Design of a Deep Learning Visual System for the Thickness Measurement of Each Coating Layer of Triso-Coated Fuel Particles

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.

Keywords: coating layer, Image segmentation, thickness measurement, Object detection, TRISO-coated fuel particles.