Yue Jiang, Zhongda Yin, Pengchao Kong, and Yong Liu, Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification, J. Univ. Sci. Technol. Beijing, 11(2004), No. 5, pp. 462-468.
Cite this article as:
Yue Jiang, Zhongda Yin, Pengchao Kong, and Yong Liu, Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification, J. Univ. Sci. Technol. Beijing, 11(2004), No. 5, pp. 462-468.
Yue Jiang, Zhongda Yin, Pengchao Kong, and Yong Liu, Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification, J. Univ. Sci. Technol. Beijing, 11(2004), No. 5, pp. 462-468.
Citation:
Yue Jiang, Zhongda Yin, Pengchao Kong, and Yong Liu, Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification, J. Univ. Sci. Technol. Beijing, 11(2004), No. 5, pp. 462-468.
A method of fuzzy identification based on T-S fuzzy model was proposed for predicting temperature Ms 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 Ms 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 Ms, and the effects of austenltizing temperature and time on Ms temperature cannot be neglected.
A method of fuzzy identification based on T-S fuzzy model was proposed for predicting temperature Ms 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 Ms 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 Ms, and the effects of austenltizing temperature and time on Ms temperature cannot be neglected.
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