Hot strength of creep resistant ferritic steels and relationship to creep rupture data

被引:15
作者
Dimitriu, R. C. [1 ]
Bhadeshia, H. K. D. H. [1 ]
机构
[1] Univ Cambridge, Dept Mat Sci & Met, Cambridge CB2 3QZ, England
关键词
hot strength; ferritic steel; creep rupture; power plant; energy;
D O I
10.1179/174328407X213332
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Experimental data on the tensile strength of ferritic steels designed for prolonged service at elevated temperatures have been assessed as a function of many variables, including the testing temperature. The resulting model has been combined with other data on the intrinsic strength of pure ferritic iron and substitutional solute strengthening to show that there is a regime in the temperature range 780-845 K beyond which there is a rapid decline in the microstructural contribution to strength. This decline cannot be attributed to changes in microstructure, but possibly to the ability of dislocations to overcome obstacles with the help of thermal activation. There is evidence of an approximate relationship between the temperature dependence of hot tensile strength and creep rupture stress.
引用
收藏
页码:1127 / 1131
页数:5
相关论文
共 29 条
[1]   Neural networks in materials science [J].
Bhadeshia, HKDH .
ISIJ INTERNATIONAL, 1999, 39 (10) :966-979
[2]   Impact toughness of C-Mn steel arc welds - Bayesian neural network analysis [J].
Bhadeshia, HKDH ;
MacKay, DJC ;
Svensson, LE .
MATERIALS SCIENCE AND TECHNOLOGY, 1995, 11 (10) :1046-1051
[3]   BAINITE - AN ATOM-PROBE STUDY OF THE INCOMPLETE REACTION PHENOMENON [J].
BHADESHIA, HKDH ;
WAUGH, AR .
ACTA METALLURGICA, 1982, 30 (04) :775-784
[4]   Atomic scale observations of bainite transformation in a high carbon high silicon steel [J].
Caballero, F. G. ;
Miller, M. K. ;
Babu, S. S. ;
Garcia-Mateo, C. .
ACTA MATERIALIA, 2007, 55 (01) :381-390
[5]  
DORN JE, 1954, J MECH PHYS SOLIDS, V3, P85
[6]   Artificial neural networks for modelling of the impact toughness of steel [J].
Dunne, D ;
Tsuei, H ;
Sterjovski, Z .
ISIJ INTERNATIONAL, 2004, 44 (09) :1599-1607
[7]   Prediction of low cycle fatigue lives of low alloy steels [J].
Goswami, T .
ISIJ INTERNATIONAL, 1996, 36 (03) :354-360
[8]   Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network [J].
Guo, Z ;
Sha, W .
COMPUTATIONAL MATERIALS SCIENCE, 2004, 29 (01) :12-28
[9]   The prediction of the hot strength in steels with an integrated phenomenological and artificial neural network model [J].
Hodgson, PD ;
Kong, LX ;
Davies, CHJ .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1999, 87 (1-3) :131-138
[10]   BAINITIC STRUCTURES AND THERMOMECHANICAL TREATMENTS APPLIED TO STEEL [J].
KALISH, D ;
KULIN, SA ;
COHEN, M .
JOURNAL OF METALS, 1965, 17 (02) :157-&