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 (Tnr, Ar3 and Ar 1) for the microalloyed steel grades. The chemical composition of the steels studied in this work are displayed in Table I.


Steel C Mn Si Al Cr Cu Nb V Ti N
C10.090.530.180.029-----0.0047
C20.150.900.210.039-----0.0053
CMn0.161.480.360.039-----0.0048
Nb0.181.340.300.025--0.033--0.0074
NbTi10.141.110.300.044--0.020-0.0150.0054
NbTi20.141.340.230.035--0.033-0.0140.0048
NbTiV0.121.500.310.038--0.0470.0510.0200.0064
NbCrCu10.161.030.410.0290.540.230.025--0.0107
NbCrCu20.130.990.380.0420.500.220.014--0.0095
Table I: Chemical compositions of steels studied in this work.


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 Tnr 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.



- REFERENCES
  1. SPITTEL, T. & SPITTEL, M. Scandinavian Journal of Metallurgy, 19(6):85-94, 1990.

  2. SPITTEL, T. & HENSEL, A. Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren. VEB Deutscher Verlag für Grundstoffindustrie, Leipzig, 1978. 476 p.

  3. TAROKH, M. & SEREDYNSKI, F. Journal of the Iron and Steel Institute, 208(7):695-697, July 1970.

  4. WHITTAKER, H.J. et alii. In: INTERNATIONAL CONFERENCE ON TECHNOLOGY OF IRON AND STEEL. Proceedings. The Iron and Steel Institute of Japan, Tokyo, 1971, 662-666.

  5. JÄCKEL, I. Neue Hütte, 34(8):287-290, August 1989.

  6. GITTINS, A. et alii. BHP Technical Bulletin, 18(1):2-8, May 1974.

  7. BERNARD, G. et alii. Revue de Metallurgie, 78(5):421-434, Mai 1981.

  8. BORATTO, F. et alii. In: THERMEC ´88. Proceedings. Iron and Steel Institute of Japan, Tokyo, 1988, p. 383-390.

  9. DUTTA, B. & SELLARS, C.M. Materials Science and Technology, March 1987, 197-206.

  10. OUCHI, C. et alii. Transactions of the ISIJ, 22(3):214-222, March 1982.


Last Update: 14 August 1997
© Antonio Augusto Gorni