{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108054"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108054","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning","abstract":"Ultrasonic metal welding (UMW) is an important manufacturing process used for joining multi-layer, thin and conductive metals. In UMW, tool wear significantly affects the weld quality and tool maintenance constitues a substantial part of production cost. Thus, tool condition monitoring (TCM) is crucial for UMW. Despite extensive literature focusing on TCM for other manufacturing processes, limited studies are available on online TCM for UMW. Existing TCM methods for UMW require a high-resolution measurement of tool surface profiles, which leads to undesirable production downtime and delayed decision making. This research proposed a completely online TCM system for UMW using sensor fusion and machine learning (ML) techniques. A data acquisition system was designed and implemented to obtain sensor signals during welding processes. A large feature pool was then extracted from the sensing signals. A subset of features were selected and subsequently used by ML based classification models. A variety of classification models were trained and tested using experimental data. The best model achieved consistent prediction accuracy of close to 100%. The proposed TCM system not only provides real-time TCM for UMW but also can support optimal decision-making in tool maintenance. The TCM system can be extended to predict remaining useful life (RUL) of tools and and integrated with a controller to adjust welding parameters accordingly.","abstract_html":"Ultrasonic metal welding (UMW) is an important manufacturing process used for joining multi-layer, thin and conductive metals. In UMW, tool wear significantly affects the weld quality and tool maintenance constitues a substantial part of production cost. Thus, tool condition monitoring (TCM) is crucial for UMW. Despite extensive literature focusing on TCM for other manufacturing processes, limited studies are available on online TCM for UMW. Existing TCM methods for UMW require a high-resolution measurement of tool surface profiles, which leads to undesirable production downtime and delayed decision making. This research proposed a completely online TCM system for UMW using sensor fusion and machine learning (ML) techniques. A data acquisition system was designed and implemented to obtain sensor signals during welding processes. A large feature pool was then extracted from the sensing signals. A subset of features were selected and subsequently used by ML based classification models. A variety of classification models were trained and tested using experimental data. The best model achieved consistent prediction accuracy of close to 100%. The proposed TCM system not only provides real-time TCM for UMW but also can support optimal decision-making in tool maintenance. The TCM system can be extended to predict remaining useful life (RUL) of tools and and integrated with a controller to adjust welding parameters accordingly.","abstract_has_math":false,"creators":["Nazir, Qasim"],"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":2020,"date_issued":"2020-08-26T21:58:06Z","date_published":"2020-08-26T21:58:06Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Ultrasonic Metal Welding, Machine Learning, Smart Manufacturing, Predictive Maintenance"],"languages":["en"],"rights":["Copyright 2020 Qasim Nazir"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108054","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":["Nazir, Qasim"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:58:06Z","2020-05-14","2020-05"]},{"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":["Ultrasonic Metal Welding, Machine Learning, Smart Manufacturing, Predictive Maintenance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Qasim Nazir"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108054"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ultrasonic metal welding (UMW) is an important manufacturing process used for joining multi-layer, thin and conductive metals. In UMW, tool wear significantly affects the weld quality and tool maintenance constitues a substantial part of production cost. Thus, tool condition monitoring (TCM) is crucial for UMW. Despite extensive literature focusing on TCM for other manufacturing processes, limited studies are available on online TCM for UMW. Existing TCM methods for UMW require a high-resolution measurement of tool surface profiles, which leads to undesirable production downtime and delayed decision making. This research proposed a completely online TCM system for UMW using sensor fusion and machine learning (ML) techniques. A data acquisition system was designed and implemented to obtain sensor signals during welding processes. A large feature pool was then extracted from the sensing signals. A subset of features were selected and subsequently used by ML based classification models. A variety of classification models were trained and tested using experimental data. The best model achieved consistent prediction accuracy of close to 100%. The proposed TCM system not only provides real-time TCM for UMW but also can support optimal decision-making in tool maintenance. The TCM system can be extended to predict remaining useful life (RUL) of tools and and integrated with a controller to adjust welding parameters accordingly.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Qasim Nazir, accepted the attached license on 2020-05-12 at 16:52.","The student, Qasim Nazir, submitted this Thesis for approval on 2020-05-12 at 17:06.","This Thesis was approved for publication on 2020-05-14 at 08:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15367 on 2020-08-25 at 17:14:30","Made available in DSpace on 2020-08-26T21:58:06Z (GMT). No. of bitstreams: 2 NAZIR-THESIS-2020.pdf: 14534671 bytes, checksum: 99056400c2c764d2afe3084a152418a5 (MD5) LICENSE.txt: 4208 bytes, checksum: f3509a7281221c8a082b36c1696e259c (MD5) Previous issue date: 2020-05-14"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning"]}]}],"canonical_facts":{"dc:contributor":["Shao, Chenhui"],"dc:creator":["Nazir, Qasim"],"dc:date":["2020-08-26T21:58:06Z","2020-05-14","2020-05"],"dc:description":["Ultrasonic metal welding (UMW) is an important manufacturing process used for joining multi-layer, thin and conductive metals. In UMW, tool wear significantly affects the weld quality and tool maintenance constitues a substantial part of production cost. Thus, tool condition monitoring (TCM) is crucial for UMW. Despite extensive literature focusing on TCM for other manufacturing processes, limited studies are available on online TCM for UMW. Existing TCM methods for UMW require a high-resolution measurement of tool surface profiles, which leads to undesirable production downtime and delayed decision making. This research proposed a completely online TCM system for UMW using sensor fusion and machine learning (ML) techniques. A data acquisition system was designed and implemented to obtain sensor signals during welding processes. A large feature pool was then extracted from the sensing signals. A subset of features were selected and subsequently used by ML based classification models. A variety of classification models were trained and tested using experimental data. The best model achieved consistent prediction accuracy of close to 100%. The proposed TCM system not only provides real-time TCM for UMW but also can support optimal decision-making in tool maintenance. The TCM system can be extended to predict remaining useful life (RUL) of tools and and integrated with a controller to adjust welding parameters accordingly.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Qasim Nazir, accepted the attached license on 2020-05-12 at 16:52.","The student, Qasim Nazir, submitted this Thesis for approval on 2020-05-12 at 17:06.","This Thesis was approved for publication on 2020-05-14 at 08:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15367 on 2020-08-25 at 17:14:30","Made available in DSpace on 2020-08-26T21:58:06Z (GMT). No. of bitstreams: 2 NAZIR-THESIS-2020.pdf: 14534671 bytes, checksum: 99056400c2c764d2afe3084a152418a5 (MD5) LICENSE.txt: 4208 bytes, checksum: f3509a7281221c8a082b36c1696e259c (MD5) Previous issue date: 2020-05-14"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/108054"],"dc:language":["en"],"dc:rights":["Copyright 2020 Qasim Nazir"],"dc:subject":["Ultrasonic Metal Welding, Machine Learning, Smart Manufacturing, Predictive Maintenance"],"dc:title":["Online tool condition monitoring for ultrasonic metal welding via sensor fusion and machine learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:47Z"}