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https://doi.org/10.2355/isijinternational.ISIJINT-2021-155

* Corresponding author: E-mail: chyim@postech.ac.kr

© 2021 The Iron and Steel Institute of Japan. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

CCBYNCND

1. Introduction

When a solid-shell of steel grows non-uniformly in the mold, the strain increases,1) and which can easily cause lon- gitudinal cracking of cast steel. In addition, if the solid-shell is uneven, heat transfer is disturbed by air gaps that form between the solid-shell and the mold wall; this disturbance extends the time for which the initial solid-shell is exposed to high temperature, and thereby promotes coarsening of grains on the surface of the cast steel.2–8) These coarse grains cause concentration of segregation and stress at grain boundaries, so surface cracking is facilitated.9–12) During continuous casting, peritectic steels in which the solid-shell is unevenly solidified also have relatively large mold level fluctuations, which degrade the surface quality of the cast steel.13–15) One way to minimize the surface defects on cast steel produced by continuous casting is to design chemi- cal compositions that yield an evenly-solidified solid-shell.

Furthermore, casting conditions such as mold flux, mold oscillation, and tundish superheat should be chosen carefully by considering the degree of solid-shell unevenness.16,17)

The composition of the steel affects its initial solidifi-

Prediction Model for Degree of Solid-shell Unevenness during Initial Solidification in the Mold

Sang-Hum KWON,1,2) Young-Mok WON,2) Gu Seul BACK,1) Hyeju KIM,1) Jae Sang LEE,1) Dong-Gyu KIM,3) Yoon Uk HEO1) and Chang Hee YIM1)*

1) Graduate Institute of Ferrous & Energy Materials Technology, Pohang University of Science and Technology, 77Cheongam-Ro, Nam-Gu, Pohang, 37673 Republic of Korea.

2) Technical Research Laboratory, POSCO, Pohang, 37877 Republic of Korea.

3) Dong-A University, 37, Nakdong-Daero 550beon-gil, Saha-Gu, Busan, Republic of Korea.

(Received on April 12, 2021; accepted on June 8, 2021)

The unevenness of the solid-shell and heat flux (Φq) during solidification were measured for various steel grades with casting apparatus. Then the data were used to develop a model to predict the degree of solid-shell unevenness according to the steel composition. The surface of the solid-shell was the least uniform when the steel composition was near the peritectic point, at which Φq was the lowest. A model to calculate an index of solid-shell unevenness was developed under the following conditions. Solid-shell unevenness is formed at the initial solidification under the constant cooling rate, which occurs only where solid fraction (fs) is from 0.9 to 1.0. In addition, solid-shell unevenness is proportional to the amount of phase transformation and inversely proportional to the temperature range of ΔTPT; between the tempera- ture of fs = 0.9 and the solidus temperature. This model shows about 87% of the prediction accuracy in the experimental results of this study and also gives reasonable explanations of solid-shell unevenness observed in previous researches on steels.

KEY WORDS: continuous casting; surface quality; solid-shell unevenness; phase transformation; heat flux.

cation behavior and heat flux (Φq) in the mold. Φq in the specific carbon region is low due to air gaps that form as a result of peritectic transformation.18–20) However, the solid-shell unevenness may not precisely coincide with Φq

of the mold,21) because both phenomena are also affected by other factors including casting speed21,22) superheat,4,23) mold lubricant,3,18,24,25) oscillation conditions and mold- level fluctuation.3,21,26,27) Some researchers have attempted to quantify the unevenness of solid-shell by measuring the solidification profile of steel according to the carbon con- centration ([C]). Many studies showed that the degree of solid-shell unevenness was large at [C] ≈ 0.1 wt%,28–32) but some studies showed large solid-shell unevenness even at ultra-low [C].33,34)

During solidification, peritectic steel grades undergo rela- tively large volume shrinkage.35–37) As a result, the uneven- ness of solid-shell due to peritectic transformation can be severe during continuous casting, so longitudinal cracking can easily occur. Longitudinal cracking depends upon the extent of peritectic transformation, so the tendency of steels to form delta-ferrite (δ) during solidification is an important factor38) and the tendency can be expressed as a ferrite potential. Studies of ferrite potential have attempted to pre- dict the severity of longitudinal cracking by calculating the

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degree to which the composition of steel is separated from the peritectic point,38–40) and relationships presented in these studies have been widely used to predict the occurrence of longitudinal cracks during continuous casting; i.e., ferrite potential has been used as a useful indicator to predict the possibility of longitudinal cracks.

