Iki boyutlu fonksiyonel kademelendirilmiş plakalarin yapay sinir aği öğrenme algoritmalari ile isil gerilme modellemesi

Functionally graded materials (FGM) which are formed according to a specific volumetric distribution function have an important place in the production of materials resistant to high temperature applications. Depending on the objective function, it is very important to determine the volumetric distribution in order to provide important features such as maximum operating performance, structural variations and safe stress values in FGM. Numerical analysis methods are used to determine the volumetric distribution and to test the appropriate volumetric distribution. In this study, volumetric distribution models were created for the equivalent stress levels which are the most important parameter in determining the thermo-mechanical behavior of two-dimensional functionally graded plates in heat flux by finite difference method. These models were obtained by two different training algorithms in artificial neural network (ANN). These training algorithms are Levenberg-Marquart and Gradient Descent Backpropagation algorithm. While it takes 1800 s to determine n and m values in numerical analysis by finite difference method, this time is 900 s with an artificial neural network model and productivity increases significantly. It is important that used training algorithms are informative for scientific studies in terms of work-time-performance values. The proposed training models will guide researchers in achieving optimum volumetric distribution in both production and theoretical studies