Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

Azamatjon Kakhramon Ugli Malikov; Manuel Fernando Flores Cuenca; Beomjin Kim; Younho Cho; Young H Kim

doi:10.1007/s12650-023-00922-6

Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks

J Vis (Tokyo). 2023 May 31:1-17. doi: 10.1007/s12650-023-00922-6. Online ahead of print.

Authors

Azamatjon Kakhramon Ugli Malikov¹, Manuel Fernando Flores Cuenca¹, Beomjin Kim¹, Younho Cho², Young H Kim³

Affiliations

¹ Graduate School of Mechanical Engineering, Pusan National University, Busan, 46241 Korea.
² School of Mechanical Engineering, Pusan National University, Busan, 46241 Korea.
³ Institute of Nuclear Safety and Management, Pusan National University, Busan, 46241 Korea.

Abstract

Abstract: The containment liner plate (CLP) is a thin layer of carbon steel material applied as a base for concrete structures protecting nuclear material. The structural health monitoring of the CLP is critical to ensure the safety of nuclear power plants. Hidden defects in the CLP can be identified utilizing ultrasonic tomographic imaging techniques such as the reconstruction algorithm for the probabilistic inspection of damage (RAPID) methodology. However, Lamb waves have a multimodal dispersion feature, which makes the selection of a single mode more difficult. Thus, sensitivity analysis was utilized since it allows for the determination of each mode's level of sensitivity as a function of frequency; the S0 mode was chosen after examining the sensitivity. Even though proper Lamb wave mode was selected, the tomographic image had blurred zones. Blurring reduces the precision of an ultrasonic image and makes it more difficult to distinguish the dimensions of the flaw. To enhance the tomographic image of the CLP, deep learning architecture such as U-Net was utilized for the segmentation of the experimental ultrasonic tomographic image, which includes an encoder and decoder part for better visualization of the tomographic image. Nevertheless, collecting enough ultrasonic images to train the U-Net model was not economically feasible, and only a small number of the CLP specimens can be tested. Thus, it was necessary to utilize transfer learning and get the values of the parameters from a pre-trained model with a much larger dataset as a starting point for a new task, rather than training a new model from scratch. Through these deep learning approaches, we were able to eliminate the blurred section of the ultrasonic tomography, leading to images with clear edges of defects and no blurred zones.

Keywords: Image segmentation; The sensitivity of the Lamb wave; Transfer learning; U-Net; Ultrasonic Lamb wave; Ultrasonic tomography.

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