Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification

A method of fuzzy identification based on T-S fuzzy model was proposed for predicting temperature M_s from chemical composition, austenitizing temperature and time for low alloy steel. The degree of membership of each sample was calculated with fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Compared with the results obtained by empirical models based on the same data, the results by the fuzzy method showed good precision. The accuracy of the fuzzy model is almost 6 times higher than that of the best empirical model. The influence of alloying elements, austenitizing temperature and time on M_s was analyzed quantitatively by using the fuzzy model. It is shown that there exists a nonlinear relationship between the contents of alloying elements in steels and their M_s, and the effects of austenltizing temperature and time on M_s temperature cannot be neglected.