Glass fiber-reinforced polymer (GFRP) is widely used in engineering fields involving aerospace, energy, transportation, etc. If internal buried defects occur due to hostile environments during fabrication and practical service, the structural integrity and safety of GFRP structures would be severely undermined. Therefore, it is indispensable to carry out effective quantitative nondestructive testing (NDT) of internal defects buried within GFRP structures. Along with the development of composite materials, microwave NDT is promising in non-intrusive inspection of defects in GFRPs. In this paper, quantitative screening of the subsurface impact damage and air void in a unidirectional GFRP via microwave reflectometry was intensively investigated. The influence of the microwave polarization direction with respect to the GFRP fiber direction on the reflection coefficient was investigated by using the equivalent relative permittivity calculated with theoretical analysis. Following this, a microwave NDT system was built up for further investigation regarding the imaging and quantitative evaluation of buried defects in GFRPs. A direct-wave suppression method based on singular-value decomposition was proposed to obtain high-quality defect images. The defect in-plane area was subsequently assessed via a proposed defect-edge identification method. The simulation and experimental results revealed that (1) the testing sensitivity to buried defects was the highest when the electric-field polarization direction is parallel to the GFRP fiber direction; and (2) the averaged evaluation accuracy regarding the in-plane area of the buried defect reached approximately 90% by applying the microwave reflectometry together with the proposed processing methods.

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%.

In virtue of the exceptional performance regarding the electric insulation, composite insulators are intensively utilized in the power transmission and distribution system. However, the intricate production process and hostile environments may leave the trunk of the composite insulator vulnerable to the internal defects such as the air gap, the carbonization defect and the groove. These defects undermine the integrity of the composite insulator, and would lead to the failure of the electrical system. In light of this, Microwave Non-destructive Testing (MNT) is investigated in this paper for imaging and evaluation of internal defects in composite-insulator trunks. The characteristics of the testing signal are scrutinized via 3D finite-element simulations. The feasibility of microwave reflectometry for detection of the internal-defects is identified. In parallel, an experimental MNT system is established for imaging and assessment of internal defects in composite-insulator trunks. In an effort to improve the quality of the defect image, an adaptive interference suppression method based on singular value decomposition is proposed. An image segmentation technique based on the pixel statistics is subsequently exploited to identify the defect region. It is indicated from the results that the localization and quantitative evaluation regarding the area of internal defects buried within the composite-insulator trunk can be realized via microwave reflectometry alongside the proposed methods.

In view of the advantage of cross-polarized inspection (CrPI) in microwave nondestructive testing (MNT), in this paper a dual-polarized narrow-beam antenna as the pivotal reflectometric sensor is systematically designed and realized particularly for enhancement of detection and imaging of subsurface defects in dielectric structures. The antenna is equipped with a compact asymmetric waveguide orthomode transducer, with Teflon used as the internal filling material, in an effort to reduce its size and narrow the beamwidth. The internal dimensions of the realized dual-polarized narrow-beam antenna are optimized via numerical simulations. Based on the optimal design parameters, the antenna is fabricated and assessed through experiments. The experimental results reveal that the fabricated antenna has better metric indicators in terms of a return loss better than 10 dB, isolation better than 40 dB in 30.0 GHz~36.0 GHz and half-power beamwidths below 36.9° at 36.0 GHz. In order to further affirm the applicability of the fabricated antenna for CrPI, an MNT system is established to perform two-dimensional scanning and imaging of back-surface volumetric defects in polyethylene specimens. Based on the image characteristics of CrPI, a flaw-recovery algorithm is proposed to retrieve the defect opening profile. The averaged contrast-to-noise ratio of the processed CrPI-based image is found to be approximately five times larger than that of the raw CrPI-based image and fourteen times bigger than that of the raw CoPI-based image. Experimental results have further indicated that the fabricated antenna is feasible for not only co-polarized inspection (CoPI) but for CrPI, which exhibits higher testing sensitivity and defect-image contrast than CoPI. In conjunction with the flaw-recovery algorithm, by utilizing the dual-polarized narrow-beam antenna with the better metric indicators for CrPI, the image quality of the back-surface flaws in polyethylene slabs can be effectively improved.

Purpose: The purpose of this study is to establish a high-resolution and quantitatively reliable microwave imaging framework for detecting and assessing interfacial debonding in rubber-to-metal bonded structures – an issue that directly threatens the operational safety and reliability of aerospace, automotive and industrial components. Traditional non-destructive testing (NDT) techniques often fail to capture weak debonding signatures. Addressing this limitation, this study aims to design an imaging-driven diagnostic approach capable of resolving small-scale debonding defects, extracting meaningful defect features from noisy metal-dominated measurements and providing automated quantitative metrics. Design/methodology/approach: This work develops a planar-scanning-based microwave reflection imaging system integrated with a spatial-wavenumber spectrum reconstruction algorithm tailored for subsurface interface inspection. A dual-layer adaptive matched-filter transfer function is constructed to compensate for phase distortions introduced by the heterogeneous layered structure, thereby improving azimuth resolution and contrast. To further suppress strong surface reflections and enhance weak defect signatures, an adaptive singular value decomposition (SVD)-based feature enhancement strategy is incorporated into the imaging pipeline. Following reconstruction, a quantitative evaluation method combining adaptive rapid image segmentation with pixel statistical analysis is introduced to automatically extract defect location, equivalent area and shape features. Findings: Experimental validation confirms that the proposed approach can reliably detect and characterize interfacial debonding as small as ∅5 mm within rubber-to-metal bonded structures. The reconstructed images exhibit notably enhanced defect visibility even in the presence of dominant metal reflections, and the quantitative evaluation achieves a defect area error below 10% and a positional error under 2 mm. The results of this study collectively verify the robustness, sensitivity and adaptability of the proposed imaging framework and highlight its suitability for inspecting various bonded structures with differing geometric and material configurations. Originality/value: This study presents a novel microwave NDT methodology that addresses the fundamental challenge of detecting weak interface debonding buried beneath strong surface reflections – an area where existing imaging or conventional NDT methods often fail. The integration of a dual-layer adaptive matched filtering strategy with SVD-based defect enhancement introduces a new mechanism for isolating subsurface dielectric discontinuities in complex heterogeneous structures. Moreover, the proposed adaptive quantitative evaluation framework enables automatic and accurate extraction of defect parameters, offering a level of assessment capability rarely achieved in microwave reflection imaging.