Premature melt solidification during mold filling and its influence on the as-cast structure

doi:10.1007/s11465-017-0437-y

Front. Mech. Eng.

2018, Vol. 13

Issue (1) : 53-65 https://doi.org/10.1007/s11465-017-0437-y

RESEARCH ARTICLE

Premature melt solidification during mold filling and its influence on the as-cast structure

M. WU^1,²(

), M. AHMADEIN³, A. LUDWIG¹

¹. Chair for Simulation and Modeling of Metallurgical Processes, University of Leoben, Leoben A-8700, Austria
². Christian-Doppler Laboratory for Advanced Process Simulation of Solidification and Melting, Department of Metallurgy, University of Leoben, Leoben A-8700, Austria
³. Production Engineering and Mechanical Design Department, Faculty of Engineering, Tanta University, Tanta 31111, Egypt

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Abstract

Premature melt solidification is the solidification of a melt during mold filling. In this study, a numerical model is used to analyze the influence of the pouring process on the premature solidification. The numerical model considers three phases, namely, air, melt, and equiaxed crystals. The crystals are assumed to have originated from the heterogeneous nucleation in the undercooled melt resulting from the first contact of the melt with the cold mold during pouring. The transport of the crystals by the melt flow, in accordance with the so-called “big bang” theory, is considered. The crystals are assumed globular in morphology and capable of growing according to the local constitutional undercooling. These crystals can also be remelted by mixing with the superheated melt. As the modeling results, the evolutionary trends of the number density of the crystals and the volume fraction of the solid crystals in the melt during pouring are presented. The calculated number density of the crystals and the volume fraction of the solid crystals in the melt at the end of pouring are used as the initial conditions for the subsequent solidification simulation of the evolution of the as-cast structure. A five-phase volume-average model for mixed columnar-equiaxed solidification is used for the solidification simulation. An improved agreement between the simulation and experimental results is achieved by considering the effect of premature melt solidification during mold filling. Finally, the influences of pouring parameters, namely, pouring temperature, initial mold temperature, and pouring rate, on the premature melt solidification are discussed.

Keywords premature solidification mold filling as-cast structure modeling

Corresponding Author(s): M. WU

Just Accepted Date: 14 April 2017 Online First Date: 11 May 2017 Issue Date: 23 January 2018

Cite this article:

M. WU,M. AHMADEIN,A. LUDWIG. Premature melt solidification during mold filling and its influence on the as-cast structure[J]. Front. Mech. Eng., 2018, 13(1): 53-65.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-017-0437-y
https://academic.hep.com.cn/fme/EN/Y2018/V13/I1/53

Fig.1 Geometric configuration of the reference ingot (Al-4.0 wt.% Cu): Grid in the domains of the ingot and mold (left half) and schematic of different phase regions during mold filling (right half) (Both 2D axisymmetric and complete 3D calculations were performed, but the grid is shown here in 2D axis symmetry.)

Fig.2 Contours of (a)

T

, (b)

f e

, (c)

n ˙

in two color scales representing the birth (gray) and death (color) of the nuclei and (d) n at a filling time of 0.4 s; (e)

T

, (f)

f e

, (g)

n ˙

in color scales representing the birth (gray) and death (color) of the nuclei and, (h) n at a filling time of 0.47 s; (i)

T

, (j)

f e

, (k)

n ˙

in two color scales representing the birth (gray) and death (color) of the nuclei and, (l) n at a filling time of 1.0 s; (m)

T

, (n)

f e

, (o)

n ˙

in two color scales representing the birth (gray) and death (color) of the nuclei and, (p) n at a filling time of 2.0 s; (q)

T

, (r)

f e

, (s)

n ˙

in two color scales representing the birth (gray) and death (color) of the nuclei and, (t) n at a filling time of 5.2 s

Fig.3 Contours of (a)

T

overlaid with

u ⇀ ℓ

, (b)

n

, and (c)

f e

at filling times of 0.4 s; (d)

T

overlaid with

u ⇀ ℓ

, (e)

n

, and (f)

f e

at filling times of 1.0 s; (g)

T

overlaid with

u ⇀ ℓ

, (h)

n

, and (i)

f e

at filling times of 2.0 s; (j)

T

overlaid with

u ⇀ ℓ

, (k)

n

, and (l)

f e

at filling times of 5.24 s (The pouring temperature is 973 K. Only half of the calculation domain is shown. The

u ⇀ ℓ

vectors are shown in the vertical section.)

Fig.4 Average T, n, and

f e

as functions of filling time for the ingot poured at 973 K

Fig.5 As-cast structure (left) and the numerically predicted phase distribution (right) in anAl-4.0 wt.% Cu ingot poured at a pouring temperature of 1073 K (The dotted line in the metallographic image presents the estimated columnar-to-equiaxed transition (CET) line.)

Fig.6 As-cast structure (left) and the numerically calculated phase distribution (right) in an Al-4.0 wt.% Cu ingot poured at a pouring temperature of 973 K (The dotted lines in the metallographic image separate regions of different grain sizes. The simulation result shows the volume fraction of the equiaxed phase overlaid with isolines of the number density of equiaxed grains.)

Pouring temperature, $T p$ /K	Initial mold temperature, $T m$ /K	Cases corresponding to varied pouring velocity ( $v p$ /(m·s⁻¹))
Pouring temperature, $T p$ /K	Initial mold temperature, $T m$ /K	0.800	1.037	1.200	1.400
963	293	?	∗	?	?
973	293	∗	∗ x	∗	∗
	423	?	∗	?	?
	573	?	∗	?	?
1023	293	?	∗ x	?	?
1073	293	?	∗ x	?	?

Tab.1 Case definitions of the pouring parameters

Fig.7 Influences of pouring temperature on (a) the average temperature of the liquid metal and mold, (b) the number density of crystals, and (c) the volume fraction of solid (

T m

=293 K;

v p

=1.037 m·s⁻¹)

Fig.8 Influences of the initial mold temperature on average

n

and

f e

(

T p

= 973 K;

v p

= 1.037 m/s)

Fig.9 Influences of pouring velocity on the average grain number density and volume fraction of solid (

T p

= 973 K;

T m

= 293 K)

Tab.2 Case definitions for the grid sensitivity study

Fig.10 Comparison of the calculated average

n

and

f e

for various 2D grid sizes and 3D

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