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All issues Volume 658 (2025) E3S Web Conf., 658 (2025) 01002 References
Open Access
Issue
E3S Web Conf.
Volume 658, 2025
Third International Conference of Applied Industrial Engineering: Intelligent Models and Data Engineering (CIIA 2025)
Article Number 01002
Number of page(s) 11
Section Industrial Optimization
DOI https://doi.org/10.1051/e3sconf/202565801002
Published online 13 November 2025
  1. J.B. Nadiu, V. Sushma, J.P. CT, M. Dhanush, V. Senthilkumar, A deep learning-based model for defect reorganisation in welding/wire arc additive manufacturing, Welding International pp. 1–10 (2024). [Google Scholar]
  2. W. Tang, Y. Shi, Y. Liao, Q. Li, Y. Luo, Research on welding defect classification methods based on convolutional neural networks, International journal of computer science and information technology 4, 150–161 (2024). 10.62051/ijcsit.v4n1.19 [Google Scholar]
  3. S. Wang, T. Chen, R. Zhang, W. Liu, D. Lv, Y. Zhang, Research on intelligent classifica-tion of weld defects based on improved resnext network (2024). [Google Scholar]
  4. D. Palma-Ramírez, B.D. Ross-Veitía, P. Font-Ariosa, A. Espinel-Hernández, A. Sanchez-Roca, H. Carvajal-Fals, J.R. Nuñez-Alvarez, H. Hernández-Herrera, Deep convolutional neural network for weld defect classification in radiographic images, Heliyon 10 (2024). [Google Scholar]
  5. B. Guo, Y. Wang, X. Li, Y. Zhou, J. Li, L. Rao, Welding defect classification based on lightweight cnn, International Journal of Pattern Recognition and Artificial Intelligence 37, 2350026 (2023). 10.1142/S021800142350026X [Google Scholar]
  6. G.A. Elhendawy, Y. El-Taybany, Machine vision-assisted welding defect detection sys-tem with convolutional neural networks, International Journal of Precision Engineering and Manufacturing pp. 1–10 (2025). [Google Scholar]
  7. T. Tyystjärvi, I. Virkkunen, P. Fridolf, A. Rosell, Z. Barsoum, Automated defect detection in digital radiography of aerospace welds using deep learning, Welding in the World 66, 643 (2022). 10.1007/s40194-022-01257-w [Google Scholar]
  8. S.B. Block, R.D. Da Silva, A.E. Lazzaretti, R. Minetto, Lohi-weld: A novel industrial dataset for weld defect detection and classification, a deep learning study, and future perspectives, IEEE Access (2024). [Google Scholar]
  9. S. Sundaram, A. Zeid, Artificial intelligence-based smart quality inspection for manufac-turing, Micromachines 14, 570 (2023). 10.3390/mi14030570 [Google Scholar]
  10. L. Song, J. Kang, Q. Zhang, S. Wang, A weld feature points detection method based on improved yolo for welding robots in strong noise environment, Signal, Image and Video Processing 17, 1801 (2022). 10.1007/s11760-022-02391-0 [Google Scholar]
  11. L. Xu, S. Dong, H. Wei, Q. Ren, J. Huang, J. Liu, Defect signal intelligent recognition of weld radiographs based on YOLO V5-IMPROVEMENT, Journal of Manufacturing Processes 99, 373 (2023). 10.1016/j.jmapro.2023.05.058 [Google Scholar]
  12. Z. Zhang, G. Wen, S. Chen, Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding, Journal of Manufacturing Processes 45, 208 (2019). 10.1016/j.jmapro.2019.06.023 [Google Scholar]
  13. R. Miao et al., Real-time defect identification of narrow overlap welds and application based on convolutional neural networks, Journal of Manufacturing Systems 62, 800 (2022). 10.1016/j.jmsy.2021.01.012 [Google Scholar]
  14. R.M. Nazarov, Z.M. Gizatullin, E.S. Konstantinov, Classification of Defects in Welds Using a Convolution Neural Network, in Proceedings of ELCONRUS (2021) [Google Scholar]
