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

  • 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.

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Metadaten
Author:Juliane Blarr, Philipp Kunze, Noah Kresin, Wilfried V. Liebig, Kaan Inal, Kay A. WeidenmannGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/111467
ISSN:0963-8695OPAC
Parent Title (English):NDT & E International
Publisher:Elsevier BV
Type:Article
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/02/20
Tag:Mechanical Engineering; Condensed Matter Physics; General Materials Science
First Page:103067
DOI:https://doi.org/10.1016/j.ndteint.2024.103067
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management / Lehrstuhl für Hybride Werkstoffe
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 50 Naturwissenschaften / 500 Naturwissenschaften und Mathematik
Latest Publications (not yet published in print):Aktuelle Publikationen (noch nicht gedruckt erschienen)
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)