Application of ANFIS for modeling of microhardness of high strength low alloy (HSLA) steels in continuous cooling

Abstract

The paper presents some results of the research connected with the development of new approach based on the Adaptive Network-based Fuzzy Inference Systems (ANFIS) of predicting the Vickers microhardness of the phase constituents occurring in five steel samples after continuous cooling. The independent variables in the model are chemical compositions, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. To construct these models, 114 different experimental data were gathered from the literature. The data used in the ANFIS model is arranged in a format of twelve input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is Vickers microhardness. In this model, the training and testing results in the ANFIS systems have shown strong potential for prediction of effects of chemical compositions and heat treatments on hardness of microalloyed steels.

The Analysis of the Microstructure and Mechanical Properties of Low Carbon Microalloyed Steels after Ultra Fast Cooling

“…The wide range of mechanical properties attainable in HSLA steels coupled with their relatively low cost are responsible for their high volume of production, which represents ~10% of the world's steel production 3 . The evolution of HSLA steel was based on low carbon content to improve weldability and suitable alloying elements were added to improve austenite hardenability [4][5][6] . Thermomechanical controlled processing (TMCP) and microalloying in order to obtain desired microstructure and properties are the essence of ultra-low carbon microalloyed steel 7 .…”