A machine vision-based defect detection system for nuclear-fuel rod groove-Reference-Cited by-同舟云学术

A machine vision-based defect detection system for nuclear-fuel rod groove

Published:2021-02-25 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Suo Xinyu,Liu Jian^ORCID,Dong Licheng,Shengfeng Chen,Enhui Lu,Ning Chen

Funder

the China-Japan Science and Technology Joint Committee of the Ministry of Science and Technology of the People's Republic of China

the Key Project of Science and Technology of Changsha

he Innovation on working methodology of Ministry of Science and Technology of the People's Republic of China

the Independent research work of State Key Laboratory of Advanced Design and Manufacture for Vehicle Body

the Foundation for Innovative Research Groups of the National Natural Science Foundation of China

the Intelligent Manufacturing Integrated Standardization and New Model Application Project of Minister of Industry and Information Technology of the People's Republic of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/content/pdf/10.1007/s10845-021-01746-7.pdf

Reference26 articles.

1. Badmos, O., Kopp, A., Bernthaler, T., et al. (2020). Image-based defect detection in lithium-ion battery electrode using convolutional neural networks. Journal of Intelligent Manufacturing, 31, 885–897. https://doi.org/10.1007/s10845-019-01484-x.

2. Boby, R. A., Sonakar, P. S., Singaperumal, M., & Ramamoorthy, B. (2011). Identification of defects on highly reflective ring components and analysis using machine vision. International Journal of Advanced Manufacturing Technology, 52(1), 217–233. https://doi.org/10.1007/s00170-010-2730-3.

3. Butler, D. (2011). Nuclear safety: Reactors, residents and risk. Nature, 472(7344), 400–401. https://doi.org/10.1038/472400a.

4. Cao, J., Cheng, Y., Wang, P., et al. (2016). Method based on bioinspired sample improves autofocusing performances. Optical Engineering, 55(10), 103103. https://doi.org/10.1117/1.OE.55.10.103103.

5. Carrera, D., Manganini, F., Boracchi, G., et al. (2017). Defect Detection in SEM Images of Nanofibrous Materials. IEEE Transactions on Industrial Informatics, 13(2), 551–561. https://doi.org/10.1109/TII.2016.2641472.

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