Ultrasonic assessment of thickness and bonding quality of coating layer based on short-time fourier transform and convolutional neural networks SCIE SCOPUS

Cited 9 time in WEB OF SCIENCE Cited 13 time in Scopus
Title
Ultrasonic assessment of thickness and bonding quality of coating layer based on short-time fourier transform and convolutional neural networks
Author(s)
Malikov, Azamatjon Kakhramon ugli; Cho,Younho; Kim, Young H.; Kim, Jeongnam; Park, Junpil; Yi, Jin Hak
KIOST Author(s)
Yi, Jin-Hak(이진학)
Alternative Author(s)
이진학
Publication Year
2021-07
Abstract
Ultrasonic non-destructive analysis is a promising and effective method for the inspection of protective coating materials. Offshore coating exhibits a high attenuation rate of ultrasonic energy due to the absorption and ultrasonic pulse echo testing becomes difficult due to the small amplitude of the second echo from the back wall of the coating layer. In order to address these problems, an advanced ultrasonic signal analysis has been proposed. An ultrasonic delay line was applied due to the high attenuation of the coating layer. A short-time Fourier transform (STFT) of the waveform was implemented to measure the thickness and state of bonding of coating materials. The thickness of the coating material was estimated by the projection of the STFT into the time-domain. The bonding and debonding of the coating layers were distinguished using the ratio of the STFT magnitude peaks of the two subsequent wave echoes. In addition, the advantage of the STFT-based approach is that it can accurately and quickly estimate the time of flight (TOF) of a signal even at low signal-to-noise ratios. Finally, a convolutional neural network (CNN) was applied to automatically determine the bonding state of the coatings. The time–frequency representation of the waveform was used as the input to the CNN. The experimental results demonstrated that the proposed method automatically determines the bonding state of the coatings with high accuracy. The present approach is more efficient compared to the method of estimating bonding state using attenuation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
ISSN
2079-6412
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/42191
DOI
10.3390/coatings11080909
Bibliographic Citation
COATINGS, v.11, no.8, 2021
Publisher
MDPI AG
Subject
ADHESIVELY BONDED JOINTS; MODEL-BASED ESTIMATION; NONDESTRUCTIVE EVALUATION; OF-FLIGHT; FREQUENCY; ATTENUATION; ALGORITHM; SIGNALS; CNN
Keywords
coating with high attenuation; thickness and bonding status; ultrasonic pulse-echo; short-time Fourier transform (STFT); convolutional neural networks (CNN)
Type
Article
Language
English
Document Type
Article
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