Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-12T22:57:21.506Z Has data issue: false hasContentIssue false
  • English
  • Français

Enabling Initial Design-Checks of Parametric Designs Using Digital Engineering Methods

Published online by Cambridge University Press:  26 May 2022

B. Gerschütz ,
S. Bickel ,
B. Schleich  and
S. Wartzack
B. Gerschütz*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
S. Bickel
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
B. Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
S. Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
*
gerschuetz@mfk.fau.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.

The world consequently gets faster, so does product development. Therefore, the stock of development and simulation data increases continuously. Unfortunately, inexperienced users cannot cope with the rising number of simulation requests in the time needed. Digital Engineering opens potentials to support the users with newly developed methods and tools. In this contribution, we present a method to assist designers, inexperienced in finite-element simulations to perform an initial check of changed parametric designs independently, quickly and with support in interpreting the results.

Keywords

digital designdata-driven designsupportive technologiesfinite element method (FEM)
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 405 - 414
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

Bickel, S., Spruegel, T., Schleich, B. and Wartzack, S. (2019). How Do Digital Engineering and Included AI Based Assistance Tools Change the Product Development Process and the Involved Engineers. Proceedings of the Design Society: International Conference on Engineering Design, 1(1), 2567–2576. https://dx.doi.org/10.1017/dsi.2019.263Google Scholar
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine 17, 37–37.Google Scholar
Gerschütz, B., Sauer, C., Kormann, A., Wallisch, A., Mehlstäubl, J., Alber-Laukant, B., Schleich, B., Paetzold, K., Rieg, F., Wartzack, S., (2021). Towards Customized Digital Engineering: Herausforderungen und Potentiale bei der Anpassung von Digital Engineering Methoden für den Produktentwicklungsprozess, in: Stuttgarter Symposium Für Produktentwicklung 2021 (SSP 2021). Stuttgart. 2021.Google Scholar
Kestel, P., Schneyer, T., & Wartzack, S. (2016). Feature-based approach for the automated setup of accurate, design-accompanying Finite Element Analyses. In Proceedings of the 14th International Design Conference. Dubrovnik.Google Scholar
Montáns, F.J., Chinesta, F., Gomez-Bombarelli, R., Kutz, J.N., (2019). Data-driven modeling and learning in science and engineering.Google Scholar
Samuel, A.L., (2000). Some studies in machine learning using the game of checkers. IBM J. Res. & Dev. 44, 206–226.CrossRefGoogle Scholar
Sauer, C., Schleich, B., and Wartzack, S. (2018). Deep learning in sheet-bulk metal forming part design. In DS92: Proceedings of the DESIGN 2018 15th International Design Conference (pp. 2999–3010). Dubrovnik, HR.Google Scholar
Schenk, M., (2011). Digitales Engineering und virtuelle Techniken zum Planen, Testen und Betreiben technischer Systeme: 13. IFF-Wissenschaftstage, 15. - 17. Juni 2010, [Magdeburg]; Fraunhofer-Verl, Stuttgart.Google Scholar
Schumann, M., Schenk, M., Schmucker, U., Saake, G., (2011). Digital Engineering - Herausforderungen, Ziele und Lösungsbeispiele, in: Digital Engineering. Presented at the 14. IFF Wissenschaftstage, Magdeburg.Google Scholar
Spruegel, T., Bickel, S., Schleich, B. and Wartzack, S. (2021). Approach and application to transfer heterogeneous simulation data from finite element analysis to neural networks. Journal of Computational Design and Engineering, Volume 8(1), 298–315. https://dx.doi.org/10.1093/jcde/qwaa079Google Scholar
Spruegel, T., Hallmann, M., Wartzack, S. (2015). A concept for FE plausibility checks in structural mechanics. In: Summary of Proceedings NAFEMS World Congress 2015, 21.-24. June 2015, San Diego, USA.Google Scholar
Spruegel, T., Rothfelder, R., Bickel, S., Grauf, A., Sauer, C., Schleich, B., & Wartzack, S. (2018). Methodology for plausibility checking of structural mechanics simulations using Deep Learning on existing simulation data. In Proceedings of NordDesign 2018. Linköping, SE: The Design Society.Google Scholar
Vajna, S.; Weber, C.; Zeman, K.; Hehenberger, P.; Gerhard, D.; Wartzack, S. (2018) CAx fuer Ingenieure. Springer Vieweg. Berlin [in german]. 10.1007/978-3-662-54624-6.Google Scholar