The goal of this study is to develop a model that can predict the degree of solid-shell unevenness according to steel composition. When developing a new steel grade, this model can be used to design a composition that can minimize the non-uniformity of the solid-shell or effectively optimize the casting operations considering the non-unifor- mity of the solid-shell.

2. Experiments

Thirteen steels (Table 1) were tested. All samples were machined to cylinders (diameter 43 mm, 950 g) for easy charging into an alumina crucible. A vacuum/pressure cast- ing machine (VTC 200 V, Indutherm) was used; it consisted of a melting chamber and a casting chamber (Fig. 1(a)).

The melting chamber fully melts the specimen, then rotates and pours the molten steel into the copper (Cu) mold of the casting chamber. The melting chamber has an induction coil that can heat the specimen in an alumina crucible; a pyrometer is installed to measure the temperature of the molten steel in the crucible. During melting and casting processes, the chambers were evacuated to ≤ 10−3 Torr, then purged with Ar gas to prevent oxidation of the specimen.

The superheat of the molten steel was set to 50°C. Inside the Cu mold in the casting chamber, cooling water was supplied to the lower part and passed through a water channel to the upper part. The cooling water circulation device was used to control the temperature and flow rate of cooling water.

Two thermocouples were installed at different depths, one thermocouple (TC#1) was 5 mm deep from the Cu mold surface, and the other (TC#2) was 10 mm deep from it as shown in Fig. 1(c). The thermocouples inside the Cu mold

are connected to a data logger, which records temperature changes in 0.1-s intervals, then calculates the Φq by using the Fourier heat-conduction equation

Heat Flux MW m

TTC TC * thermal conductivity of Cu m

( / )

/ .

# #

2

1 2 0 005

  

... (1) where ΔTTC(TC#1−TC#2) is the temperature difference between two thermocouples inserted at different depths at the same position; i.e., 5 mm apart. Surfaces of specimens in contacted with the Cu mold were observed under an opti- cal microscope to get the surface 3D-profile. The 3D-profile was then converted to seven 2D profiles by its vertically cutting with 2.5-mm intervals. Five positions were observed for each specimen. Maximum depths of 2D profiles were measured (Fig. 1(b)) and the profile depth was the average of the maximum depths. The measured profile depth was used as the degree of solid-shell unevenness.

3. Result and Discussion

3.1.  Heat Flux and Surface Profile of Test Specimens The meaured heat flux (Φq) of each specimen was shown in Fig. 2. Specimens were listed in order of liquid fraction at the start of peritectic reaction (flsp). This choice was made because a rapid volume contraction occurs due to phase transformation during solidifcation, and the liquid fraction during the phase transformation can affect the solid-shell unevenness. The flsp of each specimen was calculated using a commercial software (JmatPro). Φq of specimens with a carbon of 0.28% or more was greater than those of speci- mens with a carbon contents of less than 0.2%. It was the smallest in specimen 6 with an flsp of 0.08.

Each specimen’s surface roughness of specimen was shown as 3D profle (Fig. 3(a)) and profile depth (Fig. 3(b)).

When the initial liquid fraction at the start of the peritec- tic (flsp) was 0.08 or less, the profile depth increased with increase of flsp. On the other hand, in case that flsp was larger than 0.08, the profile depth decreased with increase of flsp.

Table 1. Chemical compositions of test specimens (wt.%).

