Nonuniformity of Carbon Element Distribution of Large Area in High Carbon Steel Continuous Casting Billet
GUO Zhongao1,2, PENG Zhiqiang1,2, LIU Qian1,2, HOU Zibing1,2()
1.College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China 2.Chongqing Key Laboratory of Vanadium-Titanium Metallurgy and New Materials, Chongqing University, Chongqing 400044, China
Cite this article:
GUO Zhongao, PENG Zhiqiang, LIU Qian, HOU Zibing. Nonuniformity of Carbon Element Distribution of Large Area in High Carbon Steel Continuous Casting Billet. Acta Metall Sin, 2021, 57(12): 1595-1606.
High carbon steel is prone to macroscopic and semimacroscopic segregation. Segregation is a phenomenon that results in the nonuniformity of solute element distribution during solidification; segregation severely impacts the quality of the casting billet. Based on qualitative analysis (macroscopic quality rating) and relatively simple quantitative analysis (segregation index, mean square error), it is found that the accuracy of existing technologies is limited for determining the nonuniformity of carbon element distribution in large areas of cast high carbon steel billets; this causes difficulties while applying such existing technologies for the evaluation of large samples under actual steelworks usage conditions. Therefore, it is essential to study the nonuniformity of carbon element distribution to precisely evaluate and optimize the quality of high carbon steel. Based on the grayscale image of the casting blank macrostructure of typical high carbon steels (mass fraction of carbon = 0.7%), the standard deviation, differential box-counting, and moment of inertia are introduced to discuss simple methods for the quantitative characterization of the nonuniformity of the major segregation element (C) in large areas. Then, the validity of the characterization results was verified using statistical homogeneity and the near-equilibrium solidification model, and the similarities and differences of the three characterization methods were discussed. The results showed that the standard deviation, differential box-counting, and moment of inertia can effectively reflect the nonuniformity of carbon element distribution. Furthermore, the mean value of the nonuniformity of carbon element distribution in the equiaxed area was 20.85% higher than that in the columnar area. The standard deviation is mainly based on the statistical characteristics of the grayscale value, while differential box-counting and moment of inertia combine the grayscale value statistical and spatial distribution information on a grayscale image of the casting blank macrostructure. In addition, differential box-counting has scale independence, which is less affected by the size and resolution of the grayscale image; the moment of inertia is sensitive to the variation of the nonuniformity of carbon element distribution in the microregion. Finally, the standard deviation is mainly influenced by large segregation points (e.g., area larger than 1 mm2), while differential box-counting and the moment of inertia are mainly influenced by medium segregation points (e.g., area of 0.1-1 mm2 ). Therefore, the three characterization methods can be combined to comprehensively evaluate the nonuniformity of carbon element distribution in large areas of cast high carbon steel billets. This research can provide a new theoretical reference for comprehensively evaluating the nonuniformity of carbon element distribution in a large area and for the fine quality evaluation of cast high carbon steel billets.
Fig.1 Schematic of sampling location in the central plane of billet (unit: mm)
Fig.2 Schematic of differential box-counting algorithm (The size of a box is 3 × 3 × 3, which is used to cover gray value)
Fig.3 Grayscale images of macrostructures of samples 1#-6# at different locations of billet (a-f)
Fig.4 Grayscale surfaces of macrostructures of samples 1#-6# at different locations of billet (a-f) (The unit of X and Y axes is pixel)
Fig.5 Standard deviation of grayscale value at different locations of billet
Fig.6 Relationship between lnN(r) and ln(1 / r) of 1# sample (R2—fitting coefficients, r—size coefficient, N(r)—number of total boxes)
Fig.7 Differential box-counting (D) at different locations of billet
Sample No.
D
R2
1#
2.6177
0.9946
2#
2.6622
0.9950
3#
2.6786
0.9946
4#
2.6820
0.9943
5#
2.7140
0.9946
6#
2.6791
0.9946
Table 1 D and corresponding R2 at different locations
Fig.8 Schematic of the nonuniformity of carbon micro distribution characterized by moment of inertia (d, θ—distance and angle between i and j, respectively )
Fig.9 Moment of inertia at different locations of billet
Fig.10 Gray histograms of samples 1#-6# at different locations of billet (a-f)
Sample No.
