基于反卷积神经网络的超声导波层合板脱粘缺陷超分辨成像

doi:10.16180/j.cnki.issn1007-7820.2023.08.002

Abstract

Abstract:

Traditional ultrasonic guided wave imaging detection methods are difficult to accurately characterize structural damage details. In order to obtain detailed features of the damage, the deconvolutional neural network model via deep learning is proposed to investigate the super-resolution imaging problem of subwavelength debonding defects in laminate plates. Initial imaging results are obtained by finite element simulation with the total focusing method. The labeled 12 550 damage images are trained and tested based on extended database expanded by data enhancement method. The results show that compared with the original full-focus imaging algorithm, the deconvolution neural network model improves the accuracy of damage location by 5%, the imaging accuracy is higher than 91%, and the positioning error is lower than 1.8 mm, indicating that the proposed method can significantly improve the resolution of network imaging results and better display the details of subwavelength damage. The above results show that the proposed method has high detection efficiency and does not require manual experience, and has good application value in engineering practice.

Key words: laminate, damage detection, finite element, total focusing method, subwave length, debonding defects, deconvolutional neural network, super resolution

CLC Number:

TP183

YUE Shengyao,XU Baiqiang,XU Guidong,XU Chenguang,ZHANG Sai. Super-Resolution Imaging of Laminate Debonding Defects via Deconvolutional Neural Network and Ultrasound Guided Waves[J].Electronic Science and Technology, 2023, 36(8): 7-13.

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References 25

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	钢板	硬橡胶板
杨氏模量/GPa	200.00	0.14
密度/ kg·m^-3	7 850.00	1 200.00
泊松比	0.30	0.49
自由四面体网格大小/ mm	0.8
时间步长/μs	0.1
检测时间/μs	150.0

数据集	损失值	精度值
训练集	0.08 734	0.931
验证集	0.05 126	0.957

Super-Resolution Imaging of Laminate Debonding Defects via Deconvolutional Neural Network and Ultrasound Guided Waves

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