Electromagnetic acoustic transducers (EMATs) have been used as a non-contact ultrasound approach for detecting and characterising surface defects in aluminium bars and billet. The characterisation was made from understanding the interaction of broadband Rayleigh surface waves with surface crack growing normal or inclined to the sample surface, based on rolling contact fatigue (RCF) cracks in rail tracks. The interaction with normal cracks have been previously reported. For inclined cracks, mode conversion of Rayleigh waves to Lamb-like waves occur in the wedge section formed by the crack, resulting in strong and prominent enhancement in the signal detected. This is confirmed by finite element analysis (FEA) models and Lamb waves arrival times calculation. Signal enhancement from the interaction creates features in B-scan images, and they have been used for initial crack classifications. Then, a number of analyses were performed to estimate the crack inclination, and accurately determine the crack vertical depth. A feature extraction and image classification program based on genetic programming have been developed (through a collaboration work) to perform automated classification on the B-scans. The program produces more than 90% accuracy using the experimental data set. The viability of EMATs to detect and fully characterise narrow cracks have been investigated through experiments using laser interferometer and comparison with EMATs measurements. The results confirmed that narrow cracks can be detected with EMATs, with initial classification (in B-scans) to normal/inclined. However, the depth sizing may not be accurate, and suggestion for better designs of EMATs have been made. FEA models have been used to study the interaction of the Rayleigh waves with branched cracks. Interesting results are observed in terms of Rayleigh waves reflections, which helps to determine the presence of a branch on RCF-like cracks. A method has been proposed for calculating the length of the branch, following a number of analyses.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:589917 |
| Date | January 2013 |
| Creators | Rosli, M. H. |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/59439/ |
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