Code wt.% liquid fraction at the

start of peritectic reaction

C Si Mn P S Ni Cu Ti Nb

Steel 1 0.03 0.02 0.006 0.008 0.01 0.02 < 0.00

Steel 2 0.09 0.21 0.35 0.015 0.015 0.05 0.16 0.01

Steel 3 0.09 0.22 0.37 0.015 0.016 0.06 0.17 0.02

Steel 4 0.06 0.26 1.44 0.006 0.001 0.45 0.14 0.01 0.01 0.04

Steel 5 0.05 0.01 1.90 0.007 0.002 0.90 0.28 0.01 0.01 0.05

Steel 6 0.06 0.28 1.92 0.006 0.002 0.50 0.18 0.01 0.03 0.08

Steel 7 0.14 0.60 0.55 0.20 0.10 0.15

Steel 8 0.15 0.28 1.31 0.011 0.004 0.01 0.01 0.02 0.23

Steel 9 0.19 0.25 0.50 0.26

Steel 10 0.28 0.22 0.36 0.015 0.012 0.06 0.18 0.48

Steel 11 0.37 0.21 0.35 0.014 0.011 0.01 0.02 0.70

Steel 12 0.44 0.23 0.60 0.010 0.002 0.01 0.01 0.95

Steel 13 0.56 0.24 0.67 0.013 0.011 0.01 0.02 > 1.00

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The largest profile depth was 1 125 μm in specimen 6. The profile depth became very small when the flsp exceeded 0.48.

The profile depth and Φq were inversely proportional to each other (Fig. 4), and the correlation coefficient value was 0.82 for all specimens, but it was 0.93 except for specimens with flsp of 0.48 or more as shown in Fig. 4.

Figure 5 shows the behavior of profile depth and Φq

according to flsp. When flsp was less than about 0.4, the profile depth and Φq were inversely proportional to each other, but when flsp was greater than about 0.4, the profile depth decreased as Φq decreased. According to the flsp, it can be thougth of as follows to have an opposite relationship between two values; profile depth and Φq. When the flsp was approximately 0.4 or more, the profile depth became about 200 μm or less and the effect of solid-shell unevenness on the heat flux became relatively small. Also, the tempera- ture of molten steel decreased as the carbon concentration increased due to applying the same superheat for each steel.

Fig. 1. Schematic diagram of experimental equipment.

Fig. 2. Heat flux of test specimens. Top x-axis: liquid fraction at the start of peritectic reaction for each specimen.

Fig. 3. Surface profile of test specimens; (a) Surface 3D profile, (b) profile depth.

Fig. 4. Relation between the heat flux and the profile depth for (a) all test specimens and (b) test specimens except high car- bon specimens (steel 10–13).

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Eventually the heat flux decreased owing to the lower tem- perature of molten steel.

3.2. Uneven Solid-shell Index

The formation of solid-shell unevenness can be thought in a viewpoint of solidification shrinkage.

In the initial solidification, molten steel contacts the mold and solidification proceeds. At this stage, the contact state between the molten steel and the mold is very good regard- less of the steel composition. Therefore, it can be regarded that the cooling rate of molten steel is constant for all steel composition during initial solidification.

As the temperature decreases, the liquid phase transforms into a solid phase and the volume contracts. If the liquid phase moves to the area where the volume contraction occurs to compensate for the volume contraction, deformation of the solid phase is difficult to occur. Therefore, it is consid- ered that only the volume contraction that occurs while the liquid phase is difficult to move can cause the deformation of the solid phase. Figure 6(a)41) is a schematic diagram showing the correlation between the flow of the residual liquid phase and the solid fraction (fs). It was known that the liquid phase could not be moved or penetrated in the area of the solid fraction above 0.9,37,42) which means the solid fraction at the liquid impenetrable temperature (LIT).

It can be said that the solid-shell unevenness occurs in the range of fs from 0.9 to 1.0 (ΔTPT). The ΔTPT according to the carbon concentration was shown in Fig. 6(b).

The chemical composition of a steel affects its phase- transformation speed, and distortion occurs severely when the phase transformation speed is fast.43) In other words, as the transformation speed increases, the amount of defor- mation causing distortion increases. Besides, at a constant cooling rate, the phase transformation speed increases as the ΔTTP decreases (Fig. 6(b)). When solidification is complete, that is, below the solidus temperature, the solid shell is suf- ficiently strong and it is difficult to deform the solid shell due to subsequent phase transformation.

