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Inferring material properties from FRP processes via sim-to-real learning

  • Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. TheFiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models.show moreshow less

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Metadaten
Author:Simon StieberORCiD, Niklas SchröterORCiD, Ewald Fauster, Marcel Bender, Alexander SchiendorferORCiD, Wolfgang ReifORCiD
Language:English
Document Type:Article
Year of first Publication:2022
published in (English):The International Journal of Advanced Manufacturing Technology
Publisher:Springer
Place of publication:London
ISSN:1433-3015
Volume:128
Issue:3-4
First Page:1517
Last Page:1533
Review:peer-review
Open Access:ja
Version:published
Tag:Architecture comparison; Digital Twin; FRP; Industry 4.0; LCM; Sequence-to-Image Learning; Transfer Learning
URN:urn:nbn:de:bvb:573-32402
Related Identifier:https://doi.org/10.1007/s00170-023-11509-8
Faculties / Institutes / Organizations:Fakultät Wirtschaftsingenieurwesen
AImotion Bavaria
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2023/03/21