Innovative self-learning disturbance compensation for straightening processes
BATHELT Lukas, DJAKOW Eugen, HENKE Christian, TRÄCHTLER Ansgar
download PDFAbstract. 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.
Keywords
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
DOI: https://doi.org/10.21741/9781644902479-216
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.
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