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. -
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 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 -
[1] 郑元鹏, 陈涛, 黄诚. CFRP加固紧凑拉伸钢试件的疲劳试验研究[J]. 复合材料学报, 2022, 39(11): 5192-5205.ZHENG Yuanpeng, CHEN Tao, HUANG Cheng. Experimental study on fatigue behavior of compact-tension specimens strengthened by CFRP[J]. Acta Materiae Compositae Sinica, 2022, 39(11): 5192-5205(in Chinese). [2] ZHANG Y, XU C, LIU P, et al. One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites[J]. Composites Part B: Engineering, 2024, 272: 111216. doi: 10.1016/j.compositesb.2024.111216 [3] 李腾, 宁志华, 吴嘉瑜. CFRP加固钢板的粘结界面剥离破坏[J]. 复合材料学报, 2021, 38(12): 4090-4105.LI Teng, NING Zhihua, WU Jiayu. Interfacial debonding failure of CFRP-strengthened steel structures[J]. Acta Materiae Compositae Sinica, 2021, 38(12): 4090-4105(in Chinese). [4] WANG Z, XIAN G, YUE Q. Finite element modeling of debonding failure in CFRP-strengthened steel beam using a ductile adhesive[J]. Composite Structures, 2023, 311: 116818. doi: 10.1016/j.compstruct.2023.116818 [5] COLOMBI P, FAVA G, SONZOGNI L. Fatigue crack growth in CFRP-strengthened steel plates[J]. Composites Part B: Engineering, 2015, 72: 87-96. [6] MOHAJER M, BOCCIARELLI M, COLOMBI P. Calibration of a cyclic cohesive-zone model for fatigue-crack propagation in CFRP-strengthened steel plates[J]. Journal of Composites for Construction, 2022, 26: 04022054. [7] ZENG J, GAO W, LIU F. Interfacial behavior and debonding failures of full-scale CFRP-strengthened H-section steel beams[J]. Composite Structures, 2018, 201: 540-552. doi: 10.1016/j.compstruct.2018.06.045 [8] WANG H, NI Y, DAI J, et al. Interfacial debonding detection of strengthened steel structures by using smart CFRP-FBG composites[J]. Smart Materials and Structures, 2019, 28(11): 115001. doi: 10.1088/1361-665X/ab3add [9] YU Q, CHEN T, GU X, et al. Fatigue behaviour of CFRP strengthened steel plates with different degrees of damage[J]. Thin-Walled Structures, 2013, 69: 10-17. doi: 10.1016/j.tws.2013.03.012 [10] RASHNOOIE R, ZEINODDINI M, AHMADPOUR F, et al. A coupled XFEM fatigue modelling of crack growth, delamination and bridging in FRP strengthened metallic plates[J]. Engineering Fracture Mechanics, 2023, 279: 109017. doi: 10.1016/j.engfracmech.2022.109017 [11] JIANG J, JIANG J W, DENG X W, et al. Detecting debonding between steel beam and reinforcing CFRP plate using active sensing with removable PZT-based transducers[J]. Sensors, 2020, 20: 41. [12] XU C, AI S, XIE J, et al. Fatigue failure process characterization for carbon fiber sheet reinforced steel rods using the acoustic emission technique[J]. Journal of Nondestructive Evaluation, 2016, 35: 24. doi: 10.1007/s10921-016-0342-z [13] MATTA F, RIZZO P, KARBHARI V, et al. Acoustic emission damage assessment of steel/CFRP bonds for rehabilitation[J]. Journal of Composites for Construction, 2006, 10: 265-274. [14] JELINEK M, SCHILP J, REINHART G. Optimised parameter sets for thermographic inspection of CFRP metal hybrid components[J]. Procedia CIRP, 2015, 37: 218-224. doi: 10.1016/j.procir.2015.08.044 [15] DING S, TIAN G, ZHU J, et al. Characterisation and evaluation of paint-coated marine corrosion in carbon steel using eddy current pulsed thermography[J]. NDT & E International, 2022, 130: 102678. [16] LIU Y, TIAN G, GAO B, et al. Depth quantification of rolling contact fatigue crack using skewness of eddy current pulsed thermography in stationary and scanning modes[J]. NDT & E International, 2022, 128: 102630. [17] LIU Z, GAO B, TIAN G. Natural crack diagnosis system based on novel L-shaped electromagnetic sensing thermography[J]. IEEE Transactions on Industrial Electronics, 2020, 67: 9703-9714. doi: 10.1109/TIE.2019.2952782 [18] PENG J, TIAN G, WANG L, et al. Investigation into eddy current pulsed thermography for rolling contact fatigue