Comparison and error estimation of 3D fibre orientation analysis of computed tomography image data for fibre reinforced composites

Pinter, Pascal; Dietrich, Stefan; Bertram, B A; Kehrer, Loredana; Elsner, Peter; Weidenmann, Kay André

doi:10.1016/j.ndteint.2018.01.001

Cited by 59 publications

(38 citation statements)

References 15 publications

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“…Structure tensor analysis is a widely used technique in image processing, including processing of micro-CT images [34][35][36][37]. For fibre-reinforced composite materials, this technique was implemented in the VoxTex software by Straumit et al [21].…”

Section: Structure Tensormentioning

confidence: 99%

Micro-CT based structure tensor analysis of fibre orientation in random fibre composites versus high-fidelity fibre identification methods

Karamov

Martulli

Kerschbaum

et al. 2020

Composite Structures

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The fibre orientation distribution controls the mechanical properties of random fibre composites. Generally accepted methods for its characterisation involve identification of fibres or their ellipsoidal cross sections as individual objects, requiring high image resolution and high computational resources. This paper investigates whether structure tensor analysis can be an alternative and whether it can work with lower resolution images. Micro-computed X-ray tomography images of random glass fibre/polypropylene injection moulded composites were processed using ellipsometry on 2D slices, 3D fibre identification (Avizo software) and analysis of the structure tensor (VoxTex software). The images had resolutions of 1.4, 3.2, 8 and 16 µm per pixel, compared to an average glass fibre diameter of 17 µm. All the methods yielded similar results for high-resolution images (1.4 and 3.2 µm). The high-fidelity, direct identification of fibres failed for low-resolution images, but the structure tensor analysis still yielded results close to the high-resolution scans.

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Section: Structure Tensormentioning

confidence: 99%

Micro-CT based structure tensor analysis of fibre orientation in random fibre composites versus high-fidelity fibre identification methods

Karamov

Martulli

Kerschbaum

et al. 2020

Composite Structures

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“…That is, the method based on an eigenvalue analysis of the Hessian matrix in each voxel of a 3D image. State of the art methods for analyzing the fiber orientation in µCT images of FRP parts determine the fiber orientation in each voxel without need to identify individual fibers [23,33,51,13]. More precisely, in each voxel belonging to the fiber system, a local fiber orientation is derived.…”

Section: Measuring Local Fiber Orientation From 3d Image Datamentioning

confidence: 99%

“…All local and single fiber based orienta- tion analysis methods described above except [12,34] require the fiber diameter to be sampled by at least 2-4 voxels [35,50,33]. Sampling the fibers coarser than this causes them to crumble in the digital image.…”

mentioning

confidence: 99%

Local fiber orientation from X-ray region-of-interest computed tomography of large fiber reinforced composite components

Baranowski

Dobrovolskij

Dremel³

et al. 2019

Composites Science and Technology

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“…For understanding the properties of a specific material, it is therefore vital to be able to analyse all relevant characteristics and distributions. X-ray computed tomography (XCT) as a non-destructive testing technique [3] is considered the most suitable technology for acquiring high-resolution scans of such materials [4]. In the step following the XCT imaging, algorithms or data processing pipelines extracting characteristics of single fibres from the computed tomography scans are required.…”

Section: Introductionmentioning

confidence: 99%

Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres

Fröhler

Elberfeld

Möller

et al. 2020

Nondestructive Testing and Evaluation

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We present visual methods for the analysis and comparison of the results of curved fibre reconstruction algorithms, i.e., of algorithms extracting characteristics of curved fibres from X-ray computed tomography scans. In this work, we extend previous methods for the analysis and comparison of results of different fibre reconstruction algorithms or parametrisations to the analysis of curved fibres. We propose fibre dissimilarity measures for such curved fibres and apply these to compare multiple results to a specified reference. We further propose visualisation methods to analyse differences between multiple results quantitatively and qualitatively. In two case studies, we show that the presented methods provide valuable insights for advancing and parametrising fibre reconstruction algorithms, and support in improving their results in characterising curved fibres.

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Comparison and error estimation of 3D fibre orientation analysis of computed tomography image data for fibre reinforced composites

Cited by 59 publications

References 15 publications

Micro-CT based structure tensor analysis of fibre orientation in random fibre composites versus high-fidelity fibre identification methods

Micro-CT based structure tensor analysis of fibre orientation in random fibre composites versus high-fidelity fibre identification methods

Local fiber orientation from X-ray region-of-interest computed tomography of large fiber reinforced composite components

Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres

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