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Binocular vision-based yarn orientation measurement of biaxial weft-knitted composites

(2022) POLYMERS. 14(9).
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Abstract
The mechanical properties of fiber-reinforced composites are highly dependent on the local fiber orientation. In this study, a low-cost yarn orientation reconstruction approach for the composite components’ surface was built, utilizing binocular structured light detection technology to accomplish the effective fiber orientation detection of composite surfaces. It enables the quick acquisition of samples of the revolving body shape without blind spots with an electric turntable. Four collecting operations may completely cover the sample surface, the trajectory recognition coverage rate reached 80%, and the manual verification of the yarn space deviation showed good agreement with the automated technique. The results demonstrated that the developed system based on the proposed method can achieve the automatic recognition of yarn paths of views with different angles, which mostly satisfied quality control criteria in actual manufacturing processes.
Keywords
Polymers and Plastics, General Chemistry, binocular vision, textile composite, preform, yarn orientation, non-destructive testing, image processing

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Citation

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MLA
Xiang, He, et al. “Binocular Vision-Based Yarn Orientation Measurement of Biaxial Weft-Knitted Composites.” POLYMERS, vol. 14, no. 9, 2022, doi:10.3390/polym14091742.
APA
Xiang, H., Jiang, Y., Zhou, Y., Malengier, B., & Van Langenhove, L. (2022). Binocular vision-based yarn orientation measurement of biaxial weft-knitted composites. POLYMERS, 14(9). https://doi.org/10.3390/polym14091742
Chicago author-date
Xiang, He, Yaming Jiang, Yiying Zhou, Benny Malengier, and Lieva Van Langenhove. 2022. “Binocular Vision-Based Yarn Orientation Measurement of Biaxial Weft-Knitted Composites.” POLYMERS 14 (9). https://doi.org/10.3390/polym14091742.
Chicago author-date (all authors)
Xiang, He, Yaming Jiang, Yiying Zhou, Benny Malengier, and Lieva Van Langenhove. 2022. “Binocular Vision-Based Yarn Orientation Measurement of Biaxial Weft-Knitted Composites.” POLYMERS 14 (9). doi:10.3390/polym14091742.
Vancouver
1.
Xiang H, Jiang Y, Zhou Y, Malengier B, Van Langenhove L. Binocular vision-based yarn orientation measurement of biaxial weft-knitted composites. POLYMERS. 2022;14(9).
IEEE
[1]
H. Xiang, Y. Jiang, Y. Zhou, B. Malengier, and L. Van Langenhove, “Binocular vision-based yarn orientation measurement of biaxial weft-knitted composites,” POLYMERS, vol. 14, no. 9, 2022.
@article{8751774,
  abstract     = {{The mechanical properties of fiber-reinforced composites are highly dependent on the local fiber orientation. In this study, a low-cost yarn orientation reconstruction approach for the composite components’ surface was built, utilizing binocular structured light detection technology to accomplish the effective fiber orientation detection of composite surfaces. It enables the quick acquisition of samples of the revolving body shape without blind spots with an electric turntable. Four collecting operations may completely cover the sample surface, the trajectory recognition coverage rate reached 80%, and the manual verification of the yarn space deviation showed good agreement with the automated technique. The results demonstrated that the developed system based on the proposed method can achieve the automatic recognition of yarn paths of views with different angles, which mostly satisfied quality control criteria in actual manufacturing processes.}},
  articleno    = {{1742}},
  author       = {{Xiang, He and Jiang, Yaming and Zhou, Yiying and Malengier, Benny and Van Langenhove, Lieva}},
  issn         = {{2073-4360}},
  journal      = {{POLYMERS}},
  keywords     = {{Polymers and Plastics,General Chemistry,binocular vision,textile composite,preform,yarn orientation,non-destructive testing,image processing}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{13}},
  title        = {{Binocular vision-based yarn orientation measurement of biaxial weft-knitted composites}},
  url          = {{http://dx.doi.org/10.3390/polym14091742}},
  volume       = {{14}},
  year         = {{2022}},
}

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