TIG welding defect detection using ResNet and Random Forest

Vu Quang Huy, Vu Minh Thuan, Hoang Van Huong, Tran Minh The Uyen, Nguyen Van-Thuc and Pham Son Minh

PLOS ONE, 2026, vol. 21, issue 7, 1-15

Abstract: Orbital TIG welding is critical in precision manufacturing, where weld defects can affect structural reliability. Manual inspection, analysis, and evaluation of welding defect images are complex because defect characteristics vary in shape, position, and size, making the process time-consuming. This study proposes a method that combines ResNet50 feature extraction with Random Forest classification to improve classification accuracy. A total of 3,219 weld image samples collected from controlled orbital TIG experiments were used for model training and evaluation and divided into four classes: normal weld, lack of fusion, overheating, and uneven weld. Using five-fold cross-validation, the method achieved an overall accuracy of 98% and demonstrated improved performance compared with CNN architectures (VGG16, VGG19, ResNet18, ResNet50) and traditional machine learning approaches such as SVM and Random Forest. ResNet50 extracts hierarchical visual representations through deep residual connections, enabling automatic feature learning, while Random Forest performs the final classification with robustness against overfitting on high-dimensional features.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0353276 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 53276&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0353276

DOI: 10.1371/journal.pone.0353276

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().