(Received 2 June 2003; accepted 18 March 2005)
Published Online: 2005
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Acoustic emission (AE) nondestructive testing was used to monitor fiberglass/epoxy I-beams. The experiment consisted of loading the I-beams in cantilever fashion with a hydraulic ram. While testing, AE waveforms were collected from the onset of loading to failure. After acquisition, the AE data from each test were filtered to include only data collected up to 50 % of the theoretical ultimate load for further analysis.
A Kohonen self-organizing map (SOM) was utilized to separate individual data points into failure mechanism clusters. Then a multiple linear regression analysis was performed using the percentage of hits associated with each failure mechanism along with the epoxy type to develop a prediction equation. The results of this analysis provided a prediction to within a 36.0 % error. A second analysis was performed utilizing a back-propagation neural network. The inputs to the network included a categorical variable for the epoxy type together with the amplitude frequencies from 30–100 dB. The optimized network contained two hidden layers having nine neurons apiece. Here the ultimate load prediction was within 48 lbf for a 9.5 % error. Thus, the back-propagation neural network produced far better results than the SOM/multiple linear regression, probably because of unwanted noise and perhaps nonlinearities in the data.
Forensic Engineer, Vector Scientific, Inc., 5245 Pacific Concourse Dr., Suite 100, Los Angeles, CA
Professor, Embry-Riddle Aeronautical University, Aerospace Engineering, 600 S. Clyde Morris Blvd., Daytona Beach, FL
Stock #: JTE11974