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APPROACH FOR THE AUTOMATED AND DATA-BASED DESIGN OF MECHANICAL JOINTS.

Published online by Cambridge University Press:  27 July 2021

Christoph Zirngibl ,
Benjamin Schleich  and
Sandro Wartzack
Christoph Zirngibl*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Benjamin Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
*
Zirngibl, Christoph, Friedrich-Alexander-Universität Erlangen-Nürnberg Engineering Design, KTmfk, Germany, [email protected]

Abstract

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As a result of the increasing challenges in the field of lightweight constructions, the demand for efficient joining technologies is continuously rising. For this purpose, cold forming processes offer an environmental friendly and fast alternative to established joining methods (e.g. welding). However, to ensure a high reliability, not only the selection of an appropriate procedure, but also the dimensioning of the individual joint is essential. While product designers can rely on a wide range of design principles for thermal processes, the dimensioning and evaluation of mechanical joining processes is mainly based on expert knowledge and a few experimental tests. Although few studies already investigated the numerical analysis of mechanical joints, an approach for the sustainable and consistent optimization of the strength and reliability of joining connections for varying use-cases is not available yet. Motivated by this lack, this paper presents an approach for the automated transfer of information within the process chain and the data-based analysis of mechanical joints by using clinching as an example. Therefore, the CRISP-DM reference model is used for the systematic data mining.

Keywords

Lightweight designOptimisationMachine learningClinchingCRISP-DM
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 521 - 530
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), 2021. Published by Cambridge University Press

References

Bye, C., Timmermann, R., Steber, M. (2014), “Mechanisches Fügen von Dünnblechen”, In: Feldmann, K., Schöppner, V., Spur, G. (Ed.), Handbuch Fügen, Handhaben und Montieren, Carl Hanser Verlag, München.Google Scholar
Coello, C.A.C. (2018), “Multi-objective Optimization”, In: Martí, R., Pardalos, P., Resende, M. (Ed.), Handbook of Heuristics, Springer, Cham. https://doi.org/10.1007/978-3-319-07124-4_17.Google Scholar
DVS (2009), “Clinchen - Überblick”, In: DVS-Fachbücher Vol. 184 (Ed.), Taschenbuch DVS-Merkblätter und -Richtlinien Mechanisches Fügen, DVS Media, Düsseldorf.Google Scholar
Drossel, W.G., Israel, M. (2013), “Sensitivitätsanalyse und Robustheitsbewertung beim mechanischen Fügen”, EFB-Forschungsbericht Nr. 323, EFB e.V., Hannover.Google Scholar
Eshtayeh, M., Hrairi, M. (2016), “Multi objective optimization of clinching joints quality using Grey-based Taguchi method”, The International Journal of Advanced Manufacturing Technology, Vol. 87, pp. 233–249. https://doi.org/10.1007/s00170-016-8471-1.CrossRefGoogle Scholar
Fahrenwaldt, H.J. (2006), “Fügen durch Umformen”, In: Fahrenwaldt, H.J., Schuler, V., Twrdek, J. (Ed.), Praxiswissen Schweißtechnik. Vieweg. https://doi.org/10.1007/978-3-8348-9192-1_6.Google Scholar
Géron, A. (2019), Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O'Reilly UK Ltd., Sebastopol.Google Scholar
Lambiase, F., Di Ilio, A. (2013), “Optimization of the clinching tools by means of integrated FE modeling and artifical intelligence techniques”, 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Ischia, 18-20 July 2012, Elsevier B.V., pp. 163–168. https://doi.org/10.1016/j.procir.2013.09.029.CrossRefGoogle Scholar
Mayer, DG., Butler, DG. (1993), “Statistical validation. Ecological Modelling”, Amsterdam, Elsevier Science Publishers B.V., Vol. 68, 21–32, https://doi.org/10.1016/0304-3800(93)90105-2CrossRefGoogle Scholar
Most, T., Will, J. (2008), “Meta-model of Optimal Prognosis - An automatic approach for variable reduction and optimal metamodel selection”, Proceedings of the Weimar Optimization and Stochastic Days 5.0, Weimar (Germany), 20.-21.11.2008, https://doi.org/10.13140/2.1.2194.4007.CrossRefGoogle Scholar
Neugebauer, R., Riedel, F. and Marx, R. (2010), “Entwicklung eines Konstruktionssystems für den rechnerischen Festigkeitsnachweis von punktförmig mechanisch gefügten Bauteilen”, EFB-Forschungsbericht Nr. 376, EFB e.V., Hannover.Google Scholar
Oudjene, M., Ben-Ayed, L., Batoz, J-L. (2009), “Shape optimization of clinching tools using the response surface methodology with Moving Least-Square approximation”, Journal of materials processing technology, Vol. 209, pp. 289–296. http://doi.org/10.1016/j.jmatprotec.2008.02.030.CrossRefGoogle Scholar
Sauer, C., Schleich, B., Wartzack, S. (2020), “Meta-model based generation of solution spaces in sheet-bulk metal forming”. Proceeding of 30th CIRP Design, http://doi.org/10.1016/j.procir.2020.02.247CrossRefGoogle Scholar
Siebertz, K., van Bebber, D., Hochkirchen, T. (2017), Statistische Versuchsplanung. Design of Experiments (DoE), Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-662-55743-3.CrossRefGoogle Scholar
Wang, X., Li, X., Shen, Z., Ma, Y., Liu, H. (2018), “Finite element simulation on investigations, modeling, and multiobjective optimization for clinch joining process design accounting for process parameters and design constraints”, The International Journal of Advanced Manufacturing Technology, Vol. 96, pp. 3481–3501. https://doi.org/10.1007/s00170-018-1708-4.CrossRefGoogle Scholar
Witten, I. H., Eibe, F., Hall, M. A., Pal, C. J. (2017), Data Mining. Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Cambridge.Google Scholar
Zirngibl, C., Schleich, B. and Wartzack, S. (2020), “Potentiale datengestützter Methoden zur Gestaltung und Optimierung mechanischer Fügeverbindungen”, Proceedings of the 31. Symposium Design for X, online, 16.-17.09.2020, Krause, D.; Paetzold, K.; Wartzack, S., pp. 71–80. https://doi.org/10.35199/dfx2020.8.CrossRefGoogle Scholar