Red neuronal artificial para predecir la dependencia a la composición química de la energía de falla de apilamiento en aceros inoxidables austeníticos - Alfonso M. Román; Bernando Campillo; Arturo Molina; Horacio Martínez; Itzel Reyes; Osvaldo Flores (Programación Matemática y Software, 2020)
Stacking fault energy (SFE) is an important parameter to be considered in the design of austenitic stainless steels (SS) due to its influence on magnetic susceptibility, atomic order changes and intergranular corrosion resistance. An extensive review of specialized literature was examined in order to understand the different methods that have been developed for the calculation of SFE. Characterization by transmission electron microscopy (TEM), linear expressions from data processing and first-principles quantum mechanics approximations are some techniques that have been used for the calculation of SFE. In the present work a feed forward backpropagation artificial neural network (ANN) was developed to predict the SFE within given specific ranges of chemical compositions for austenitic SS. The experimental data were extracted from a research work reported by Yonezawa et al [1], and then were analyzed for three different heat treatment conditions. The present model predicts SFE values with a correlation coefficient of 0.99, which is a minor error when is compared with other works in the literature.