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  4. Novel thresholding method and convolutional neural network for fiber volume content determination from 3D μCT images
 
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2024
Journal Article
Title

Novel thresholding method and convolutional neural network for fiber volume content determination from 3D μCT images

Abstract
In order to determine fiber volume contents (FVC) of low contrast CT images of carbon fiber reinforced polyamide 6, a novel thresholding method and a convolutional neural network are implemented with absolute deviations from experimental values of 2.7% and, respectively, 1.46% on average. The first method is a sample thickness based adjustment of the Otsu threshold, the so-called “average or above (AOA) thresholding”, and the second is a mixed convolutional neural network (CNN) that directly takes 3D scans and the experimentally determined FVC values as input. However, the methods are limited to the specific material combination, process-dependent microstructure and scan quality but could be further developed for different material types.
Author(s)
Blarr, Juliane
Kunze, Philipp
Kresin, Noah
Liebig, Wilfried
Fraunhofer-Institut für Chemische Technologie ICT  
Inal, Kaan
Weidenmann, Kay André  
Fraunhofer-Institut für Chemische Technologie ICT  
Journal
NDT & E International  
Open Access
DOI
10.1016/j.ndteint.2024.103067
Language
English
Fraunhofer-Institut für Chemische Technologie ICT  
Keyword(s)
  • Carbon fiber reinforced polymers

  • Deep learning

  • Low contrast

  • Thermoplastics

  • X-ray tomography

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