Analysis of the package diameter in winding processes by image analysis and a linear regression model
Source abstract
Abstract
Currently, industrial winding processes are often optimized by trial and error. A digital twin of winding processes could be helpful in order to assist industry to optimize the winding processes. Formulating the kinematic equations that form the basis of such a simulation of the winding process is straightforward in principle. However, a major challenge is to model the increase of the package diameter as a function of time or length of wound up yarn, respectively. In this paper, a kinematic model for the winding process is first outlined. The focus of the paper is the description of a workflow in order to find a model for the package diameter increase dependent on the wound yarn length. For that purpose, a new image analysis method is presented to derive the general class of the model function for the diameter increase. Then, the measurement results of a series of experiments are analyzed to find a parameterization of the model function. Here, the input process parameters winding tension, cradle pressure, winding speed, and traverse ratio are varied at two levels. Finally, the linear regression model for the package diameter increase is presented.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Industrial Vision Systems and Defect Detection · Material Properties and Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Fraunhofer ITWM, Kaiserslautern, Germany
Frankfurt University of Applied Sciences, Frankfurt am Main, Germany
Institut für Textiltechnik of RWTH Aachen University, Aachen, Germany
Fraunhofer Center for Machine Learning, Munich, Germany
Funding
Fraunhofer Cluster of Excellence Cognitive Internet Technologies · AiF Projekt
References (26)
Show source-provided references
1. SSM Schärer Schweiter Mettler AG. Dye packages/rewinding. Technical report, Horgen, Switzerland, 2014.
2. Industrievereinigung Chemiefaser eV. IVC - Jahresbroschüre 2019/2020, 2020.
3. Mueller K · Indian Text J · 2007 · 12 · 182
4. Kretschmer A · Textil-Praxis Int · 1989 · 7 · 622
5. Heetjans JH · 2005
6. 10.1533/978178242072910.1533/9781782420729 ↗
7. Aslan B, Wischnowski M. InnoSpul. Schlussbericht zum Forschungsvorhaben AiF.Nr.17514N, Aachen, 2014.
8. Wischnowski M · Melliand Int · 2018 · 24 · 71
9. 10.1533/9780857097088.26610.1533/9780857097088.266 ↗
10. Wischnowski M
11. 10.1201/978020300958110.1201/9780203009581 ↗
12. Durur G · 2000
13. Kyosev Y · 2005 · 11
14. Busin L · 2008 · 151 · 65
15. 10.1109/TSMC.1979.431007610.1109/TSMC.1979.4310076 ↗
16. Soille P · 2013
17. 10.1109/TPAMI.1986.476785110.1109/TPAMI.1986.4767851 ↗
18. 10.1016/S0167-8655(00)00045-310.1016/S0167-8655(00)00045-3 ↗
19. Hough PV. Method and means for recognizing complex patterns. US Patent 3,069,654, USA, 1962.
20. 10.1145/361237.36124210.1145/361237.361242 ↗
21. van Ginkel M, Hendriks CL, van Vliet LJ. A short introduction to the radon and hough transforms and how they relate to each other. Technical Report, Delft University of Technology, Delft, The Netherlands, 2004.
22. Fraunhofer ITWM. ToolIP tool for image processing, http://itwm.fraunhofer.de/toolip (2020, accessed 2 June 2020).
23. Geron A · 2017
24. Jansen W · Melliand Textilberichte · 2006 · 318
25. Finckh V, Riethmüller C, Planck H. Entwicklung einer neuen Spultechnologie bei Färbespulen zur Erschließung breiter, direkter Einsatzmöglichkeiten. Schlussbericht zum Forschungsvorhaben AiF-Nr. 15923 N, Denkendorf, 2011.
26. Pedregosa F · J Mach Learn Res · 2011 · 12 · 2825