ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels

作者：Khalaj Gholamreza^*; Nazari Ali; Yoozbashizadeh Hossein; Khodabandeh Alireza; Jahazi Mohammad

来源：Neural Computing & Applications, 2014, 24(2): 301-308.

DOI：10.1007/s00521-012-1233-6

• Artificial neural network (ANN)
• Microalloyed steel
• Continuous cooling
• High strength low alloy (HSLA) steel

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the transformation start temperature of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 138 different experimental data were gathered from the literature. The data used in the ANN model are arranged in a format of fourteen input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is transformation start temperature. In this model, the training and testing results in the ANN have shown strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels.

• 出版日期2014-2