Xiao, J., Yu, Z., Zuo, P., & Chen, J. (2025). Non-contact guided wave tomographic imaging of invisible defect in curved composite structures. Composites Communications, 60, 102651. https://doi.org/10.1016/j.coco.2025.102651
Abstract:
Composite components with complex shapes are widely utilized as reinforcement structures in the aerospace industry. However, their structural and geometric complexity poses significant challenges for effective defect inspection and monitoring. Air-coupled guided wave inspection is a promising approach for rapid, non-contact detection in such structures. This study investigates the behavior of air-coupled Lamb waves and tomographic imaging techniques for defect reconstruction in curved composite plates. Finite element (FE) modeling was employed to analyze the effect of structural curvature on Lamb wave propagation, including mode interactions with delaminations in bent regions. A probabilistic tomography (PT) method, incorporating the air-coupled Lamb wave technique, was developed to image delaminations in curved composite structures. To improve imaging accuracy, specific transmitter-receiver configurations were designed and evaluated through FE simulations. The feasibility of this approach was validated on a curved CFRP composite specimen through both simulations and experimental testing. Results show that composite curvature significantly influences wave dispersion, mode conversion, and guided wave propagation directivity. Imaging outcomes confirm that the probabilistic tomography method effectively visualizes delaminations in curved laminates. These findings establish air-coupled Lamb wave tomography as an efficient, non-contact solution for inspecting curved composite structures.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A∗STAR - RIE 2025 – Industry Alignment Fund – Pre Positioning (IAF-PP) funding scheme - Battery Remanufacturing for Improved Circular Ecosystems
Grant Reference no. : M24N2a0076
This research / project is supported by the Zhejiang Province - Key R&D program of Zhejiang Province
Grant Reference no. : 2024C01128