The objective of this research was to classify acoustic emission (AE) -data associated with fatigue cracks in aluminum fatigue specimens and to use early cycle life AE data to predict failure in such members. An AE data acquisition system coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used to classify the AE flaw growth signals. The signals were classified into three categories based on their AE parameters: plastic deformation, plane strain fracture and mixed mode (plane strain and plane stress) fracture.
Acoustic emission amplitude data from the twenty-four low cycle fatigue tests were used to train and test a back propagation neural network for prediction of cycles to failure. The input data consisted of amplitude frequency histograms (30-100 dB) and the actual cycle lives. The output was the predicted cycles to failure or fatigue life. A network capable of predicting cycles to failure with a worst case error of- 9.30% was the final result.
","abstract_html":"<p>The objective of this research was to classify acoustic emission (AE) -data associated with fatigue cracks in aluminum fatigue specimens and to use early cycle life AE data to predict failure in such members. An AE data acquisition system coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used to classify the AE flaw growth signals. The signals were classified into three categories based on their AE parameters: plastic deformation, plane strain fracture and mixed mode (plane strain and plane stress) fracture.</p> <p>Acoustic emission amplitude data from the twenty-four low cycle fatigue tests were used to train and test a back propagation neural network for prediction of cycles to failure. The input data consisted of amplitude frequency histograms (30-100 dB) and the actual cycle lives. The output was the predicted cycles to failure or fatigue life. A network capable of predicting cycles to failure with a worst case error of- 9.30% was the final result.</p>","abstract_has_math":false,"creators":["Ibekwe, Emeka Chigozie"],"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","Frank J. Radosta","Jean-Michel Dhainaut"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004-05-01T07:00:00Z","date_published":"2004-05-01T07:00:00Z","updated_at":"2026-07-27T19:25:29Z","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/276","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","Frank J. Radosta","Jean-Michel Dhainaut"]},{"key":"dc:creator","label":"Author","values":["Ibekwe, Emeka Chigozie"]}]},{"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/276"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The objective of this research was to classify acoustic emission (AE) -data associated with fatigue cracks in aluminum fatigue specimens and to use early cycle life AE data to predict failure in such members. An AE data acquisition system coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used to classify the AE flaw growth signals. The signals were classified into three categories based on their AE parameters: plastic deformation, plane strain fracture and mixed mode (plane strain and plane stress) fracture.
Acoustic emission amplitude data from the twenty-four low cycle fatigue tests were used to train and test a back propagation neural network for prediction of cycles to failure. The input data consisted of amplitude frequency histograms (30-100 dB) and the actual cycle lives. The output was the predicted cycles to failure or fatigue life. A network capable of predicting cycles to failure with a worst case error of- 9.30% was the final result.
"]},{"key":"dc:title","label":"Title","values":["Neural Network Fatigue Life Prediction in 7075-T6 Aluminum from Acoustic Emission Data"]}]}],"canonical_facts":{"dc:contributor":["Eric v. K. Hill","Frank J. Radosta","Jean-Michel Dhainaut"],"dc:creator":["Ibekwe, Emeka Chigozie"],"dc:description.abstract":["The objective of this research was to classify acoustic emission (AE) -data associated with fatigue cracks in aluminum fatigue specimens and to use early cycle life AE data to predict failure in such members. An AE data acquisition system coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used to classify the AE flaw growth signals. The signals were classified into three categories based on their AE parameters: plastic deformation, plane strain fracture and mixed mode (plane strain and plane stress) fracture.
Acoustic emission amplitude data from the twenty-four low cycle fatigue tests were used to train and test a back propagation neural network for prediction of cycles to failure. The input data consisted of amplitude frequency histograms (30-100 dB) and the actual cycle lives. The output was the predicted cycles to failure or fatigue life. A network capable of predicting cycles to failure with a worst case error of- 9.30% was the final result.
"],"dc:identifier":["https://commons.erau.edu/db-theses/276"],"dc:subject":["neural network","fatigue","aluminum","acoustic emission","Aerospace Engineering","Structures and Materials"],"dc:title":["Neural Network Fatigue Life Prediction in 7075-T6 Aluminum from Acoustic Emission Data"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:29Z"}