Numerical Simulation of Macrosegregation inSteel Ingot Casting
Houfa SHEN(), Kangxin CHEN, Baicheng LIU
Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
Cite this article:
Houfa SHEN, Kangxin CHEN, Baicheng LIU. Numerical Simulation of Macrosegregation inSteel Ingot Casting. Acta Metall Sin, 2018, 54(2): 151-160.
Many key forging components of heavy equipment are manufactured by large steel ingots. Macrosegregation in steel ingots is a key defect formed during the solidification process. Over the past few decades, numerical modeling has played a more and more important role in the study of macrosegregation. Various models have been developed and applied to different ingot casting processes. This paper focused on the application of macrosegregation models to the steel ingot. Firstly, the formation mechanism and influencing factors of macrosegregation were introduced. Then, the existing macrosegregation models and their recent development were summarized. Macrosegregation models accounting for such mechanisms as solidification shrinkage-induced flow and mushy zone deformation were analyzed, respectfully. To model macrosegregation due to solidification shrinkage, the key was to solve the free surface. A simple derivation showed that the multi-phase (including gas phase) models were equivalent to the VOF-based segregation models in dealing with the shrinkage-induced flow. Finally, our recent research work on numerical modeling of macrosegregation in steel ingots was illustrated, including application of the developed multi-component and multi-phase macrosegregation model to a 36 t steel ingot, and numerical simulation of multiple pouring process. The carbon and sulphur concentrations at about 1800 sampling points, covering the full section of a 36 t ingot, were measured. By detailed temperature recording, accurate heat transfer conditions between the ingot and mould were obtained. Typical macrosegregation patterns, including the bottom-located negative segregation and the pushpin-like positive segregation zone in the top riser, have been reproduced both in the measurements and the predictions. The carbon and sulphur concentrations predicted by the three dimensional multi-component and multi-phase macrosegregation models agreed well with the measurements, thus proving that the model can well predict the macrosegregation formation in steel ingots. As for the multi-pouring process simulation, the results show a high concentration of carbon at the bottom and a low concentration of carbon at the top of the mould after the multi-pouring process with carbon content high in the first ladle and low in the last ladle. Therefore, the multiple pouring process could get the initial solute distribution with the opposite form of segregation. Such carbon concentration distribution would reduce the negative segregation at the bottom and the positive segregation at the top of the solidified ingot, thus proving the ability of the multiple pouring process for the control of macrosegregation.
Fig.1 Steel ingot production of the whole world and China from year 2006 to 2015
Fig.2 Schematic of typical macrosegregation patterns in large steel ingots
Fig.3 Macro and microscopic phenomena in alloy solidification process (R(1) is the outer radius of the dendrite, R=R(1)+δl, δl is the solute diffusion length in liquid phase)
Fig.4 Evolutions of solid fraction and carbon concentration in 36 t steel ingot during solidification at solidification time t=0.33 h (a), 2.0 h (b) and 6.0 h (c)
Fig.5 Predicted (a) and measured (b) carbon concentration distributions and extracted segregation pattern in 3-D (c) in 36 t steel ingot after solidification[41]
Fig.6 Concentrations of carbon and sulphur in 36 t steel ingot along the centerline (a), section A (b), section B (c) and section C (d)[41]
Fig.7 Carbon concentration variation at tundish outlet during multi-pouring (MP) process[43]
Fig.8 Evolutions of carbon concentration in mold at time t=1070 s (a), 1770 s (b), 2410 s (c) and 3030 s (d)[43]
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