Abstract
Welding is essential in our life. It is crucial to nurture welding skills in Japan nowadays. The experts have to evaluate the many beginners' welding. Since the experts' burden is critical, a computational assistant for evaluating beginners' welding is required. This paper describes a simple evaluation system of welding plates by beginners. The authors considered four types of beginners' typical defects: lack of welding metal, linear misalignment, welding metal unevenness, and angular distortion. To capture these defects simultaneously, the authors propose an original equipment to photograph the welding plates. The computer extracts only the part of the welding plate using color markers. CNN (Convolutional Neural Network) evaluates the defects. As a first step, the authors addressed evaluating only angular distortion. The angular distortion is one of the typical failures by beginners. In the experiment, the authors conducted the validation of CNN. In the conclusion part, we discuss the experimental result and future works.
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References
Niles, R.W., Jackson, C.E.: Weld thermal efficiency of the GTAW process. Weld. J. 54, 25–32 (1975)
Asai, S., Ogawa, T., Takebayashi, H.: Visualization and digitation of welder skill for education and training. Weld. World 56, 26–34 (2012)
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)
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)
Kato, S., Hino, T., Yoshikawa, N.: Fundamental study on evaluation system of beginner’s welding using CNN. In: Lecture Notes in Networks and Systems, vol. 96, pp. 821–827 (2019)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Kato, S., Hino, T., Kumeno, H., Kagawa, T., Nobuhara, H.: Automatic detection of beginner's welding joint. In: Proceedings of 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, pp. 465–467 (2020).
Mochizuki, M., Okano, S.: Effect of welding process conditions on angular distortion induced by bead-on-plate welding. ISIJ Int. 58(1), 153–158 (2018)
Krizhevsky, A., Sutskever, I., Hinton, CE.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS 2012), pp. 1097–1105 (2012)
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Priddy, K.L., Keller, P.E.: Artificial Neural Networks - An Introduction, Chapter 11 Dealing with Limited Amounts of Data, pp. 101–102. SPIE Press, Bellingham (2005)
Wong, T.-T.: Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn. 48(9), 2839–2846 (2015)
MathWorks: Transfer Learning Using AlexNet. https://jp.mathworks.com/help/deeplearning/ug/transfer-learning-using-alexnet.html;jsessionid=10ee690544b1eb830e5dc2412cf0?lang=en. Accessed 27 Feb 2021
Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Park, J.-K., , An, W.-H., Kang, D.-J.: Convolutional neural network based surface inspection system for non-patterned welding defects. Int. J. Precis. Eng. Manuf. 20(3), 363–374 (2019)
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This research is supported by the Japan Welding Engineering Society's grant.
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Kato, S. et al. (2022). Evaluation for Angular Distortion of Welding Plate. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_23
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DOI: https://doi.org/10.1007/978-3-030-82193-7_23
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