Abstract
The pearlite growth rate during the isothermal austenite-to-pearlite transformation has been analyzed using a neural network technique within a Bayesian framework. An extensive database consisting of the detailed chemical composition considering elements such as Mn, Cr, Ni, Si and Mo, and isothermal temperature was compiled for this purpose using data from the published literature. With the aim of modeling the pearlite growth rate during the austenite-to-pearlite transformation a neural network has been proposed. The model allows us to examine the relative importance of the alloying elements in pearlite growth. The results from the network analysis were consistent with those expected from phase transformation theory.
