1,443
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

Identification and analysis of fibers in ultra-large micro-CT scans of nonwoven textiles using deep learning

ORCID Icon, , , ORCID Icon & ORCID Icon
Pages 1647-1657 | Received 19 Apr 2022, Accepted 10 Oct 2022, Published online: 24 Nov 2022

References

  • Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Retrieved from https://arxiv.org/abs/1603.04467
  • Axelsson, M. (2009). Estimating 3D fibre orientation in volume images < SE-END> [Paper presentation].</SE-END> January, 2008, pp. 1–4. https://doi.org/10.1109/ICPR.2008.4761631
  • Badran, A., Marshall, D., Legault, Z., Makovetsky, R., Provencher, B., Piché, N., & Marsh, M. (2020). Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning. Journal of Materials Science, 55(34), 16273–16289. https://doi.org/10.1007/s10853-020-05148-7
  • Becker, J., Biebl, F., Cheng, L., Glatt, E., Grießer, A., Groß, M., … Wiegmann, A. (2021). September). GeoDict Software. GeoDict Software. Retrieved from https://www.math2market.de/GeoDict/geodict_download.php
  • Beckman, I. P., Berry, G., Cho, H., & Riveros, G. (2021). Digital twin geometry for fibrous air filtration media. Fibers, 9(12), 84. https://doi.org/10.3390/fib9120084
  • Beucher, S., & Lantuéjoul, C. (1979, January). Use of Watersheds in Contour Detection. 132.
  • Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. 3D U-Net: Learning dense volumetric segmentation from sparse annotation.
  • Corte, H., & Kallmes, O. J. (1960). The structure of paper. I The statistical geometry ofindeal two dininsional fiber networks. Tappi Journal, 43(9), 737–752.
  • Depriester, D., Rolland Du Roscoat, S., Orgéas, L., Geindreau, C., Levrard, B., & Brémond, F. (2022). Individual fibre separation in 3D fibrous materials imaged by X-ray tomography. Journal of Microscopy, 286(3), 220–239. https://doi.org/10.1111/jmi.13096
  • Grießer, A., Westerteiger, R., Glatt, E., De Boever, W., Hagen, H., & Wiegmann, A. (2022). Identified fibers and validation data for fiber identification, https://doi.org/10.30423/data.math2market-2022-02.sample-c.fiberfind
  • Henyš, P., & Čapek, L. (2021). Individual yarn fibre extraction from micro-CT: Multilevel machine learning approach. The Journal of The Textile Institute, 0, 1–8. https://doi.org/10.1080/00405000.2020.1865503
  • Hilden, J., Rief, S., & Planas, B. (2021, August). FiberGeo User Guide 2022. Tech report. https://doi.org/10.30423/userguide.geodict2022-fibergeo
  • Hoess, K. M., Hahn, F. J., Schmauder, S., & Keller, F. (2021). Predicting the mechanical behavior of a polypropylene-based nonwoven using 3D microstructural simulation. The Journal of The Textile Institute, 0, 1–12. https://doi.org/10.1080/00405000.2021.2001891
  • Huang, X., Wen, D., Zhao, Y., Wang, Q., Zhou, W., & Deng, D. (2016). Skeleton-based tracing of curved fibers from 3D X-ray microtomographic imaging. Results in Physics, 6, 170–177. https://doi.org/10.1016/j.rinp.2016.03.008
  • Kallel, H., & Joulain, K. (2022). Design and thermal conductivity of 3D artificial cross-linked random fiber networks. Materials & Design, 220, 110800. https://doi.org/10.1016/j.matdes.2022.110800
  • Kermani, I. D., Schmitter, M., Eichinger, J. F., Aydin, R. C., & Cyron, C. J. (2021). Computational study of the geometric properties governing the linear mechanical behavior of fiber networks. Computational Materials Science, 199, 110711. https://doi.org/10.1016/j.commatsci.2021.110711
