{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117718"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117718","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning predictions of crack paths in brittle and ductile media","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Worthington, Michael"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Chew, Huck Beng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Machine Learning","Artificial Neural Networks","Warp3d","Crack Growth","Void Defects","Micromechanics Model","Crack Patterns","Crack Paths","Additive Manufacturing","Genetic Algorithm","Brittle Fracture","Ductile Fracture","Fracture Toughness"],"languages":["en","eng"],"rights":["Copyright 2022 Michael Worthington"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117718","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chew, Huck Beng"]},{"key":"dc:creator","label":"Author","values":["Worthington, Michael"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-09-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace 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":["Machine Learning","Artificial Neural Networks","Warp3d","Crack Growth","Void Defects","Micromechanics Model","Crack Patterns","Crack Paths","Additive Manufacturing","Genetic Algorithm","Brittle Fracture","Ductile Fracture","Fracture Toughness"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Michael Worthington"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117718"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Michael Worthington, accepted the attached license on 2022-09-06 at 14:26.","The student, Michael Worthington, submitted this Thesis for approval on 2022-09-06 at 14:37.","This Thesis was approved for publication on 2022-09-08 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18482 on 2023-04-12 at 07:23:27","Despite their modern widespread use, additively manufactured (AM) materials suffer from a frequent problem: defects. Defects, such as “lack of fusion” or “keyhole” defects, result in a porous, inhomogeneous final material containing a distribution of voids. These void defects control the fracture, and thus failure, behavior, limiting the application of AM materials. In this thesis, we first develop machine learning techniques using artificial neural networks (ANNs) to predict how voids affect crack growth in an algorithmically-described brittle-like material and apply this to predict crack paths as a proof of concept. The ANNs are trained using inputs describing the porosity distribution surrounding the crack tip and outputs representing the ensuing crack growth direction. We then extend this technique to porous ductile media, representing defect-filled AM materials. Results showed excellent performance in predicting both individual steps of crack growth and full crack paths for both brittle and ductile media. Individual crack growth directions were predicted with over 98% accuracy for brittle media and accuracies neared 90% for ductile media. Common prediction errors for both brittle and ductile fracture resulted from higher void densities and insufficiencies in the ANN training data for particular types of void configurations. Multi-step crack path predictions were in good alignment with the true paths. The high predictive performance for crack paths allowed for the design of porosity distributions with an evolutionary genetic algorithm to achieve crack propagation along desired crack paths. A limited ability to design for a targeted fracture toughness was demonstrated. However, it was noted that ductile fracture is governed by multiple mechanisms. The mechanism accounted for in this thesis is primarily void-by-void, where the crack tip interacts with a single void at a time. However, it was found that many cracks are governed by multiple void interaction, where multiple voids grow simultaneously, creating multiple damage zones that eventually coalesce. Training of the ANN to account for only void-by-void crack growth thus limited its ability to predict full ductile crack paths. Extensions to more accurately account for multiple void interaction mechanisms are discussed, and is a continuing subject of future work."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning predictions of crack paths in brittle and ductile media"]}]}],"canonical_facts":{"dc:contributor":["Chew, Huck Beng"],"dc:creator":["Worthington, Michael"],"dc:date":["2022-12","2022-09-08"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Michael Worthington, accepted the attached license on 2022-09-06 at 14:26.","The student, Michael Worthington, submitted this Thesis for approval on 2022-09-06 at 14:37.","This Thesis was approved for publication on 2022-09-08 at 15:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18482 on 2023-04-12 at 07:23:27","Despite their modern widespread use, additively manufactured (AM) materials suffer from a frequent problem: defects. Defects, such as “lack of fusion” or “keyhole” defects, result in a porous, inhomogeneous final material containing a distribution of voids. These void defects control the fracture, and thus failure, behavior, limiting the application of AM materials. In this thesis, we first develop machine learning techniques using artificial neural networks (ANNs) to predict how voids affect crack growth in an algorithmically-described brittle-like material and apply this to predict crack paths as a proof of concept. The ANNs are trained using inputs describing the porosity distribution surrounding the crack tip and outputs representing the ensuing crack growth direction. We then extend this technique to porous ductile media, representing defect-filled AM materials. Results showed excellent performance in predicting both individual steps of crack growth and full crack paths for both brittle and ductile media. Individual crack growth directions were predicted with over 98% accuracy for brittle media and accuracies neared 90% for ductile media. Common prediction errors for both brittle and ductile fracture resulted from higher void densities and insufficiencies in the ANN training data for particular types of void configurations. Multi-step crack path predictions were in good alignment with the true paths. The high predictive performance for crack paths allowed for the design of porosity distributions with an evolutionary genetic algorithm to achieve crack propagation along desired crack paths. A limited ability to design for a targeted fracture toughness was demonstrated. However, it was noted that ductile fracture is governed by multiple mechanisms. The mechanism accounted for in this thesis is primarily void-by-void, where the crack tip interacts with a single void at a time. However, it was found that many cracks are governed by multiple void interaction, where multiple voids grow simultaneously, creating multiple damage zones that eventually coalesce. Training of the ANN to account for only void-by-void crack growth thus limited its ability to predict full ductile crack paths. Extensions to more accurately account for multiple void interaction mechanisms are discussed, and is a continuing subject of future work."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117718"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Michael Worthington"],"dc:subject":["Machine Learning","Artificial Neural Networks","Warp3d","Crack Growth","Void Defects","Micromechanics Model","Crack Patterns","Crack Paths","Additive Manufacturing","Genetic Algorithm","Brittle Fracture","Ductile Fracture","Fracture Toughness"],"dc:title":["Machine learning predictions of crack paths in brittle and ductile media"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}