A hybrid algorithm for modeling and studying of the effect of material and mechanical uncertainties on stability of sandwich FGM materials under thermal shock

Khayat, Majid; Baghlani, Abdolhossein; Najafgholipour, Mohammad Amir

doi:10.1016/j.compstruct.2022.115657

Cited by 10 publications

(2 citation statements)

References 99 publications

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“…1,2 So far, various studies have been performed on the structural behavior of these materials, including vibration, buckling, and stability analyses. [3][4][5] Nowadays, artificial intelligence-based approaches such as machine learning (ML) models have attained remarkable attention from researchers toward predicting the mechanical response of FGMs under various loading conditions and environments. Jodaei et al 6 used the differential quadrature method and artificial neural network (ANN) to investigate vibration analysis of functionally graded annular plates.…”

Section: Introductionmentioning

confidence: 99%

Natural frequency prediction of functionally graded graphene-reinforced nanocomposite plates using ensemble learning and support vector machine models

Pashmforoush

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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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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Section: Introductionmentioning

confidence: 99%

Natural frequency prediction of functionally graded graphene-reinforced nanocomposite plates using ensemble learning and support vector machine models

Pashmforoush

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Belli sınır koşullar altında optimum kompozisyonel gradyant üst değerini bulmak için YSA kullanarak bir model oluşturdular. Khayat ve arkadaşları [13], Budiansky ve Ruth'un kriterlerine dayalı silindirik kabukların termal ve mekanik gerilme tesirindeki FKM'de dinamik kararlılığını araştırdılar. Bu çalışmada belirsiz değerleri ortandan kaldırmak için nokta tahmini ve YSA ile termal yükleme altındaki davranışı araştırdılar.…”

Section: Introductionunclassified

Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem

Demirbaş¹,

Çakir

2022

International Scientific and Vocational Studies Journal

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Functionally graded materials (FGMs) are materials composed of metals and ceramics in which the distribution of material components varies according to a particular volumetric function. FGMs are often used in high-temperature applications. In our study, models were created in the Artificial Neural Network depending on the equivalent stress levels in the compositional gradient exponent, which is the most important parameter in determining the thermo-mechanical behavior of circular plates functionally staggered in two directions, and the performances of these models were evaluated. These models were obtained with four different training algorithms: Levenberg-Marquardt, Backpropagation Algorithm, Resilient Propagation Algorithm, Conjugate Gradient Backpropagation with Powell-Beale Restarts To train the ANN, equivalent stress levels were obtained by performing numerical analyzes at different compositional gradient upper values. The data sets were created by considering the largest value of the equivalent stress levels, the smallest value of the largest value, the largest value of the smallest value, and the smallest value of the smallest value. In this study, training stages and performance values were examined and interpreted with 4 training algorithms in detail.

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Non-Fourier thermoelastic interaction of two collinear cracks in a functionally graded layer

Yang

Pourasghar

Chen

et al. 2023

Applied Mathematical Modelling

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A hybrid algorithm for modeling and studying of the effect of material and mechanical uncertainties on stability of sandwich FGM materials under thermal shock

Cited by 10 publications

References 99 publications

Natural frequency prediction of functionally graded graphene-reinforced nanocomposite plates using ensemble learning and support vector machine models

Natural frequency prediction of functionally graded graphene-reinforced nanocomposite plates using ensemble learning and support vector machine models

Evaluation of the Performance of ANN Algorithms with the Bidirectional Functionally Graded Circular Plate Problem

Non-Fourier thermoelastic interaction of two collinear cracks in a functionally graded layer

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