Date of Award

Fall 2001

Document Type

Thesis - Open Access

Degree Name

Master of Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Eric v. K. Hill

Committee Member

Frank J. Radosta

Committee Member

Ashok H. Gurjar

Committee Member

Richard P. Anderson

Abstract

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 was utilized to separate each individual data point (hit) 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 nonlinearities in the data and unwanted noise.

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