Analysis of the package diameter in winding processes by image analysis and a linear regression model

Journal of Engineered Fibers and Fabrics, vol. 17

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.

Authors 4

1. Simone Gramsch corresponding

   Frankfurt University of Applied Sciences · Fraunhofer Institute for Industrial Mathematics

   Affiliation as printed

   Frankfurt University of Applied Sciences, Frankfurt am Main, Germany

   Fraunhofer ITWM, Kaiserslautern, Germany

2. Erik Gordon Bell Aachen Institut für Textiltechnik

   RWTH Aachen University

   Affiliation as printed

   Institut für Textiltechnik of RWTH Aachen University, Aachen, Germany

3. Ali Moghiseh

   Fraunhofer Institute for Industrial Mathematics

   Affiliation as printed

   Fraunhofer ITWM, Kaiserslautern, Germany

4. Andre Schmeißer

   Fraunhofer-Gesellschaft · Fraunhofer Institute for Industrial Mathematics

   Affiliation as printed

   Fraunhofer Center for Machine Learning, Munich, Germany

   Fraunhofer ITWM, Kaiserslautern, Germany