Enhanced electromagnetic induction thermography detection of internal damage in CFRP-steel adhesively bonded structures

ZHANG Yubin; CHEN Lina; LIU Pengqian; ZHAO Qing; LIU Rui; WANG Longbo; XIE Jing; XU Changhang

doi:10.13801/j.cnki.fhclxb.20240423.002

Volume 41 Issue 9

Sep. 2024

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Article Navigation > Acta Materiae Compositae Sinica > 2024 > 41(9): 4930-4941

ZHANG Yubin, CHEN Lina, LIU Pengqian, et al. Enhanced electromagnetic induction thermography detection of internal damage in CFRP-steel adhesively bonded structures[J]. Acta Materiae Compositae Sinica, 2024, 41(9): 4930-4941. doi: 10.13801/j.cnki.fhclxb.20240423.002

Citation:

ZHANG Yubin, CHEN Lina, LIU Pengqian, et al. Enhanced electromagnetic induction thermography detection of internal damage in CFRP-steel adhesively bonded structures[J]. Acta Materiae Compositae Sinica, 2024, 41(9): 4930-4941. doi: 10.13801/j.cnki.fhclxb.20240423.002

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Enhanced electromagnetic induction thermography detection of internal damage in CFRP-steel adhesively bonded structures

doi: 10.13801/j.cnki.fhclxb.20240423.002

College of Mechanical and Electronic Engineering, China University of Petroleum (East China), Qingdao 266580, China

Funds: National Key Research and Development Program of China (2023YFC3009202); CNPC’s Major Science and Technology Projects (ZD2019-184-004-04)

Received Date: 2024-03-04
Accepted Date: 2024-04-13
Rev Recd Date: 2024-04-07

Available Online: 2024-04-23

Publish Date: 2024-09-15

Abstract

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.
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References

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