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| Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification | |
| 引用本文： | YueJiang ZhongdaYin PengchaoKang YongLiu. Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification[J]. 北京科技大学学报(英文版), 2004, 11(5): 462-468 |
| 作者姓名： | YueJiang ZhongdaYin PengchaoKang YongLiu |
| 作者单位： | SchoolofMaterialsScienceandEngineering,HarbinInstituteofTechnology,Harbin150001,China |
| 摘 要： | 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 austenitizing temperature and time on Ms temperature cannot be neglected. |
| 关 键 词： | 马氏体 低合金钢 模糊识别 预测模型 初始温度 |
Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification |
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| Yue Jiang,Zhongda YIN,Pengchao Kang,Yong Liu. Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification[J]. Journal of University of Science and Technology Beijing, 2004, 11(5): 462-468 | |
| Authors: | Yue Jiang Zhongda YIN Pengchao Kang Yong Liu |
| Abstract: | 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 austenitizing temperature and time on Ms temperature cannot be neglected. |
| Keywords: | fuzzy identification prediction model martensite transformation start-temperature Ms alloying element low alloy steel |
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