{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/20627"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/20627","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Machine-Learning-Based Non-Destructive Evaluation of Refractory Anchor Welds via Analysis of Percussion-Induced Acoustic Signals","abstract":"Welding is a critical process in modern infrastructure, particularly for securing refractory anchors in high-temperature vessels used across refineries, power plants, and chemical facilities. Ensuring the quality of these welds is essential, but traditional inspection methods are often destructive, time-consuming, or limited in accuracy. This thesis investigates a novel non-destructive evaluation (NDE) technique that leverages machine learning (ML) and percussion-induced audio analysis to assess weld quality. A custom weld plate was fabricated containing both properly welded and intentionally flawed refractory anchors. Controlled mechanical impacts—using tools such as a hammer and chisel—were applied to the exposed ends of these anchors. The resulting audio signals were recorded and transformed into Mel-Frequency Cepstral Coefficients (MFCCs). These MFCCs served as input features for multiple machine learning models including supervised and unsupervised models. The supervised models used were support vector machines (SVM), logistic regression, recurrent neural networks (RNN). The unsupervised models used were k-means clustering. All models were evaluated using three progressively independent tests: a dependent 70:30 train-test split, a semi-independent test using newly recorded data from the same weld plate, and a fully independent test involving unseen anchors. Despite increased variability across tests, the supervised models achieved 100% classification accuracy, while the unsupervised clustering method reached 99.42%. These results demonstrate that audio-based machine learning offers a fast, cost-effective, and objective alternative for weld inspection, with strong potential for improving industrial quality control practices.","abstract_html":"Welding is a critical process in modern infrastructure, particularly for securing refractory anchors in high-temperature vessels used across refineries, power plants, and chemical facilities. Ensuring the quality of these welds is essential, but traditional inspection methods are often destructive, time-consuming, or limited in accuracy. This thesis investigates a novel non-destructive evaluation (NDE) technique that leverages machine learning (ML) and percussion-induced audio analysis to assess weld quality. A custom weld plate was fabricated containing both properly welded and intentionally flawed refractory anchors. Controlled mechanical impacts—using tools such as a hammer and chisel—were applied to the exposed ends of these anchors. The resulting audio signals were recorded and transformed into Mel-Frequency Cepstral Coefficients (MFCCs). These MFCCs served as input features for multiple machine learning models including supervised and unsupervised models. The supervised models used were support vector machines (SVM), logistic regression, recurrent neural networks (RNN). The unsupervised models used were k-means clustering. All models were evaluated using three progressively independent tests: a dependent 70:30 train-test split, a semi-independent test using newly recorded data from the same weld plate, and a fully independent test involving unseen anchors. Despite increased variability across tests, the supervised models achieved 100% classification accuracy, while the unsupervised clustering method reached 99.42%. These results demonstrate that audio-based machine learning offers a fast, cost-effective, and objective alternative for weld inspection, with strong potential for improving industrial quality control practices.","abstract_has_math":false,"creators":["Chirigos, Mason"],"institution":"University of Houston","degree_name":"Master of Science in Mechanical Engineering","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Song, Gangbing"],"committee_chairs":[],"committee_members":["Chen, Xuemin","Zhu, Weihang"],"year":2025,"date_issued":"2025-08","date_published":"2025-08","updated_at":"2026-07-24T02:32:47Z","subjects":["Mechanical engineering"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/20627","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Song, Gangbing"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Chen, Xuemin","Zhu, Weihang"]},{"key":"dc:creator","label":"Author","values":["Chirigos, Mason"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-24T17:05:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mechanical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Mechanical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10657/20627"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Welding is a critical process in modern infrastructure, particularly for securing refractory anchors in high-temperature vessels used across refineries, power plants, and chemical facilities. Ensuring the quality of these welds is essential, but traditional inspection methods are often destructive, time-consuming, or limited in accuracy. This thesis investigates a novel non-destructive evaluation (NDE) technique that leverages machine learning (ML) and percussion-induced audio analysis to assess weld quality. A custom weld plate was fabricated containing both properly welded and intentionally flawed refractory anchors. Controlled mechanical impacts—using tools such as a hammer and chisel—were applied to the exposed ends of these anchors. The resulting audio signals were recorded and transformed into Mel-Frequency Cepstral Coefficients (MFCCs). These MFCCs served as input features for multiple machine learning models including supervised and unsupervised models. The supervised models used were support vector machines (SVM), logistic regression, recurrent neural networks (RNN). The unsupervised models used were k-means clustering. All models were evaluated using three progressively independent tests: a dependent 70:30 train-test split, a semi-independent test using newly recorded data from the same weld plate, and a fully independent test involving unseen anchors. Despite increased variability across tests, the supervised models achieved 100% classification accuracy, while the unsupervised clustering method reached 99.42%. These results demonstrate that audio-based machine learning offers a fast, cost-effective, and objective alternative for weld inspection, with strong potential for improving industrial quality control practices."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine-Learning-Based Non-Destructive Evaluation of Refractory Anchor Welds via Analysis of Percussion-Induced Acoustic Signals"]}]}],"canonical_facts":{"dc:contributor.advisor":["Song, Gangbing"],"dc:contributor.committeemember":["Chen, Xuemin","Zhu, Weihang"],"dc:creator":["Chirigos, Mason"],"dc:date.accessioned":["2025-09-24T17:05:28Z"],"dc:date.issued":["2025-08"],"dc:description.abstract":["Welding is a critical process in modern infrastructure, particularly for securing refractory anchors in high-temperature vessels used across refineries, power plants, and chemical facilities. Ensuring the quality of these welds is essential, but traditional inspection methods are often destructive, time-consuming, or limited in accuracy. This thesis investigates a novel non-destructive evaluation (NDE) technique that leverages machine learning (ML) and percussion-induced audio analysis to assess weld quality. A custom weld plate was fabricated containing both properly welded and intentionally flawed refractory anchors. Controlled mechanical impacts—using tools such as a hammer and chisel—were applied to the exposed ends of these anchors. The resulting audio signals were recorded and transformed into Mel-Frequency Cepstral Coefficients (MFCCs). These MFCCs served as input features for multiple machine learning models including supervised and unsupervised models. The supervised models used were support vector machines (SVM), logistic regression, recurrent neural networks (RNN). The unsupervised models used were k-means clustering. All models were evaluated using three progressively independent tests: a dependent 70:30 train-test split, a semi-independent test using newly recorded data from the same weld plate, and a fully independent test involving unseen anchors. Despite increased variability across tests, the supervised models achieved 100% classification accuracy, while the unsupervised clustering method reached 99.42%. These results demonstrate that audio-based machine learning offers a fast, cost-effective, and objective alternative for weld inspection, with strong potential for improving industrial quality control practices."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/20627"],"dc:language.iso":["English"],"dc:subject":["Mechanical engineering"],"dc:title":["Machine-Learning-Based Non-Destructive Evaluation of Refractory Anchor Welds via Analysis of Percussion-Induced Acoustic Signals"],"dc:type":["Thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_name":["Master of Science in Mechanical Engineering"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:47Z"}