The aim of this study is to investigate the application of acoustic emission (AE) techniques to the defect detection and monitoring of adhesively-bonded joints. Pencil Lead Breaks (PLBs) have been used as a simulated AE source to experimentally investigate the characteristics of AE wave-propagation in adhesively-bonded joints, and have been combined with Artificial Neural Networks (ANNs) to provide a novel method of defect detection and sizing. Modal AE analysis has been applied to destructive testing of adhesively-bonded specimens as a novel method to differentiate between fracture modes. Dynamic Finite Element Analysis (FEA) has been utilised to simulate the AE generation and propagation to further investigate the findings of the experimental studies and to assess the applicability of the findings over a broader range of conditions than could be achieved experimentally. PLB tests have been conducted on large (500mm x 500mm x 1mm) aluminium sheet specimens to identify the effects of an adhesive layer on AE wave-propagation. Three specimens were considered: a single sheet; two sheets placed together without adhesive; and an adhesively-bonded specimen. The simulated AE source was applied to the specimens at varying propagation distances and orientations. The acquired signals were processed using wavelet transforms to explore time-frequency features, and compared with modified group velocity curves based on the Rayleigh-Lamb equations to allow identification of wave modes and edge reflections. The effects of propagation distance and source orientation were investigated, while comparison has been made between the three specimens. PLB tests were also used to detect, size and investigate the effect of void-type adhesive defects. Defect-free specimens were used for reference, and specimens with two different void sizes were tested. The PLB source was used to generate simulated AE which would propagate through the defect region and then be recorded with the AE system. Four configurations were tested to assess the effects of source-sensor propagation distance, and source and sensor proximity to the defect. Typical AE parameters of peak amplitude, rise time, decay time, duration, number of counts and AE energy were investigated. Frequency analyses by Fast Fourier Transformation (FFT), partial powers and wavelet transform (WT) were also implemented. ANNs, using the raw Time-Domain signal as an input, were successfully trained and tested to differentiate between the tested specimen types and to estimate the defect sizes. AE-instrumented Double Cantilever-Beam (Mode-I fracture) and Lap-Shear (Mode-II fracture) tests were conducted on similar adhesively-bonded aluminium specimens. Linear source location was used to identify the source-to-sensor propagation distance of each recorded hit. Theoretical dispersion curves were used to identify regions of the signal corresponding to the symmetric and asymmetric wave-modes. Additionally, peak wavelet transform coefficients for the wave modes were compared between the two fracture modes and assessed as an indicator of fracture mode. It was concluded that there is a relationship between the fracture mode and the generated wave modes, with Mode-II fracture typically generating a relatively greater symmetric wave mode than Mode-I fracture. Dynamic FEA was used to replicate both the PLB tests and the destructive tests, and to investigate the effects of a range of parameters that could not all be practically varied in experimental work. Adhesive Young's modulus (representative of different adhesive types), adhesive layer thickness and adhesive void size were varied in the simulated PLB tests. FEA was also used to investigate the effects of fracture mode on the generated acoustic emissions in simulated mixed mode-bending tests, conducted over a range of mode mixities. The FEA results were found to corroborate the results of the experimental work and support a relationship between fracture mode and generated wave modes. It was also identified that a variety of other parameters may also affect the wave modes, and thus need to be considered to achieve effective use of modal analysis to differentiate between fracture modes.
CRAWFORD, A.R. 2021. Defect detection and condition assessment of adhesively-bonded joints using acoustic emission techniques. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1603651