Thermal Stress Control in Functionally Graded Plates with Artificial Neural Network

Yıl 2018, Cilt: 2 Sayı: 1, 39 - 55, 30.06.2018

Munise Didem Demirbaş Didem Sofuoğlu

Öz

In this
study, trained models were obtained by using Artificial Neural Network (ANN) in
order to determine the equivalent stress levels of one dimensional functionally
graded rectangular plates. In this training set, a single layer sensor model
was used according to our linear problem. With ANN, the models were trained by
changing parameters the number of different iterations, number of neurons and
learning algorithms. and the trained model was tested and its performance was
measured.

In our
study, thermal stress analyses were performed for different compositional
gradient exponents using finite difference method to constitute data sets. The
data sets were constructed for the smallest value of the largest value of the
equivalent stress levels, the greatest value of the greatest value of the
equivalent stress levels, the greatest value of the smallest value of the
equivalent stress levels, and the smallest value of the smallest value of the
equivalent stress levels. Five different training algorithms were used in our
training network: Levenberg-Marquardt, Back Propagation Algorithm, Momentum
Coefficient Back Propagation Algorithm, Adaptive Back Propagation Algorithm and
Momentive Adaptive Back Propagation Algorithm. The
Levenberg-Marquardt algorithm is found to be more efficient than the other
algorithms.

With this
study, trained models have been developed to provide time and job savings to
determine equivalent stress levels in functionally graded plates, which are
very important for high temperature applications. These educated models will
provide important contributions to the literature and will be a source for the
work to be done in this regard.

Anahtar Kelimeler

Functionally graded plates, artificial neural network, single layer model, Levenberg-Marquardt algorithm, finite difference method, thermal stress analysis.

Thermal Stress Control in Functionally Graded Plates with Artificial Neural Network

Öz

Kaynakça

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