Application of neural networks to forecasting the CCT diagrams

The paper presents the methodology of modelling using the neural networks of the relationship between the chemical composition and austenitizing temperature, and the supercooled austenite transformation kinetics during the continuous cooling. The model worked out makes it possible to calculate a complete CCT diagram for the steel with a known chemical composition and analysis of the influence of particular elements on the characteristic points and transformation curves of the supercooled austenite, and also the hardness resulting from cooling. It makes also possible forecasting of the structure developed in steel as a result of cooling at a particular rate, by the quantitative description of the percentages of ferrite, pearlite, bainite, and martensite with the retained austenite.