T. MAUDER, J. STETINA: IMPROVEMENT OF THE CASTING OF SPECIAL STEEL WITH ...
3–6
IMPROVEMENT OF THE CASTING OF SPECIAL STEEL WITH A
WIDE SOLID-LIQUID INTERFACE
IZBOLJ[ANJE ULIVANJA POSEBNEGA JEKLA S [IROKIM
INTERVALOM TRDNO-TEKO^E
Tomas Mauder, Josef Stetina
Brno University of Technology, Faculty of Mechanical Engineering, Technicka 2, 616 69 Brno, Czech Republic
mauder@fme.vutbr.cz, stetina@fme.vutbr.cz
Prejem rokopisa – received: 2014-07-29; sprejem za objavo – accepted for publication: 2015-03-03
doi:10.17222/mit.2014.122
In the last few years, steelmakers have been facing a significant decrease in the steel demand caused by the global economic
crisis. Positive economic results have mostly been reached in the steel factories that have focused on special steel production
with higher product capabilities, such as higher strength grades, steel design for acidic environments, steel for the offshore
technology, etc. These steels must keep the mechanical properties, such as the resistance to rapture, compression strength,
stress-strain properties, etc., within strict limits. The numerical calculations and optimization of casting parameters were pro-
vided. The results show the recommended casting parameters and differences between the examined steel and classic
low-carbon steels.
Keywords: continuous casting, experimental measurement, numerical simulation, optimization
Proizvajalci jekel se zadnja leta spopadajo z zmanj{evanjem povpra{evanja po jeklu, kar je posledica globalne ekonomske krize.
Pozitivne ekonomske rezultate so dosegle `elezarne, ki so se osredinile na proizvodnjo posebnih jekel in z ve~jimi proizvodnimi
zmogljivostmi, kot so visokotrdnostna jekla, jekla za delo v kislem okolju, jekla za naftne plo{~adi itd. Ta jekla morajo v ozkih
intervalih obdr`ati svoje mehanske lastnosti, kot so pretrg, tla~na trdnost, raztezek pri nategu itd. Izvr{eni so bili izra~uni in
optimizacija postopka ulivanja. Rezultati ka`ejo predlagane parametre litja in razlike med preiskovanim jeklom in navadnim
malooglji~nim jeklom.
Klju~ne besede: kontinuirano litje, eksperimentalne meritve, numeri~na simulacija, optimizacija
1 INTRODUCTION
Continuous casting (CC) of steel, as an industrialized
method of solidification processing, has a relatively short
history of only about 60 years. In fact, the CC ratio in the
world of steel industry now reaches more than 95 % of
crude-steel output (Figure 1).1–3 Through the years, the
product quality, production efficiency, operating safety
and casting of special steels and alloys have increased.
Today, nearly all steel grades can be produced and
productivity goals exceed by far those envisaged in the
1960s/1970s; the limits of casting-section sizes have
been increased to support new steel-grade developments
like thick high-strength steel plates or to realize new pro-
cess routes like the direct link between the casting and
rolling steps.4
In the early 1990s, continuous casting was an estab-
lished and already matured technology. The production
was focused on cost reductions through higher casting
speeds, a better utilization of energy, the optimization of
equipment performance and a reduction of the main-
tenance expenses using the equipment with a longer
lifetime. The key factor, which made continuous casting
the "main-stream technology", was the continuous inno-
vation. From the metallurgical point of view, the state-
of-the-art continuous casters have the features that
enable strand treatment through special cooling and
soft-reduction technologies.5 Sophisticated process mo-
dels allow an online process simulation and closed-loop
control to further optimize the product quality and pro-
ductivity goals.
Today, steelmakers in the European Union are facing
a significantly decreasing steel demand caused by the
global economic crisis. Figure 1 shows the crude-steel
production progress in the EU between 2005 and 2012
Materiali in tehnologije / Materials and technology 50 (2016) 1, 3–6 3
UDK 669.18:621.74.047:519.61/.64 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 50(1)3(2016)
Figure 1: Crude-steel production in EU2
Slika 1: Proizvodnja surovega jekla v EU2
promulgated by The World Steel Association.2 The
economic crisis in 2008 caused a deep slump in the steel
production in the EU. Positive economic results were
mostly reached by the steel factories that focused on
special steel production with higher product capabilities,
such as higher-strength grades, steel plates for barrel
boilers, steel design for acidic environments and steel for
the offshore technology. The production of special steel
is the only prospective for the EU to keep the
competitiveness with the Asian market.
