{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110539"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110539","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning-based non-destructive evaluation of fatigue damage in metals","abstract":"Non-destructive evaluation (NDE) of fatigue damage in metals is crucial for ensuring high product performance and safety. In remanufacturing, NDE for the incoming recycled metal materials is also essential to maximize the benefits of utilizing such materials. However, critical challenges exist in the development of NDE techniques for used components: an individual NDE technology is only sensitive to specific fatigue conditions; and analytics methods are lacking for quantitatively measuring accumulated mechanical damage and conducting prognostics in an early fatigue stage. In this thesis, we propose a novel machine learning-based NDE technology by combining the strengths of linear ultrasonic (LU) and nonlinear ultrasonic (NLU) methods to characterize material properties and flaws at multiple length scales. Besides, a remaining useful life (RUL) estimation framework with hierarchical classifiers and S-N curves for identifying fatigue damage levels and inferring RUL is developed. In addition, regression models are developed to estimate residual stress and full width at half maximum (FWHM). The effectiveness of the proposed methods is demonstrated by using life cycle fatigue testing data for 5052-H32 aluminum alloy.","abstract_html":"Non-destructive evaluation (NDE) of fatigue damage in metals is crucial for ensuring high product performance and safety. In remanufacturing, NDE for the incoming recycled metal materials is also essential to maximize the benefits of utilizing such materials. However, critical challenges exist in the development of NDE techniques for used components: an individual NDE technology is only sensitive to specific fatigue conditions; and analytics methods are lacking for quantitatively measuring accumulated mechanical damage and conducting prognostics in an early fatigue stage. In this thesis, we propose a novel machine learning-based NDE technology by combining the strengths of linear ultrasonic (LU) and nonlinear ultrasonic (NLU) methods to characterize material properties and flaws at multiple length scales. Besides, a remaining useful life (RUL) estimation framework with hierarchical classifiers and S-N curves for identifying fatigue damage levels and inferring RUL is developed. In addition, regression models are developed to estimate residual stress and full width at half maximum (FWHM). The effectiveness of the proposed methods is demonstrated by using life cycle fatigue testing data for 5052-H32 aluminum alloy.","abstract_has_math":false,"creators":["Hsu, Min-Hsiu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Shao, Chenhui"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:11:12Z","date_published":"2021-09-17T01:11:12Z","updated_at":"2026-07-22T22:24:52Z","subjects":["metal fatigue","non destructive testing","non destructive evaluation","machine learning","remaining useful life","remanufacturing","sensor fusion","linear ultrasound","nonlinear ultrasound","residual stress","full width at half maximum"],"languages":["en"],"rights":["Copyright 2021 Min-Hsiu Hsu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110539","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shao, Chenhui"]},{"key":"dc:creator","label":"Author","values":["Hsu, Min-Hsiu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:11:12Z","2021-04-29","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["metal fatigue","non destructive testing","non destructive evaluation","machine learning","remaining useful life","remanufacturing","sensor fusion","linear ultrasound","nonlinear ultrasound","residual stress","full width at half maximum"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Min-Hsiu Hsu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110539"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Non-destructive evaluation (NDE) of fatigue damage in metals is crucial for ensuring high product performance and safety. 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The effectiveness of the proposed methods is demonstrated by using life cycle fatigue testing data for 5052-H32 aluminum alloy.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Min-Hsiu Hsu, accepted the attached license on 2021-04-27 at 11:32.","The student, Min-Hsiu Hsu, submitted this Thesis for approval on 2021-04-27 at 12:27.","This Thesis was approved for publication on 2021-04-29 at 13:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16485 on 2021-09-16 at 16:46:50","Made available in DSpace on 2021-09-17T01:11:12Z (GMT). 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The effectiveness of the proposed methods is demonstrated by using life cycle fatigue testing data for 5052-H32 aluminum alloy.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Min-Hsiu Hsu, accepted the attached license on 2021-04-27 at 11:32.","The student, Min-Hsiu Hsu, submitted this Thesis for approval on 2021-04-27 at 12:27.","This Thesis was approved for publication on 2021-04-29 at 13:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16485 on 2021-09-16 at 16:46:50","Made available in DSpace on 2021-09-17T01:11:12Z (GMT). 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