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Determination of fibers volume fraction in layered composite materials by optical methods
V.A. Komarov 1, A.A. Pavlov 1

Samara National Research University, 443086, Samara, Russia, Moskovskoye Shosse 34

 PDF, 1389 kB

DOI: 10.18287/2412-6179-CO-1068

Pages: 473-478.

Full text of article: Russian language.

Abstract:
A problem of determining the fiber volume fraction in fiber-reinforced strands of fabric-based laminated composites is considered. As a source of information about the structure of the material, digital micrographs of the ground surface of the cross-sections of the composites are used. Methods and features of the analysis of raster microscopic images of heterogeneous material associated with variable pixel brightness and blurring of the "fiber-binder" boundaries are discussed. To make the image processing less labor-intensive and more objective, a special autoencoder is proposed and built. The study of the structure of a typical structural carbon fiber-reinforced plastic is illustrated by an end-to-end demonstration example. A significant acceleration of the image processing process using the convolutional autoencoder and a good agreement of the results with a careful manual analysis are shown.

Keywords:
composite, fiber, volume fraction, micrograph, image processing, autoencoder.

Citation:
Komarov VA, Pavlov AA. Determination of fibers volume fraction in layered composite materials by optical methods. Computer Optics 2022; 46(3): 473-478. DOI: 10.18287/2412-6179-CO-1068.

Acknowledgements:
This work was financially supported by the RF Ministry of Science and Higher Education of the Russian Federation under the project FSSS-2020-0016.

References:

  1. Niu MCY. Composite airframe structures: Practical design information and data. 3rd ed. Granada Hills: Adaso/Adastra Engineering Center; 2000. ISBN: 978-962-7128-06-6.
  2. Lomov S, Ivanov DS, Verpoest I, Zako M. Full-field strain measurements for validation of meso-FE analysis of textile composites. Compos Part A Appl Sci Manuf 2008; 39(8): 1218-1231. DOI: 10.1016/j.compositesa.2007.09.011.
  3. Gommer F, Endruweit A, Long A. Quantification of micro-scale variability in fibre bundles. Compos Part A Appl Sci Manuf 2016; 87: 131-137. DOI: 10.1016/j.compositesa.2016.04.019.
  4. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979; 9(1): 62-66. DOI: 10.1109/tsmc.1979.4310076.
  5. Shi Z, Setlur S, Govindaraju V. Digital image enhancement using normalization techniques and their application to palm leaf manuscripts. 2005. Source: <https://cedar.buffalo.edu/~zshi/Papers/kbcs04_261.pdf>.
  6. Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Recognit 2000; 33(2): 225-236. DOI: 10.1016/s0031-3203(99)00055-2.
  7. Su B, Lu S, Tan C. Binarization of historical document images using the local maximum and minimum. Proc 9th IAPR Int Workshop on Document Analysis Systems 2010: 159-165. DOI: 10.1145/1815330.1815351.
  8. Castellanos FJ, Gallego AJ. A selectional auto-encoder approach for document image binarization. Pattern Recognit 2019; 86: 37-47. DOI: 10.1016/j.patcog.2018.08.011.
  9. Pastor-Pellicer J, España-Boquera S, Zamora-Martínez F, Zeshan Afzal M, Castro-Bleda MJ. Insights on the use of convolutional neural networks for document image binarization. In Book: Rojas I, Joya G, Catala A, eds. Advances in Computational Intelligence. Part II. Springer International Publishing; 2015: 115-126. DOI: 10.1007/978-3-319-19222-2_10.
  10. Peng X, Cao H, Natarajan P. Using convolutional encoder-decoder for document image binarization. 14th IAPR Int Conf on Document Analysis and Recognition 2017: 708-713. DOI: 10.1016/j.patcog.2018.08.011.
  11. Rehman A, Saba T. Neural networks for document image preprocessing: State of the art. Artif Intell Rev 2014; 42(2): 253-273. DOI: 10.1007/s10462-012-9337-z.
  12. Bank D, Koenigsteain N, Giryes R. Autoencoders. Source: <https://www.researchgate.net/publication/339945889_Autoencoders>.
  13. About Keras. The Functional API. Source: <https://keras.io/guides/functional_api/>.
  14. About Keras. Keras API reference: Convolution 2D layers. Source: <https://keras.io/api/layers/convolution_layers/convolution2d/>.
  15. Turchenko V, Luczak A. Creation of a deep convolutional auto-encoder in caffe. Source: <https://www.researchgate.net/publication/286302172_Creation_of_a_Deep_Convolutional_Auto-Encoder_in_Caffe>.
  16. About Keras. Keras API reference: MaxPooling2D layer. Source: <https://keras.io/api/layers/pooling_layers/max_pooling2d/>.
  17. About Keras. Keras API reference: UpSampling2D layer. Source: <https://keras.io/api/layers/reshaping_layers/up_sampling2d/>.
  18. About Keras. Keras API reference: Optimizers_Adam. Source: <https://keras.io/api/optimizers/adam/>.
  19. Kingma D, Ba J. Adam: A method for stochastic optimization. Source: <https://www.researchgate.net/publication/269935079_Adam_A_Method_for_Stochastic_Optimization>.

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