Modeling of Globular and Dendritic Structure Evolution in Solidification of an Al-7mass%Si Alloy.

Abstract

The evolution of globular and dendritic structures in solidification of an Al-7mass%Si alloy has been investigated by a modified cellular automaton model (MCA). Besides retaining the probabilistic aspects of the classical CA model for the heterogeneous nucleation and the preferential growth orientations of the nuclei, the present MCA model is coupled with the curvature, the solute partition between liquid and solid as well as diffusion in both phases. The effects of constitutional undercooling and curvature undercooling are incorporated on the equilibrium interface temperature. The relationship between the growth velocity of a dendrite tip and the local undercooling is calculated according to the KGT (Kurz-Giovanola-Trivedi) model. The finite volume method, coupled with the cellular automaton model, was used to calculate the solute field in the computational domain. The effects of pouring temperature, cooling rate, and inoculation on the growth morphology of the primary phase were studied. The simulation results were compared with those obtained experimentally. It can be concluded that the present simulation model can successfully predict the evolution of dendritic and globular structures in solidification of alloys.KEY WORDS: solidification microstructure; modified cellular automaton; nucleation; growth kinetics; solute redistribution; diffusion; curvature; semi-solid casting; globular structure; dendrite structure.formation of dendritic structures in solidification of alloys.14)The purpose of the present study is to study the morphology evolution of the primary phase, i.e., dendritic, intermediate rosette-like and globular, in solidification of Al-7mass%Si alloy using a modified cellular automaton (MCA) model. The effects of pouring temperature, cooling rate and inoculation on the morphology of the primary phase were examined. The simulated results were compared with those obtained experimentally. Experimental ProcedureAn Al-7.0mass%Si alloy was prepared using commercial pure aluminum (99.9%) and metallic silicon in an electrical resistance furnace. The inoculation was achieved by the addition of Al-5Ti-B master alloy to the level of 0.01 mass% Ti. The melt temperature was initially kept at 700°C for a specified period, and then slowly cooled down to various temperatures in a crucible. Subsequently, the melt was poured into a preheated steel mold having a thermally insulated bottom placed in a temperature holding furnace in order to cool the specimen with various cooling rates. The experimental apparatus is shown in Fig. 1. A thermocouple was positioned in the center of the mold cavity to measure the cooling curves of the slurry. The temperature-time curve of the slurry was recorded using a data-acquisition unit. The slurry specimen was quenched into water when it was cooled down to a specified temperature. The specimens for metallographic observation were cut from the area near to the thermocouple, polished and then etched with Tucker solution. Mathematical Formulation and Numerical Method Model DescriptionTh...

Cooling slope casting to obtain thixotropic feedstock II: observations with A356 alloy

“…Uggowitzer and Kaufmann [13] argued that the nuclei are formed by partial quenching of the melt. This was backed up by simulation results from Zhu et al [14]. One central aspect of the NRC process [11] is the laminar filling of the crucible, which is achieved by controlled pouring of the melt along the wall of the inclined crucible (see Ref.…”

Dynamic formation of primary grain structures on squared steel billets produced by continuous casting (computer simulation)

“…Then routines for solidification and pre-solidification are executed. The criterions (t CCT ) and (t CET ) are usually established according with mushy times (t mushy I,J ) as is was explained; nevertheless new criterions can be established using the classification exposed in this work [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The points shown in Fig.…”

“…The shortest time difference is calculated using the Eq. (10). Here the sub indexes (i) and (j) are used to identify the neighbors.…”

“…The models developed by other authors [9][10][11][12][13][14][15][16][17][18] have incorporated physical theory related with the microstructure formation of these materials during solidification. About it, all the models also [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] treat the solidification problem as a function of the nucleation and grain growth.…”

“…About it, all the models also [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] treat the solidification problem as a function of the nucleation and grain growth. Some authors [16] have simulated this process using parallel computing methods.…”