doi.org/10.1155/2015/474157CrossRefGoogle Scholar