{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T02:42:00Z","timestamp":1775011320035,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819688883","type":"print"},{"value":"9789819688890","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-96-8889-0_38","type":"book-chapter","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T08:55:53Z","timestamp":1751273753000},"page":"447-459","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WeldViT: A Lightweight Network for Online Identification of Multi-label Welding Defects"],"prefix":"10.1007","author":[{"given":"Yue","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1016\/j.jmapro.2022.05.029","volume":"79","author":"F Xu","year":"2022","unstructured":"Xu, F., Xu, Y., Zhang, H., Chen, S.: Application of sensing technology in intelligent robotic arc welding: a review. J. Manuf. Process. 79, 854\u2013880 (2022)","journal-title":"J. Manuf. Process."},{"key":"38_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmsy.2020.07.021","volume":"57","author":"W Cai","year":"2020","unstructured":"Cai, W., Wang, J., Jiang, P., Cao, L., Mi, G., Zhou, Q.: Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: a critical review of recent literature. J. Manuf. Syst. 57, 1\u201318 (2020)","journal-title":"J. Manuf. Syst."},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, Z.: Visual sensing technologies in robotic welding: recent research developments and future interests. Sens. Actuators A: Phys. 320 (2021)","DOI":"10.1016\/j.sna.2021.112551"},{"key":"38_CR4","doi-asserted-by":"publisher","first-page":"908","DOI":"10.1016\/j.jmapro.2020.04.059","volume":"56","author":"Y Cheng","year":"2020","unstructured":"Cheng, Y., Wang, Q., Jiao, W., Yu, R., Chen, S., Zhang, Y., et al.: Detecting dynamic development of weld pool using machine learning from innovative composite images for adaptive welding. J. Manuf. Process. 56, 908\u2013915 (2020)","journal-title":"J. Manuf. Process."},{"key":"38_CR5","doi-asserted-by":"publisher","first-page":"3899","DOI":"10.1007\/s00170-023-11035-7","volume":"125","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Li, L., Chen, H., Wu, X., Dong, Y., Tian, J., et al.: Penetration recognition based on machine learning in arc welding: a review. Int. J. Adv. Manuf. Technol. 125, 3899\u20133923 (2023)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"38_CR6","doi-asserted-by":"crossref","unstructured":"Mattera, G., Nele, L., Paolella, D.: Monitoring and control of the wire arc additive manufacturing process using artificial intelligence techniques: a review. J. Intell. Manuf. 34 (2023)","DOI":"10.1007\/s10845-023-02085-5"},{"key":"38_CR7","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.jmsy.2023.05.026","volume":"68","author":"T Liu","year":"2023","unstructured":"Liu, T., Zheng, P., Bao, J.: Deep learning-based welding image recognition: a comprehensive review. J. Manuf. Syst. 68, 601\u2013625 (2023)","journal-title":"J. Manuf. Syst."},{"key":"38_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2022.102513","volume":"81","author":"J Wang","year":"2023","unstructured":"Wang, J., Gao, P., Zhang, J., et al.: Knowledge augmented broad learning system for computer vision based mixed-type defect detection in semiconductor manufacturing. Robot. Comput.-Integr. Manuf. 81, 102513 (2023)","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"38_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122795","volume":"242","author":"S Chen","year":"2024","unstructured":"Chen, S., Huang, Z., Wang, T., et al.: Mixed-type wafer defect detection based on multi-branch feature enhanced residual module. Expert Syst. Appl. 242, 122795 (2024)","journal-title":"Expert Syst. Appl."},{"key":"38_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102737","volume":"62","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Zhan, Q., Ma, Z.: EfficientNet-ECA: a lightweight network based on efficient channel attention for class-imbalanced welding defects classification. Adv. Eng. Inform. 62, 102737 (2024)","journal-title":"Adv. Eng. Inform."},{"key":"38_CR11","doi-asserted-by":"publisher","first-page":"4563","DOI":"10.3390\/s23094563","volume":"23","author":"S Xiao","year":"2023","unstructured":"Xiao, S., Liu, Z., Yan, Z., Wang, M.: Grad-MobileNet: a gradient-based unsupervised learning method for laser welding surface defect classification. Sensors 23, 4563 (2023)","journal-title":"Sensors"},{"key":"38_CR12","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531\u201311539. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: EfficientViT: memory efficient vision transformer with cascaded group attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14420\u201314430. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510\u20134520. IEEE (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Dollar, P.: Designing network design spaces. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10425\u201310433. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"38_CR16","doi-asserted-by":"publisher","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient cnn architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"38_CR17","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML), vol. 97, pp. 6105\u20136114. PMLR (2019)"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9992\u201310002. IEEE (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"38_CR19","unstructured":"Mehta, S., Rastegari, M.: MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer (2022). arXiv preprint arXiv:2110.02178"},{"key":"38_CR20","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Lecture Notes in Computer Science","Advances and Trends in Artificial Intelligence. Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8889-0_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T01:33:09Z","timestamp":1775007189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8889-0_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"ISBN":["9789819688883","9789819688890"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8889-0_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"assertion":[{"value":"1 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IEA\/AIE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kytakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"38","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ieaaie2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.i-somet.org\/iea-aie2025\/committees.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}