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

The acoustic emission technique is a passive non-destructive testing technique that has significant potential for use as a structural health monitoring technique for many large-scale structures, allowing continuous global monitoring. The location capability of the acoustic emission technique is its most beneficial attribute; however, the location accuracy can often be limited in complex materials and structures. This article discusses recent advances in the location of acoustic emission signals. The key sources of errors are identified as signal arrival time measurement and processing algorithm limitations. A series of strategies for reducing the effects of both causes of error are presented. Additionally, the results of a case study are used to demonstrate a novel mapping technique for acoustic emission source location of fatigue crack signals in an aircraft landing gear component. Improvements in location accuracy of up to 87.5% were observed when compared with standard location calculation algorithms.

As many as 25% to 40% of unicompartmental knee replacement (UKR) revisions are performed for pain, a possible cause of which is proximal tibial strain. The aim of this study was to examine the effect of UKR implant design and material on cortical and cancellous proximal tibial strain in a synthetic bone model. Composite Sawbone tibiae were implanted with cemented UKR components of different designs, either all-polyethylene or metal-backed. The tibiae were subsequently loaded in 500 N increments to 2500 N, unloading between increments. Cortical surface strain was measured using a digital image correlation technique. Cancellous damage was measured using acoustic emission, an engineering technique that detects sonic waves (‘hits’) produced when damage occurs in material.Anteromedial cortical surface strain showed significant differences between implants at 1500 N and 2500 N in the proximal 10 mm only (p < 0.001), with relative strain shielding in metal-backed implants. Acoustic emission showed significant differences in cancellous bone damage between implants at all loads (p = 0.001). All-polyethylene implants displayed 16.6 times the total number of cumulative acoustic emission hits as controls. All-polyethylene implants also displayed more hits than controls at all loads (p < 0.001), more than metal-backed implants at loads ≥ 1500 N (p < 0.001), and greater acoustic emission activity on unloading than controls (p = 0.01), reflecting a lack of implant stiffness. All-polyethylene implants were associated with a significant increase in damage at the microscopic level compared with metal-backed implants, even at low loads. All-polyethylene implants should be used with caution in patients who are likely to impose large loads across their knee joint.Cite this article: Bone Joint J 2013;95-B:1339–47.