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
In order to overcome the difficulties in applying traditional time-of-arrival techniques for locating acoustic emission events in complex structures and materials, a technique termed 'Delta-t mapping' was developed. This article presents a significant improvement on this, in which the difficulties in identifying the precise arrival time of an acoustic emission signal are addressed by incorporating the Akaike information criteria. The performance of the time of arrival, the Delta-t mapping and the Akaike information criteria Delta-t mapping techniques is assessed by locating artificial acoustic emission sources, fatigue damage and impact events in aluminium and composite materials, respectively. For all investigations conducted, the improved Akaike information criteria Delta-t technique shows a reduction in average Euclidean source location error irrespective of material or source type. For locating Hsu-Nielsen sources on a complex aluminium specimen, the average source location error (Euclidean) is 32.6 (time of arrival), 5.8 (Delta-t) and 3 mm (Akaike information criteria Delta-t). For locating fatigue damage on the same specimen, the average error is 20.2 (time of arrival), 4.2 (Delta-t) and 3.4 mm (Akaike information criteria Delta-t). For locating Hsu-Nielsen sources on a composite panel, the average error is 19.3 (time of arrival), 18.9 (Delta-t) and 4.2 mm (Akaike information criteria Delta-t). Finally, the Akaike information criteria Delta-t mapping technique had the lowest average error (3.3 mm) when locating impact events when compared with the Delta-t (18.9 mm) and time of arrival (124.7 mm) techniques. Overall, the Akaike information criteria Delta-t mapping technique is the only technique which demonstrates consistently the lowest average source location error (greatest average error of 4.2 mm) when compared with the Delta-t (greatest average error of 18.9 mm) and time of arrival (greatest average error of 124.7 mm) techniques. These results demonstrate that the Akaike information criteria Delta-t mapping technique is a viable option for acoustic emission source location, increasing the accuracy and likelihood of damage detection, irrespective of material, geometry and source type.
Rhys 2015. Localisation and identification of fatigue matrix cracking and delamination in a carbon fibre panel by acoustic emission. Composites Part B: Engineering 74 , pp.
AbstractThe use of Acoustic Emission (AE) as a Structural Health Monitoring (SHM) technique is very attractive due to its ability to detect not only damage sources in real-time but also to locate them. To demonstrate the AE capabilities on known damage modes, a carbon fibre panel was manufactured with cut fibres in a central location, and subjected to fatigue loading to promote matrix cracking. AE signals were located within the crack area; next, a delamination within the panel using an impact was created. Again, AE signals detected under fatigue loading from this area were located and used for further analysis. The application of an unsupervised neural network based classification technique successfully separated the two damage mechanisms. The results obtained allowed a more detailed understanding of matrix cracking and delamination sources of AE in carbon fibre laminates.
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