  15. V. Vasan, N.V. Sridharan, R.J. Balasundaram, S. Vaithiyanathan, Ensemble-based deep learning model for welding defect detection and classification, Engineering Applications of Artificial Intelligence 136 (2024). 10.1016/j.engappai.2024.108961 [Google Scholar]
  16. J.A. Ortega Triana, Aprendizaje profundo para la detección automática de fisuras de hormigón usando redes neuronales convolucionales (2021), http://hdl.handle.net/10251/174954 [Google Scholar]
  17. X. Wan, Y. Wang, D. Zhao, Y. Huang, A comparison of two types of neural network for weld quality prediction in small scale resistance spot welding, Mechanical Systems and Signal Processing 93, 634 (2017). [Google Scholar]
  18. M.P. Ho, W.K. Ngai, T.W. Chan, H.w. Wai, An artificial neural network approach for parametric study on welding defect classification, The International Journal of Advanced Manufacturing Technology 120, 527 (2022). 10.1007/s00170-022-08700-8 [Google Scholar]
  19. P. Sassi, P. Tripicchio, C. Avizzano, A smart monitoring system for automatic weld-ing defect detection, IEEE Transactions on Industrial Electronics 66, 7078 (2019). 10.1109/TIE.2019.2896165 [Google Scholar]
  20. T. Feng, S. Huang, J. Liu, J. Wang, X. Fang, Welding surface inspection of armatures via cnn and image comparison, IEEE Sensors Journal 21, 1 (2021). 10.1109/JSEN.2021.3079334 [Google Scholar]
  21. M. Kesse, E. Buah, H. Handroos, G. Ayetor, Development of an artificial intelligence powered tig welding algorithm for the prediction of bead geometry for tig welding processes using hybrid deep learning, Metals 10, 451 (2020). 10.3390/met10040451 [Google Scholar]
  22. A. Khumaidi, E.M. Yuniarno, M.H. Purnomo, Welding defect classification based on convolution neural network (CNN) and Gaussian kernel, in 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA) (2017) [Google Scholar]
  23. K. Ramesh, E. Ramana, L. Srikanth, C. Sri Harsha, N. Kiran Kumar, in Recent Advances in Materials Processing and Characterization: Select Proceedings of ICMPC 2021 (Springer, 2022), pp. 339–350 [Google Scholar]
  24. W. Khalifa, O. Abouelatta, E. Gadelmawla, I. Elewa, Classification of welding defects using gray level histogram techniques via neural network., MEJ-Mansoura Engineering Journal 39, 1 (2020). [Google Scholar]
  25. Trial, Welding quality dataset (2024), accessed on 2025-06-04, https://universe.roboflow.com/trial-3ftsn/welding-quality-4rfjj [Google Scholar]
  26. B. Kalanoor, Weld detection dataset (2023), accessed on 2025-06-04, https://universe.roboflow.com/basanth-kalanoor-k9v2p/weld-detection-slz4d [Google Scholar]
  27. D.A. Pitaloka, A. Wulandari, T. Basaruddin, D.Y. Liliana, Enhancing cnn with prepro-cessing stage in automatic emotion recognition, Procedia computer science 116, 523 (2017). [Google Scholar]
  28. L. Fan, F. Zhang, H. Fan, C. Zhang, Brief review of image denoising techniques, Visual computing for industry, biomedicine, and art 2, 7 (2019). [Google Scholar]
  29. X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, M. Parmar, A review of convolu-tional neural networks in computer vision, Artificial Intelligence Review 57, 99 (2024). 10.1007/s10462-024-10721-6 [Google Scholar]
  30. E. Jeczmionek, P.A. Kowalski, Flattening layer pruning in convolutional neural networks, Symmetry 13, 1147 (2021). [Google Scholar]
  31. V. Moya, A. Quito, A. Pilco, J.P. Vásconez, C. Vargas, Crop detection and maturity classification using a yolov5-based image analysis, Emerging Science Journal 8, 496 (2024). [Google Scholar]

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