Z.G. Li
,
H.B. Chang
,
T. Y. Hsu
,
Z. Y. Xu
,
and X. Y. Ruan (Department of Plasticity of Technology
,
Shanghai Jiao Tong University
,
Shanghai 200030
,
China)(Department of Metallurgical Engineering
,
Shanghai Technical College of Metallurgy
,
Shanghai 200233
,
China)(Department of Materials Science
,
Shanghai Jiao Tong University
,
Shanghai 200030
,
China)
金属学报(英文版)
Supposing carbon contents of ferrite phases in pearlite precipitating from austenite in multicomponent steel at temperature T and in Fe-C ystem at T' are the same the pearlite formation temperature diference, can be calculated from the FeX phase diagrams and the equilibrium temperature Al. Using Tp and Fe-C binary thermodynamic model, the driving forces for phase transformation from austenite to pearlite in multicomponent steels have been successfully calculated. Through the combination of simplified Zener and Hillert's model for pearlite growth with Johnson-Mehl equation, using data from known TTT diagrams, the interfacial energy parameter and activation energy for pearlite formation can be determined and expressed as functions of chemical composition in steels by regression analysis. The calculated starting curves of pearlitic transformation in some commercial steels agree well with the experimental data.
关键词:
pearlite formation temperature difference
,
null
,
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J. T. Liu
,
H.B. Chang
,
R.H. Wu
,
T. Y. Hsu(Xu Zuyao) and X.R. Ruan( 1)Department of Plasticity Technology
,
Shanghai Jiao Tong University
,
Shanghai 200030
,
China 2)School of Materials Science and Engineering
,
Shanghai Jiao Tong University
,
Shanghai 200030
,
China)
金属学报(英文版)
The hot deformation behavior of TI (18W-4Cr-1V) high-speed steel was investigated by means of continuous compression tests performed on Gleeble 1500 thermomechan- ical simulator in a wide range of tempemtures (950℃-1150℃) with strain rotes of 0.001s-1-10s-1 and true strains of 0-0. 7. The flow stress at the above hot defor- mation conditions is predicted by using BP artificial neural network. The architecture of network includes there are three input parameters:strain rate,temperature T and true strain , and just one output parameter, the flow stress ,2 hidden layers are adopted, the first hidden layer includes 9 neurons and second 10 negroes. It has been verified that BP artificial neural network with 3-9-10-1 architecture can predict flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress by using Zaped-Holloman parumeter and hyperbolic sine stress function, the prediction method by using BP artificial neurul network has higher efficiency and accuracy.
关键词:
T1 high-speed steel
,
null
,
null
,
null
,
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