Assessment of different solution methods for receding contact problems in functionally graded layered mediums

Yaylacı, Murat; Eyüboğlu, Ayşegül; Adıyaman, Gökhan; Yaylacı, Ecren Uzun; Öner, Erdal; Birinci, Ahmet

doi:10.1016/j.mechmat.2020.103730

Cited by 29 publications

(10 citation statements)

References 33 publications

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“…The (N/2 + 1)th equation is established automatically [47], and can be safely deleted. Thus, Equations ( 35)- (38) only contains 2N + 1 equations, as well as 2N + 1 unknowns, i.e., the contact pressure p 2 (s k ), q 2 (s k ), (k = 1, 2, • • • , N) and the half-length of receding contact b. It is worth noting that, among all the unknowns in Equations ( 35)- (38), only b is non-linear.…”

Section: Solution To Dual Singular Integral Equationsmentioning

confidence: 99%

“…Thus, Equations ( 35)- (38) only contains 2N + 1 equations, as well as 2N + 1 unknowns, i.e., the contact pressure p 2 (s k ), q 2 (s k ), (k = 1, 2, • • • , N) and the half-length of receding contact b. It is worth noting that, among all the unknowns in Equations ( 35)- (38), only b is non-linear. We can initialize b with a random value and substitute it into Equations ( 35)- (38); the remaining unknowns p 2 (s k ) and q 2 (s k ), (k = 1, 2, • • • , N) can then be solved iteratively.…”

Section: Solution To Dual Singular Integral Equationsmentioning

confidence: 99%

“…It is worth noting that, among all the unknowns in Equations ( 35)- (38), only b is non-linear. We can initialize b with a random value and substitute it into Equations ( 35)- (38); the remaining unknowns p 2 (s k ) and q 2 (s k ), (k = 1, 2, • • • , N) can then be solved iteratively. The numerical results of the iteration method can be verified by Equation (38), if the absolute error δ satisfies:…”

Section: Solution To Dual Singular Integral Equationsmentioning

confidence: 99%

“…Çömez et al [37] investigated the problem of frictional receding contact of an FG layer resting on a homogeneously coated half-plane. Recently, Yaylacı et al [38] used the semianalytical method, the finite element method (FEM), and the multi-layer perceptron method to analyze the receding contact problem between an FG layer and a homogeneous elastic layer resting on a Winkler foundation which loaded with a rigid cylindrical indenter. Öner et al [39] used the semi-analytical method, FEM, and an artificial neural network to study the double receding contact problem of multi-layered FGM structures under a distributive compressive traction field.…”

Section: Introductionmentioning

confidence: 99%

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Receding Contact Problem of Multi-Layered Elastic Structures Involving Functionally Graded Materials

Yan

2022

Crystals

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This paper studies a receding contact problem of a functionally graded layer laminate pressed against a functionally graded coated homogeneous half-plane substrate by a rigid flat indenter. The shear modulus of the functionally graded materials with a constant Poisson’s ratio is modeled by an exponential function which varies along the thickness direction. Both the governing equations and the boundary conditions of the receding contact problem are converted into a pair of singular integral equations using the Fourier integral transforms, which are numerically integrated by the Chebyshev–Gauss quadrature. The contact pressure and the contact size at both contact interfaces are eventually obtained iteratively, as developed from the steepest descent algorithm. Extensive parametric studies suggest that it is possible to regulate the contact pressure and contact size by constructing the top layer from a soft functionally graded material.

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Section: Solution To Dual Singular Integral Equationsmentioning

confidence: 99%

Section: Solution To Dual Singular Integral Equationsmentioning

confidence: 99%

Section: Solution To Dual Singular Integral Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Receding Contact Problem of Multi-Layered Elastic Structures Involving Functionally Graded Materials

Yan

2022

Crystals

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“…In recent years, artificial neural networks (ANNs), which can describe nonlinear and complex relationships between data without any assumptions, have become an alternative to traditional regression models [32][33]. These are used to predict the outcome of any problem or situation in different disciplines by using some input values and relations [34]. ANNs imitate the functioning of the human brain.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to Predict Inhibition in Probiotic Experiments

Yaylacı

2021

International Journal of Engineering and Applied Sciences

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Artificial neural networks (ANNs) provide a modeling approach that can be used in the in vitro stages of probiotic studies. The aim of the study was to evaluate the ability of multilayer perceptron (MLP) and radial-basis function (RBF) ANNs to predict the inhibition level of indicator bacteria in co-culture experiments performed at various initial concentrations. In both types of networks, time, initial concentrations of L. lactis and Aeromonas spp. were the input variables and the inhibition concentration of Aeromonas spp. was the output value. In the construction of the models, different numbers of neurons in the hidden layer, and different activation functions were examined. The performance of the developed MLP and RBF models was tested with root mean square error (RMSE), coefficient of determination (R 2 ) and relative error (e) statistical analysis. Both ANN models were showed a strong agreement between the predicted and experimental values. However, the developed MLP models showed higher accuracy and efficiency than the RBF models. The results indicated that ANNs developed in this study can successfully predict the inhibition concentration of Aeromonas spp. co-cultured with L. lactis in vitro and can be used to determine bacterial concentrations in the design of further experiments.

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An integral shear and normal deformation theory for bending analysis of functionally graded sandwich curved beams

et al. 2021

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Assessment of different solution methods for receding contact problems in functionally graded layered mediums

Cited by 29 publications

References 33 publications

Receding Contact Problem of Multi-Layered Elastic Structures Involving Functionally Graded Materials

Receding Contact Problem of Multi-Layered Elastic Structures Involving Functionally Graded Materials

Application of Artificial Neural Networks to Predict Inhibition in Probiotic Experiments

An integral shear and normal deformation theory for bending analysis of functionally graded sandwich curved beams

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