Yang Fang

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.

This study proposes a multiscale electromagnetic method that combines far-field angular-domain analysis with local near-field imaging to detect damage in radar-absorbing coatings on aircraft surfaces. Using an intact state as a reference, continuous anomalous angular intervals and their principal anomalous azimuths are identified via robust statistical criteria, employing the electromagnetic scattering energy ratio (ESER), defined here as a normalized squared-field response ratio, and the azimuth difference factor. These angular characteristics, combined with target geometry and a predefined detection area, are used to determine the direction and azimuthal range of candidate near-field scanning windows. Subsequently, coherent back-projection is applied to the local near-field measurements. The centroid and equivalent electromagnetic dimensions of the differential response are estimated using complex differential imaging, multithreshold analysis, and squared-magnitude-weighted central second moments. Numerical analysis of a simplified aircraft-like model demonstrates angular-domain screening and local characterization of impact, coating loss and bulging. For the rectangular coating-loss simulation, the equivalent electromagnetic length and width deviate from the nominal side lengths by 8.6% and 2.3%, respectively. The corresponding experimental deviations for localized 1 cm × 1 cm coating loss on a coated conical specimen are 9.2% and 6.3%. These results support two-stage screening and local equivalent-scale extraction under the investigated geometry, material and reference-matching conditions.

Microwave imaging is promising for non-contact inspection of radar-absorbing materials (RAMs); however, strong structured non-damage responses in reconstructed microwave images can resemble surface defects and trigger false detections, while hard masking may remove weak damage responses near ambiguous boundaries. To address these issues, this paper proposes dual-channel scale-adaptive clutter suppression with confidence-derived gating as an application-specific front end for surface-damage detection in curved RAM specimens. A normalized microwave-response map and a fixed morphology-enhanced map, both deterministically derived from the same measurement, are jointly encoded. The input-conditioned scale-adaptive pyramid-pooling module (SA-PPM) reweights multi-scale context branches, while confidence-derived adaptive-threshold gating (C-ATG) generates a bounded pixel-wise threshold from the retention confidence and its ambiguity proxy. Continuous-weight filtering (CW-FI) then produces continuous retention weights, enabling gradual attenuation instead of forced binary removal. Experiments were conducted on a paired visible-light/microwave RAM dataset. On the held-out test subset, the method achieved pixel-level discrimination between clutter and retained responses and improved detection of strip-like cracks and circular spalling under matched detector-side settings.

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.

To address the challenges of low excitation efficiency, weak echo signals and limited detection accuracy in electromagnetic acoustic transducers (EMATs) for defect detection in metal plates, this paper proposes a high-precision detection method integrating arc-shaped focused phased arrays with phase-coherent synthetic aperture imaging compared to traditional methods. First, an arc-shaped phased array EMAT is constructed. Through array geometric focusing and superposition of array elements, ultrasonic energy undergoes dual enhancement, thereby increasing the signal-to-noise ratio (SNR) of defect echo signals. Second, based on phase-coherent synthetic aperture imaging principles, propagation delay compensation and phase correction are applied to multi-channel echo signals. This enables coherent accumulation and virtual focusing of echo energy, effectively improving imaging accuracy and resolution. Furthermore, compressed sensing theory is integrated into the imaging framework. By establishing a sparse representation model for defect signals, acceptable image recovery is achieved even with a limited number of receiving channels. Experimental results demonstrate that the proposed arc-shaped focusing phased array combined with phase-coherent synthetic aperture imaging improves detection accuracy and image quality. The compressed sensing extension strategy reduces channel requirements while preserving acceptable imaging performance under half-channel sparse sampling conditions. This research provides a novel technical approach for high-precision and high-efficiency EMAT non-destructive testing.

