Characterization of the Hot Mechanical Behavior of Steels

CHARACTERIZATION OF THE HOT MECHANICAL/METALLURGICAL BEHAVIOR OF COSIPA STEELS

Antonio Augusto Gorni

Companhia Siderúrgica Paulista - COSIPA, Brazil

The aim of this work was the determination of the hot flow stress behavior of carbon and microalloyed steels processed at COSIPA's Plate Mill, using isothermal hot torsion tests, as well the determination of critical temperatures regarding controlled rolling (T_nr, Ar₃ and Ar₁) for the microalloyed steel grades. The chemical composition of the steels studied in this work are displayed in Table I.

Table I: Chemical compositions of steels studied in this work.
Steel	C	Mn	Si	Al	Cr	Cu	Nb	V	Ti	N
C1	0.09	0.53	0.18	0.029	-	-	-	-	-	0.0047
C2	0.15	0.90	0.21	0.039	-	-	-	-	-	0.0053
CMn	0.16	1.48	0.36	0.039	-	-	-	-	-	0.0048
Nb	0.18	1.34	0.30	0.025	-	-	0.033	-	-	0.0074
NbTi1	0.14	1.11	0.30	0.044	-	-	0.020	-	0.015	0.0054
NbTi2	0.14	1.34	0.23	0.035	-	-	0.033	-	0.014	0.0048
NbTiV	0.12	1.50	0.31	0.038	-	-	0.047	0.051	0.020	0.0064
NbCrCu1	0.16	1.03	0.41	0.029	0.54	0.23	0.025	-	-	0.0107
NbCrCu2	0.13	0.99	0.38	0.042	0.50	0.22	0.014	-	-	0.0095

The hot flow stress of these steels were modeled in function of temperature, strain and strain rate according to several empirical formulas (Tarokh, Hajduk, Samanta, Tegart, Rossard and Jäckel), as well neural networks [1-7]. Besides that, it was studied the effect of chemical composition on the hot strength, through its effects on the values of the activation energy of hot deformation Q, mean hot strength and parameters of the Hajduk equation. However, the best model to quantify the effects of alloy elements over hot strength was developed using the technique of neural networks.

The critical temperatures for controlled rolling were determined using the method developed by BORATTO and JONAS [8], which uses data from the evolution of the average hot flow strength determined from multiple deformation hot torsion tests. The effect of alloy elements over T_nr were studied using the equation of BORATTO and JONAS [8], the model of interation between precipitation and recristallization proposed by DUTTA & SELLARS [9] and neural networks. The same procedure was performed regarding the Ar₃ temperature, using the equation of OUCHI [10] and neural networks. In both cases, the models developed using neural networks showed the best precision in the forecasting of these critical temperatures in function of the chemical composition of the steel.

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	Last Update: 14 August 1997
	© Antonio Augusto Gorni