Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network-Reference-Cited by-同舟云学术

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Inspection of Welding Defect Based on Multi-feature Fusion and a Convolutional Network

Published:2021-10-19 Issue:4 Volume:40 Page:
ISSN:0195-9298
Container-title:Journal of Nondestructive Evaluation
language:en
Short-container-title:J Nondestruct Eval

Author:

Yang Lei,Fan Junfeng,Huo Benyan,Liu Yanhong

Funder

National Natural Science Foundation of China

National Key Research & Development Project of China

Science & Technology Research Project in Henan Province of China

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Link

https://link.springer.com/content/pdf/10.1007/s10921-021-00823-4.pdf

Reference47 articles.

1. Stavridis, J., Papacharalampopoulos, A., Stavropoulos, P.: Quality assessment in laser welding: a critical review. Int. J. Adv. Manuf. Technol. 94(5–8), 1825–1847 (2018)

2. Tao, X., Wang, Z., Zhang, Z., Zhang, D., Xu, D., Gong, X., Zhang, L.: Wire defect recognition of spring-wire socket using multitask convolutional neural networks. IEEE Trans. Compon. Packag. Manuf. Technol. 8(4), 689–698 (2018)

3. Alfaro, S.C., Franco, F.D.: Exploring infrared sensoring for real time welding defects monitoring in GTAW. Sensors 10(6), 5962–5974 (2010)

4. Jain, D.K., et al.: An evaluation of deep learning based object detection strategies for threat object detection in baggage security imagery. Patt. Recogn. Lett. 120, 112–119 (2019)

5. Duan, F., Yin, S., Song, P., Zhang, W., Zhu, C., Yokoi, H.: Automatic welding defect detection of X-ray images by using cascade adaboost with penalty term. IEEE Access 7, 125 929-125 938 (2019)

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