–
Novel approach for data-driven modelling of multi-stage straightening and bending processes
PETERS Henning, DJAKOW Eugen, ROSTEK Tim, MAZUR Andreas, TRÄCHTLER Ansgar, HOMBERG Werner, HAMMER Barbara
download PDFAbstract. In multi-stage bending and straightening operations cross-stage and quantity-dependent effects crucially affect the quality of the end product. Using punch-bending units in combination with a mechatronic straightening device can improve the accuracy and repeatability of product features remarkably well. In this work a concept for an innovative hybrid model of a roll straightener in a multi-stage straightening and multi-stage bending process is proposed. This model combines data-driven elements with expert knowledge and aims to minimise residual errors of the roll straightener to reliably decrease the risk of disadvantageous cross-stage and quantity-dependent effects on a subsequent punch-bending process.
Keywords
Straightening Machine, Punch-Bending Process, Hybrid Modelling
Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: PETERS Henning, DJAKOW Eugen, ROSTEK Tim, MAZUR Andreas, TRÄCHTLER Ansgar, HOMBERG Werner, HAMMER Barbara, Novel approach for data-driven modelling of multi-stage straightening and bending processes, Materials Research Proceedings, Vol. 41, pp 2289-2298, 2024
DOI: https://doi.org/10.21741/9781644903131-252
The article was published as article 252 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] M. Paech, Advanced semi-automatic straightening technology, Wire Journal International, vol. 41, no. 7 (2008), 74-79.
[2] K. Richter, F. Reuther, R. Müller, D. Landgrebe, Investigating the influence of bending parameters on the springback behavior of ultra-high strength spring strips, Materials Science Forum, vol. 918 (2018), 125–133. https://doi.org/10.4028/www.scientific.net/MSF.918.125
[3] M. Grüber, Konzepte zur Steuerung des Richtwalzprozesses bei variierenden Richtguteigenschaften, Dissertation, RWTH Aachen Universität, Verlagshaus Mainz GmbH Aachen, 2019.
[4] A. Amor, M. Rachik, H. Sfar, “Combination of finite-element and semi-analytical models for sheet metal leveling simulation,” Key Engineering Materials, vol. 473 (2011), 182–189. https://doi.org/10.4028/www.scientific.net/KEM.473.182
[5] A. Trächtler, W. Homberg, Entwicklung eines intelligenten Einrichtassistenzsystems für Richtapparate, um die Prozessgrenzen der Verarbeitung hochfester Drahtwerkstoffe zu erweitern, Forschung für die Praxis P 1273 / IGF-Nr. 19799 N, 2021.
[6] G. Knoblauch, Steckverbinder: Systemkonzepte und Technologien, Expert Verlag, Esslingen, 2002.
[7] N. Auserhoff, Integrierte Produkt- und Prozessgestaltung für die Herstellung von Blechbiegeteilen, Shaker, 1999.
[8] S. Chatti, Optimierung der Fertigungsgenauigkeit beim Profilbiegen. Shaker, 1998.
[9] K. Siegert, Blechumformung: Verfahren, Werkzeuge und Maschinen. Springer-Verlag, 2015.
[10] U.-H. Damerow, Korrigierende Eingriffsmöglichkeiten in den Herstellungsprozess von Stanzbiegeteilen, Dissertation (D 466), Universität Paderborn, Shaker Verlag Aachen, 2017.
[11] L.-S. Henrich, Theoretische und experimentelle Untersuchungen zum Richtwalzen von Blechen, Dissertation, Universität-Gesamthochschule Siegen, TIM-Bericht Nr. T06-08.94, 1994.
[12] V. Shinkin, Arithmetical method of calculation of power parameters of 2n-roller straightening machine under flattening of steel sheet, CIS Iron and Steel Review, vol. 14 (2017), 22–27. https://dx.doi.org/10.17580/cisisr.2017.02.05
[13] L. Steinwender, A. Kainz, K. Krimpelstätter, K. Zeman, Numerical simulation of tension losses and reaction forces in tension levellers, Special Edition: 10th International Conference on Technology of Plasticity, ICTP (2011), 343-348.
[14] F. Martínez-de Pisón, R. Lostado, A. Pernia, R. Fernandez, Optimising tension levelling process by means of genetic algorithms and finite element method, Ironmaking & Steelmaking, vol. 38, no. 1 (2011), 45–52. https://doi.org/10.1179/030192310X12700328926029
[15] C. F. Mertin, Experimentelle und numerische Analyse von mehrstufigen Stanzbiegeprozessen für Bauteile mit federnden Eigenschaften, Dissertation, RWTH Aachen Universität, Verlagshaus Mainz GmbH, Aachen, 2019.
