Data driven prognosis of clinch joints in multi-material design
Jean-Patrick Ludwig, Seraphin Tsi-Nda Lontsi, Jonas Neumann, Lukas Kappis, Christian Scharr, Wilko Flügge, Marion Merklein, Gerson Meschut
Abstract. Clinching is a conventional mechanical joining process used in Multi-Material Design in the automotive sector. To receive the desired geometrical characteristics in clinch joints, correct process design is required. To reduce the cost of finding fitting process parameters, numerical simulation of the joining process can be used to predict the geometrical characteristics, such as interlock, instead of an experimental approach. These numerical simulation models consume computational resources and time. In this paper machine learning is used to find correlations between features of the joining process and geometrical characteristics in the joint. This serves the purpose of predicting the joint’s target values more resource-efficiently. Modelling with machine learning requires a structured dataset with sufficient parameter variation. To create this data base the following procedure was used. For joining partners, a HC340LA steel alloy with 2 mm material thickness was used punch-sided and an EN AW 5182 aluminum alloy with 1.5 mm thickness was used die-sided. For this combination a suitable tool combination and punch distance was experimentally identified. A finite element model was created to reproduce the joining process. For the modelling of the material of both joining partners flow curves determined by Vallaster et al. were used [1]. The punch and die were recreated digitally by opto-electronic measurements and transformed into a mesh suitable for numerical simulation. The model was validated by comparing process values like the maximum force applied by the punch and geometrical values in the joints cross section. Additionally, a process window for suitable punch distances was experimentally determined. Afterwards a variation of 70 different process designs was conducted with variants inside and outside the process window. The results were used for training, testing and validating various machine learning models. All models competed against each other to find the must suited model to predict every geometric value. To ensure good model performances and prevent the model from overfitting, a tenfold cross validation was used for validating the models. Analysis of the results gives the following key findings: i) Good predictability is reached for the interlock and sheet thickness of the joint. ii) Prediction neck thickness showed low error values, but also low correlation. iii)The prediction of those key values for evaluating clinch joint characteristics by machine learning models positively impacts needed resources in comparison to numerical models.
Keywords
Mechanical Joining, Clinching, Numerical Simulation, Machine Learning
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Jean-Patrick Ludwig, Seraphin Tsi-Nda Lontsi, Jonas Neumann, Lukas Kappis, Christian Scharr, Wilko Flügge, Marion Merklein, Gerson Meschut, Data driven prognosis of clinch joints in multi-material design, Materials Research Proceedings, Vol. 54, pp 1449-1458, 2025
DOI: https://doi.org/10.21741/9781644903599-157
The article was published as article 157 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] E. Vallaster, S. Wiesenmayer, and M. Merklein, “Effect of a strain rate dependent material modeling of a steel on the prediction accuracy of a numerical deep drawing process,” Prod. Eng. Res. Devel., vol. 18, no. 1, pp. 47–60, 2024. https://doi.org/10.1007/s11740-023-01222-6
[2] G. Meschut, V. Janzen, and T. Olfermann, “Innovative and Highly Productive Joining Technologies for Multi-Material Lightweight Car Body Structures,” J. of Materi Eng and Perform, vol. 23, no. 5, pp. 1515–1523, 2014. https://doi.org/10.1007/s11665-014-0962-3
[3] Europäische Forschungsgesellschaft für Blechverarbeitung e.V., Merkblatt DVS/EFB 3420: Clinchen – Überblick. Düsseldorf: DVS Media GmbH, 2021.
[4] J. Varis and J. Lepistö, “A simple testing-based procedure and simulation of the clinching process using finite element analysis for establishing clinching parameters,” Thin-Walled Structures, vol. 41, no. 8, pp. 691–709, 2003. https://doi.org/ 10.1016/S0263-8231(03)00026-0
[5] S. Coppieters, P. Lava, S. Baes, H. Sol, P. van Houtte, and D. Debruyne, “Analytical method to predict the pull-out strength of clinched connections,” Thin-Walled Structures, vol. 52, pp. 42–52, 2012. https://doi.org/10.1016/j.tws.2011.12.002
[6] C. Bielak, M. Böhnke, R. Beck, M. Bobbert, and G. Meschut, “Numerical analysis of the robustness of clinching process considering the pre-forming of the parts,” Journal of Advanced Joining Processes, vol. 3, p. 100038, 2021. https://doi.org/10.1016/j.jajp.2020.100038
[7] C. R. Bielak, M. Böhnke, M. Bobbert, and G. Meschut, “Further development of a numerical method for analyzing the load capacity of clinched joints in versatile process chains,” ESAFORM 2021, 2021. https://doi.org/10.25518/esaform21.4298
[8] C. R. Bielak, M. Böhnke, M. Bobbert, and G. Meschut, “Development of a Numerical 3D Model for Analyzing Clinched Joints in Versatile Process Chains,” pp. 165–172. https://doi.org/10.1007/978-3-031-06212-4_15
[9] J. Friedlein; C. R. Bielak; M. Böhnke; M. Bobbert; G. Meschut; J. Mergheim; P. Steinmann, “Influence of plastic orthotropy on clinching of sheet metal,” Materials Research Proceedings, no. 25, pp. 133–140, Mar. 2023. https://doi.org/10.21741/9781644902417-17
[10] C. Zirngibl, B. Schleich, and S. Wartzack, “APPROACH FOR THE AUTOMATED AND DATA-BASED DESIGN OF MECHANICAL JOINTS,” Proc. Des. Soc., vol. 1, pp. 521–530, 2021. https://doi.org/10.1017/pds.2021.52
[11] C. Zirngibl, B. Schleich, and S. Wartzack, “Robust estimation of clinch joint characteristics based on data-driven methods,” Int J Adv Manuf Technol, vol. 124, 3-4, pp. 833–845, 2023. https://doi.org/10.1007/s00170-022-10441-7
[12] C. Zirngibl, F. Dworschak, B. Schleich, and S. Wartzack, “Application of reinforcement learning for the optimization of clinch joint characteristics,” Prod. Eng. Res. Devel., vol. 16, 2-3, pp. 315–325, 2022. https://doi.org/10.1007/s11740-021-01098-4
[13] P. Schober, C. Boer, and L. A. Schwarte, “Correlation Coefficients: Appropriate Use and Interpretation,” Anesthesia and analgesia, vol. 126, no. 5, pp. 1763–1768, 2018. https://doi.org/10.1213/ANE.0000000000002864
[14] G. Meschut, M. Merklein, A. Brosius, D. Drummer, L. Fratini, U. Füssel, M. Gude, W. Homberg, P.A.F. Martins, M. Bobbert, M. Lechner, R. Kupfer, B. Gröger, D. Han, J. Kalich, F. Kappe, T. Kleffel, D. Köhler, C.-M. Kuball, J. Popp, D. Römisch, J. Troschitz, C. Wischer, S. Wituschek, M. Wolf, “Review on mechanical joining by plastic deformation,” Journal of Advanced Joining Processes, vol. 5, p. 100113, 2022. https://doi.org/10.1016/j.jajp.2022.100113
[15] B. Schramm, J. Friedlein, B. Gröger, C. Bielak, M. Bobbert, M. Gude, G. Meschut, T. Wallmersperger, J. Mergheim, “A Review on the Modeling of the Clinching Process Chain—Part II: Joining Process,” Journal of Advanced Joining Processes, vol. 6, p. 100134, 2022. https://doi.org/10.1016/j.jajp.2022.100134
