Affiliation:
1. Department of Mechanical Engineering, Tech University of Korea, Siheung-si 15073, Republic of Korea
Abstract
In this study, the Convolution Neural Network (CNN) algorithm is applied for non-destructive evaluation of aluminum panels. A method of classifying the locations of defects is proposed by exciting an aluminum panel to generate ultrasonic Lamb waves, measuring data with a sensor array, and then deep learning the characteristics of 2D imaged, reflected waves from defects. For the purpose of a better performance, the optimal excitation location and sensor locations are investigated. To ensure the robustness of the training model and extract the feature effectively, experimental data are collected by slightly changing the excitation frequency and shifting the location of the defect. The high classification accuracy for each defect location can be achieved. It is found that the proposed algorithm is also successfully applied even when a bar is attached to the panel.
Funder
National Research Foundation of Korea
GRRC program of Gyeonggi province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference59 articles.
1. Non-Destructive Detection of Fatigue Damage in Thick Composites by Pulse-Echo Ultrasonics;Mouritz;Compos. Sci. Technol.,2000
2. Application of Ultrasonic Pulse-Echo Method to Insulation Condition Diagnosis for Large Generators;Hao;IEEE Trans. Dielectr. Electr. Insul.,2005
3. Tian, F., Hao, Y., Zou, Z., Zheng, Y., He, W., Yang, L., and Li, L. (2019). An Ultrasonic Pulse-Echo Method to Detect Internal Defects in Epoxy Composite Insulation. Energies, 12.
4. Filament-Wound Composite Pressure Vessel Inspection Based on Rotational through-Transmission Laser Ultrasonic Propagation Imaging;Lee;Compos. Struct.,2020
5. Liquid Film Thickness Measurement by an Ultrasonic Pulse Echo Method;Park;Nucl. Eng. Technol.,1985
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