Based on the above review results, the model is suggested under the following three conditions.

1) During initial solidification, the contact between the molten steel and the mold is perfect. Therefore, the cooling rate of molten steel is constant regardless of the steel composition.

2) The solid phase deformation that causes solid-shell

unevenness occurs only at temperatures between LIT and TfS = 1.0 (ΔTPT).

3) The amount of deformation increases as phase transfor- mation speed increases. Therefore, it is inversely pro- portional to the ΔTPT and proportional to the amount of phase transformation.

The degree of deformation occurrence, that is, the degree of solid-shell unevenness, was defined as Uneven Solid- shell Index (USI) (Eq. (2)).

USIa     













 f

T

f T

f T

L Ln

L L n

L L ( / )

( / )

( )/

( )/

( / ) ( /





 

 

b c 

 )n ... (2) Where Δf(L/δ) is the amount of liquid transformed to delta ferrite (Fig. 6(b), area ), and Δf(L+δ)/γ is the amount of peritectic transformation (Fig. 6(b), area ), Δf(L/γ) is the amount of liquid transformed to austenite (Fig. 6(b), area

), and ΔT(i/j) is the temperature range at which the phase is transformed from i to j.

a, b and c are average volume changes according to the phase transformation for 13 steel grades.

Each value was a = 0.0028 b = 0.0033 and c = 0.0040.

Changes of phase fraction (Δf(L/δ), Δf(L+δ)/γ and Δf(L/γ)) and ΔTPT (ΔT(L/δ), ΔT(L+δ)/γ and ΔT(L/γ)) (Fig. 7) were calculated using JMatPro (version 11). The consistency between the profile depth and the calculated USI value in Eq. (2) was investigated using the fLIT and an exponent term of ΔTPTn

as variables. The consistency between the two factors was

Fig. 5. Correlation between profile depth and heat flux according to liquid fraction at the start of peritectic reaction. (Online version in color.)

Fig. 6. Schematic drawings for temperature range where solid shell unevenness forms. a) Relation between the tempera- ture range of solid shell unevenness formation and den- dritic structure, b) Calculation temperature range (T) for solid-shell unevenness according to carbon content, LIT ≤ T ≤ TfS = 1.0, is the L/δ transformation, is the (L + δ)/γ transformation, is the L/γ transformation. (Online version in color.)

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the best when fLIT = 0.9 and n = 0.3. The USI showed a good match with the measured profile depths (R2 = 0.87;

Fig. 7(a)). In addition, it was confirmed that the composi- tion region with a large profile depth could be accurately predicted by USI (Fig. 7(b)).

This result confirmed that the USI gave a good expla- nation of the solid-shell unevenness caused by phase transformation during initial solidification of the steel in the mold.

Figure 8 shows an example of the calculation results for two steel grades and the yellow part indicates fs from 0.9 to 1.0. The calculated results of the phase fraction change for steel 6 were Δf(L/δ) = 2.4, Δf(L+δ)/γ = 49, and each transforma- tion temperature range was ΔT(L/δ) = 2.72°C and ΔT(L+δ)/γ = 2.67°C (Fig. 8(a)). The calculation result of steel 8 was shown in Fig. 8(b). Each phase fraction change and trans- formation temperature range were Δf(L+δ)/γ = 17.4, Δf(L/γ) = 7.2, and ΔT(L+δ)/γ = 0.76°C, ΔT(L/γ) = 10.6°C respectively.

Because of these differences, in particular the differences in Δf(L+δ)/γ and ΔT(L+δ)/γ, the USI changed according to the steel composition.

The developed prediction model was compared with

Fig. 7. Comparison of USI calculation result and profile depth. a) Relation between USI and Profile depth, b) Behavior of USI and profile depth according to liquid fraction at the start of peritectic reaction.

Fig. 8. Example of USI calculation for specimens. a) steel 6, b) steel 8, the calculation temperature range is from Tfs = 0.9

to Tfs = 1.0 (Yellow box). Fig. 9. Relation between USI and measured results in previous studies of solid-shell unevenness.

