Innovative self-learning disturbance compensation for straightening processes

Innovative self-learning disturbance compensation for straightening processes

BATHELT Lukas, DJAKOW Eugen, HENKE Christian, TRÄCHTLER Ansgar

download PDF

Abstract. To increase the sustainability of forming processes such as punch bending, homogenization of the processed semi-finished product is an essential step in the manufacturing process. High-strength wire materials are usually available as strip material before being further processed in a forming process. For storage and transport, the material is coiled onto coils and transported to the customer. During the coiling process, residual stresses and plastic deformation are introduced into the wire. Thus, the final product quality is also influenced by the geometry of the coil. Straightening machines are used in production lines to compensate for these. Once a straightening machine has been set up, the settings for the roll positions are usually not changed. As a result, there is no reaction to material fluctuations, which means that the components to be produced do not meet the dimensional accuracy requirements. This leads to an increase in the rejection rate in manufacturing processes. To reduce the rejection rate, it is necessary to enable dynamic and flexible infeed of the straightening rollers. In this context, an innovative control concept with disturbance compensation was developed for the straightening process. The disturbance compensation uses a disturbance model that predicts the change in bending curvature over the coil radius. With this prediction, the straightening machine can be adjusted specifically. The roller positions are adjusted by a subordinate position control. The additional feedback from measured geometric product properties from the following punching-bending process enables the straightening machine to be adjusted even in the case of unforeseen fluctuations in the material properties. In this way, it is possible to react to any material fluctuations as required. This novel, demand-oriented adjustment of the straightening machine is expected to result in a high increase in the efficiency of the production process and a reduction of the rejection rate.

Mechatronic Straightening Machine, Self-Learning Disturbance Compensation, Punch-Bending Process

Published online 4/19/2023, 10 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: BATHELT Lukas, DJAKOW Eugen, HENKE Christian, TRÄCHTLER Ansgar, Innovative self-learning disturbance compensation for straightening processes, Materials Research Proceedings, Vol. 28, pp 2005-2014, 2023


The article was published as article 216 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.

[1] M. Paech, Semi-automatic straightening technology, Wire 58 (2008) 40-46.
[2] M. Oligschläger, Modellbasierte Steuerung von Richtwalzanlagen mithilfe inverser Modellierung und schnell berechenbarer Metamodelle, Dissertation, RWTH Aachen, Aachen, 2015
[3] W. Guericke, Theoretische und experimentelle Untersuchungen der Kräfte und Drehmomente beim Richten von Walzgut auf Rollenrichtmaschinen, Magdeburg, T. H., F. f. Maschinenbau, Diss. v. 27. Jan. 1966 (Nicht f. d. Aust.), Magdeburg, 1966.
[4] A. Pernía, F.J. Martínez-de-Pisón, J. Ordieres, F. Alba, J. Blanco, Fine tuning straightening process using genetic algorithms and finite element methods, Ironmaking and Steelmaking 37 (2010) 119-125.
[5] F.J. Martínez-de-Pisón, R. Lostado, A. Pernía, R. Fernández, Optimising tension levelling process by means of genetic algorithms and finite element method, Ironmaking and Steelmaking 38 (2011) 45-52.
[6] R. Kaiser, T. Hatzenbichler, B. Buchmayr, T. Antretter, Simulation of the Roller Straightening Process with Respect to Residual Stresses and the Curvature Trend, Mater. Sci. Forum 768–769 (2013) 456-463.
[7] M. Oligschläger, G. Hirt, Implementation of Closed-loop Control Systems in Finite Element Simulations for Roller Leveling, Matériaux et techniques 100 (2012) 1-14.
[8] M. Grüber, Konzepte zur Steuerung des Richtwalzprozesses bei variierenden Richtguteigenschaften, Dissertation, RWTH Aachen, 2019.
[9] L. Bathelt, F. Bader, E. Djakow, C. Henke, A. Trächtler, W. Homberg, Innovative Assistance System for Setting up a Mechatronic Straightening Machine, Key Eng. Mater. 926 (2022) 2397-2405.
[10] R. Haberland, G. Lauer, Sensorik für die geregelte Blechrichtmaschine, Bleche Rohe Profile 40 (1993) 599-601.
[11] B.-A. Behrens, R. Krimm, Automatisierung des Richtprozesses mit Hilfe einer computergestützten Regelung unter Berücksichtigung der Restkrümmung – Abschlussbericht, FWF, Frankfurt am Main, 2006.
[12] M. Paech, W. Van Raemdonck, Inline wire diagnosis, Wire J. Int. (2015) 92-97.
[13] D. Ashton, V. Deigelmann, M. Stolzenberg, B. Wolter, Closed loop automatic shape and residual stress control during levelling – Final report, Off. for Official Publ. of the European Communities, Vol. 22824, Luxembourg, 2007
[14] F. Bader, L. Bathelt, E. Djakow, W. Homberg, C. Henke, A. Trächtler, Self-optimized, Intelligent Open-Loop-Controlled Steel Strip Straightening Machine for Advanced Formability, in: G. Daehn, J. Cao, B. Kinsey, E. Tekkaya, A. Vivek, Y. Yoshida, (Eds.), Forming the Future. The Minerals, Metals and Materials Series, Springer, Cham, 2021.\_1
[15] F. Bader, L. Bathelt, E. Djakow, W. Homberg, C. Henke, A. Trächtler, Innovative Measurement Of Stress Superposed Steel Strip For Straightening Machines, ESAFORM 2021, 24th International Conference on Material Forming, Liège, Belgique, 2021.
[16] L. Bathelt, F. Bader, E. Djakow, C. Henke, A. Trächtler, W. Homberg, Mechatronische Richtapparate: Intelligente Richttechnik von hochfesten Flachdrähten, in: Fachtagung VDI MECHATRONIK 2022 , Darmstadt, 2022, pp. 19-24.
[17] O. Föllinger, Regelungstechnik: Einführung in die Methoden und ihre Anwendung, 13. überarbeitete Auflage 2022, VDE Verlag GMBH, Berlin, ISBN 978-3-8007-5518-9