Fatigue of hybrid fibre-reinforced plastics.

Understanding the fatigue behaviour of hybrid fibre-reinforced plastics is desirable for exploiting their features in safe, durable and reliable industrial components. The fatigue performance of hybrid composites has not been extensively investigated yet. The paper presents an overview of the available knowledge on the fatigue of hybrid fibre-reinforced plastics, and, more specifically, reports the fatigue behaviour of a quasi-isotropic pseudo-ductile all-carbon fibre interlayer hybrid composite by experimental measurements and observations, with emphasis on the damage development. The fatigue conditions are tension-tension stress- and strain-controlled cyclic loading. The results include fatigue life for different maximum stress and strain levels, stiffness evolution and damage observations by X-ray micro-computed tomography. The studied hybrid all-carbon fibre quasi-isotropic composite exhibits pseudo-ductility in quasi-static testing. For stress-controlled fatigue, the fatigue load over the limit of elastic response is not sustained. Contrary to that, the composite retains its load-carrying ability in the pseudo-ductile regime for a strain-controlled regime, albeit with lowered stiffness. This article is part of the theme issue 'Ageing and durability of composite materials'.

Super-Resolution Processing of Synchrotron CT Images for Automated Fibre Break Analysis of Unidirectional Composites.

Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is not sufficient for a fully automated analysis of continuous mechanical deformations. We therefore investigate the possibility of increasing the quality of low-resolution in situ scans by means of super-resolution (SR) using 3D deep learning techniques, thus facilitating the subsequent fibre break identification. We trained generative neural networks (GAN) on datasets of high-(0.3 µm) and low-resolution (1.6 µm) statically acquired images. These networks were then applied to a low-resolution (1.1 µm) noisy image of a continuously loaded specimen. The statistical parameters of the fibre breaks used for the comparison are the number of individual breaks and the number of 2-plets and 3-plets per specimen volume. The fully automated process achieves an average accuracy of 82% of manually identified fibre breaks, while the semi-automated one reaches 92%. The developed approach allows the use of faster, low-resolution in situ tomography without losing the quality of the identified physical parameters.

A dataset of micro-scale tomograms of unidirectional glass fiber/epoxy and carbon fiber/epoxy composites acquired via synchrotron computed tomography during in-situ tensile loading.

We have performed synchrotron computed tomography on two different fiber-reinforced composites while they were being continuously in-situ loaded in 0° tension. One material is a glass/epoxy laminate and the other is a carbon/epoxy laminate. The voxel size is 1.1 µm, which allows clear recognition of the glass fibers, but not distinct individual carbon fibers. For each material, four loading steps are selected with approximately 0, 40, 73, and 95% of the failure load, and the 3D images of the four volumes from each material are overlaid. A volume of interest in the middle 0° ply is chosen and located in the 3D image of each loading step (Fig. 1). The cropped volumes of interest for each material are presented in this publication and are publicly available on Mendeley Data[1]. As examples of two frequently-used type of unidirectional fiber-reinforced composites, the presented data can be used for different microstructural analyses, including investigation of the 3D variability in fiber distribution and orientation, and their evolution during tensile loading. For example, we have performed fiber orientation analysis on this data, using our digital image correlation-based technique, in [2]. Moreover, real-time formation of fiber breaks with tensile loading can be investigated in the data.