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Neural network-based damage identification in composite laminated plates using frequency shifts

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Abstract

Delamination is the principal mode of failure of laminated composites. It is caused by the rupture of the fiber–matrix interface and results in the separation of the layers. This failure is induced by interlaminate tension and shear that is developed due to a variety of factors such as fatigue. Composites are known for excellent structural performance, which can be significantly affected by delamination. Such damages are not always visible on the surface and can lead to sudden catastrophic failures. To ensure a structural performance and integrity, accurate methods to monitor damages are required. This work presents a methodology for damage detection and identification on laminated composite plates using artificial neural networks fed with modal data obtained by finite element analysis. The proposed neural network to quantify damage severity achieved up to 95% success rate. A comparative study was done to evaluate the effect of boundary conditions on damage location. With the comparative study results, another neural network was proposed to locate damage position, achieving excellent results by successfully locating or by significantly reducing the search area. Both proposed ANNs use only frequency variation values as inputs, an easily obtainable quantity that requires few equipment to be acquired. The obtained results from these numerical examples indicate that the proposed approach can detect true damage locations and estimate damage magnitudes with satisfactory accuracy for this particular geometry, even under high measurement noise.

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Abbreviations

\(E_{1}\) :

Young’s modulus in longitudinal direction

\(E_{2}\) :

Young’s modulus in lateral direction

\(G_{12}\) :

In-plane shear modulus

M :

Mass matrix

n :

Generic counter

K :

Stiffness matrix

\(\alpha \) :

Stiffness multiplier

\(\Delta \omega \) :

Natural frequency shift

\(\lambda \) :

Eigenvalues

\(\nu _{12}\) :

Poisson’s ratio

\(\rho \) :

Density

\(\Phi \) :

Eigenvectors

\(\omega \) :

Natural frequency

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Acknowledgements

The authors are grateful to the Brazilian Funding Institutions CAPES, CNPq (Grant Number 431219/2018-4) and FAPEMIG (Grant Number APQ-00385-18) for the financial supports.

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Authors and Affiliations

  1. Mechanical Engineering Institute, Federal University of Itajubá, Av. BPS, 1303, Itajubá, Brazil

    Guilherme Antonio Oliver, Antonio Carlos Ancelotti Jr & Guilherme Ferreira Gomes

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  1. Guilherme Antonio Oliver
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  2. Antonio Carlos Ancelotti Jr
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  3. Guilherme Ferreira Gomes
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Correspondence to Guilherme Antonio Oliver.

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Oliver, G.A., Ancelotti, A.C. & Gomes, G.F. Neural network-based damage identification in composite laminated plates using frequency shifts. Neural Comput & Applic 33, 3183–3194 (2021). https://doi.org/10.1007/s00521-020-05180-3

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