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

Yue Jiang; Zhongda Yin; Pengchao Kang; Yong Liu

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

Yue Jiang^*
, Zhongda Yin
, Pengchao Kang
, Yong Liu

^*Corresponding author for this work

Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

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 austenitizing temperature and time on M_s temperature cannot be neglected.

Original language	English
Pages (from-to)	462-468
Number of pages	7
Journal	Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed)
Volume	11
Issue number	5
State	Published - Oct 2004
Externally published	Yes

Keywords

Alloying element
Fuzzy identification
Low alloy steel
Martensite transformation start-temperature M
Prediction model

Cite this

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title = "Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification",

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 = "Alloying element, Fuzzy identification, Low alloy steel, Martensite transformation start-temperature M, Prediction model",

author = "Yue Jiang and Zhongda Yin and Pengchao Kang and Yong Liu",

year = "2004",

month = oct,

language = "英语",

volume = "11",

pages = "462--468",

journal = "Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed)",

issn = "1005-8850",

publisher = "University of Science and Technology Beijing",

number = "5",

}

TY - JOUR

T1 - Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification

AU - Jiang, Yue

AU - Yin, Zhongda

AU - Kang, Pengchao

AU - Liu, Yong

PY - 2004/10

Y1 - 2004/10

N2 - 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.

AB - 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.

KW - Alloying element

KW - Fuzzy identification

KW - Low alloy steel

KW - Martensite transformation start-temperature M

KW - Prediction model

UR - https://www.scopus.com/pages/publications/10444265333

M3 - 文章

AN - SCOPUS:10444265333

SN - 1005-8850

VL - 11

SP - 462

EP - 468

JO - Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed)

JF - Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed)

IS - 5

ER -

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

Abstract

Keywords

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