Karen M. Holford

Carbon Fibre-Reinforced Polymers (CFRPs) in aerospace applications are expected to operate in moist environments where carbon fibres have high resistance to water absorption; however, polymers do not. To develop a truly optimised structure, it is important to understand this degradation process. This study aims to expand the understanding of the role of water absorption on fibrous/polymeric structures, particularly in a matrix-dominant property, namely interlaminar strength. This work used Acoustic Emission (AE), which could be integrated into any Structural Health Monitoring System for aerospace applications, optical strain measurements, and microscopy to provide an assessment of the gradual change in failure mechanisms due to the degradation of a polymer’s structure with increasing water absorption. CFRP specimens were immersed in purified water and kept at a constant temperature of 90 °C for 3, 9, 24 and 43 days. The resulting interlaminar strength was investigated through short-beam strength (SBS) testing. The SBS values decreased as immersion times were increased; the decrease was significant at longer immersion times (up to 24.47%). Failures evolved with increased immersion times, leading to a greater number of delaminations and more intralaminar cracking. Failure modes, such as crushing and multiple delaminations, were observed at longer immersion times, particularly after 24 and 43 days, where a pure interlaminar shear failure did not occur. The observed transition in failure mechanism showed that failure of aged specimens was triggered by a crushing of the upper surface plies leading to progressive delamination at multiple ply interfaces in the upper half of the specimen. The crushing occurred at a load below that required to initiate a pure shear failure and hence represents an under prediction of the true SBS of the sample. This is a common test used to assess environmental degradation of composites and these results show that conservative knockdown factors may be used in design. AE was able to distinguish different material behaviours prior to final fracture for unaged and aged specimens suggesting that it can be integrated into an aerospace asset management system. AE results were validated using optical measurements and microscopy.

Thermoelastic stress analysis (TSA) is a non-contact technique for measuring the distribution of stress in the surface of a component subject to cyclic loading by using a sensitive infrared camera. The stress concentrations indicative of a crack can be located and tracked using an optical flow method, allowing the position of the crack-tip to be identified at a given time. Acoustic emission (AE) has been used to validate the TSA algorithm. AE events from cracking, located using the Delta-T Mapping method, were detected several seconds before the TSA algorithm first detected cracking; however, TSA provided significantly more accurate location information.

Acoustic Emission (AE) sensors were used to detect signals arising from a cylindrical roller bearing with artificial defects seeded onto the outer raceway. An SKF N204ECP roller bearing was placed between two double row spherical roller bearings, type SKF 22202E, and loaded between 0.29 and 1.79kN. Speed was constant at 5780rpm. High frequency analysis allowed insight into the condition of the bearings through the determination of an increase in the structural resonances of the system as the size of an artificial defect was increased. As higher loads were applied, frequencies around 100kHz were excited, indicating the release of AE possibly attributed to friction and the plastic deformation as peaks, induced through engraving of the raceway, were flattened and worn down. Sensitivity of AE to this level in bearings indicates the potential of the technique to detect the early stages of bearing failure during life tests.

Acoustic emission is widely used for mechanical diagnostics and to characterise damage in composite materials. Distinction between different damage mechanisms is still one of the major challenges and remains an unresolved issue. The objective of cluster analysis is to separate an acoustic emission data set into multiple classes that reflect different acoustic emission sources. This article is concerned with the implementation of unsupervised clustering techniques to classify acoustic emission transients from a carbon fibre laminate buckling test. A new approach to signal feature extraction was utilised, whereby principal components provide signal features that represent the greatest data variance while remaining linearly uncorrelated with each other; feature selection was undertaken using a hierarchical clustering method and finally a cluster analysis was performed using k-means and Fuzzy C-means techniques. The aim of the work is to reduce the data required in the classification process, thereby reducing the processing time and computational power required, without significantly affecting the classification result. Thus, an approach which is more suited to online processing, allowing fast and efficient processing and storage of data is provided. The proposed unsupervised clustering analysis was able to separate acoustic emission signals into two different clusters that were correlated to the damage mechanisms observed. The results show that the clustering groups have a good fit with ultrasonic C-scan and digital image correlation strain data. The application of a clustering process that uses the most effective acoustic emission features as input data is an objective method, and this investigation shows that it may be a useful complement in the field of non-destructive evaluation.