Abstract
The continuous casting technological parameters have a great influence on the secondary dendrite arm spacing of the slab, which determines the segregation behavior of materials. Therefore, the identification of technological parameters of continuous casting process directly impacts the property of slab. The relationships between continuous casting technological parameters and cooling rate of slab for spring steel were built using BP neural network model, based on which, the relevant secondary dendrite arm spacing was calculated. The simulation calculation was also carried out using the industrial data. The simulation results show that compared with that of the traditional method, the absolute error of calculation result obtained with BP neural network model reduced from 0.015 to 0.0005, and the relative error reduced from 6.76% to 0.22%. BP neural network model had a more precise accuracy in the optimization of continuous casting technological parameters.
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References
SHI Ming-hui, ZHOU Chang-le, WU Qing-feng, et al. Uncertain Inference Based on Certainty Degree and Its Implementation by Artificial Neural Network [J]. Application Research of Computers, 2007, 24(1): 241 (in Chinese).
MIN Xi-lin, LIU Tu-hua. Application of BP Network Developed Using MATLAB Neural Network Toolbox [J]. Computer Applications, 2001, 21(8): 163 (in Chinese).
HOU Zhi-bin, WEN Bi-teng, PENG Hua, et al. The Study and Application of the BP Neural Network [J]. Computer Knowledge and Technology, 2009, 15(15): 3982 (in Chinese).
ZHANG Ling, CAI Cheng-wen. Secondary Cooling Control of Continuous Casting Based on BP Networks [J]. Computer and Modernization, 2008 (10): 66 (in Chinese).
CAI Kai-ke, CHENG Shi-fu. Theory and Technology of Continuous Casting [M]. Beijing: Metallurgical Industry Press, 2008 (in Chinese).
Alkemper J, Voorhees P W. Three Dimensional Characterization of Dendrite Microstructures [J]. Acta Mater, 2001, 49 (5): 897.
ZHAI Shen-qiu, WEI Ya-jie. Metal Dendrite Growth Behavior [J]. Journal of Shandong University of Technology, 1997, 27(2): 181 (in Chinese).
WANG Ming-cheng, YAN Yun-hui, HAN Ying-li, et al. Application of Neural Network in the Surface Quality Testing of Cool Rolled Strip [J]. Machinery, 2006, 44(12): 71 (in Chinese).
GE Zhe-xue, SUN Zhi-qiang. Neural Network Theory and MATLAB R2007 Realization [M]. Beijing: Electronic Industry Press, 2007 (in Chinese).
Pryds N H, Huang X. The Effect of Cooling Rate on the Microstructures Formed During Solidification of Ferritic Steel [J], Metallurgical and Materials Transactions, 2000, 31A(12): 3155.
Cicutti C, Boeri R. On the Relationship Between Primary and Secondary Dendrite Arm Spacing in Continuous Casting Products [J]. Scripta Materialia, 2001, 45(12): 1455.
Lasse Makkonen. Spacing in Solidification of Dendritic Arrays [J]. Journal of Crystal Growth, 2000, 208(1/2/3/4): 772.
Cabrera-Marrero J M, Carreno-Galindon V, Morales R D, et al. Macro-Micro Modeling of the Dendritic Microstructure of Steel Billets Processed by Continuous Casting [J]. ISIJ International, 1998, 38(8): 812.
El-Bealy M, Thomas B G. Prediction of Dendrite Arm Spacing for Low Alloy Steel Casting Processes [J]. Metallurgical and Materials Transactions, 1996, 278B(8): 689.
WANG Kun. Interior Quality Research of High-Carbon Steel Rectangular Bloom [D]. Beijing: University of Science and Technology, Beijing, 2006 (in Chinese)
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Jiang, Lh., Wang, Ag., Tian, Ny. et al. BP Neural Network of Continuous Casting Technological Parameters and Secondary Dendrite Arm Spacing of Spring Steel. J. Iron Steel Res. Int. 18, 25–29 (2011). https://doi.org/10.1016/S1006-706X(11)60099-X
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DOI: https://doi.org/10.1016/S1006-706X(11)60099-X