modelling plate mill rolling an expert practical system approach · 2019. 5. 3. · modelling plate...

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

Charles Hatchett Seminar Plate mill: an expert practical system 2

• 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


• 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


• 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


• 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


An industry case

Rolling 300 mm slab to 16 mm plate


Microstructure at ¼ thickness

Fine grains

Coarse grain

Mixed microstructure




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


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


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


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


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


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


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


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



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




After Before


The model

Description


• Two modules: reheating and hot rolling

• Sellars’ type model

• Uses equations available in the literature

Reheating module


Reheating module


Inputs Outputs

Alloy design

Process parameters • Slab thickness • Furnace geometry • Furnace temperatures

Nb content in solution

Time needed to dissolve Nb

Hot rolling module


Hot rolling module


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.

Charles Hatchett Seminar 24 Plate mill: an expert practical system


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

Charles Hatchett Seminar 26

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


Physically based models

Among strong points…


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


Physically based models

…however…


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


Physically based + Numerical models

Possible strong and weak points


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


Empirical + Numerical models



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


Empirical models


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




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


Summary Empirical models


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.


modelling plate mill rolling an expert practical system approach · 2019. 5. 3. · modelling plate...

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