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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

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

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【作者】 岳圣尧许伯强徐桂东徐晨光张赛

【Author】 YUE Shengyao;XU Baiqiang;XU Guidong;XU Chenguang;ZHANG Sai;School of Physics and Electronic Engineering, University of Jiangsu;

【机构】 江苏大学物理与电子工程学院

【摘要】 针对传统超声导波成像检测方法难以精确表征结构损伤细节特征的问题,文中提出了基于深度学习的反卷积神经网络模型,对层合板中亚波长脱粘缺陷的超分辨成像问题进行研究,以获得损伤的细节特征。通过有限元仿真与全聚焦成像算法获取初始成像结果,再使用数据增强方法扩充数据库,最后对标注好的12 550张损伤图像进行训练和测试。研究结果表明,与原始全聚焦成像算法相比,反卷积神经网络模型下损伤的成像位置准确度提高了5%,成像精度高于91%,定位误差低于1.8 mm,说明文中所提方法能够明显提高网络成像结果分辨率并较好地显现亚波长损伤的细节特征。上述结果表明文中所提方法具有较高的检测效率,且无需人工经验,在工程实践中具有良好的应用价值。

【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.

【基金】 国家自然科学基金(62071205)~~
  • 【文献出处】 电子科技 ,Electronic Science and Technology , 编辑部邮箱 ,2023年08期
  • 【分类号】TB553;TP183;TP391.41
  • 【网络出版时间】2022-05-12 18:24:00
  • 【下载频次】231
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