detection and characterization[J]. NDT & E International, 2015, 74: 72-80. [19] YI Q, TIAN G, MALEKMOHAMMADI H, et al. New features for delamination depth evaluation in carbon fiber reinforced plastic materials using eddy current pulse-compression thermography[J]. NDT & E International, 2019, 102: 264-273. [20] GAO B, LI X, WOO W, et al. Quantitative validation of Eddy current stimulated thermal features on surface crack[J]. NDT & E International, 2017, 85: 1-12. [21] RODRÍGUEZ-MARTÍN M, LAGÜELA S, GONZÁLEZ-AGUILERA D, et al. Thermographic test for the geometric characterization of cracks in welding using IR image rectification[J]. Automation in Construction, 2016, 61: 58-65. doi: 10.1016/j.autcon.2015.10.012 [22] XIE J, ZHANG Y, HE Z, et al. Automated leakage detection method of pipeline networks under complicated backgrounds by combining infrared thermography and Faster R-CNN technique[J]. Process Safety and Environmental Protection, 2023, 174: 39-52. doi: 10.1016/j.psep.2023.04.006 [23] HE M, ZHANG L, LI J, et al. Methods for suppression of the effect of uneven surface emissivity of material in the moving mode of eddy current thermography[J]. Applied Thermal Engineering, 2017, 118: 612-620. doi: 10.1016/j.applthermaleng.2017.03.023 [24] 张玉彬, 刘鹏谦, 陈丽娜, 等. 基于YOLO v5的带涂层钢结构亚表面缺陷脉冲涡流热成像智能检测[J]. 红外技术, 2023, 45(10): 1029-1037.ZHANG Yubin, LIU Pengqian, CHEN Lina, et al. YOLO v5-based intelligent detection for eddy current pulse thermography of subsurface defects in coated steel structures[J]. Infrared Technology, 2023, 45(10): 1029-1037(in Chinese). [25] LI X, LIU Z, JIANG X, et al. Method for detecting damage in carbon-fibre reinforced plastic-steel structures based on eddy current pulsed thermography[J]. Nondestructive Testing and Evaluation, 2016, 33(1): 1-19. [26] ZOU X, WANG L, WANG J, et al. Nondestructive evaluation of carbon fiber reinforced polymer (CFRP)-steel interfacial debonding using eddy current thermography[J]. Composite Structures, 2022, 284: 115133. doi: 10.1016/j.compstruct.2021.115133 [27] XIE J, WU C, GAO L, et al. Detection of internal defects in CFRP strengthened steel structures using eddy current pulsed thermography[J]. Construction and Building Materials, 2021, 282: 122642. doi: 10.1016/j.conbuildmat.2021.122642 [28] XIE J, XU C, WU C, et al. Visualization of de-fects in CFRP-reinforced steel structures using improved eddy current pulsed thermography[J]. Automation in Construction, 2023, 145: 104643. doi: 10.1016/j.autcon.2022.104643 [29] CHEN L, ZHANG Y, XIE J, et al. Simultaneous inspection of multi-kind defects in adhesively bonded CFRP/steel structures by inductive thermography[J]. Infrared Physics and Technology, 2024, 138: 105254. doi: 10.1016/j.infrared.2024.105254 [30] WU J, ZHU J, XIA H, et al. DC-biased magnetization based eddy current thermography for subsurface defect detection[J]. IEEE Transactions on Industrial Informatics, 2019, 15(12): 6252-6259. doi: 10.1109/TII.2019.2891107 [31] MIAO L, GAO B, LI H, et al. Novel interventional electromagnetic thermography for subsurface defect detection[J]. International Journal of Thermal Sciences, 2023, 184: 107960. doi: 10.1016/j.ijthermalsci.2022.107960 [32] LI K, TIAN G, AHMED J, et al. Emissivity correction and thermal pattern reconstruction in eddy current pulsed thermography[J]. Sensors, 2023, 23(5): 2646. doi: 10.3390/s23052646 [33] CIESLAK M, CASTELFRANCO A, RONCALLI V, et al. t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis[J]. Marine genomics, 2020, 51: 100723. doi: 10.1016/j.margen.2019.100723 [34] OSWALD-TRANTA B. Comparative study of thermal contrast and contrast in thermal signal derivatives in pulse thermography[J]. NDT & E International, 2017, 91: 36-46. [35] GRYS S. New thermal contrast definition for defect characterization by active thermography[J]. Measurement, 2012, 45: 1885-1892. doi: 10.1016/j.measurement.2012.03.017