To improve the low sensitivity of electromagnetic acoustic testing (EMAT) to micro-debonding defects in metal–rubber bonded structures, this study proposes a detection framework combining a magnetic-field-enhanced focusing EMAT with entropy-weighted multi-feature fusion imaging. First, a Halbach-type focusing magnet was designed and evaluated through finite element simulations, showing a substantial enhancement of the effective bias magnetic field in the working region. Then, three complementary echo features, namely amplitude (AMP), time-domain integral (TDI), and power spectral density (PSD), were extracted from the acquired resonance signals and integrated using an adaptive entropy-weighted fusion strategy. Comparative and ablation analyses were further conducted to distinguish the respective contributions of probe enhancement and feature fusion, and to compare entropy-weighted fusion with single-feature imaging and equal-weight fusion. The results indicate that the focused probe mainly improves the defect-response strength at the hardware level, whereas feature fusion mainly improves image contrast, background suppression, and segmentation consistency at the image level. Among the compared methods and under the present experimental conditions, entropy-weighted fusion provides the best overall imaging performance. Under the present experimental conditions, the proposed framework enables reliable detection of 5 mm debonding defects in aluminum-alloy–rubber bonded specimens and 10 mm debonding defects in titanium-alloy–rubber bonded specimens. These results suggest that the combined use of magnetic-field focusing and adaptive multi-feature fusion is a promising approach for the detection and quantitative characterization of micro-debonding defects in metal–rubber bonded structures.

Reliable identification of steel surface defects is an important requirement for automated quality control in modern manufacturing. In practice, however, defects on steel surfaces may have low visual contrast, irregular geometric patterns, substantial background interference, and considerable scale differences, which complicate both localization and classification. Real-Time DEtection Transformer (RT-DETR) offers an end-to-end detection framework without non-maximum suppression (NMS) and supports real-time inference, but its standard feature extraction and aggregation mechanisms are not specifically optimized for weak, fragmented, or boundary-ambiguous surface defects. This work develops a Defect-aware Adaptive Fusion RT-DETR (DAF-RTDETR) based on RT-DETR with a ResNet-18 backbone (RT-DETR-R18). Three complementary modifications are incorporated into the framework. EdgeEnhance is deployed in the backbone to preserve local detail-sensitive information while limiting redundant computation. Faster-CGLU Mixed Aggregation Network (FC-MANet) is used in place of the original fusion blocks to improve nonlinear defect-oriented representation, whereas Adaptive Cross-scale Fusion (ACF) substitutes static concatenation with adaptive interaction across adjacent feature scales. The method is evaluated primarily on the Northeastern University surface defect dataset (NEU-DET) and is additionally assessed on the Metallic Surface Defect Detection dataset (GC10-DET) as a supplementary benchmark under more diverse steel-surface conditions. On NEU-DET, DAF-RTDETR increases mean Average Precision at an Intersection over Union threshold of 0.5 (mAP@0.5) from 75.4% for RT-DETR-R18 to 79.5%, while decreasing the parameter count from 19.88M to 16.33M and retaining an inference rate of 92 frames per second (FPS). On GC10-DET, the proposed detector obtains the highest mAP@0.5 among the evaluated detectors, reaching 73.2%. The experimental findings show that DAF-RTDETR achieves a favorable compromise among detection performance, model size, and inference efficiency for steel surface defect inspection.

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.

Accurate measurement of a Radar Cross-Section (RCS) is a critical technical challenge in assessing the stealth performance and scattering characteristics of radar targets. Traditional RCS measurement methods are limited by high costs, sensitivity to environmental conditions, and difficulties in distinguishing local scattering features of targets. To address these challenges, this paper proposes a novel RCS measurement method based on enhanced imaging and scattering center extraction. This method integrates sub-aperture imaging with image fusion techniques to improve imaging quality and enhance the detail of target scattering characteristics. Additionally, an improved sequence CLEAN algorithm is employed to effectively suppress sidelobe effects and ensure the accuracy of scattering center extraction. Experimental results demonstrate that this method achieves higher precision in RCS measurement of complex targets and is particularly effective in environments with strong interference, where it successfully separates the scattering contributions of the target from those of the interference sources. This method offers a new technological approach for precise RCS measurement of radar stealth targets in the future.