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Enhancing Weld Inspection Through Comparative Analysis of Traditional Algorithms and Deep Learning Approaches

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Abstract

Automated inspection is vital in modern industrial manufacturing, optimizing production processes and ensuring product quality. Welding, a widely used joining technique, is susceptible to defects like porosity and cracks, compromising product reliability. Traditional nondestructive testing (NDT) methods suffer from inefficiency and limited accuracy. Many researchers have tried to apply deep learning for defect detection to address these limitations. This study compares traditional algorithms with deep learning methods, specifically evaluating the SwinUNet model for weld segmentation. The model achieves an impressive F1 score of 96.31, surpassing traditional algorithms. Feature analysis utilizing class activation maps confirms the model's robust recognition and generalization capabilities. Additionally, segmentation results for different welding defects were compared among various models, further substantiating the recognition capabilities of SwinUNet. The findings contribute to the automation of weld identification and segmentation, driving industrial production efficiency and enhancing defect detection.

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Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

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All authors agree to the publication.

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Acknowledgements

The work is sponsored by the Beijing Institute of Technology Young Scholar Startup Program (no grant number). The authors would like to acknowledge support from the Experimental Center of Advanced Materials (ECAM) of the Beijing Institute of Technology and the National Key Laboratory of Science and Technology on Materials under Shock and Impact.

Geolocation Information

Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing, 100081, China.

Funding

The Beijing Institute of Technology Young Scholar Startup Program supported this work.

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Authors and Affiliations

  1. School of Materials Science & Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Baoxin Zhang, Xiaopeng Wang, Jinhan Cui, Juntao Wu & Xinghua Yu

  2. Chongqing Innovation Center, Beijing Institute of Technology, Chongqing, 401147, China

    Xinghua Yu

  3. Chongqing Special Equipment Inspection and Research Institute, Chongqing, 401121, China

    Zhi Xiong & Wenpin Zhang

  4. Key Laboratory of Electromechanical Equipment Security in Western Complex Environment for State Market Regulation, Chongqing, 401121, China

    Zhi Xiong & Wenpin Zhang

  5. Chongqing University, Chongqing, 400044, China

    Wenpin Zhang

Authors
  1. Baoxin Zhang
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  2. Xiaopeng Wang
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  3. Jinhan Cui
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  4. Juntao Wu
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  5. Zhi Xiong
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Contributions

Baoxin Zhang: Conceptualization, Methodology, Software, Original Draft Preparation. Xiaopeng Wang: Data Curation. Jinhan Cui: Visualization, Investigation. Juntao Wu: Data Curation. Zhi Xiong: Data Curation, Wenpin Zhang: Data Provider. Xinghua Yu: Supervision, Writing—Review and Editing, Project Administration.

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Correspondence to Xinghua Yu.

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Zhang, B., Wang, X., Cui, J. et al. Enhancing Weld Inspection Through Comparative Analysis of Traditional Algorithms and Deep Learning Approaches. J Nondestruct Eval 43, 38 (2024). https://doi.org/10.1007/s10921-024-01047-y

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  • DOI: https://doi.org/10.1007/s10921-024-01047-y

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