Prediction of hot deformation resistance during processing of microalloyed steels in plate rolling process

Mean flow stress of microalloyed high-strength steels during plate rolling has been thoroughly studied. It has been found out by both thermomechanical tests and measurements taken in the industrial plate mill. For this purpose, log data obtained from the plate rolling mills have been converted to mean flow stress using the Sims approach. The agreement between thermomechanical tests and mill data has been tested in order to confirm that thermomechanical testing can provide an easy, convenient and very effective simulation of industrial hot rolling process. The results are analysed and compared to the predictions of some mathematical models developed in literature. Subsequently, the best performing formula, namely the Poliak's equation, has been optimised by means of genetic algorithms and the standard Gauss–Newton method. This latter has allowed a finer tuning of the models' parameters in order to fit at best the available data. The Poliak's formula optimised by genetic algorithms is shown to accurately predict the mean flow stress and therefore it provides a useful tool for the determination of the milling settings before the incoming strip enters the mill.

[1]  Derek A. Linkens,et al.  Modelling of the flow stress using BP network , 1999, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296).

[2]  C. M. Sellars,et al.  Modelling Microstructure and Its Effects during Multipass Hot Rolling , 1992 .

[3]  P. Hodgson.  Models of the recrystallisation behaviour of C-Mn and Nb microalloyed steels during hot working processes. , 1993 .

[4]  Numerical Simulation of Austenite Recrystallization in CSP Hot Rolled C-Mn Steel Strip , 2007 .

[5]  Peter Hodgson,et al.  Integrated model for thermo-mechanical controlled process in rod (or bar) rolling , 2002 .

[6]  John J. Jonas,et al.  Mathematical Modeling of the Mean Flow Stress, Fractional Softening and Grain Size during the Hot Strip Rolling of C-Mn Steels , 1996 .

[7]  Mahdi Mahfouf,et al.  Integration of Systems Engineering-based Paradigms for the Scheduling and Control of an Experimental Hot-rolling Mill , 2009 .

[8]  L. Cser,et al.  The role of neural networks in the optimisation of rolling processes , 1998 .

[9]  John J. Jonas,et al.  Determination of Recrystallization Stop Temperature from Rolling Mill Logs and Comparison with Laboratory Simulation Results , 1994 .

[10]  John J. Jonas,et al.  A one-parameter approach to determining the critical conditions for the initiation of dynamic recrystallization , 1996 .

[11]  J. Jonas,et al.  Modeling of Flow Stress and Rolling Load of a Hot Strip Mill by Torsion Testing , 1989 .

[12]  A. Zaky.  Determinations of the non-recrystallization temperature for X52 steel produced by compact slab process combined with direct hot rolling , 2006 .

[13]  Sebastián F. Medina,et al.  DISCREPANCIES IN THE REGRESSION MODELLING OF RECRYSTALLIZATION RATE AS USING THE DATA FROM PHYSICAL SIMULATION TESTS , 2004 .

[14]  C. M. Sellars,et al.  Modelling the kinetics of strain induced precipitation in Nb microalloyed steels , 2001 .

[15]  Nenad Radovié,et al.  Effect of Interpass Time and Cooling Rate on Apparent Activation Energy for Hot Working and Critical Recrystallization Temperature of Nb-microalloyed Steel , 1999 .