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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Lendzioszek, Daniel R."],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Eric v. K. Hill","Yi Zhao","David J. Sypeck"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2002,"date_issued":"2002-10-01T07:00:00Z","date_published":"2002-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:23Z","subjects":["acoustic emission","fiberglass/epoxy","Aerospace Engineering","Structures and Materials"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/db-theses/119","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Eric v. K. Hill","Yi Zhao","David J. Sypeck"]},{"key":"dc:creator","label":"Author","values":["Lendzioszek, Daniel R."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["acoustic emission","fiberglass/epoxy","Aerospace Engineering","Structures and Materials"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/db-theses/119"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Modeling of Acoustic Emission Failure Mechanism Data from a Unidirectional Fiberglass/Epoxy Tensile Test Specimen"]}]}],"canonical_facts":{"dc:contributor":["Eric v. K. Hill","Yi Zhao","David J. Sypeck"],"dc:creator":["Lendzioszek, Daniel R."],"dc:description.abstract":["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.
"],"dc:identifier":["https://commons.erau.edu/db-theses/119"],"dc:subject":["acoustic emission","fiberglass/epoxy","Aerospace Engineering","Structures and Materials"],"dc:title":["Modeling of Acoustic Emission Failure Mechanism Data from a Unidirectional Fiberglass/Epoxy Tensile Test Specimen"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:23Z"}