Vibration-based damage identification in beam-like composite laminates by using artificial neural networks

Date

2003-01-01

Author

Şahin, Melin

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This paper investigates the effectiveness of the combination of global (changes in natural frequencies) and local (curvature mode shapes) vibration-based analysis data as input for artificial neural networks (ANNs) for location and severity prediction of damage in fibre-reinforced plastic laminates. A finite element analysis tool has been used to obtain the dynamic characteristics of intact and damaged cantilever composite beams for the first three natural modes. Different damage scenarios have been introduced by reducing the local stiffness of the selected elements at different locations along the finite element model of the beam structure. After performing the sensitivity analyses aimed at finding the necessary parameters for the damage detection, different input-output sets have been introduced to various ANNs. In order to check the robustness of the input used in the analysis, random noise has been generated numerically and added to noise-free data during the training of the ANNs. Finally, trained feedforward back-propagation ANNs have been tested using new damage cases and checks have been made for severity and location prediction of the damage.

Subject Keywords

Mechanical Engineering

URI

https://hdl.handle.net/11511/42264

Journal

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE

DOI

https://doi.org/10.1243/095440603321919581

Collections

Department of Aerospace Engineering, Article

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Citation Formats

M. Şahin, “Vibration-based damage identification in beam-like composite laminates by using artificial neural networks,” PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, pp. 661–676, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42264.