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the measured solid-shell unevenness of the previously reported results (Fig. 9). The components of the three steel grades compared were Si 0.2, Mn 0.5, P 0.02, S 0.02, Al 0.5 wt% (Fig. 9(a)),29) Si 0.3, Mn 0.3, P 0.01, S 0.01, Al 0.03 wt% (Fig. 9(b))28) and Si 0.21–0.25, Mn 1.17–1.25, P 0.011–0.015, S 0.001–0.002, Cr 0.12–0.179, Al 0.035–0.049 wt% (Fig. 9(c)).31,32) As shown in Fig. 9, the calculated USI values showed excellent consistency with previously studied results.

These results confirmed that the USI of this paper could accurately predict the degree of solid-shell unevenness according to the steel composition. We believe the USI can be useful to guide the design of chemical compositions of new steel grades, or for optimization of casting operation to achieve the stable surface quality of slabs. In the future, this model will be developed to predict the solid-shell uneven- ness of high-alloy steels such as high Mn steel, high Si/Ni steel and stainless steel.

4. Conclusion

To quantify the effect of the steel composition on solid- shell unevenness, the profile depth and Φq were measured for 13 steel grades. A model to predict the degree of solid- shell unevenness was developed according to the steel composition. The main results are as follows.

(1) An apparatus and method that uses a 950 g ingot were developed to accurately measure solid-shell uneven- ness and heat flux according to the steel composition.

(2) The model to predict solid-shell unevenness was developed under the following conditions:

(a) The molten steel has very good contact with the mold at initial solidification. Therefore, the mold cooling rate is constant regardless of the steel composition.

(b) Solid-shell unevenness occurs between TfS = 0.9 and TfS = 1.0 (ΔTPT).

(c) The solid-shell unevenness is proportional to the amount of phase transformation (Δf ) and inversely proportional to the temperature range of the phase transformation (ΔTPT).

The USI is

0 0028. ( / )0 3 0 0033. 0 3 0 00.

( / ).

( )/

( .)/

    









 f

T

f T

L L

L L





 

 

440 0 3

 f T

L L ( / ) ( / ).





(3) The developed model (USI) in this study showed approximately 87% consistency with the experimental results, and could also explain the previously reported solid- shell unevenness behaviors.

REFERENCES

1) T. Matsumiya, T. Saeki, J. Tanaka and T. Ariyoshi: Tetsu-to-Hagané, 68 (1982), 1782 (in Japanese).

2) P. Lyu, W. Wang, X. Long, K. Zhang, E. Gao and R. Qin: Metall.

Mater. Trans. B, 49 (2018), 78.

3) W. Wang, X. Long, H. Zhang and P. Lyu: ISIJ Int., 58 (2018), 1695.

4) W. Wang, C. Zhu and L. Zhou: Steel Res. Int., 88 (2017), 1600488.

5) H. Zhang and W. Wang: Metall. Mater. Trans. B, 48 (2017), 779.

6) Z. Niu, Z. Cai and M. Zhu: ISIJ Int., 59 (2019), 283.

7) K. Furumai, A. Phillion and H. Zurob: ISIJ Int., 59 (2019), 2036.

8) M. Hanao, M. Kawamoto and A. Yamanaka: ISIJ Int., 52 (2012), 1310.

9) R. Dippenaar, S. C. Moon and E. S. Szekeres: Iron Steel Technol., 4 (2007), 105.

10) Y. Zhang, W. Wang and H. Zhang: Metall. Mater. Trans. B, 47 (2016), 2244.

11) F. Zarandi and S. Yue: ISIJ Int., 44 (2004), 1705.

12) S. Kencana, M. Ohno, K. Matsuura and K. Isobe: ISIJ Int., 50 (2010), 1965.

13) Y. Li, X. Zhang, P. Lan and J. Zhang: Int. J. Miner. Metall. Mater., 20 (2013), 138.

14) Z. Jiang, Z. Su, C. Xu, J. Chen and J. He: J. Iron Steel Res. Int., 27 (2020), 160. https://doi.org/10.1007/s42243-019-00299-7

15) H. Mizukami and A. Yamanaka: ISIJ Int., 50 (2010), 435.

