Genetic programming for fiber-threading for fiber-reinforced plastics

  • Setting up fiber-threading for a pultrusion line is tedious, error prone and takes a long time. Between 100 and 1000 fibers have to be arranged into a two-dimensional shape, which have to be threaded between several support plates without causing crossovers. When manually planning this process based on intuition, it is hard to keep track of the complexity. This slows the process down to where it can take several hours or several days, and shortening this duration reduces the cost considerably. As planning the setup takes up a large chunk of time, we are proposing a simulation and an algorithm to automatically calculate how the fiber bundles need to be threaded from the creels through the support plates to result in the desired shape. Using a three-dimensional simulation for collision detection in conjunction with a genetic algorithm, we are able to shorten the planning of the fibers to around 10 minutes on a modern 8-core personal computer. Based on this data, further work can be doneSetting up fiber-threading for a pultrusion line is tedious, error prone and takes a long time. Between 100 and 1000 fibers have to be arranged into a two-dimensional shape, which have to be threaded between several support plates without causing crossovers. When manually planning this process based on intuition, it is hard to keep track of the complexity. This slows the process down to where it can take several hours or several days, and shortening this duration reduces the cost considerably. As planning the setup takes up a large chunk of time, we are proposing a simulation and an algorithm to automatically calculate how the fiber bundles need to be threaded from the creels through the support plates to result in the desired shape. Using a three-dimensional simulation for collision detection in conjunction with a genetic algorithm, we are able to shorten the planning of the fibers to around 10 minutes on a modern 8-core personal computer. Based on this data, further work can be done to further improve, visualize or permanently store the data in a digitized company.show moreshow less

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Metadaten
Author:Jonas Wilfert, Simon StieberORCiDGND, Frederik Wilhelm, Wolfgang ReifORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1000363
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/100036
ISBN:978-1-7281-2990-7OPAC
Parent Title (English):2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vasteras, Sweden, 7-10 September 2021
Publisher:IEEE
Place of publication:Piscataway, NJ
Editor:Moris Behnam, Lucia Lo Bello, Thomas Nolte, Marco Porta
Type:Conference Proceeding
Language:English
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2022/12/08
First Page:1
Last Page:8
DOI:https://doi.org/10.1109/etfa45728.2021.9613726
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Software & Systems Engineering
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Softwaretechnik / Lehrstuhl für Softwaretechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht