Akademik Veri Yönetim Sistemi
INTERNATIONAL SCIENTIFIC AND VOCATIONAL JOURNAL , cilt.6, sa.2, ss.103-115, 2022 (Hakemli Dergi)
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.