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A Machine Learning-Based Approach for Quick Evaluation of Live Simulations in Embodiment Design

Published online by Cambridge University Press:  26 May 2022

C. Sauer ,
B. Gerschütz ,
J. Bernsdorf ,
B. Schleich  and
S. Wartzack
C. Sauer*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
B. Gerschütz
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
J. Bernsdorf
Affiliation:
CADFEM GmbH, Germany
B. Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
S. Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
*
sauer@mfk.fau.de

Abstract

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Supporting product developers in early design phases with Live-Simulation can enhance the quality of early product designs. Live-Simulation can also facilitate a democratization of simulation and puts away pressure from simulation experts. In this paper, a machine learning based quick evaluation tool is proposed to support product developers in interpreting Live-Simulation results. The proposed tool enables a quick evaluation of the Live-Simulation results and enables product developers to further enhance their simulations. The tool is shown within a use case in bike rocker switch design.

Keywords

data-driven designsimulation-based designstructural analysismachine learninglive-simulation
Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1757 - 1766
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

Abbey, T. (2019), Meshless FEA Opportunities. [online] Digital Engineering 247. Available at: http://web.archive.org/web/20211021121117/https://www.digitalengineering247.com/article/meshless-fea-opportunities/simulate (accessed 21.10.2021).Google Scholar
Alawadhi, E. M., 2009, Finite Element Simulations Using Ansys, CRC Press, Boca Raton, Florida, USA. 10.1201/9781439801611CrossRefGoogle Scholar
Argyris, J. H. (1955), “Energy theorems and structural analysis”, Aircraft Engineering , Vol. 27, pp. 125–144.CrossRefGoogle Scholar
Bickel, S., Sprügel, T., Schleich, B., 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 22nd International Conference on Engineering Design (ICED19), Delft, NL: Cambridge, United Kingdom: Cambridge University Press, pp. 2567–2576.CrossRefGoogle Scholar
Breiman, L., Friedman, J., Olshen, R., Stone, C. (1984) Classification and Regression Trees, Wadsworth, Belmont, CA, 1984.Google Scholar
Ciarlet, P. (1978), The Finite Element Method for Elliptic Problems, North Holland, New York, USA.Google Scholar
Courant, R. (1942), “Variational methods for the solution of problems of equilibrium and vibrations”, Bulletin American Mathematical Society, Vol. 69, pp. 1–23.Google Scholar
Fleischmann, C., Leher, I., Hartwich, R., Hainke, M., Sesselmann, S., (2019), “A new approach to quickly edit geometries and estimate stresses and displacements of implants in real-time”, Current Directions in Biomedical Engineering, Vol. 5, pp. 553–556. 10.1515/cdbme-2019-0139Google Scholar
Gerschütz, B.; Sauer, C., Wallisch, A., Mehlstäubl, J., Kormann, A. Schleich, B., Alber-Laukant, 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”, Stuttgarter Symposium für Produktentwicklung SSP 2021, Stuttgart, Deutschland, pp. 93–104. 10.18419/opus-11478Google Scholar
Glatzel, T., Litterst, C., Cupelli, C., Lindemann, T., Moosmann, C., Niekrawietz, R., Streule, W., Zengerle, R., Koltay, P., 2008, “Computational fluid dynamics (CFD) software tools for microfluidic applications – A case study”, Computers & Fluids , Vol 37, pp. 218–235. 10.1016/j.compfluid.2007.07.014CrossRefGoogle Scholar
Kestel, P., Kügler, P., Zirngibl, C., Schleich, B., Wartzack, S. (2019), “Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes”, Advanced Engineering Informatics, Vol. 39, pp. 292–305. 10.1016/j.aei.2019.02.001CrossRefGoogle Scholar
Kim, Y. S. (2008), “Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size”, Expert Systems with Applications, Vol. 34(2), pp. 1227–1234. 10.1016/j.eswa.2006.12.017Google Scholar
Martínez-Martínez, F., et al. . (2017) “A Finite Element-Based Machine Learning Approach for Modeling the Mechanical Behavior of the Breast Tissues under Compression in Real-Time.” Computers in Biology and Medicine, vol. 90, Nov. 2017, pp. 116–124. 10.1016/j.compbiomed.2017.09.019CrossRefGoogle ScholarPubMed
Most, T., Will, J. (2008), Metamodel of Optimal Prognosis - an automatic approach for variable reduction and optimal metamodel selection, Proceedings Weimarer Optimierungs- und Stochastiktage 5 (2008): 20–21Google Scholar
Nikitin, I., Nikitina, L., Frolov, P., Goebbels, G., Göbel, M., Klimenko, S., Nielson, G. M. (2003), Real-time simulation of elastic objects in virtual environments using finite element method and pre-computed Green's functions, Исследовано в России, Vol 6.Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011), “Scikit-learn: Machine learning in Python”, Journal of Machine Learning Research, Vol. 12, pp. 2825–2830.Google Scholar
Sprügel, 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”, 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 für Ingenieure, 3 Ed., Springer, Berlin/Heidelberg. 10.1007/978-3-662-54624-6Google Scholar
Zhou, Y., Lu, H., Wang, G., Wang, J., Li, W., (2020). “Voxelization modelling based finite element simulation and process parameter optimization for Fused Filament Fabrication”, Materials & Design, Vol 187, 108409. 10.1016/j.matdes.2019.108409CrossRefGoogle Scholar
Zienkiewicz, O. C. (1977), The Finite Element Method, McGraw-Hill, New York, USA.Google Scholar