Mohammadreza Khanzadeh - Department of Material, Majlesi Branch, Islamic Azad University, Iran
Ali Nazari - Department of Technical and Engineering Sciences, Islamic Azad University
Gholamreza Khalaj - Department of Technical and Engineering Sciences, Islamic Azad University, Saveh Branch, Saveh, Iran

In the present paper, a model based on adaptive network-based fuzzy inference systems (ANFIS) 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. For purpose of building the model, training and testing using experimental results from 140 specimens produced from two basic composites were conducted. The data used for the input data in ANFIS models are arranged in a format of six input parameters that cover the FGS 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 ANFIS, the ductile to brittle transition temperature of each FGS specimen was predicted. The training and testing results in the ANFIS model have shown a strong potential for predicting the ductile to brittle transition temperature of each FGS specimen

Functionally graded steels, DBTT, ANFIS, crack divider, crack arrester, ESR