عنوان مجله
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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
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کلیدواژهها
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Functionally graded materials, nanocomposites, machine learning, vibration analysis, shapley values
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چکیده
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Functionally graded materials (FGMs) are modern engineering materials with increasing application in various industrial
fields. In this study, the free vibration behavior of graphene-reinforced FGM plate is investigated using finite element
method and machine learning (ML) approaches. For this purpose, three advanced ML models including ensemble
learning algorithms (bootstrap aggregation and gradient boosting) and Gaussian support vector machine are employed
to predict the natural frequency of functionally graded graphene/epoxy nanocomposite plates. In this regard, first,
hyperparameter optimization is carried out using Bayesian optimization algorithm. Then, regression analysis is
performed using the aforementioned ML approaches. According to the obtained results, all ML models have a high
coefficient of determination (more than 96%) with low mean squared error (MSE) values. However, the best
performance is related to the gradient boosting method, followed by support vector machine and bootstrap
aggregation, respectively. Finally, the significance degree of involved parameters on natural frequency is estimated using
the Shapley values concept. The obtained results reveal that the most significant parameters affecting the natural
frequency of graphene-reinforced FGM plates are clamp type, the volume fraction of graphene, followed by thickness
ratio and distribution pattern, respectively.
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