Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals
The structural integrity of bolts in civil structures is critical to ensuring safety and operational efficiency. Traditional visual inspection methods often fail to detect internal defects of installed bolts, necessitating more advanced approaches. On the other hand, ultrasonic testing which c...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | deu |
| Published: |
NDT.net
2025-03-01
|
| Series: | e-Journal of Nondestructive Testing |
| Online Access: | https://www.ndt.net/search/docs.php3?id=30801 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387551134056448 |
|---|---|
| author | Abdul Azziz Abd Talib Chun Yee Lim Chin Kian Liew |
| author_facet | Abdul Azziz Abd Talib Chun Yee Lim Chin Kian Liew |
| author_sort | Abdul Azziz Abd Talib |
| collection | DOAJ |
| description |
The structural integrity of bolts in civil structures is
critical to ensuring safety and operational efficiency.
Traditional visual inspection methods often fail to detect
internal defects of installed bolts, necessitating more
advanced approaches. On the other hand, ultrasonic testing
which can detect internal defects is not as straightforward
to apply with signals difficult to analyse without the
relevant certified non-destructive testing personnel
qualifications and experience. This paper integrates
machine learning to automate ultrasonic testing signal
analysis in enhancing the reliability and precision of
defect detection of bolts. A-scan signals were analysed to
extract key features for machine learning models, with
Gradient Boosting Classifier emerging as the optimal model,
achieving an accuracy of 97% in defect classification. This
methodology targets bolt defects such as thinning and
corrosion, utilizing normalised signal features to
distinguish severity of defects while enabling automated
classification.
|
| format | Article |
| id | doaj-art-05427d7289de45a09fbca35ed41576e5 |
| institution | Kabale University |
| issn | 1435-4934 |
| language | deu |
| publishDate | 2025-03-01 |
| publisher | NDT.net |
| record_format | Article |
| series | e-Journal of Nondestructive Testing |
| spelling | doaj-art-05427d7289de45a09fbca35ed41576e52025-08-20T03:52:52ZdeuNDT.nete-Journal of Nondestructive Testing1435-49342025-03-0130310.58286/30801Defect Detection of Bolts through Machine Learning ofUltrasonic Testing SignalsAbdul Azziz Abd TalibChun Yee LimChin Kian Liew The structural integrity of bolts in civil structures is critical to ensuring safety and operational efficiency. Traditional visual inspection methods often fail to detect internal defects of installed bolts, necessitating more advanced approaches. On the other hand, ultrasonic testing which can detect internal defects is not as straightforward to apply with signals difficult to analyse without the relevant certified non-destructive testing personnel qualifications and experience. This paper integrates machine learning to automate ultrasonic testing signal analysis in enhancing the reliability and precision of defect detection of bolts. A-scan signals were analysed to extract key features for machine learning models, with Gradient Boosting Classifier emerging as the optimal model, achieving an accuracy of 97% in defect classification. This methodology targets bolt defects such as thinning and corrosion, utilizing normalised signal features to distinguish severity of defects while enabling automated classification. https://www.ndt.net/search/docs.php3?id=30801 |
| spellingShingle | Abdul Azziz Abd Talib Chun Yee Lim Chin Kian Liew Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals e-Journal of Nondestructive Testing |
| title | Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals |
| title_full | Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals |
| title_fullStr | Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals |
| title_full_unstemmed | Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals |
| title_short | Defect Detection of Bolts through Machine Learning ofUltrasonic Testing Signals |
| title_sort | defect detection of bolts through machine learning ofultrasonic testing signals |
| url | https://www.ndt.net/search/docs.php3?id=30801 |
| work_keys_str_mv | AT abdulazzizabdtalib defectdetectionofboltsthroughmachinelearningofultrasonictestingsignals AT chunyeelim defectdetectionofboltsthroughmachinelearningofultrasonictestingsignals AT chinkianliew defectdetectionofboltsthroughmachinelearningofultrasonictestingsignals |