Date of Award

Fall 2002

Document Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering


Aerospace Engineering

Committee Chair

Dr. Eric v. K. Hill

Committee Member

Dr. Yi Zhao

Committee Member

Dr. David J. Sypeck


The purpose of this work was to model the acoustic emission (AE) flaw growth data that resulted from the tensile test of a unidirectional fiberglass/epoxy specimen. The data collected and stored during the test were the six standard AE quantification parameters for each event. A classification neural network was used to sort the data into five failure mechanism clusters. The resulting frequency histograms of the sorted data were then mathematically modeled herein using the three types of Johnson distributions: bounded, lognormal, and unbounded. These provided a reasonably good fit for all six AE parameter distributions for each of the five failure mechanisms.