Μ-Computed TOMOGRAPHY FOR MICRO-STRUCTURE CHARACTERIZATION OF CARBON FIBER REINFORCED PLASTIC (CFRP)

Stoessel, R.; Guenther, T.; Dierig, Tobias; Schladitz, Katja; Godehardt, Michael; Kessling, P.‐M.; Fuchs, T.

doi:10.1063/1.3591888

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…The main problem is to distinguish between matrix and pores. The method used to decrease this error is based on a grayscale value comparison using a reference sample (only matrix), adjusting the threshold level to a certain grayscale value, as reported by Stoessel et al (2010). ImageJ software version 1.52 was used for image analysis, and r eff and φ micro were determined through Image > Adjust > Threshold and Analyze > Measure instructions.…”

Section: Image Processingmentioning

confidence: 99%

Quantification of the Microporosity Effect on Permeability of Porous Rocks

Alfonso¹,

Abatal

Castro

et al. 2020

J Por Media

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The contributions of microporosity (φ micro ) and micropermeability (k micro ) to the total (Darcy) permeability (k) were estimated for limestones from Sierra de Chiapas region. To achieve this purpose, microporosity (φ micro ) was measured using scanning electron microscopy (SEM) image analysis and compared to experimental permeability. Moreover, k micro was estimated using the modified Kozeny-Carman equation, whose parameters were also determined from SEM image analysis of the porous network. The obtained equations that establish the correlations k micro -k and φ micro -k were in excellent agreement with experimental permeabilities, demonstrating their efficacies. Besides, the importance of the use of SEM as complementary tool for the analysis of small pores was demonstrated.

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Section: Image Processingmentioning

confidence: 99%

Quantification of the Microporosity Effect on Permeability of Porous Rocks

Alfonso¹,

Abatal

Castro

et al. 2020

J Por Media

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“…FBP or iterative methods, including selection of artefact reduction strategies), and to the final image processing and feature extraction techniques. 13 Additionally, morphological features of the porosity, such as the shape factor of voids 14 and the scale of the dominant void size (e.g., micro-voids, macro-voids a combination of both), 15,16 also play an important role in the segmentation outcome. Whilst there are clearly many variables/variations possible in this overall process of extracting porosity values, the effect of the voxel size can be seen to be critical.…”

Section: Introductionmentioning

confidence: 99%

The effect of X-ray computed tomography scan parameters on porosity assessment of carbon fibre reinfored plastics laminates

Galvez-Hernandez,

Smith,

Gaska

et al. 2023

Journal of Composite Materials

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Combinations of X-ray Computed Tomography (XCT) scan times, from 30 s to 60 min, and voxel sizes, from 6 to 50 µm, were investigated for their effect on the porosity measurements of a unidirectional carbon fibre epoxy composite volume. The sample had a total void volume of around 2%, which is typical of the tolerance expected in the aerospace industry. The volume contained localised voids that create sub-volumes with representative high (5%) and low (1%) porosity regions. The ability to detect small-size voids in the lower porosity regions decreased as the voxel size increased. Scan resolutions above 25 µm resulted in a coarser segmentation and overestimation of the porosity due to the presence of partial volume effects. Scan times shorter than 2 min resulted in noisy images, requiring aggressive filtering that affected the segmentation of voids. Porosity segmentation was performed by thresholding and Deep Learning methods. Deep Learning segmentation was found to recognise noise better, providing more consistent and cleaner segmented data than thresholding. To capture micro-voids that contribute to porosity levels at the typical aerospace tolerance of 2%, scan parameters with a voxel size equal to or smaller than 25 µm, scan times of 2 to 8 min, and deep learning segmentation were found to be the most promising. These shorter scan times can be used to increase the productivity of CT scanning for porosity or observing time-resolved events. The data provided here contributes to the body of knowledge studying X-ray hardware settings and optimising image segmentation.

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Computed Tomography

Sause¹

2016

In Situ Monitoring of Fiber-Reinforced Composites

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Μ-Computed TOMOGRAPHY FOR MICRO-STRUCTURE CHARACTERIZATION OF CARBON FIBER REINFORCED PLASTIC (CFRP)

Cited by 7 publications

References 3 publications

Quantification of the Microporosity Effect on Permeability of Porous Rocks

Quantification of the Microporosity Effect on Permeability of Porous Rocks

The effect of X-ray computed tomography scan parameters on porosity assessment of carbon fibre reinfored plastics laminates

Computed Tomography

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