Making evaluations on the ever-increasing data and thus obtaining meaningful outcome becomes more and more important
in the digital age. Thanks to the recent advances in artificial intelligence technologies, processing this data and making predictions
is very popular. Within the scope of this study, S235 JR steel, which is used in many areas such as bridges, railways, industrial
buildings, vehicle manufacturing and oil-gas exploration stations, has been deformed at different rates in the laboratory environment.
Microstructure images of the deformed materials were obtained with the help of a microscope after some metallographic processes.
As a result of the study, it is aimed to contribute to the literature by creating a data set containing microstructure images of S235 JR
steel, which has been deformed at different rates. In addition, Convolutional Neural Network (CNN) and Multilayer Artificial Neural
Network (ANN) models, which are among the deep learning methods of artificial intelligence technology, were used to classify the
deformation rates. In order to minimize the error in both deep learning models, Adam Optimization Algorithm has been preferred. The
results obtained with the Adam Optimization Algorithm in CNN and Multilayer ANN models were compared and the highest success
in the classification process was obtained in the CNN model.