Modelling Plate Mill Rolling An Expert Practical System Approach
M. Rebellato* and R. Barbosa** *Companhia Brasileira de Mineração e Metalurgia,
**Universidade Federal de Minas Gerais
The Charles Hatchett Seminar, 16th July 2014, London
Introduction
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• For structural steels, optimized mechanical properties are heavily dependent on how fine and homogeneous the cross section ferrite grains become
• The key is to determine how to apply basic metallurgical fundamentals to the production line
In the field
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• Engineers face limitations such as plant layout design, customer and societal specifications, costly downgrades
• There is little time to decide; models must give outputs very rapidly
Motivation
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• An expert, metallurgically sound, practical system is necessary
http://www.industry.siemens.com/datapool/industry/industrysolutions/metals/siroll/en/Dongkuk-Plate-Mill-No.2-en.pdf
This work
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• In what follows the first steps of a larger project aimed at building a practical expert system for industry rolling of microalloyed steels is presented
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An industry case
Rolling 300 mm slab to 16 mm plate
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Microstructure at ¼ thickness
Fine grains
Coarse grain
Mixed microstructure
Rolling 300 mm slab to 16 mm plate
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Microstructure at ¼ thickness
Fine grains
Coarse grain
Mixed microstructure
Grain sizes Average ~ 10 μm However, grain sizes ranging from ~ 20-25 to ~ 5-6 μm
Very inhomogeneous structure
Plate: chemical composition
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Element % weight
C 0.046
Mn 1.08
Nb 0.04
Ti 0.014
V 0
Cu 0
Cr, Ni, Mo < 0.35
N2 0.0051
Plate: possible precipitation
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Ti = 0.014
N2 = 0.0051 3.4 : 1
All Ti as TiN
0.0009 N2 available (almost no N2 left)
N2 left ≈ 0 Nb = 0.040
7.75 : 1
• 0.046 C takes 0.006 Nb • Nb left in solid solution = 0.040 – 0.006 = 0.034
Form TiN + NbCN (few) + NbC , ie, mixed particles + leaving Nb in solid solution during rolling
Most Nb in solution Low RLT and RST
Hot rolling schedule
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Time
Tem
per
atu
re
Broadsizing @ 1150oC
Roughing R1 @ 1140oC and R8 @ 1130oC
RLT @ 970oC
RST @ 890oC
Finishing F1 @ 940oC and F6 @ 855oC
Acc Tstart @ 830oC and Tfinish @ 500oC Time = 25 s
Holding period 240 s
Possible partial recrystallization case due to low strain accumulation
Finishing stands: Partial recrystallization of austenite
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Pass Recrystallized fraction after pass
(%)
Accumulated strain up to a given pass
F1 17 0.33
F2 73 0.61
F3 90 0.50
F4 22 0.29
F5 18 0.46
F6 5 0.50
Model indicates: possible partial recrystallization case low strain accumulation before transformation
Suggested alternative Start finishing below RST, stop above Ar3
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Time
Tem
pe
ratu
re
Broadsizing @ 1150oC
Roughing R1 @ 1140oC and R8 @ 1130oC
RLT @ 970oC
RST @ 890oC
Finishing F1 to F6 below RST and above AR3
Holding period 300 s
Acc Tstart @ 830oC and Tfinish @ 500oC Time = 25 s
Suggested improvement in finishing
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Reference temperature Temperature (oC)
RST 890
Finishing start temperature (F1) 910 (940 previous schedule)
Finishing stop temperature (F6) 825 (855 previous schedule)
AR3 810
Temperatures
Possible outcome
No partial recrystallization and Increase in strain accumulation before transformation
Finishing Recrystallization and strain accumulation after changes
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Pass Recrystallized fraction after pass
(%)
Accumulated strain up to a given pass
F1 Nil 0.38
F2 Nil 0.75
F3 Nil 1.08
F4 Nil 1.29
F5 Nil 1.51
F6 Nil 1.65
Model indicates: no recrystallization in all passes substantial increase in accumulated strain
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Microstructure at ¼ thickness
Grain sizes Average ~ 7 μm Grain sizes ranging from ~ 10-12 to ~ 3-6 μm
More homogeneous structure
Rolling 300 mm slab to 16 mm plate New trial
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Microstructure at ¼ thickness
Rolling 300 mm slab to 16 mm plate
After Before
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The model
Description
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• Two modules: reheating and hot rolling
• Sellars’ type model
• Uses equations available in the literature
Reheating module
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Reheating module
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Inputs Outputs
Alloy design
Process parameters • Slab thickness • Furnace geometry • Furnace temperatures
Nb content in solution
Time needed to dissolve Nb
Hot rolling module
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Hot rolling module
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Inputs Outputs
Process parameters • Pass temperatures • Strain • Strain rate • Grain size • Delay times
Recrystallization data Precipitation data Ferrite grain size Schedule optimization
Nb in solution (from reheating module)
Schedule optimization
Schedule thicknesses, mm
Stage at rolling
Slab 300
End broadsizing 227
Hold 64
Final 16
Reduction %
Stage at rolling
Reduction (%)
Recommended
Sizing + broadsizing
24
Roughing 70 ≥ 50 – 60%
Finishing 75 ≥ 20%
0
5
10
15
20
25
30
35
B1 B2 B3 R1 R2 R3 R4 R5 F1 F2 F3 F4 F5 F6
Re
du
ctio
n [
%]
Pass Number
Suggested schedule Heaviest thickness reduction applied at last roughing pass
BS
Ro
ugh
ing
Fin
ish
ing
Obs.: a) Heaviest thickness reduction at last roughing pass; b) Pass reductions are progressive along metallurgical
roughing phase.
