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基于微波的玻璃纤维增强塑料分层缺陷可视化定量检测

Visual Quantitative Detection of Delamination Defects in GFRP via Microwave.

作者信息

Yang Xihan, Fang Yang, Wang Ruonan, Li Yong, Chen Zhenmao

机构信息

State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Research Centre of NDT and Structural Integrity Evaluation, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

出版信息

Sensors (Basel). 2023 Jul 13;23(14):6386. doi: 10.3390/s23146386.

DOI:10.3390/s23146386
PMID:37514680
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10384237/
Abstract

Glass Fiber reinforced polymers (GFRPs) are widely used and play an important role in modern society. The multilayer structure of GFRPs can lead to delamination defects during production and service, which can have a significant impact on the integrity and safety of the equipment. Therefore, it is important to monitor these delamination defects during equipment service in order to evaluate their effects on equipment performance and lifespan. Microwave imaging testing, with its high sensitivity and noncontact nature, shows promise as a potential method for detecting delamination defects in GFRPs. However, there is currently limited research on the quantitative characterization of defect images in this field. In order to achieve visual quantitative nondestructive testing (NDT), we propose a 2D-imaging visualization and quantitative characterization method for delamination defects in GFRP, and realize the combination of visual detection and quantitative detection. We built a microwave testing experimental system to verify the effectiveness of the proposed method. The results of the experiment indicate the effectiveness and innovation of the method, which can effectively detect all delamination defects of 0.5 mm thickness inside GFRP with high accuracy, the signal-to-background ratio (SBR) of 2D imaging can reach 4.41 dB, the quantitative error of position is within 0.5 mm, and the relative error of area is within 11%.

摘要

玻璃纤维增强聚合物(GFRP)在现代社会中被广泛使用并发挥着重要作用。GFRP的多层结构在生产和使用过程中可能导致分层缺陷,这会对设备的完整性和安全性产生重大影响。因此,在设备使用过程中监测这些分层缺陷,以评估它们对设备性能和寿命的影响非常重要。微波成像检测具有高灵敏度和非接触性的特点,有望成为检测GFRP中分层缺陷的一种潜在方法。然而,目前该领域对缺陷图像的定量表征研究有限。为了实现可视化定量无损检测(NDT),我们提出了一种用于GFRP中分层缺陷的二维成像可视化和定量表征方法,并实现了视觉检测和定量检测的结合。我们构建了一个微波测试实验系统来验证所提方法的有效性。实验结果表明了该方法的有效性和创新性,它能够高精度地有效检测出GFRP内部所有厚度为0.5毫米的分层缺陷,二维成像的信背比(SBR)可达4.41分贝,位置定量误差在0.5毫米以内,面积相对误差在11%以内。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/a48a6adf4e4e/sensors-23-06386-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/864401bb96a6/sensors-23-06386-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/0a6007e88bc2/sensors-23-06386-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/8bde13b29172/sensors-23-06386-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/b7ea597c4f43/sensors-23-06386-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/0c4e1af6748f/sensors-23-06386-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/374338e955ca/sensors-23-06386-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/a48a6adf4e4e/sensors-23-06386-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/864401bb96a6/sensors-23-06386-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/c43430b6b312/sensors-23-06386-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/82a9508bd980/sensors-23-06386-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/e551386e70bb/sensors-23-06386-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/ef86bb47427b/sensors-23-06386-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/0a6007e88bc2/sensors-23-06386-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/8bde13b29172/sensors-23-06386-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/b7ea597c4f43/sensors-23-06386-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/0c4e1af6748f/sensors-23-06386-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/374338e955ca/sensors-23-06386-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197c/10384237/a48a6adf4e4e/sensors-23-06386-g013.jpg

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