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Date of Award
Thesis - Open Access
Master of Science in Aerospace Engineering
Dr. Eric v. K. Hill
Dr. Yi Zhao
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.
Scholarly Commons Citation
Lendzioszek, Daniel R., "Modeling of Acoustic Emission Failure Mechanism Data from a Unidirectional Fiberglass/Epoxy Tensile Test Specimen" (2002). Theses - Daytona Beach. 119.