Wavelet-based acoustic emission analysis of composite materials

In this dissertation, a methodology for time-frequency analysis of acoustic emission (AE) signals generated due to static loading of composite specimen is presented. The tool is based on a recently developed mathematical transform called the wavelet transform. Two aspects of AE-based nondestructive evaluation (NDE) are failure mode identification and residual strength prediction. In this work, the wavelet-based AE method is applied to these two aspects of AE-based NDE.

Presently, the public literature review indicates that AE techniques are dominated by time domain analysis methods. It can be seen that these methods have matured into tools which provide satisfactory results. There are limited results available that use frequency domain techniques, however, there is valuable information available in the frequency domain. Thus, it is evident that there is a need for an AE analysis technique that simultaneously utilizes both the time and frequency domains. In this dissertation, a hybrid technique is developed.

With the application of wavelet transforms to the failure mode identification, the AE signals are decomposed into different wavelet levels. A general trend is observed by investigating the energy-frequency distribution of the decomposed AE signals. This trend indicates that the energy in the AE signals is essentially concentrated in three levels (seven, eight, and nine), representing frequency rages of 50-150 kHz, 150-250 kHz, and 250-310 kHz. Furthermore, the energy percentages in levels seven, eight, and nine are determined to be 8%, 15%, and 75%, respectively. The analysis indicates that the three dominant wavelet levels may be related to different failure modes associated with the fracture of CFR composites.

In the prediction of residual strength, the ability of the wavelet transform to enhance the signal to noise ratio is employed. The exponential constant in value used to determine the relationship between stress and stress intensity factor are compared relative to classical fracture mechanics and AE techniques. In the comparison study, the conventional and wavelet-based AE techniques are presented side-by-side to show the advantage of wavelet-based methods. The results verify that the wavelet-based method improves on the results relative to classical fracture mechanics methods.