A hybrid theoretical–numerical–experimental framework for robust health monitoring of thin-walled hollow composite members using guided waves
Abstract Thin-walled hollow composite members (HCM) are extensively employed in aerospace and automotive industries due to their high strength-to-weight ratio and design flexibility. This study introduces a hybrid -numerical–experimental framework for robust detection and characterisation of barely...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-96150-z |
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| Summary: | Abstract Thin-walled hollow composite members (HCM) are extensively employed in aerospace and automotive industries due to their high strength-to-weight ratio and design flexibility. This study introduces a hybrid -numerical–experimental framework for robust detection and characterisation of barely visible damage in HCM using guided waves (GW). It focuses on assessing surface abrasion and hairline cracks, two common yet challenging damage types encountered in the field. A semi-analytical finite element (SAFE) formulation is developed for the dispersion analysis alongside numerical simulations using finite element software COMSOL Multiphysics®, and experimental validation is performed to ensure accurate and reliable results. The study focuses on GW propagation and scattering behaviour under varying damage scenarios, exploring the effects of damage size, position, and its offset on wave features. Parametric analyses show significant variations in wave characteristics such as group velocity, amplitude, and mode features. A waveform and statistical approach incorporating continuous wavelet transform (CWT) and energy enables precise damage classification. Results show that abrasion-induced damages cause substantial changes in GW features in terms of DIs and statistical parameters, while hairline cracks marginally affect the damage indices and wave features, aiding in distinguishing between different damage types. These findings contribute to the development of robust damage identification algorithms for structural health monitoring, providing valuable insights for optimising the maintenance and performance of composite structures in critical engineering environments, ensuring safety and operational efficiency. |
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| ISSN: | 2045-2322 |