Modelling of One-directional Functionally Graded Circular Plates with Artificial Neural Network

Abstract

In functionally graded materials (FGMs), a combination is provided based on a volume ratio to prevent cracks in the interfaces of different materials and to prevent irregularities in the material transition region. The volumetric distribution between the components determines the mechanical performance of the FGMs.  In this study, the thermo-mechanical behavior of the functionally graded circular plate (FGCPs) was investigated. The thermo-mechanical behavior depends on the equivalent stress values, and the equivalent stress values depend on the volumetric distribution of the components of the material, ie the compositional gradient upper values. Numerical analysis was performed for 60 different compositional gradient peaks in the range [0.01-5], models based on volumetric distribution were established and equivalent stress values were calculated. In the artificial neural network (ANN), three different training algorithms, Levenberg-Marquardt, Gradient Descent With Momentum Backpropagation and Gradient Descent With Adaptive Learning Rate Backpropagation, were created and compared. According to the results of the analysis, Levenberg-Marquart algorithm showed an average success rate of over 90%. It is thought that the models installed in ANN will provide insight in determining the thermo-mechanical behavior of FGCPs and will save work-timei.

Keywords

• One-Directional Functionally Graded Circular Plates,Artificial Neural Network

Modelling of One-directional Functionally Graded Circular Plates with Artificial Neural Network

Abstract

In functionally graded materials (FGMs), a combination is provided based on a volume ratio to prevent cracks in the interfaces of different materials and to prevent irregularities in the material transition region. The volumetric distribution between the components determines the mechanical performance of the FGMs.  In this study, the thermo-mechanical behavior of the functionally graded circular plate (FGCPs) was investigated. The thermo-mechanical behavior depends on the equivalent stress values, and the equivalent stress values depend on the volumetric distribution of the components of the material, ie the compositional gradient upper values. Numerical analysis was performed for 60 different compositional gradient peaks in the range [0.01-5], models based on volumetric distribution were established and equivalent stress values were calculated. In the artificial neural network (ANN), three different training algorithms, Levenberg-Marquardt, Gradient Descent With Momentum Backpropagation and Gradient Descent With Adaptive Learning Rate Backpropagation, were created and compared. According to the results of the analysis, Levenberg-Marquart algorithm showed an average success rate of over 90%. It is thought that the models installed in ANN will provide insight in determining the thermo-mechanical behavior of FGCPs and will save work-timei.

Keywords

• One-Directional Functionally Graded Circular Plates,Artificial Neural Network,Training Algorithms,Finite Difference Method

References

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