Finite Element and Neural Network Based Predictive Model to Determine Natural Frequency of Laminated Composite Plates with Eccentric Cutouts under Free Vibration

This research proposes a predictive model to identify changes in the mechanical and geometrical properties of composite plates with eccentric cutouts based on natural frequency. Finite elements (FE) and neural networks are used to develop the model based on machine learning. First, the numerical ana...

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Bibliographic Details
Published in:Advances in technology innovation 2022-03, Vol.7 (2), p.131-142
Main Authors: Seba, Mohamed Rida, Kebdani, Said
Format: Article
Language:English
Subjects:
Artificial neural networks
Boundary conditions
Composite structures
Free vibration
Laminar composites
Machine learning
Mathematical models
Neural networks
Numerical analysis
Numerical models
Prediction models
Resonant frequencies
Stacking sequence (composite materials)
Vibration analysis
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Summary:This research proposes a predictive model to identify changes in the mechanical and geometrical properties of composite plates with eccentric cutouts based on natural frequency. Finite elements (FE) and neural networks are used to develop the model based on machine learning. First, the numerical analysis of free vibration is performed by the FE model on the laminated composite plates with a stacking sequence [0/90]2s under a clamped-free (CFFF) boundary condition. The outputs of the FE model (520 configurations) are then utilized to train the artificial neural network (ANN) model through the Levenberg-Marquardt method, and the developed ANN model is then used to evaluate the influence of various parameters on the natural frequency. The results show that the changes in the mechanical and geometrical properties of composite plates have impacts on the natural frequency. Furthermore, the findings of the ANN model are substantially identical to those of the numerical model, with a small margin of error.
ISSN:2415-0436
2518-2994
DOI:10.46604/aiti.2022.8909