Detection of Small Debonding Defects in Metal-Rubber Bonded Structures Using an Enhanced EMAT and Multi-Feature Fusion Imaging.

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.