H / %
Grayscale value in the range
of content tolerance
1#
11.98
[97,110]
2#
9.76
[103,116]
3#
8.39
[115,130]
4#
7.35
[121,136]
5#
6.02
[121,136]
6#
7.33
[118,133]
Table 2 The weight ratios of grayscale values in the range of content tolerance
Fig.11 Relationship between H and standard deviation (a), differential box-counting (b), and moment of inertia (c)
Fig.12 Relationship between mass fraction of carbon in the liquid phase (CL) and solid fraction (fs) (kE—effective distribution coefficient of solute)
Fig.13 Area ratios of I, II, and III three kinds of segregation points (5#-6#)
1
Wang G D. Technology innovation and development direction of iron and steel industry [J]. Iron Steel, 2015, 50(9): 1
王国栋. 钢铁行业技术创新和发展方向 [J]. 钢铁, 2015, 50(9): 1
2
Song X F, He A R, Qiu Z S. Influence of initial temperature difference on high strength strip buckling in laminar cooling [J]. Iron Steel, 2016, 51(5): 52
Li J, Wu M H, Ludwig A, et al. Simulation of macrosegregation in a 2.45-ton steel ingot using a three-phase mixed columnar-equiaxed model [J]. Int. J. Heat Mass Transf., 2014, 72: 668
4
Ge H H, Ren F L, Li J, et al. Four-phase dendritic model for the prediction of macrosegregation, shrinkage cavity, and porosity in a 55-ton ingot [J]. Metall. Mater. Trans., 2017, 48A: 1139
5
Pickering E J. Macrosegregation in steel ingots: The applicability of modelling and characterisation techniques [J]. ISIJ Int., 2013, 53: 935
6
Choudhary S K, Ganguly S. Morphology and segregation in continuously cast high carbon steel billets [J]. ISIJ Int., 2007, 47: 1759
7
Mayer F, Wu M, Ludwig A. On the formation of centreline segregation in continuous slab casting of steel due to bulging and/or feeding [J]. Steel Res. Int., 2010, 81: 660
8
Miyamura K, Kitamura S Y, Sakaguchi S, et al. Development of segregation etch print method and its application to investigation of CC slab segregation [J]. Trans. Iron Steel Inst. Japan, 1984, 24: 718
9
Sang B G, Kang X H, Li D Z. A novel technique for reducing macrosegregation in heavy steel ingots [J]. J. Mater. Process. Technol., 2010, 210: 703
10
Lesoult G. Macrosegregation in steel strands and ingots: Characterisation, formation and consequences [J]. Mater. Sci. Eng., 2005, A413-414: 19
11
Yoshida N, Umezawa O, Nagai K. Influence of phosphorus on solidification structure in continuously cast 0.1 mass% carbon steel [J]. ISIJ Int., 2003, 43: 348
12
Wang H Z. Original position statistic distribution analysis (original position analysis)—A new analytical method in research and quality evaluation of materials [J]. Sci. China, 2003, 46B: 119
13
Chen Y, Yang S B, Zhu M Y. Key technologies of internal quality control for continuously cast high-speed rail steel bloom [J]. Iron Steel, 2006, 41(12): 36
Chang Y, Hou Z B, Wang W, et al. Characteristics and mechanism of carbon element and manganese element distribution along casting direction in high-carbon billet [J]. Iron Steel, 2016, 51(11): 43
Zhang N, Yang P, Mao W M. Influence of columnar grains on the cold rolling texture evolution in Fe-3%Si electrical steel [J]. Acta Metall. Sin., 2012, 48: 782
Hou Z B, Guo Z A, Guo D W, et al. A new method for carbon content distribution based on grayscale image of casting blank macrostructure in carbon steel [J]. J. Iron Steel Res., 2019, 31: 620
Liao K W, Lee Y T. Detection of rust defects on steel bridge coatings via digital image recognition [J]. Automat. Constr., 2016, 71: 294
21
Huang Z L. The high-temperature billet crack detection research based on digital image processing [D] Chengdu: University of Electronic Science and Technology of China, 2013
黄志良. 基于数字图像处理的高温钢坯裂纹检测研究 [D]. 成都: 电子科技大学, 2013
22
Gong H J. Estimating paddy yield based on fractal and image texture analysis [D]. Nanjing Agricultural University, 2008
Sarkar N, Chaudhuri B B. An efficient differential box-counting approach to compute fractal dimension of image [J]. IEEE Trans. Syst. Man Cybern., 1994, 24: 115
28
Haralick R M. Statistical and structural approaches to texture [J]. Proc. IEEE, 1957, 67: 786
29
Renzetti F R, Zortea L. Use of a gray level co-occurrence matrix to characterize duplex stainless steel phases microstructure [J]. Frattura ed Integrità Strutturale, 2013, 5: 43
30
Tong X Y, Li H X, Yao L J, et al. Feature extraction and analysis of surface microscopic image of pure copper subjecting low cycle fatigue [J]. Mech. Sci. Technol. Aerosp. Eng., 2015, 34: 1446
Hou Z B, Cao J H, Chang Y, et al. Morphology characteristics of carbon segregation in die steel billet by ESR based on fractal dimension [J]. Acta Metall. Sin., 2017, 53: 769
Wang H Z, Zhao P, Chen J W, et al. In situ statistical distribution analysis of low alloy steel continuous casting slab [J]. Sci. China, 2005, 35E: 260
Bowe T F, Brody H D, Flemings M C. Measurements of solute redistribution in dendritic solidification [J]. Trans. Metall. Soc. AIME, 1996, 236: 624
34
Hou Z B, Guo D W, Cao J H, et al. A method based on the centroid of segregation points: A Voronoi polygon application to solidification of alloys [J]. J. Alloys Compd., 2018, 762: 508
35
Burton J A, Prim R C, Slichter W P. The distribution of solute in crystals grown from the melt. Part I. Theoretical [J]. J. Chem. Phys., 1953, 21: 1987
