{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:49:17Z","timestamp":1773510557292,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031184604","type":"print"},{"value":"9783031184611","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-18461-1_9","type":"book-chapter","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T07:15:14Z","timestamp":1665558914000},"page":"133-144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of Portable Crack Evaluation System for Welding Bend Test"],"prefix":"10.1007","author":[{"given":"Shigeru","family":"Kato","sequence":"first","affiliation":[]},{"given":"Takanori","family":"Hino","sequence":"additional","affiliation":[]},{"given":"Tomomichi","family":"Kagawa","sequence":"additional","affiliation":[]},{"given":"Hajime","family":"Nobuhara","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/BF03321378","volume":"56","author":"S Asai","year":"2012","unstructured":"Asai, S., Ogawa, T., Takebayashi, H.: Visualization and digitation of welder skill for education and training. Welding in the world 56, 26\u201334 (2012)","journal-title":"Welding in the world"},{"issue":"12","key":"9_CR2","first-page":"389","volume":"94","author":"AP Byrd","year":"2015","unstructured":"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\u2013395 (2015)","journal-title":"Weld. J."},{"issue":"5","key":"9_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.14723\/tmrsj.44.181","volume":"44","author":"T Hino","year":"2019","unstructured":"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\u2013186 (2019)","journal-title":"Trans. Mat. Res. Soc. Japan"},{"key":"9_CR4","unstructured":"The Japanese Welding Engineering Society http:\/\/www.jwes.or.jp\/en\/ Accessed 28 Mar 2022"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1016\/j.engfailanal.2020.104825"},{"issue":"11","key":"9_CR6","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"9_CR7","unstructured":"Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"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\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks. In: CVPR, 1(2), p. 3 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"9_CR11","unstructured":"Tan, M., Le, Q.V.: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ArXiv Preprint ArXiv:1905.1194 (2019)"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1007\/s12541-019-00074-4"},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1016\/j.autcon.2019.02.013"},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.jmapro.2019.06.023","volume":"45","author":"Z Zhang","year":"2019","unstructured":"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\u2013216 (2019)","journal-title":"J. Manuf. Process."},{"key":"9_CR15","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.jmapro.2020.12.015","volume":"62","author":"W Dai","year":"2021","unstructured":"Dai, W., et al.: Deep learning assisted vision inspection of resistance spot welds. J. Manuf. Process. 62, 262\u2013274 (2021)","journal-title":"J. Manuf. Process."},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"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\u20132967 (2021)","DOI":"10.1016\/j.matpr.2020.12.806"},{"issue":"16","key":"9_CR17","doi-asserted-by":"publisher","first-page":"3312","DOI":"10.3390\/app9163312","volume":"9","author":"H Zhu","year":"2019","unstructured":"Zhu, H., Ge, W., Liu, Z.: Deep learning-based classification of weld surface defects. Appl. Sci. 9(16), 3312 (2019)","journal-title":"Appl. Sci."},{"key":"9_CR18","unstructured":"The Japanese Industrial Standards Committee https:\/\/www.jisc.go.jp\/eng\/index.html Accessed 29 Mar 2022"},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1117\/3.633187","volume-title":"Artificial Neural Networks - An Introduction, Chapter 11","author":"KL Priddy","year":"2005","unstructured":"Priddy, K.L., Keller, P.E.: Artificial Neural Networks - An Introduction, Chapter 11, pp. 101\u2013105. Dealing with Limited Amounts of Data. SPIE Press, Bellingham, WA, USA (2005)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"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\u201995 - International Conference on Neural Networks, 1, pp.101\u2013104 (1995)","DOI":"10.1109\/ICNN.1995.488074"}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18461-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T16:49:22Z","timestamp":1728146962000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18461-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,13]]},"ISBN":["9783031184604","9783031184611"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18461-1_9","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,13]]},"assertion":[{"value":"13 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FTC 2022","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of the Future Technologies Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ftc2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/FTC","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}