Inspection of Carbon Fibre Reinforced Polymers: 3D identification and quantification of components by X-ray CT

Dilonardo, Elena; Nacucchi, Michele; Pascalis, Fabio De; Zarrelli, Mauro; Giannini, Cinzia

doi:10.1007/s10443-021-09976-x

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…The line probe determined the correlation between the grayscale intensity and the material composition, and between the cursor point position on the sample slice and the grayscale associated with the threshold values for each of the sample components. The voxels with a grayscale value less than 100 were classified as the pores, and those with a value higher than 100 were classified as the fibers and matrix [ 31 , 32 ].…”

Section: Resultsmentioning

confidence: 99%

Manufacturing of Carbon Fibers/Polyphenylene Sulfide Composites via Induction-Heating Molding: Morphology, Mechanical Properties, and Flammability

et al. 2022

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Conventional thermosetting composites exhibit advantageous mechanical properties owing to the use of an autoclave; however, their wide usage is limited by high production costs and long molding times. In contrast, the fabrication of thermoplastic composites involves out-of-autoclave processes that use press equipment. In particular, induction-heating molding facilitates a quicker thermal cycle, reduced processing time, and improved durability of the thermoplastic polymers; thus, the process cost and production time can be reduced. In this study, carbon fiber/polyphenylene sulfide thermoplastic composites were manufactured using induction-heating molding, and the relationships among the process, structure, and mechanical properties were investigated. The composites were characterized using optical and scanning electron microscopy, an ultrasonic C-scan, and X-ray computed tomography. In addition, the composites were subjected to flammability tests. This study provides novel insights into the optimization of thermoplastic composite manufacturing and thermoset composite curing processes.

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Section: Resultsmentioning

confidence: 99%

Manufacturing of Carbon Fibers/Polyphenylene Sulfide Composites via Induction-Heating Molding: Morphology, Mechanical Properties, and Flammability

et al. 2022

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“…21 Denoising was done using a Gaussian denoise filter of variable size, depending on the quality of the input scan. This special case of mean filter 22 was successfully used for the denoising of CT data of composite materials in previous studies 6,23,24 and was applied in this study throughout the plugin "3D Gaussian Blur 3D" available in Fiji. However, the application of a denoise filter generates a smoothing of the image, which limits the detection of the smallest features.…”

Section: Image Processingmentioning

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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“…X-ray computed tomography detection is a nondestructive testing technique with the benefits of short wavelength, strong penetrability, intuitive imaging, and the identification of micronscale defects (Wang X. et al, 2021). XCT is used in a wide range of applications in aerospace, food, and construction, such as assessing the porosity of composite materials for airplanes (Dilonardo et al, 2020), inspecting the quality of agricultural products (Du et al, 2019), and measuring the density and moisture content of wood (Wang et al, 2019c;Wang J. et al, 2019). In recent years, XCT detection technology has been extensively utilized for the detection of defects in insulating materials (Hao et al, 2019).…”

Section: Computed Tomography Testingmentioning

confidence: 99%

A review of non-destructive methods for the detection tiny defects within organic insulating materials

et al. 2022

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In recent years, solid organic materials such as silicone rubber and epoxy resin have been widely used in electrical equipment due to their excellent insulation properties. However, as a result of manufacturing and design flaws as well as aging issues during operation, the insulating materials in the linked state no longer fit tightly and tiny structural defects (defect size less than 10 mm) develop, such as debonding at the composite interface, pores or cracks within the insulating material, etc. Tiny defects are prone to partial discharges and breakdowns, compromising the safety of high-voltage power equipment, particularly when subjected to strong electric fields. Therefore, it is necessary to carry out non-destructive testing (NDT) for such tiny defects. Such defects are small in size, easily buried in the material, and even some are wrapped in metal, which in turn requires very high detection accuracy, but traditional methods are difficult to achieve, so NDT technologies for tiny defects within insulating materials have become a research hotspot in the field of electric power in recent years. This paper firstly introduces the sources of tiny defects in solid organic insulating materials for electrical equipment. Secondly, the harm caused by structural defects is elaborated. Finally, emerging NDT methods and their advantages and limitations in defect detection are described in detail. The review aims to provide the reader with a comprehensive overview of most of the NDT techniques used in the detection of tiny defects within solid organic insulating materials for electrical equipment and their most salient features.

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Inspection of Carbon Fibre Reinforced Polymers: 3D identification and quantification of components by X-ray CT

Cited by 9 publications

References 33 publications

Manufacturing of Carbon Fibers/Polyphenylene Sulfide Composites via Induction-Heating Molding: Morphology, Mechanical Properties, and Flammability

Manufacturing of Carbon Fibers/Polyphenylene Sulfide Composites via Induction-Heating Molding: Morphology, Mechanical Properties, and Flammability

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

A review of non-destructive methods for the detection tiny defects within organic insulating materials

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