  • Konopczyński, T., Kröger, T., Zheng, L., & Hesser, J. (2019). Instance segmentation of fibers from low resolution CT Scans via 3D deep embedding learning.
  • Krause, M., Hausherr, J., Burgeth, B., Herrmann, C., & Krenkel, W. (2010). Determination of the fibre orientation in composites using the structure tensor and local X-ray transform. Journal of Materials Science, 45(4), 888–896. https://doi.org/10.1007/s10853-009-4016-4
  • Kroutilova, J., Maas, M., Mecl, Z., Wagner, T., Klaska, F., & Kasparkova, P. (2020, May 28). Patent No. WO 2020/103964 A1. Retrieved from https://patents.google.com/patent/WO2020103964A1/en
  • May, D., Willenbacher, B., Semar, J., Sharp, K., & Mitschang, P. (2020). Out-of-plane permeability of 3D woven fabrics for composite structures. The Journal of The Textile Institute, 111(7), 1047–1053. https://doi.org/10.1080/00405000.2019.1682759
  • Page, D. H., Seth, R. S., Jorda, B. D., & Barb, M. C. (1985). Curl, crimps, kinks and microcompressions in pulp fibres - their origin, measurement and significance. In V. Punton (Ed.), Papermaking raw materials, Trans. VIIIth Fund. Res. Symp. Oxford (pp. 183–227).
  • Pourdeyhimi, B., & Ramanathan, R. (1995). Image analysis method for estimating 2-D fiber orientation and fiber length in discontinuous fiber reinforced composites. Polymers and Polymer Composites, 3, 277–287.
  • Rigby, A. J., Anand, S. C., & Horrocks, A. R. (1997). Textile materials for medical and healthcare applications. Journal of the Textile Institute, 88(3), 83–93. https://doi.org/10.1080/00405009708658589
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI). 9351, 234–241. Retrieved from http://lmb.informatik.uni-freiburg.de/Publications/2015/RFB15a
  • Schladitz, K., Peters, S., Reinel-Bitzer, D., Wiegmann, A., & Ohser, J. (2006). Design of acoustic trim based on geometric modeling and flow simulation for nonwoven. Computational Materials Science, 38(1), 56–66. https://doi.org/10.1016/j.commatsci.2006.01.018
  • Soltani, P., Johari, M., & Zarrebini, M. (2015, January). 3D fiber orientation characterization of nonwoven fabrics using X-ray Micro-computed Tomography. World Journal of Textile Engineering and Technology, 1, 41–47.
  • Suragani Venu, L., Shim, E., Anantharamaiah, N., & Pourdeyhimi, B. (2012). Three-dimensional structural characterization of nonwoven fabrics. Microscopy and Microanalysis, 18(6), 1368–1379. https://doi.org/10.1017/S143192761201375X
  • Townsend, P., Larsson, E., Karlson, T., Hall, S. A., Lundman, M., Bergström, P., Hanson, C., Lorén, N., Gebäck, T., Särkkä, A., & Röding, M. (2021). Stochastic modelling of 3D fiber structures imaged with X-ray microtomography. Computational Materials Science, 194, 110433. https://doi.org/10.1016/j.commatsci.2021.110433
  • Viguié, J., Latil, P., Orgéas, L., Dumont, P. J. J., Rolland Du Roscoat, S., Bloch, J.-F., Marulier, C., & Guiraud, O. (2013). Finding fibres and their contacts within 3D images of disordered fibrous media. Composites Science and Technology, 89, 202–210. https://doi.org/10.1016/j.compscitech.2013.09.023
  • Weber, M., Grießer, A., Glatt, E., Wiegmann, A., & Schmidt, V. (2022). Modeling curved fibers by fitting R-vine copulas to their Frenet representations. Accepted in Microscopy and Microanalysis.
  • Westenberger, P., Estrade, P., & Lichau, D. (2012). Fibre orientation visualization with AvizoFire. NDTnet, Retrieved from https://www.ndt.net/search/docs.php3?id=13711#