[16] A. Forcellese, F. Gabrielli, R. Ruffini, Effect of the training set size on springback control by neural network in an air bending process, Journal of Materials Processing Technology, vol. 80 (1998), 493–500. https://doi.org/10.1016/S0924-0136(98)00122-8
[17] S. Liu, Y. Xia, Z. Shi, H. Yu, Z. Li, J. Lin, Deep learning in sheet metal bending with a novel theory-guided deep neural network, IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 3 (2021), 565–581, 2021. https://doi.org/10.1109/JAS.2021.1003871
[18] J. Havinga, T. Van den Boogaard, F. Dallinger, P. Hora, Feedforward control of sheet bending based on force measurements, Journal of Manufacturing Processes, vol. 31 (2018), 260 272. https://doi.org/10.1016/j.jmapro.2017.10.011
[19] A. Karpatne, G. Atluri, J. H. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, V. Kumar, Theory-guided data science: A new paradigm for scientific discovery from data, IEEE Transactions on knowledge and data engineering, vol. 29, no. 10 (2017), 2318 2331. https://doi.org/10.1109/TKDE.2017.2720168
[20] L. Bathelt, M. Scurk, E. Djakow, C. Henke, A. Trächtler, Novel straightening-machine design with integrated force measurement for straightening of high-strength flat wire, Sensors, vol. 23, no. 22: 9091 (2023). https://doi.org/10.3390/s23229091
[21] J. Li, Z. Wang, S. Zhang, Y. Lin, L. Jiang, J. Tan, Task incremental learning-driven Digital-Twin predictive modeling for customized metal forming product manufacturing process, Robotics and Computer-Integrated Manufacturing, Volume 85 (2023), 1-16. https://doi.org/10.1016/j.rcim.2023.102647
[22] X. Hu, L. Chu, J. Pei, W. Liu, J. Bian, Model complexity of deep learning: A survey, Knowledge and Information Systems, vol. 63 (2021), 2585–2619. https://doi.org/10.48550/arXiv.2103.05127
[23] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature, vol. 521, no. 7553 (2015), 436 444. https://doi.org/10.1038/nature14539
[24] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation, vol. 9, no. 8 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
[25] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, Advances in neural information processing systems, vol. 30 (2017). https://doi.org/10.48550/arXiv.1706.03762
[26] C. E. Rasmussen, C. K. I. Williams, Gaussian Processes for Machine Learning, The MIT Press, 2005. https://doi.org/10.7551/mitpress/3206.001.0001
[27] E. Pintelas, I. E. Livieris, P. Pintelas, A grey-box ensemble model exploiting black-box accuracy and white-box intrinsic interpretability, Algorithms, vol. 13, no. 1 (2020). https://doi.org/10.3390/a13010017
[28] C. Molnar, Interpretable machine learning, (2020). Information on https://christophm.github.io/interpretable-ml-book
[29] T. Miller, Explanation in artificial intelligence: Insights from the social sciences, Artificial intelligence, vol. 267 (2019), 1–38. https://doi.org/10.1016/j.artint.2018.07.007
[30] A. Fisher, C. Rudin, F. Dominici, All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously. J. Mach. Learn. Res., vol. 20, no. 177 (2019), 1–81. https://doi.org/10.48550/arXiv.1801.01489
[31] T. Rojat, R. Puget, D. Filliat, J. Del Ser, R. Gelin, N. Díaz-Rodríguez, “Explainable artificial intelligence (xai) on timeseries data: A survey,” arXiv preprint arXiv:2104.00950 (2021). https://doi.org/10.48550/arXiv.2104.00950
[32] A. Ali, T. Schnake, O. Eberle, G. Montavon, K.-R. Müller, L. Wolf, Xai for transformers: Better explanations through conservative propagation, in International Conference on Machine Learning. PMLR (2022), 435–451. https://doi.org/10.48550/arXiv.2202.07304
[33] Z. Qin, F. Yu, C. Liu, X. Chen, How convolutional neural network see the world-A survey of convolutional neural network visualization methods, arXiv preprint arXiv:1804.11191 (2018). https://doi.org/10.3934/mfc.2018008
[34] K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, arXiv preprint arXiv:1312.6034, 2013. https://doi.org/10.48550/arXiv.1312.6034
