Enhanced electromagnetic induction thermography detection of internal damage in CFRP-steel adhesively bonded structures
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摘要: 碳纤维增强复合材料(Carbon fiber reinforced polymer，CFRP)通过胶接方式广泛应用于钢结构加固，因此对于加固后形成的CFRP-钢胶接结构进行检测以确保其结构完整性和安全性变得至关重要。然而，CFRP、环氧树脂和钢各自具有不同的物理性质，给准确检测此类特殊混合结构的内部损伤带来了挑战。为解决这一问题，本研究提出了一种增强型电磁感应热成像检测方法，以增强CFRP-钢胶接结构内部损伤的检测。该方法首先利用常规电磁感应热成像系统获得被检物体表面的温度数据，然后对表面温度数据进行预处理。接着，采用设计的卷积自编码器(Convolutional autoencoder，CAE)模型从预处理后的表面温度数据中提取像素级深度热特征，最后利用提取的深度热特征生成增强的检测结果，从而提高损伤的可见性。对含有脱粘、分层和裂纹的CFRP-钢胶接结构试件进行的实验结果表明，增强型电磁感应热成像能够有效提高内部损伤的可见性，这有助于准确评估CFRP-钢胶接结构的质量，从而提高此类结构的安全性。
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关键词:
- CFRP-钢胶接结构 /
- 增强型电磁感应热成像 /
- 卷积自编码器 /
- 特征提取 /
- 无损检测
Abstract: Carbon fiber reinforced polymer (CFRP) composites are widely used in steel structure reinforcement through adhesively bonding, making it crucial to inspect CFRP-steel adhesively bonded structures (ABCSS) to ensure their structural integrity and safety. However, the distinct physical properties of CFRP, epoxy resin, and steel pose challenges in accurately detecting internal damages in such specialized hybrid structures. To address this issue, this study proposes an enhanced electromagnetic induction thermography detection method to enhance the detection of internal damages in ABCSS. This method initially utilizes a conventional electromagnetic induction thermography system to obtain surface temperature data of the object under inspection, followed by preprocessing of the surface temperature data. Subsequently, a designed Convolutional autoencoder (CAE) model is employed to extract pixel-level deep thermal features from the preprocessed surface temperature data. Finally, the extracted deep thermal features are utilized to generate enhanced detection results, thereby improving the visibility of damages. Experimental results on ABCSS specimens containing delamination, debonding, and cracks demonstrate that enhanced electromagnetic in-duction thermography effectively enhances the visibility of internal damages. This enhancement contributes to accurately assessing the quality of ABCSS, thereby improving the safety of such structures. -
图 1 所提增强型电磁感应热成像方法的原始热成像数据后处理框架
Figure 1. Post-processing framework of raw thermographic data for the proposed enhanced electromagnetic induction thermography method
图 2 电磁感应热成像实验系统示意图
Figure 2. Electromagnetic induction thermography experimental system
图 4 常规电磁感应热成像得到的CFRP-钢胶接结构试件原始热成像图像
Figure 4. Raw thermographic images of CFRP-steel adhesively bonded specimens obtained by conventional electromagnetic induction thermography
图 5 CFRP-钢胶接结构试件数据特征可视化结果(缺陷和非缺陷区域在图四中被标出)
Figure 5. Data features visualization results of CFRP-steel adhesively bonded specimens (Defect and non-defect regions are marked in Fig. 4, respectively.)
图 6 CFRP-钢胶接结构试件增强结果
Figure 6. Enhanced results of CFRP-steel adhesively bonded specimens
图 7 CFRP-钢胶接结构试件沿损伤的灰度曲线
Figure 7. Gray scale profile of CFRP-steel adhesively bonded specimens along the damage
图 8 CFRP-钢胶接结构试件原始结果与增强结果的对比度对比
Figure 8. Contrast between raw and enhanced results of CFRP-steel adhesively bonded specimens
表 1 CAE模型参数
Table 1. Parameters of CAE model
Layers Network parameters Activation function Kernel number Kernel size Stride step Padding Convolution 1 16 3 1 Same Relu Max-pooling 1 1 2 2 Valid Convolution 2 32 3 1 Same Relu Max-pooling 2 1 2 2 Valid Convolution 3 64 3 1 Same Relu Max-pooling 3 1 2 2 Valid Up-sampling 1 1 2 2 Valid Convolution 4 64 3 1 Same Relu Up-sampling 2 1 2 2 Valid Convolution 5 64 3 1 Same Relu Up-sampling 3 1 2 2 Valid Convolution 6 32 3 1 Same Relu Convolution 7 1 3 1 Same Sigmoid 
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表 2 CFRP -钢胶接结构中的预制缺陷信息
Table 2. Details of prefabricated defects in CFRP-steel adhesively bonded structures specimens
Defect Specimen Defect information Type Steel substrate Length/mm Width/mm Thickness/mm 1# CFRP/steel-1 Debonding Q235 20 20 0.1 2# CFRP/steel-2 Delamination Q235 20 10 0.1 3# CFRP/steel-3 Crack A106 11.05 2 0.55 4# CFRP/steel-3 Crack A106 19.25 2 2.55
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