16) T. Emi and H. Fredriksson: Mater. Sci. Eng. A, 413–414 (2005), 2.

17) W. Lyu, W. Wang and H. Zhang: Metall. Mater. Trans. B, 48 (2017), 18) S. N. Singh and K. E. Blazek: Proc. National Open Hearth and Basic 247.

Oxygen Steel Conf., Vol. 59, AIME, New York, (1976), 264.

19) H. Mizukami, Y. Shirai and S. Hiraki: ISIJ Int., 60 (2020), 1968.

20) T. Nishimura, K. Morishita, M. Yoshiya, T. Nagira and H. Yasuda:

Tetsu-to-Hagané, 105 (2019), 290 (in Japanese).

21) J. Suni and H. Henein: Metall. Mater. Trans. B, 27 (1996), 1045.

22) T. Kanazawa, S. Hiraki, M. Kawamoto, K. Nakai, K. Hanazaki and T. Murakami: Tetsu-to-Hagané, 83 (1997), 701 (in Japanese).

23) M. Suzuki and Y. Yamaoka: Mater. Trans., 44 (2003), 836.

24) V. A. Ulyanov, E. M. Kitaev and A. A. Skvortsov: Steel USSR, 8 (1978), 617.

25) K. Saito and M. Tate: Proc. National Open Hearth and Basic Oxygen Steel Conf., Vol. 56, AIME, Warrendale, PA, (1973), 238.

26) H. Zhang and W. Wang: Metall. Mater. Trans. B, 47 (2016), 920.

27) J. Yang, X. Meng and M. Zhu: ISIJ Int., 58 (2018), 2071.

28) Y. Sugitani and M. Nakamura: Tetsu-to-Hagané, 65 (1979), 1702 (in Japanese).

29) H. Murakami, M. Suzuki, T. Kitagawa and S. Miyahara: Tetsu-to- Hagané, 78 (1992), 105 (in Japanese).

30) Y. M. Won, T. Yeo, D. J. Seol and K. H. Oh: Metall. Mater. Trans.

B, 31 (2000), 779.

31) D. Pu, G. Wen, D. Fu, P. Tang and J. Guo: Metals, 8 (2018), 982.

32) J. Guo, G. Wen, D. Pu and P. Tang: Materials, 11 (2018), 571.

33) H. Shibata, M. Suzuki and T. Emi: Conf. Current Advances in Mate- rials and Processes, Vol. 11, ISIJ, Tokyo, (1998), 36.

34) M. Suzuki, C. H. Yu, H. Sato, Y. Tsui, H. Shibata and T. Emi: ISIJ Int., 36 (1996), Suppl., S171.

35) S. Saleem, M. Vynnycky and H. Fredriksson: Metall. Mater. Trans.

B, 48 (2017), 1625.

36) H. Fredriksson: Met. Sci., 10 (1976), 77.

37) K. H. Kim, T. J. Yeo, K. H. Oh and D. N. Lee: ISIJ Int., 36 (1996), 38) M. M. Wolf: Continuous Casting, Vol. 9, Iron and Steel Socity, 284.

Warrendale, PA, (1997), 63.

39) R. Sarkar, A. Sengupta, V. Kumar and S. K. Choudhary: ISIJ Int., 55 (2015), 781.

40) J. Xu, S. He, T. Wu, X. Long and Q. Wang: ISIJ Int., 52 (2012), 1856.

41) Y. Won: Ph.D. thesis, Seoul National University, (1999), http://

d c o l l e c t i o n . s n u . a c . k r / j s p / c o m m o n / D c L o O r g P e r . jsp?sItemId=000000071447, (accessed 2021-08-24).

42) T. W. Clyne, M. Wolf and W. Kurz: Metall. Trans. B, 13 (1982), 259.

43) S. Griesser: Ph.D. thesis, University of Wollongong, (2013), https://

ro.uow.edu.au/cgi/viewcontent.cgi?article= 4918&context = theses, (accessed 2021-08-25).

References

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