Classifying the type of damage occurring within a structure using a structural health monitoring system can allow the end user to assess what kind of repairs, if any, that a component requires. This paper investigates the use of acoustic emission (AE) to locate and classify the type of damage occurring in a composite, carbon fibre panel during buckling. The damage was first located using a bespoke location algorithm developed at Cardiff University, called delta-T mapping. Signals identified as coming from the regions of damage were then analysed using three AE classification techniques; artificial neural network (ANN) analysis, unsupervised waveform clustering (UWC) and corrected measured amplitude ratio (MAR). A comparison of results yielded by these techniques shows a strong agreement regarding the nature of the damage present in the panel, with the signals assigned to two different damage mechanisms, believed to be delamination and matrix cracking. Ultrasonic C-scan images and a digital image correlation (DIC) analysis of the buckled panel were used as validation. MAR's ability to reveal the orientation of recorded signals greatly assisted the identification of the delamination region, however, ANN and UWC have the ability to group signals into several different classes, which would prove useful in instances where several damage mechanisms were generated. Combining each technique's individual merits in a multi-technique analysis dramatically improved the reliability of the AE investigation and it is thought that this cross-correlation between techniques will also be the key to developing a reliable SHM system

Dissolving microneedles are especially attractive for transdermal drug delivery as they are associated with improved patient compliance and safety. Furthermore, microneedles made of sugars offer the added benefit of biomolecule stabilisation making them ideal candidates for delivering biological agents such as proteins, peptides and nucleic acids. In this study, we performed experimental and finite element analyses to study the mechanical properties of sugar microneedles and evaluate the effect of sugar composition on microneedle ability to penetrate and deliver drug to the skin. Results showed that microneedles made of carboxymethylcellulose/maltose are superior to those made of carboxymethylcellulose/trehalose and carboxymethylcellulose/sucrose in terms of mechanical strength and the ability to deliver drug. Buckling was predicted to be the main mode of microneedle failure and the order of buckling was positively correlated to the Young's modulus values of the sugar constituents of each microneedle.

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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.

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