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Is this a reasonable type of model?
Proposed and other types of models
• Physically based
• Physically based + Numerical
• Numerical models
• Empirically based + Numerical
• Empirically based
Plate mill: an expert practical system
There are several types
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Wide plate mill
http://www.danieli.com/products/Flat-Products-Hot-Rolling-Mills/Plate-Mills/PLATE-MILLS/Wide-Plate-Mill on April, 12, 2014.
Type of models (not exhaustive)
• They render a better understanding of the physical variables and metallurgical phenomena behind the process
Plate mill: an expert practical system
Physically based models
Among strong points…
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C.L. MIAO, C.J. SHANG, H.S. ZUROB, G.D. ZHANG, and S.V. SUBRAMANIAN, Recrystallization, Precipitation Behaviors, and Refinement of Austenite Grains in High Mn, High Nb Steel, METALLURGICAL AND MATERIALS TRANSACTIONS A VOLUME 43A, FEBRUARY 2012—66
Predicted results showing evolution of net drive force of recrystallization with time.
• They rely on many variables that are difficult to obtain with a specific degree of accuracy
Plate mill: an expert practical system
Physically based models
…however…
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S. Hore, S.K. Das, S. Banerjee, S. Mukherjee, A multiscale coupled Monte Carlo model to characterize microstructure evolution during hot rolling of Mo-TRIP steel, Acta Materialia 61 (2013) 7251–7259.
• Still provides better understanding however,
• Difficulty in obtaining key variables persists and
• It is usually time consuming
Plate mill: an expert practical system
Physically based + Numerical models
Possible strong and weak points
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S. Hore, S.K. Das, S. Banerjee, S. Mukherjee, A multiscale coupled Monte Carlo model to characterize microstructure evolution during hot rolling of Mo-TRIP steel, Acta Materialia 61 (2013) 7251–7259.
Multiscale model
Obs: Model uses a continuum dislocation density evolution model coupled with a heat transfer model integrated with a mesoscale Monte Carlo (MC) simulation technique.
• Usually very easy to use
• Lack of generalization
• Must be tuned by numerical algorithm
Plate mill: an expert practical system
Empirical + Numerical models
Possible strong and weak points
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Average prediction errors for MFS values calculated using equations available in the literature
Antonella DIMATTEO, Marco VANNUCCI and Valentina COLLA , Prediction of Mean Flow Stress during Hot Strip Rolling Using Genetic Algorithms, ISIJ International, Vol. 54 (2014), No. 1, pp. 171–178
Plate mill: an expert practical system
Empirical models
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V. V. Orlov and E. I. Khlusova , SIMULATION OF THROUGH PRODUCTION PROCESSES FOR MANUFACTURING THICK-WALLED PLATE IN HOT ROLLING REVERSING MILLS, Metallurgist, Vol. 56, Nos. 11–12, March, 2013 (Russian Original Nos. 11–12, Nov.–Dec., 2012). The authors are at the Prometey Central Research Institute of Structural Materials.
“A concept of structure formation (and consequently properties) for thick rolled sheet was developed as applied to equipment of reversing hot-rolling mills with different properties”
• Usually very easy to use
• Continues to lack generalization
• However, it is possibly a type of model suitable for on-site industry multipass rolling
Plate mill: an expert practical system
Possible strong and weak points
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Comparison of calculated results for ferrite grain size with experimental data (steel X80, dγ = 17 μm, cooling rate 1°C/sec).
V. V. Orlov and E. I. Khlusova , SIMULATION OF THROUGH PRODUCTION PROCESSES FOR MANUFACTURING THICK-WALLED PLATE IN HOT ROLLING REVERSING MILLS, Metallurgist, Vol. 56, Nos. 11–12, March, 2013 (Russian Original Nos. 11–12, Nov.–Dec., 2012). The authors are at the Prometey Central Research Institute of Structural Materials.
Empirical models
• Easy to use
• Lack of generalization
• Might be useful for on-site modelling of multipass rolling
Plate mill: an expert practical system
Summary Empirical models
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Physically based model
• Enhances understanding
• Difficulty with some variables
• Might be not practical for on-site multipass rolling modelling
Conclusions
• First steps were taken to develop a simple, reliable, applicable on-site model.
• Results from plate 16-mm thick plate have been used to validate the model. The suggested optimized schedule showed potential to improve mechanical properties of the plate.
• The proposed model seems suitable to be used as an on-site tool.
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