Modeling of Acoustic Emission Failure Mechanism Data from a Unidirectional Fiberglass/Epoxy Tensile Test Specimen

<p>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.</p>

Name thesis:degree_name

Master of Science in Aerospace Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year

2002

Author dc:creator

• Lendzioszek, Daniel R.

Contributors dc:contributor

• Eric v. K. Hill
• Yi Zhao
• David J. Sypeck

• acoustic emission
• fiberglass/epoxy
• Aerospace Engineering
• Structures and Materials

Repository record dc:identifier

https://commons.erau.edu/db-theses/119

OAI identifier oai:identifier

oai:commons.erau.edu:db-theses-1165