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

Abstract

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.

Keywords

• “Two-Directional Functionally Graded Circular Plates
• Finite Difference Method
• Thermal Stress Analysis
• Artificial Neural Network
• Training Algorithms”

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