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

Abstract

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.