APPLICATION OF MACHINE LEARNING FOR RECOGNIZING SURFACE WELDING DEFECTS IN VIDEO SEQUENCES

Authors

DOI:

https://doi.org/10.37943/16RYGE2979

Keywords:

weld defects, classification, feature extraction, SVM, ANN

Abstract

The paper offers a solution to the problem of detecting and recognizing surface defects in welded joints that appear during tungsten inert gas welding of metal edges. This problem belongs to the machine vision. Welding of stainless-steel edges is carried out automatically on the pipe production line. Therefore, frames of video sequences are investigated. Images of some welding defects are shown in the paper. An algorithm proposed by the authors is used to detect welding defects in the video sequence frames, the efficiency of which has been confirmed experimentally. The problem solution of welding defects recognition is based on the use of traditional machine learning methods: support vector machine and artificial neural network. To build classification models, a labeled dataset containing automatically extracted texture features from the areas of welding defects detected in the video sequences was created. An analysis was performed to identify the strength of the correlation of texture features between each other and the dependent variable in the dataset for dimensionality reduction of the feature vector. The models were trained and tested on datasets with different numbers of features. The quality of the classification models was evaluated based on the accuracy metric values. The best results were achieved by the classifier built using the support vector machine with a chi-square kernel on a training sample with two features. The build models allow automatic recognition of such welding defects as lack of fusion and metal oxidation. The computational experiments with real video sequences obtained with a digital camera confirmed the possibility of using the proposed solution for recognizing surface welding defects in the process of manufacturing stainless steel pipes.

References

Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021). Surface defect detection methods for industrial products: a review. Applied Sciences, 11(16), 7657. https://doi.org/10.3390/app11167657

Rasheed, A., Zafar, B., Rasheed, A., Ail, N., Sajid, M., Dar, S.H., Habib, U., Shehryar, T., & Mahmood, M.T. (2020). Fabric defect detection using computer vision techniques: a comprehensive review. Mathematical Problems in Engineering, 2. https://doi.org/10.1155/2020/8189403

Sipko, E., Kravchenko, O., Karapetyan, A., Plakasova, Zh., & Gladka, M. (2020). The system recognizes surface defects of marble slabs based on segmentation methods. Scientific Journal of Astana IT University, 1, 50-59. https://doi.org/10.37943/AITU.2020.1.63643

He, Y., Song, K., Meng, Q., & Yan, Y. (2020). An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Transactions on Instrumentation and Measurement, 69 (4), 1493-1504. https://doi.org/10.1109/TIM.2019.2915404

Luo, Q., Fang, X., Su, J., & et al. (2020). Automated visual defect classification for flat steel surface: a survey. IEEE Transactions on Instrumentation and Measurement, 69(12), 9329-9349. https://doi.org/10.1109/TIM.2020.3030167

Tang, B., Chen, L., Sun, W., & Lin, Z. (2023). Review of surface defect detection of steel products based on machine vision. IET Image Processing, 17(2), 303-322. https://doi.org/10.1049/ipr2.12647

Li, Y., Hu, M., & Wang, T. (2020). Visual inspection of weld surface quality. Journal of Intelligent and Fuzzy Systems, 39, 1-10. https://doi.org/10.3233/JIFS-179993

Tripicchio, P., Camacho-Gonzalez, G. & D’Avella, S. (2020). Welding defect detection: coping with artifacts in the production line. The International Journal of Advanced Manufacturing Technology, 111, 1659-1669. https://doi.org/10.1007/s00170-020-06146-4

Ding, K., Niu, Z., Hui, J., Zhou, X., & Chan, F.T.S. (2022). A weld surface defect recognition method based on improved mobilenetv2 algorithm. Mathematics, 10, 3678. https://doi.org/10.3390/math10193678

Park, J.-K., An, W.-H. & Kang, D. (2019). Convolutional neural network based surface inspection system for non-patterned welding defects. International Journal of Precision Engineering and Manufacturing, 20. https://doi.org/10.1007/s12541-019-00074-4

Hou, W., Zhang, D., Wei, Y., Guo, J., & Zhang, X. (2020). Review on computer-aided weld defect detection from radiography images. Applied Sciences, 10(5), 1878. https://doi.org/10.3390/app10051878

Ai-Ghamdi, S., Emam, A. & Abouelatta, O. (2017). Automatic classification of welding defects using neural network and image processing techniques. Albaha University Journal of Basic and Applied Sciences, 1, 17-25.

Jiang, H., Zhao, Y., Gao, J., & Zhao, W. (2016). Weld defect classification based on texture features and principal component analysis. Insight - Non-destructive testing and condition monitoring, 58(4), 194-200. https://doi.org/10.1784/insi.2016.58.4.194

Patil, R. & Reddy P, Y. (2021). An autonomous technique for multi-class weld imperfections detection and classification by support vector machine. Journal of Nondestructive Evaluation, 40. https://doi.org/10.1007/s10921-021-00801-w

Dong, S., Sun, X., Xie, S. & Wang, M. (2019). Automatic defect identification technology of digital image of pipeline weld. Natural Gas Industry B, 6. https://doi.org/10.1016/j.ngib.2019.01.016

Sassi, P., Tripicchio, P., & Avizzano, C.A. (2019). A smart monitoring system for automatic welding defect detection. IEEE Transactions on Industrial Electronics, 66(12), 9641-9650. https://doi.org/10.1109/TIE.2019.2896165

Ajmi, C., Zapata Pérez, J., Ferchichi, S., Zaafouri, A., & Laabidi, K. (2020). Deep learning technology for weld defects classification based on transfer learning and activation features. Advances in Materials Science and Engineering, 1-16. https://doi.org/10.1155/2020/1574350

Yemelyanova, M., Smailova, S., & Baklanova, O. (2023). Detection of surface defects in welded joints during visual inspections using machine vision methods. Computer Optics, 47(1), 112-117. https://doi.org/10.18287/2412-6179-CO-1137

Yemelyanova, M. (2023). Algorithm for automatic detection of defects in welded joints in a video sequence based on comparison of brightness histograms. Bulletin of D. Serikbayev EKTU, 3, 186-194.

Andriyanov, N.A., Dementiev, V.E., & Tashlinskii, A.G. (2022). Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks. Computer Optics, 46(1), 139-159. https://doi.org/10.18287/2412-6179-CO-922

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Published

2023-11-14

How to Cite

Yemelyanova, M., & Smailova , S. (2023). APPLICATION OF MACHINE LEARNING FOR RECOGNIZING SURFACE WELDING DEFECTS IN VIDEO SEQUENCES. Scientific Journal of Astana IT University, 16(16). https://doi.org/10.37943/16RYGE2979

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