This purpose of this research was to identify fatigue crack growth and predict failure for 7075-T6 aluminum notched bars under uniaxial tensile loading using acoustic emission (AE) data. The experiments performed in this study extend the results obtained by previous researchers who used maximum cyclic loads of 4,000, 3,000, and 2,000 pounds at a stress ratio of R = 0.0 and a frequency of 1 Hz to perform the fatigue tests. For this research the cyclic load remained at 2,000 pounds, but an additional ten specimens were tested in order to increase the amount of AE data available to the backpropagation neural network (BPNN) for prediction of cyclic life to failure. In addition, the AE data obtained from cyclic testing were filtered and successfully classified using a Kohonen self-organizing map (SOM) to identify the plane stress and plane strain failure mode data. Furthermore, the early cycle (< 25% of fatigue life) AE amplitude distribution data from the test samples were used to predict fatigue lives using the BPNN. The increased AE data from the ten new specimens allowed the neural network to predict fatigue lives on ten total samples with a worst case error of -9.39%. The prediction results are presented along with comparisons to the previous research. Thus, neural network analysis of acoustic emission data provided both accurate fatigue life prediction and classification of the failure mechanisms involved.
","abstract_html":"<p>This purpose of this research was to identify fatigue crack growth and predict failure for 7075-T6 aluminum notched bars under uniaxial tensile loading using acoustic emission (AE) data. The experiments performed in this study extend the results obtained by previous researchers who used maximum cyclic loads of 4,000, 3,000, and 2,000 pounds at a stress ratio of R = 0.0 and a frequency of 1 Hz to perform the fatigue tests. For this research the cyclic load remained at 2,000 pounds, but an additional ten specimens were tested in order to increase the amount of AE data available to the backpropagation neural network (BPNN) for prediction of cyclic life to failure. In addition, the AE data obtained from cyclic testing were filtered and successfully classified using a Kohonen self-organizing map (SOM) to identify the plane stress and plane strain failure mode data. Furthermore, the early cycle (< 25% of fatigue life) AE amplitude distribution data from the test samples were used to predict fatigue lives using the BPNN. The increased AE data from the ten new specimens allowed the neural network to predict fatigue lives on ten total samples with a worst case error of -9.39%. The prediction results are presented along with comparisons to the previous research. Thus, neural network analysis of acoustic emission data provided both accurate fatigue life prediction and classification of the failure mechanisms involved.</p>","abstract_has_math":false,"creators":["Okur, Muhammed Arif"],"institution":null,"degree_name":"Master of Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Eric v. K. Hill","Yi Zhao","Ilteris Demirkiran"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-04-01T07:00:00Z","date_published":"2010-04-01T07:00:00Z","updated_at":"2026-07-27T19:25:45Z","subjects":["neural network","fatigue","aluminum","acoustic emission","Aerospace Engineering","Structures and Materials"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/db-theses/246","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","Ilteris Demirkiran"]},{"key":"dc:creator","label":"Author","values":["Okur, Muhammed Arif"]}]},{"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 Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["neural network","fatigue","aluminum","acoustic emission","Aerospace Engineering","Structures and Materials"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/db-theses/246"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This purpose of this research was to identify fatigue crack growth and predict failure for 7075-T6 aluminum notched bars under uniaxial tensile loading using acoustic emission (AE) data. The experiments performed in this study extend the results obtained by previous researchers who used maximum cyclic loads of 4,000, 3,000, and 2,000 pounds at a stress ratio of R = 0.0 and a frequency of 1 Hz to perform the fatigue tests. For this research the cyclic load remained at 2,000 pounds, but an additional ten specimens were tested in order to increase the amount of AE data available to the backpropagation neural network (BPNN) for prediction of cyclic life to failure. In addition, the AE data obtained from cyclic testing were filtered and successfully classified using a Kohonen self-organizing map (SOM) to identify the plane stress and plane strain failure mode data. Furthermore, the early cycle (< 25% of fatigue life) AE amplitude distribution data from the test samples were used to predict fatigue lives using the BPNN. The increased AE data from the ten new specimens allowed the neural network to predict fatigue lives on ten total samples with a worst case error of -9.39%. The prediction results are presented along with comparisons to the previous research. Thus, neural network analysis of acoustic emission data provided both accurate fatigue life prediction and classification of the failure mechanisms involved.
"]},{"key":"dc:title","label":"Title","values":["Neural Network Fatigue Life Prediction in Notched Aluminum Specimens from Acoustic Emission Data"]}]}],"canonical_facts":{"dc:contributor":["Eric v. K. Hill","Yi Zhao","Ilteris Demirkiran"],"dc:creator":["Okur, Muhammed Arif"],"dc:description.abstract":["This purpose of this research was to identify fatigue crack growth and predict failure for 7075-T6 aluminum notched bars under uniaxial tensile loading using acoustic emission (AE) data. The experiments performed in this study extend the results obtained by previous researchers who used maximum cyclic loads of 4,000, 3,000, and 2,000 pounds at a stress ratio of R = 0.0 and a frequency of 1 Hz to perform the fatigue tests. For this research the cyclic load remained at 2,000 pounds, but an additional ten specimens were tested in order to increase the amount of AE data available to the backpropagation neural network (BPNN) for prediction of cyclic life to failure. In addition, the AE data obtained from cyclic testing were filtered and successfully classified using a Kohonen self-organizing map (SOM) to identify the plane stress and plane strain failure mode data. Furthermore, the early cycle (< 25% of fatigue life) AE amplitude distribution data from the test samples were used to predict fatigue lives using the BPNN. The increased AE data from the ten new specimens allowed the neural network to predict fatigue lives on ten total samples with a worst case error of -9.39%. The prediction results are presented along with comparisons to the previous research. Thus, neural network analysis of acoustic emission data provided both accurate fatigue life prediction and classification of the failure mechanisms involved.
"],"dc:identifier":["https://commons.erau.edu/db-theses/246"],"dc:subject":["neural network","fatigue","aluminum","acoustic emission","Aerospace Engineering","Structures and Materials"],"dc:title":["Neural Network Fatigue Life Prediction in Notched Aluminum Specimens from Acoustic Emission Data"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:45Z"}