The effect of X-ray computed tomography scan parameters on porosity assessment of carbon fibre reinfored plastics laminates
The effect of X-ray computed tomography scan parameters on porosity assessment of carbon fibre reinfored plastics laminates
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DOI：
10.1177/00219983231209383
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发表时间：
2023-10-26
影响因子：
2.9
通讯作者：
Kratz，James
中科院分区：
文献类型：
--
作者：
Galvez-Hernandez，Pedro;Smith，Ronan;Kratz，James
x射线计算机断层扫描（XCT）扫描时间从30秒到60分钟，体素尺寸从6到50µm，研究了它们对单向碳纤维环氧复合材料体积孔隙度测量的影响。样品的总空隙量约为2%，这是航空航天工业中期望的典型公差。该体积包含局部空隙，形成具有代表性的高（5%）和低（1%）孔隙度区域的子体积。随着体素尺寸的增加，在低孔隙度区域检测小尺寸孔隙的能力下降。扫描分辨率高于25µm时，由于部分体积效应的存在，会导致孔隙度的分割和高估。扫描时间短于2分钟会导致图像噪声，需要积极滤波，影响空隙的分割。采用阈值分割和深度学习方法进行孔隙度分割。深度学习分割被发现可以更好地识别噪声，提供比阈值更一致和更清晰的分割数据。为了捕获典型航空公差为2%的孔隙度水平，扫描参数等于或小于25 μ m，扫描时间为2至8分钟，深度学习分割被认为是最有前途的。这些较短的扫描时间可用于提高CT扫描孔隙度或观察时间分辨事件的生产率。这里提供的数据有助于研究x射线硬件设置和优化图像分割的知识体系。
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.