Steel grades for acidic environments and for the off-
shore technology must keep the mechanical properties,
such as the resistance to rapture, compression strength,
stress-strain properties and so on, within strict limits. A
breakdown situation caused by a low quality of steel
could have a catastrophic effect on the material and
human losses. The defects of the steel for acidic envi-
ronments have been known more than 50 years. Despite
that, the world oil, gas and engineering companies still
make huge efforts to improve the operations in acidic
environments and to avoid critical situations. The me-
chanical properties for these grades of steel are specified
by the European Standard EN 10020. Their production,
unlike the classic low-carbon steel grades, requires a
special treatment like the soft reduction, electromagnetic
stirring, different cooling conditions, etc., to avoid crack
defects.
The casting of special steel (C0.18, Ni0.04, V0.004,
N0.003 w/%) was performed by the steelmaker Vitkovice
Steel, a. s. A macroscopic examination (the Baumann
method) shows many defects in the final quality of the
steel, such as high porosity, centerline segregation and
cracks.
This paper deals with the results of a numerical
simulation of the temperature field and optimization of a
casting process by analyzing the casting parameters and
their influences on the quality of the steel.6
2 DATA FROM THE MACROGRAPHY
The testing set contains twenty-five samples from
four heats (casting sequences). With a macroscopic test,
we evaluated the cracks in the transverse and longitu-
dinal directions, the centerline segregation according to
SMS DEMAG, the segregation index, the discontinuity
and the lack of homogeneity.
The chemical composition of the steel is in Table 1
and the macrography results are shown in Table 2 and
Figures 2 to 5.
Table 1: Chemical composition of the examined steel in mass frac-
tions, w/%
Tabela 1: Kemijska sestava preiskovanega jekla v masnih dele`ih,
w/%
C Si Mn P Cr
0.18 0.38 1.49 0.021 0.07
S Ni Mo Cu Al
0.004 0.05 0.017 0.028 0.029
Nb Ti V Ca
0.001 0.003 0.004 0.003
Table 2: Macrostructure results
Tabela 2: Ocena makrostrukture
H
ea
t
(c
as
ti
ng
se
qu
en
ce
)
Macrostructure
S
ur
fa
ce
cr
ac
ks
C
ol
um
na
r
cr
ac
ks
C
en
te
rl
in
e
cr
ac
ks
M
id
w
ay
cr
ac
ks
T
ra
ns
ve
rs
e
cr
ac
ks
S
eg
re
ga
ti
on
in
de
x
S
M
S
D
E
M
A
G
26 393 6 106 23 106 5 3 2
26 394 5 107 23 107 5 2 2
26 395 6 103 23 105 5 1-2 2
26 396 5 108 22 107 5 2-3 3
occurrence of defects Centerline segregation, cracks, localdiscontinuity
From the results, it is obvious that the quality of the
steel is not sufficient and has to be improved.
3 NUMERICAL SIMULATIONS
The simulation of the continuous-casting process is
based on a transient numerical model of the temperature
field. This model was specially modified to simulate the
real casting machine operated in Vitkovice Steel, a.s. The
T. MAUDER, J. STETINA: IMPROVEMENT OF THE CASTING OF SPECIAL STEEL WITH ...
4 Materiali in tehnologije / Materials and technology 50 (2016) 1, 3–6
Figure 2: Steel sample from heat 26 393
Slika 2: Vzorec jekla iz taline 26 393
Figure 3: Steel sample from heat 26 394
Slika 3: Vzorec jekla iz taline 26 394
model represents a unique combination of numerical mo-
deling and a large number of experimental measure-
ments. Its results are validated with long-time measure-
ments made during the real casting process. A detailed
description of the numerical model can be found in7.
Thermophysical properties are calculated by the IDS
solidification package. The results for the examined steel
are in Figure 6.
The casting parameters, such as the casting speed, the
pouring temperature, the heat removal from the mold, the
cooling intensity in the secondary cooling zone, etc., for
the numerical simulation were taken from the real
measurement data for heats 26 393–26 396. The results
from the simulation of heat 26 393 are in Figures 7 and
8.
The numerical simulation reveals that the exanimated
steel is characterized by a long mushy zone in compa-
rison with the classic low-carbon steels. The metallur-
gical length reaches 20.08 m and the mushy zone is
almost 10 m long. For the classic low-carbon steels, the
mushy zone is proximately 4–6 m long. The inner
quality of steel is also influenced by the position of the
metallurgical length. The steel should be fully solidified
in close distance to the caster unbending point. The ca-
ster operating in Vitkovice Steel, a.s., has the unbending
point located 12.6 m from the meniscus. So, the defects
can also be caused by the long distance between the
position of the metallurgical length and the unbending
point. These rules8 together with the method for the opti-
mum cooling9 give a set of conditions for the optimiza-
tion of the casting parameters.
