Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

Kallannavar, Vinayak; Kattimani, Subhaschandra; Soudagar, Manzoore Elahi M.; Alshahrani, Saad; Imran, Muhammad

doi:10.3390/ma14123170

Cited by 26 publications

(8 citation statements)

References 43 publications

(70 reference statements)

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“…The shiny region marked the blue lines depict the reflection of the light source because of the presence of Diamond-Like Carbon (DLC) entities, such as MWCNTs nanoparticles. The breakage of the polymer chains at a higher level of reinforcement can be seen in Figure 10 , which also indicates the poor distribution of MWCNTs and the lack of interfacial adhesion between the polymer chains [ 29 , 30 , 31 , 32 ]. The reinforcement at 0.3 wt.% of the epoxy/MWCNTs, as shown in Figure 9 , indicates good dispersion and good interfacial linkage in-between the fillers-matrix.…”

Section: Resultsmentioning

confidence: 99%

Statistical Analysis of Polymer Nanocomposites for Mechanical Properties

et al. 2021

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Epoxy resins, due to their high stiffness, ease of processing, good heat, and chemical resistance obtained from cross-linked structures, have found applications in electronics, adhesives coatings, industrial tooling, and aeronautic and automotive industries. These resins are inherently brittle, which has limited their further application. The emphasis of this study is to improve the properties of the epoxy resin with a low-concentration (up to 0.4% by weight) addition of Multi-Walled Carbon Nanotubes (MWCNTs). Mechanical characterization of the modified composites was conducted to study the effect of MWCNTs infusion in the epoxy resin. Nanocomposites samples showed significantly higher tensile strength and fracture toughness compared to pure epoxy samples. The morphological studies of the modified composites were studied using Scanning Electron Microscopy (SEM).

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Section: Resultsmentioning

confidence: 99%

Statistical Analysis of Polymer Nanocomposites for Mechanical Properties

et al. 2021

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“…The proposed method could accurately capture the effect of temperature and volume fraction index on the natural frequency of FGM plates. Kallannavar et al 8 employed ANN to study the influence of moisture and temperature on the vibration behavior of skew laminated sandwich plates. The dataset used for network training was prepared using first-order shear deformation theory.…”

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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“…Timchenko and Osetrov [32] proposed convolutional neural networks (CNN) for predicting the natural frequency of composite plates. ANN was also used to evaluate the environmental effect on the vibrational response of a skew composite laminated sandwich plate [33].…”

Section: Introductionmentioning

confidence: 99%

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

Seba

Kebdani

2022

Adv. technol. innov.

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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.

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Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

Cited by 26 publications

References 43 publications

Statistical Analysis of Polymer Nanocomposites for Mechanical Properties

Statistical Analysis of Polymer Nanocomposites for Mechanical Properties

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

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

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