Abstract
The processing quality of the grooves of a nuclear-fuel rod will directly affect the quality of the finished nuclear-fuel rod. Due to the highly reflective, microscopic, and annular characteristics of nuclear-fuel rod grooves, it has been quite challenging to realize imaging and microscopic defect detection for these grooves. In this work, a machine vision-based defect detection system was developed for nuclear-fuel rod grooves. Through the performance improvement and application of the self-reference template defect detection method, efficient online inspection of nuclear-fuel rod grooves was realized. In the developed system, a combined-light-source imaging system was first designed by combining a coaxial light and a ring light, which realized the clear imaging of a groove. After that, an image expansion strategy was employed to expand the annular groove into a strip-shaped region of interest (ROI). Then, according to the turning processing characteristic of the nuclear-fuel rod groove, the large-size defect detection effect of the self-reference template method was improved by eliminating the anomalous columns prior to generating the self-reference template. The experimental results indicated that the average inspection efficiency of the developed system was 8.026 s/rod, the average false detection rate was 0.183%. The accuracy of the self-reference template method was 87.6%, higher than that of YOLOv2 and Faster R-CNN. The developed system exhibits high inspection efficiency and accuracy, so it can meet the actual detection functions and requirements of production lines, and now it has been successfully applied to actual production.
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source imaging system: a overall appearance of the imaging system, b internal details of the imaging system
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Acknowledgements
This work is supported by the China-Japan Science and Technology Joint Committee of the Ministry of Science and Technology of the People's Republic of China (Grant No. 2017YFE0128400), the Key Project of Science and Technology of Changsha (Grant No. kq1902049), the Innovation on working methodology of Ministry of Science and Technology of the People's Republic of China (Grant No. 2016IM030300), the Independent research work of State Key Laboratory of Advanced Design and Manufacture for Vehicle Body (Grant No. 71675001), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51621004), and the Intelligent Manufacturing Integrated Standardization and New Model Application Project of Minister of Industry and Information Technology of the People's Republic of China (Grant No. 2016ZXFM02016). Also, the authors would like to express their thanks to the reviewers for their valuable suggestions. We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company. The data materials in this paper are all real and available. We will provide the original data and MATLAB original code.
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Suo, X., Liu, J., Dong, L. et al. A machine vision-based defect detection system for nuclear-fuel rod groove. J Intell Manuf 33, 1649–1663 (2022). https://doi.org/10.1007/s10845-021-01746-7
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DOI: https://doi.org/10.1007/s10845-021-01746-7