Artificial neural network modeling for the prediction of critical transformation temperatures in steels

Accurate knowledge of critical transformation temperatures in steels such as the austenitizing temperature, Tγ, isothermal bainite and martensite start temperatures, BS and MS, is of unquestionable significance from an industrial and research point of view. Therefore a significant amount of work has been devoted not only in understanding the physical mechanism lying beneath those transformations, but also obtaining quantitatively accurate models. Nowadays, with modern computing systems, more rigorous and complex data analysis methods can be applied whenever required. Thus, Artificial Neural Network (ANN) analysis becomes a very attractive alternative, for being easily distributed, self-sufficient and for its ability of accompanying its predictions by an indication of their reliability.