Design of a deep learning visual system for the thickness measurement of each coating layer of TRISO-coated fuel particles. (15th March 2022)

• Record Type: Journal Article
• Authors: Zhang, Hang; Liu, Jian; Hu, Zhaochuan; Chen, Ning; Yang, Zhiyuan; Shen, Junhua
• Is Part Of: Measurement. Volume 191(2022)
• Publication Date: 2022-03-15
• Subjects: Coating layer -- High-temperature gas-cooled reactors -- Intelligent visual system -- Thickness measurement -- TRISO-coated fuel particles; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.110806
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A complete set of automatic microscopic imaging equipment is designed. An automatic image acquisition method is proposed for the coated fuel particle. An accurate thickness measurement method is proposed based on the designed DCNNs. A tracing method is proposed to enhance the reliability and consistency. 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.