Modeling ductile to brittle transition temperature of functionally graded steels by artificial neural networks

In the present paper, a model based on artificial neural networks for predicting ductile to brittle transition temperature of functionally graded steels in both crack divider and crack arrester configurations has been presented. Functionally graded steels containing graded ferritic and austenitic regions together with bainite and martensite intermediate layers were produced by electroslag remelting. To build the model, training and testing were conducted using experimental results from 140 specimens produced of two basic composites. The utilized data in the multilayer feed forward neural networks models are arranged in a format of six input parameters that cover the specimen type, the crack tip configuration, the thickness of graded ferritic region, the thickness of graded austenitic region, the distance of the notch from bainite or martensite intermediate layer and temperature. According to these input parameters, in the neural networks models, the ductile to brittle transition temperature of each specimen was predicted. The training and testing results in the neural network model have shown a strong potential for predicting the ductile to brittle transition temperature of each specimen.