Abstract
This paper describes a system to evaluate the crack severity for the welding bend test fragment. The examination in the welding qualification test in Japan, is conducted by human visual inspection and its burden is concern. The authors constructed an equipment to photograph the fragment specimens under the stable optical condition. The proposed system is also designed for portability to assist the evaluator in the field. We employed Resnet18 to evaluate the given image. The image input layer of original Resnet18 was remodeled from 224-by-224 to 500-by-500 to capture the crack feature in detail. The output layer is replaced with three classification nodes such as “Bad,” “Good,” and “Neutral” expressing the crack severity levels. Experiments showed that 83% accuracy were obtained, confirming that CNN adequately captured the surface crack conditions. The experimental details, remarks, and future works are discussed.
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References
Asai, S., Ogawa, T., Takebayashi, H.: Visualization and digitation of welder skill for education and training. Welding in the world 56, 26–34 (2012)
Byrd, A.P., Stone, R.T., Anderson, R.G., Woltjer, K.: The use of virtual welding simulators to evaluate experimental welders. Weld. J. 94(12), 389–395 (2015)
Hino, T., et al.: Visualization of gas tungsten arc welding skill using brightness map of backside weld pool. Trans. Mat. Res. Soc. Japan 44(5), 181–186 (2019)
The Japanese Welding Engineering Society http://www.jwes.or.jp/en/ Accessed 28 Mar 2022
Wan, Y., Jiang, W., Li, H.: Cold bending effect on residual stress, microstructure and mechanical properties of Type 316L stainless steel welded joint. Engineering Failure Analysis 117,104825 (2020)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks. In: CVPR, 1(2), p. 3 (2017)
Tan, M., Le, Q.V.: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ArXiv Preprint ArXiv:1905.1194 (2019)
Park, J.-K., An, W.-H., Kang, D.-J.: Convolutional neural network based surface inspection system for non-patterned welding defects. Int. J. Precision Eng. Manufacturing 20(3), 363-374 (2019)
Dung, C.V., Sekiya, H., Hirano, S., Okatani, T., Miki, C.: A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks. Automation in Construction 102, 217-229 (2019)
Zhang, Z., Wen, G., Chen, S.: Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding. J. Manuf. Process. 45, 208–216 (2019)
Dai, W., et al.: Deep learning assisted vision inspection of resistance spot welds. J. Manuf. Process. 62, 262–274 (2021)
Abdelkader, R., Ramou, N., Khorchef, M., Chetih, N., Boutiche, Y.: Segmentation of x-ray image for welding defects detection using an improved Chan-Vese model. Materials Today: Proceedings 42(5), 2963–2967 (2021)
Zhu, H., Ge, W., Liu, Z.: Deep learning-based classification of weld surface defects. Appl. Sci. 9(16), 3312 (2019)
The Japanese Industrial Standards Committee https://www.jisc.go.jp/eng/index.html Accessed 29 Mar 2022
Priddy, K.L., Keller, P.E.: Artificial Neural Networks - An Introduction, Chapter 11, pp. 101–105. Dealing with Limited Amounts of Data. SPIE Press, Bellingham, WA, USA (2005)
Ueda, N., Nakano, R.: Estimating expected error rates of neural network classifiers in small sample size situations: a comparison of cross-validation and bootstrap. In: Proceedings of ICNN’95 - International Conference on Neural Networks, 1, pp.101–104 (1995)
Acknowledgments
The authors would like to thank Ueno in MathWorks for technical advice. This work was supported by a Grant-in-Aid from JWES (The Japan Welding Engineering Society.)
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Kato, S., Hino, T., Kagawa, T., Nobuhara, H. (2023). Development of Portable Crack Evaluation System for Welding Bend Test. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_9
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DOI: https://doi.org/10.1007/978-3-031-18461-1_9
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