Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review
Non-destructive evaluation (NDE) and structural health monitoring (SHM) play a critical role in ensuring the safety, reliability, and longevity of engineering structures and materials. Among the various NDE techniques, ultrasonic methods are widely regarded as the most effective for damage detection...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-06-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/16878132251347390 |
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| _version_ | 1850209773727776768 |
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| author | Boris I Kseniia Barashok Yongjoon Choi Yeongil Choi Mohammed Aslam Jaesun Lee |
| author_facet | Boris I Kseniia Barashok Yongjoon Choi Yeongil Choi Mohammed Aslam Jaesun Lee |
| author_sort | Boris I |
| collection | DOAJ |
| description | Non-destructive evaluation (NDE) and structural health monitoring (SHM) play a critical role in ensuring the safety, reliability, and longevity of engineering structures and materials. Among the various NDE techniques, ultrasonic methods are widely regarded as the most effective for damage detection and material characterization due to their high sensitivity and versatility. However, conventional ultrasonic approaches face challenges in analyzing complex signals, limiting their accuracy and efficiency in certain applications. The advent of machine learning (ML) has revolutionized ultrasonic data analysis by utilizing advanced data mining and pattern recognition capabilities to decode intricate signal patterns. This review provides a comprehensive overview of ML techniques applied to ultrasonic-based damage detection and material characterization, including key processes such as data preprocessing and feature engineering. Emphasis is placed on case studies and real-world applications, highlighting ML’s role in defect detection, localization, and material property assessment. Additionally, the paper addresses critical challenges, limitations, and future directions, offering insights into the transformative potential of ML in ultrasonic NDE and SHM. |
| format | Article |
| id | doaj-art-e37b0c9b513f4eda92c6df7710317ec2 |
| institution | OA Journals |
| issn | 1687-8140 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj-art-e37b0c9b513f4eda92c6df7710317ec22025-08-20T02:09:56ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402025-06-011710.1177/16878132251347390Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive reviewBoris I0Kseniia Barashok1Yongjoon Choi2Yeongil Choi3Mohammed Aslam4Jaesun Lee5Department of Smart Manufacturing Engineering, Changwon National University, Changwon, South KoreaNDE and SHM Lab, Research Institute of Mechatronics, Changwon National University, Changwon, South KoreaDepartment of Smart Manufacturing Engineering, Changwon National University, Changwon, South KoreaDepartment of Smart Manufacturing Engineering, Changwon National University, Changwon, South KoreaExtreme Environment Design and Manufacturing Innovation Center, Changwon National University, Changwon, South KoreaSchool of Mechanical Engineering, Changwon National University, Changwon, South KoreaNon-destructive evaluation (NDE) and structural health monitoring (SHM) play a critical role in ensuring the safety, reliability, and longevity of engineering structures and materials. Among the various NDE techniques, ultrasonic methods are widely regarded as the most effective for damage detection and material characterization due to their high sensitivity and versatility. However, conventional ultrasonic approaches face challenges in analyzing complex signals, limiting their accuracy and efficiency in certain applications. The advent of machine learning (ML) has revolutionized ultrasonic data analysis by utilizing advanced data mining and pattern recognition capabilities to decode intricate signal patterns. This review provides a comprehensive overview of ML techniques applied to ultrasonic-based damage detection and material characterization, including key processes such as data preprocessing and feature engineering. Emphasis is placed on case studies and real-world applications, highlighting ML’s role in defect detection, localization, and material property assessment. Additionally, the paper addresses critical challenges, limitations, and future directions, offering insights into the transformative potential of ML in ultrasonic NDE and SHM.https://doi.org/10.1177/16878132251347390 |
| spellingShingle | Boris I Kseniia Barashok Yongjoon Choi Yeongil Choi Mohammed Aslam Jaesun Lee Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review Advances in Mechanical Engineering |
| title | Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review |
| title_full | Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review |
| title_fullStr | Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review |
| title_full_unstemmed | Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review |
| title_short | Machine learning techniques in ultrasonics-based defect detection and material characterization: A comprehensive review |
| title_sort | machine learning techniques in ultrasonics based defect detection and material characterization a comprehensive review |
| url | https://doi.org/10.1177/16878132251347390 |
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