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The study and measurement of the shielding effectiveness (SE) of planar materials is required to predict the suitability of a certain material to form an enclosed electromagnetic shield. One of the most widely used standards for measuring the SE of planar materials is ASMT D4935-18. It is based on a coaxial sample holder (CSH) that operates up to 1.5 GHz. Due to this standard’s frequency limitations, new variants with higher frequency limits have been developed by decreasing the size of the CSH conductors and the samples. However, this method and its high-frequency variants require two types of samples with very specific geometries and sizes. This method is unsuitable for certain types of nanomaterials due to their complex mechanization at such undersized scales. This contribution proposes an alternative SE measurement method based on an absorber box that mitigates the problems presented by the ASTM D4935-18 standard. The SE of rigid nanomaterial samples based on several concentrations of multi-walled carbon nanotubes (MWCNT) and two different fiber reinforcements have been obtained.

Porosity severely reduces the mechanical performance of composite laminates and methods for automatic segmentation of void phases are growing. This study investigates porosity in composite materials that take the form of interlaminar voids and dry tow areas. Deep Learning was used for the segmentation of X-ray micrographs via the implementation of eight state-of-the-art Convolutional Neural Network (CNN) architectures trained with data sets containing twenty-five, fifty, and one-hundred images. The combination of hyperparameters providing the highest accuracy for each architecture and training set size was achieved through the optimisation of six relevant hyperparameters, including the cut-off probability applied to output probability maps. Additionally, the properties of the CNN architectures ( e.g., layer typology, connections, density…) were found to play a determining role, not only in the segmentation results but also in the associated computing effort. U-Net and FCDenseNet outperformed the FCN-8s, FCN-16, SegNet, LinkNet, ResNet18 and Xception CNN architectures. However, the CNNs generally outperformed the standard thresholding approaches, especially in sub-volumes containing low porosity (1.07%) where the influence on strength is very sensitive in high-performance composites. In low porosity samples, U-Net and FCDenseNet consistently segmented voids to 85% + accuracy, whereas thresholding was only half as accurate, at around 40%. The results provide a strong motivation to replace thresholding as a segmentation method for composite X-ray micrographs. In terms of efficiency, the reduced complexity of the U-Net network allowed for an average reduction of the training time (−36%) and prediction time (−17%) when compared to FCDenseNet.