Quantitative assessment of delamination in composites using multiple local-defect-resonance modes

Hu, Yajie; Li, Weibin; Zhang, Changyu; Lan, Zifeng; Deng, Mingxi

doi:10.1016/j.jsv.2024.118499

Journal of Sound and Vibration

2024

DOI: 10.1016/j.jsv.2024.118499

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Quantitative assessment of delamination in composites using multiple local-defect-resonance modes

Yajie Hu,

Weibin Li,

Changyu Zhang

et al.

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Deep learning-assisted local resonance strategy for accurate internal damage imaging in composites

Zhang,

Hu,

Deng

et al. 2024

Applied Physics Letters

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In this paper, we propose a deep neural network-assisted strategy to accurately and efficiently identify local defect resonance (LDR) modes and accurately image the internal damage in composites. A two-dimensional convolutional neural network (2D-CNN) model was constructed to identify LDR modes. The frequency-domain contour maps were used as input data, given that the LDR phenomenon exhibits discernible physical attributes in the frequency domain that are conducive to deep neural network assimilation. The obtained results demonstrate effective training outcomes and transferability, even with a limited number of samples. The LDR modes are efficiently extracted by the developed 2D-CNN model and used to obtain the accurate imaging of internal damages in composites.

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Deep learning-assisted local resonance strategy for accurate internal damage imaging in composites

Zhang,

Hu,

Deng

et al. 2024

Applied Physics Letters

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show abstract

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Quantitative assessment of delamination in composites using multiple local-defect-resonance modes

Cited by 1 publication

References 32 publications

Deep learning-assisted local resonance strategy for accurate internal damage imaging in composites

Deep learning-assisted local resonance strategy for accurate internal damage imaging in composites

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