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- W4210494794 endingPage "110806" @default.
- W4210494794 startingPage "110806" @default.
- W4210494794 abstract "In the new generation of nuclear energy system, the thickness of the coating layer of tristructural isotropic (TRISO)-coated fuel particles is one of the most important parameters. Recently, some visual-based methods have been developed for the thickness measurement of each coating layer, but the existing method still lacks of practicality. In this study, an advanced visual system combined with the ceramographic section method and deep learning algorithms is designed to automatically measure the thickness values of each coating layer. In the designed visual system, an automatic image acquisition method is first achieved. After that, an accurate thickness measurement method is proposed based on the designed image segmentation model. Finally, to enhance the reliability and consistency of the measurement results, a tracing method is developed for the designed measurement system. The experimental results demonstrate that the designed system can accurately automatically measure the thickness values of each coating layer." @default.
- W4210494794 created "2022-02-08" @default.
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- W4210494794 date "2022-03-01" @default.
- W4210494794 modified "2023-10-16" @default.
- W4210494794 title "Design of a deep learning visual system for the thickness measurement of each coating layer of TRISO-coated fuel particles" @default.
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- W4210494794 doi "https://doi.org/10.1016/j.measurement.2022.110806" @default.
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