Abstract
We consider the problem of quality control of fuel pellets for nuclear reactors. Various methods of obtaining and processing images of pellet surfaces were investigated when developing the testing system. The main difficulty of this task is the imperfect quality of the resulting image of the test object as well as limited time for its processing. For high-performance testing of the geometry of fuel pellets, hardware and software tools and algorithms have been developed to considerably increase the reliability of testing results. As a result of this work, we obtained stable images with a high degree of repeatability and with sufficient resolution that are suitable for subsequent high-performance reliable mathematical processing. A high degree of independence of the image and of the processing results of specific characteristics of individual products and their batches has been achieved.
REFERENCES
Reshetnikov, G., Bibilashvili, Yu.K., Golovnin, I.S., et al., Razrabotka, proizvodstvo i ekspluatatsiya teplovidelyayuschikh elementov energeticheskikh reaktorov (Development, Production, and Operation of Nuclear Reactor Fuel Elements), Moscow: Energoatomizdat, 1995.
Beloborodov, A.V., Vlasov, E.V., Finogenov, L.V., and Zav’yalov, P.S., High productive optoelectronic pellets surface inspection for nuclear reactors, Key Eng. Mater., 2010, vol. 437, pp. 165–169.
Finogenov, L.V., Beloborodov, A.V., Ladygin, V.I., Chugui, Yu.V., Zagoruiko, N.G., Gulyaevskii, S.E., Shul’man, Yu.S., Lavrenyuk, P.I., and Pimenov, Yu.V., An optoelectronic system for automatic inspection of the appearance of fuel pellets, Defektoskopiya, 2007, vol. 43, no. 10, pp. 692–699.
Zav’yalov, P.S., Finogenov, L.V., and Vlasov, E.V., A dedicated optical system for the quality inspection of cylindrical surfaces, Russ. J. Nondestr. Test., 2016, vol. 52, no. 7, pp. 415–420.
Zhang, B., Liu, M., Tian, Y., Wu, G., Yang, X., Shi, S., and Li, J., Defect inspection system of nuclear fuel pellet end faces based on machine vision, J. Nucl. Sci. Technol., 2020, vol. 57, no. 6, pp. 617–623. https://doi.org/10.1080/00223131.2019.1708827
Zhang, B., Miao, Y., Tian, Y., Zhang, W., Wu, G., Wang, X., and Zhang, C., Implementation of surface crack detection method for nuclear fuel pellets guided by convolution neural network, J. Nucl. Sci. Technol., 2021, vol. 58, no. 7, pp. 787–796. https://doi.org/10.1080/00223131.2020.1869622
Bardin, B.V., Fast algorithm of median filtering, Sci. Instrum., 2011, vol. 21, no. 3, pp. 135–139.
Sauvola, J. and Pietikainen, M., Adaptive document image binarization, Pattern Recognition, 2000, vol. 33, pp. 225–236.
Shafait, F., Keysers, D., and Breuel, T.M., Efficient implementation of local adaptive thresholding techniques using integral images, Document Recognit. Retrieval XV (San Jose, 2008).
Funding
This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors of this work declare that they have no conflicts of interest.
Rights and permissions
About this article
Cite this article
Vlasov, E.V., Beloborodov, A.V., Zav’yalov, P.S. et al. Monitoring the Appearance of the End Faces of Fuel Pellets under Conditions of Conveyor Production. Russ J Nondestruct Test 59, 815–825 (2023). https://doi.org/10.1134/S106183092370047X
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S106183092370047X