Terahertz Detection of Interface Defects Within Composite Insulators Using a Gated Recurrent Neural Network

Li, Yu; Xie, Congzhen; Gou, Bin; Yu, Song; Yang, Chang

doi:10.1109/tdei.2023.3330838

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Based on the good penetration ability of THz in most non-polar materials, the use of THz for the non-destructive testing of multi-layer lightweight composites is an effective technical method for ensuring product quality and safety [3][4][5]. THz non-destructive testing has emerged as a cutting-edge technique capable of identifying specific defects by examining diverse spectral features within the THz wavelength range [6][7][8]. Previous studies, such as those by H. Jiang et al, utilized three feature parameters, namely, the maximum absolute value, the signal power, and the envelope area, to image mid-gap defects of various shapes [9].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Detection of Defects in Multi-Layer Lightweight Composite Structures Using THz-TDS Based on a U-Net-BiLSTM Network

Zhang,

Li,

Zhang

et al. 2024

Materials

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Multi-layer lightweight composite structures are widely used in the field of aviation and aerospace during the processes of manufacturing and use, and, as such, they inevitably produce defects, damage, and other quality problems, creating the need for timely non-destructive testing procedures and the convenient repair or replacement of quality problems related to the material. When using terahertz non-destructive testing technology to detect defects in multi-layer lightweight composite materials, due to the complexity of their structure and defect types, there are many signal characteristics of terahertz waves propagating in the structures, and there is no obvious rule behind them, resulting in a large gap between the recognition results and the actual ones. In this study, we introduced a U-Net-BiLSTM network that combines the strengths of the U-Net and BiLSTM networks. The U-Net network extracts the spatial features of THz signals, while the BiLSTM network captures their temporal features. By optimizing the network structure and various parameters, we obtained a model tailored to THz spectroscopy data. This model was subsequently employed for the identification and quantitative analysis of defects in multi-layer lightweight composite structures using THz non-destructive testing. The proposed U-Net-BiLSTM network achieved an accuracy of 99.45% in typical defect identification, with a comprehensive F1 score of 99.43%, outperforming the CNN, ResNet, U-Net, and BiLSTM networks. By leveraging defect classification and thickness recognition, this study successfully reconstructed three-dimensional THz defect images, thereby realizing quantitative defect detection.

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Section: Introductionmentioning

confidence: 99%

Quantitative Detection of Defects in Multi-Layer Lightweight Composite Structures Using THz-TDS Based on a U-Net-BiLSTM Network

Zhang,

Li,

Zhang

et al. 2024

Materials

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Design and Numerical Modeling of Terahertz Metasurface with Dual Functions of Sensing and Filtering

Zhang,

Sun,

Chen

et al. 2024

Sensors

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This study proposes a dual-functional terahertz device based on the Dirac semimetal, serving as both a sensing element and a band-pass filter. The device’s operating mode can switch between these two functions by utilizing the phase transition property of vanadium dioxide (VO2). When VO2 is in the insulating state, the device functions as a sensing element. The simulation results demonstrate an impressive refractive index sensitivity of 374.40 GHz/RIU (Refractive Index Unit). When VO2 is in the metallic state, the device functions as a band-pass filter, exhibiting a center frequency of 2.01 THz and a 3 dB fractional bandwidth of 0.91 THz. The integration of these dual functionalities within a single terahertz device enhances its utility in both sensing and filtering applications.

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Terahertz Detection of Interface Defects Within Composite Insulators Using a Gated Recurrent Neural Network

Cited by 2 publications

References 31 publications

Quantitative Detection of Defects in Multi-Layer Lightweight Composite Structures Using THz-TDS Based on a U-Net-BiLSTM Network

Quantitative Detection of Defects in Multi-Layer Lightweight Composite Structures Using THz-TDS Based on a U-Net-BiLSTM Network

Design and Numerical Modeling of Terahertz Metasurface with Dual Functions of Sensing and Filtering

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