Ultrasonic Assessment of Thickness and Bonding Quality of Coating Layer Based on Short-Time Fourier Transform and Convolutional Neural Networks

Azamatjon Kakhramon Ugli Malikov,Jin-Hak Yi,Junpil Park,Younho Cho,Jeongnam Kim,Young H Kim

doi:10.3390/coatings11080909

Azamatjon Kakhramon Ugli Malikov, Jin-Hak Yi + Show 4 more

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https://doi.org/10.3390/coatings11080909

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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.

Highlights

Coatings are widely applied to offshore structures as corrosion protection layers
The main purpose of this experiment was to measure the thickness of the coating layer and as mentioned in Section 2, the time of flight (TOF) of the wave packet can be determined by the local maximum value of projection of the short-time Fourier transform (STFT) magnitude into the time-domain
We propose a combined technique of the STFT spectrogram with a convolutional neural network (CNN) for automatically classifying the state of the coating layers

Summary

Introduction

Coatings are widely applied to offshore structures as corrosion protection layers. Coating layer thinning or lack of adhesion between the coating layer and structure may affect the safety of the structure and ignoring this can result in complete system failure. The lifetime of the coated structure is directly related to the quality of the adhesion of the coating material with the structure. The thickness and adhesion state are two important features of coatings that must be monitored periodically. The coating is significantly thin and the thickness may vary up to 3 mm. In some applications, the coating material is applied as a thin layer on the surface of the metallic structure. In such cases, the thickness of the coating material may be close to 1 mm or even thinner. The coating material is usually applied by conventional rollers [1] that create many pores on the inside of the polymer. Porosity greatly increases the ultrasonic attenuation of the coating layer, ultrasonic testing becomes difficult

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