Open Access
| Issue |
EPJ Web Conf.
Volume 376, 2026
6th International Conference on Recent Advances in Mechanical Engineering and Nanomaterials (ICRAMEN 2026)
|
|
|---|---|---|
| Article Number | 02006 | |
| Number of page(s) | 9 | |
| Section | Production and Manufacturing Processes | |
| DOI | https://doi.org/10.1051/epjconf/202637602006 | |
| Published online | 01 July 2026 | |
- J. Ren, H. Zhang, M. Yue, YOLOv8-WD: Deep learning-based detection of defects in automotive brake joint laser welds. Appl. Sci. 15(3), 1184 (2025). https://doi.org/10.3390/app15031184 [Google Scholar]
- J. Qi, Z. Xu, L. Cheng, S. Liu, K. Zhong, D. Ai, Welding image defect detection based on YOLO11. Anti-Corros. Methods Mater. (2026), ahead-of-print. https://doi.org/10.1108/ACMM-02-2025-3184 [Google Scholar]
- M. Siva Ramkumar, S.K. Prasanna Lakshmi, K. Balakrishnan, T. Rajendran, M. Arif, M. Sivaramkrishnan, D. Kavitha, A hybrid Swin-DeiT transformer framework for automated welding defect detection in radiographic images. Comput. Electr. Eng. 134, 111135 (2026). https://doi.org/10.1016/j.compeleceng.2026.111135 [Google Scholar]
- M. Zhang, Y. Hu, B. Xu et al., DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion. Sci. Rep. 15, 23305 (2025). https://doi.org/10.1038/s41598-025-06811-2 [Google Scholar]
- K. Parmar, A. Biber, M. Olesch et al., Leveraging thermal images for automatic surface defect detection in TIG welding process of thin metal sheets. Weld. World 70, 14871503 (2026). https://doi.org/10.1007/s40194-026-02340-2 [Google Scholar]
- G. Jocher, A. Chaurasia, J. Qiu, YOLO by Ultralytics (2023) [Google Scholar]
- D.P. Kingma, M. Welling, Auto-encoding variational Bayes, in International Conference on Learning Representations (2014) [Google Scholar]
- S. Chalapathy, S. Chawla, Deep learning for anomaly detection: A survey. ACM Comput. Surv. 54, 1–38 (2021) [Google Scholar]
- S. Zhang, X. Wu, Z. You, Automatic weld defect detection based on deep learning. IEEE Access 7, 17250–17259 (2019) [Google Scholar]
- Dataset Author, The welding defect dataset - v2, Kaggle (2023) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