4 RESULTS AND DISCUSSION
According to the optimization criteria, the numerical
model and fuzzy-regulation algorithm6 were used to
calculate new casting parameters for the exanimated
steel. In order to get the metallurgical-length position
between 12–15 m, the casting speed has to decrease to
89 % of the original speed. The cooling intensity for the
particular cooling circuit increases, on average, to
T. MAUDER, J. STETINA: IMPROVEMENT OF THE CASTING OF SPECIAL STEEL WITH ...
Materiali in tehnologije / Materials and technology 50 (2016) 1, 3–6 5
Figure 8: Distribution of liquid and solid steel
Slika 8: Porazdelitev teko~ega in trdnega jekla
Figure 6: Thermophysical properties of the examined steel
Slika 6: Fizikalno-termi~ne lastnosti preiskovanega jekla
Figure 5: Steel sample from heat 26 396
Slika 5: Vzorec jekla iz taline 26 396
Figure 7: Temperature distribution before optimization
Slika 7: Porazdelitev temperature pred optimizacijoFigure 4: Steel sample from heat 26 395
Slika 4: Vzorec jekla iz taline 26 395
13.4 %. The results of the numerical simulation and opti-
mization are in Figures 9 and 10.
The result was given to the Vitkovice Steel, a.s., as a
casting recommendation for this type of steels. Despite
the fact that the first macroscopic results (Figure 11)
show a quality improvement, more experimental
measurements and numerical simulations are required in
order to get a general view of the behavior of the
examined steel.
5 CONCLUSION
Numerical modeling and optimization will play an
increasing role in the future improvements to the conti-
nuous casting of steel. A combination of the optimiza-
tion algorithm based on fuzzy logic with the numerical
model of the temperature field improves the casting
parameters. With the optimum casting parameters, a
better final quality of cast steel can be achieved. The
algorithm is very general and its calculations can be used
for any steel grade and caster geometry.
Acknowledgement
This work is an output of the research and scientific
activities of NETME Centre, the regional R&D center
built with the financial support from the Operational
Programme Research and Development for Innovations
within the project NETME Centre (New Technologies
for Mechanical Engineering), Reg. No. CZ.1.05/2.1.00/
01.0002 and, in the follow-up sustainability stage,
supported through NETME CENTRE PLUS (LO1202)
with the financial means from the Ministry of Education,
Youth and Sports under the "National Sustainability
Programme I".
6 REFERENCES
1 J. P. Birat et al., The Making, Shaping and Treating of Steel: Casting
Volume, 11th edition, AISE Steel Foundation, Pittsburgh, PA 2003,
1000
2 Crude steel production, The World Steel Association [online],
Brussels, Belgium 2013 [cit. 2013-03-27], http://www.world-
steel.org/
3 C. A. Däcker et al., The History of Mould Slag Films Downwards
the Mould and How it Affects Heat Flux and Shell Growth in Conti-
nuous Casting of Steels, Proceedings of the METEC InSteelCon
2011, Düsseldorf 2011, 8
4 A. Flick, Ch. Stoiber, Trends in Continuous Casting of Steel – Yes-
terday, Today and Tomorrow, Proceedings of the METEC InSteelCon
2011, Düsseldorf 2011, 8
5 Y. H. Chang et al., Development and Application of Dynamic
Secondary Cooling and Dynamic Soft Reduction Control for Slab
Castings, Proceedings of the METEC InSteelCon 2011, Düsseldorf
2011, 6
6 T. Mauder, C. Sandera, J. Stetina, A Fuzzy-Based Optimal Control
Algorithm for a Continuous Casting Process, Mater. Tehnol., 46
(2012) 4, 325–328
7 T. Mauder, C. Sandera, J. Stetina, Optimal control algorithm for con-
tinuous casting process by using fuzzy logic, Steel Res. Int., 86
(2015) 7, 785–798, doi:10.1002/srin.201400213
8 G. S. Jansto, Steelmaking and Continuous Casting Process
Metallurgy Factors Influencing Hot Ductility Behavior of Niobium
Bearing Steels, Proceedings of the METAL 2013, Brno, 2013, 32–39
9 A. A. Ivanova, V. A. Kapitanov, A. V. Kuklev, Method of calculating
the optimum parameters for the air-mist cooling of a continuous-cast
slab, Metallurgist, 56 (2012), 173–179, doi:10.1007/s11015-012-
9555-2
T. MAUDER, J. STETINA: IMPROVEMENT OF THE CASTING OF SPECIAL STEEL WITH ...
6 Materiali in tehnologije / Materials and technology 50 (2016) 1, 3–6
Figure 11: Steel sample from the casting after optimization
Slika 11: Vzorec litega jekla po optimizaciji
Figure 10: Distribution of liquid and solid steel
Slika 10: Porazdelitev teko~ega in trdnega jekla
Figure 9: Temperature distribution after optimization
Slika 9: Porazdelitev temperature po optimizaciji