Pertanika Journal of Science & Technology
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Pertanika ยท Universiti Putra Malaysia Press

Pertanika Journal of Science & Technology

Official journal of Universiti Putra Malaysia for scholarly work across science, engineering and related technologies.

e-ISSN 2231-8526 ISSN 0128-7680
Research article

Optimisation and Predictive Modelling of Natural Frequencies on Carbon/Glass Hybrid Composite Laminates

Abdul Azim Taredi, Siti Mariam Abdul Rahman, Mohd Nor Azmi Ab Patar and Jamaluddin Mahmud

https://doi.org/10.47836/pjst.33.6.09
KeywordsANSYS, artificial neural network, finite element analysis, hybrid composite laminate, natural frequency, response surface methodology
Article content

Abstract

A hybrid laminated composite is a laminate formed by integrating composite layers from various fibre types to achieve optimal properties. Nonetheless, considerable knowledge remains to be acquired regarding the vibration behaviour associated with the hybridisation of composite laminates. In structural design, natural frequency is a critical factor for preventing resonance, which may result in significant structural failure. This study aims to assess the inherent natural frequency response of hybrid composite laminates under free vibration, influenced by varying plate thicknesses, layer fractions, and orientation angles. Finite element models were developed using a commercial finite element software, ANSYS, to precisely characterise the natural frequencies of hybrid composite laminates under free vibration. The design of experiments was employed to identify 17 case study runs and to assess significant factors, with a comprehensive examination of each factor's impact on natural frequencies conducted through modal analysis. Given the limited dataset, employing techniques such as cross-validation with response surface methodology (RSM) and artificial neural networks (ANN) enhances the reliability of performance assessment for the model. Optimisation was conducted utilising RSM via analysis of variance, while ANN serves as a tool to ascertain data accuracy. The accuracy and robustness of the models are corroborated by a comparison of predictions from finite element analysis and RSM, demonstrating a strong correlation with percentage errors of 16 and 10% for ANN, respectively.