1. Mobility Database of Al-based
Alloys
PanAl2020_MB is an atomic mobility database for Al-based alloys, which is
compatible with the PanAl2020_TH thermodynamic database and suitable for
the simulation of diffusion controlled phenomena using the PanDiffusion
module, PanPrecipitation module, and/or PanSolidification module.
1.1 Components (34)
Ag
Al
B
Be
Bi
C
Ca
Ce
Co
Cr
Cu
Fe
Gd
Ge
Hf
K
Li
Mg
Mn
Na
Nb
Ni
Pb
Sb
Sc
Si
Sn
Sr
Ti
V
W
Y
Zn
Zr
1.2 Phases
The atomic mobility within the Liquid, Bcc, Fcc, and Hcp solution phases are
assessed in this database.
1.3 Self-diffusivity of Pure Elements
The self-diffusivity of an element is usually described by an analytical
expression. For the stable crystal structures, these expressions can be
obtained using the available experimental data, while those for the
metastable/unstable states are usually estimated from those of the stable
states. In the following tables, we use different color to represent different
status:
: Validated
: Estimated
: No data
Table 1.1: Assessed self-diffusivity of pure elements with different crystal structures
Ag
Be
Bi
Ca
Ce
Co
Cr
Cu
Fe
Gd
Ge
Hf
K
Li
Mg
Mn
Na
Nb
Bcc
Fcc
Hcp
Ni
Pb
Sb
Sc
Si
Sn
Sr
Ti
V
W
Y
Zn
Zr
Bcc
Fcc
Hcp
1.4 Assessed Systems
In addition to the assessed self-diffusivities shown above, the impurity
diffusion data for all elements included in the current mobility database are
also assessed. Moreover, chemical-diffusivities available in some binary and
ternary systems are also used to assess the interaction parameters. These
binary and ternary systems are listed below for the Bcc, Fcc, and Hcp phases,
respectively.
Fcc Phase
Ag-Al
Ag-Cu
Ag-Sn
Ag-Zn
Al-Cu
Al-Mg
Al-Ni
Al-Si
Al-W
Al-Zn
Cr-Fe
Cr-Ni
Cu-Fe
Cu-Mg
Cu-Si
Cu-Sn
Cu-Ti
Cu-Zn
Fe-Mn
Fe-Ni
Fe-Si
Ge-Ni
Mn-Ni
Nb-Ni
Ni-Ti
Ni-V
Ni-W
Ni-Zn
Ag-Al-Zn
Al-Cr-Ni
Al-Cu-Mg
Al-Cu-Si
Al-Cu-Zn
Al-Mg-Zn
Al-Mn-Ni
Al-Nb-Ni
Cr-Cu-Ni
Cr-Fe-Ni
Cr-Nb-Ni
Cu-Fe-Mn
Cu-Fe-Ni
Cu-Mn-Ni
Cu-Ni-Zn
Fe-Mn-Si
Bcc phase
Al-Fe
Al-Ti
Cr-Fe
Cr-Ti
Cu-Ti
Fe-Ti
Hf-Zr
Nb-Ti
Nb-V
Nb-W
Nb-Zr
Ti-V
Ti-Zr
V-Zr
Al-Cr-Ti
Al-Fe-Ti
Cr-Fe-Ni
Hcp phase
Al-Mg
Mg-Zn
Al-Mg-Zn
1.5 Database Validation
The simulated concentration profiles of a series of aluminum alloys are used to
validate the current mobility database for Al-based alloys. A few examples of
such simulation are shown below.
Figure 1.1: Concentration profiles of Al-6.35Zn/Al-5.14Ag (at.%) aged at 796K for 55080s [1]
Figure 1.2: Concentration profile of Al-4Zn/Al-0.99Cu (at.%) annealed at 850K for 72h [2]
Figure 1.3: Concentration profiles of Al-0.96Cu/Al-1.41Mg (at.%) aged at 853K for 7290s [3, 4]
Figure 1.4: Concentration profiles of Al-2.72Mg/Al-4.81Zn (at.%) aged at 868K for 5400s [5]
1.6 Applications
This mobility database is combined with the thermodynamic database for Al-
based alloys, PanAl_TH, to simulate the diffusion-controlled phenomena of Al-
based alloys. A few examples are given below.
1.6.1: Precipitation kinetics of aluminum alloys
The PanPrecipitation module was developed for the simulation of
precipitation kinetics of multi-component alloys. It has been seamlessly
integrated with the thermodynamic calculation engine of Pandat software, and
has been used to simulate the evolution of microstructure and the
corresponding mechanical property responses to heat treatment of 2xxx, 6xxx
and 7xxx series of aluminum alloys [6]. Below shows an example simulation
performed for the Al-2.3Mg-6.1Zn (wt%) alloy aged at 160
o
C for 1000 hours.
The simulated particle size and yield strength evolution with time are compared
with experimental data as shown in Figure 1.5. As is seen, the particles grow
and coarsen with ageing time, while the yield strength reaches peak between 1
to 10 hours. The yield strength decreases quickly after 10 hours of ageing at
160
o
C. The database used to do this simulation is the combined
thermodynamic and mobility database of Al-based alloys: PanAl_TH+MB. More
information regading to precipitation simulation can be found in
PanPrecipitation module under the Software section.
Figure 1.5: Simulated and measured particle size and yield strength evolution with time for Al-
2.3Mg-6.1Zn (wt%) alloy aged at 160
o
C for 1000 hours
1.6.2: Dissolution of aluminum alloys
The PanDiffusion module was developed for the simulation of diffusion
kinetics of multi-component alloys. In Figure 1.6, dissolution of Si particle in
Al-Si binary system was simulated and compared with the experimentally
determined data [7]. In this simulation, the combined thermodynamic and
mobility database of Al-based alloys, PanAl_TH+MB, is used. More information
regading to diffusion simulation can be found in PanDiffusion module under
the Software section.
Figure 1.6: Comparison of simulated and experimentally determined dissolution of Si particle
in Al-Si binary system
1.6.3: Solidification of aluminum alloys
The PanSolidification module was developed for the simulation of
solidification behavior of multi-component alloys considering the effects of
back-diffusion in the solid matrix phase, cooling rate, and dendrite arm
coarsening. As shown in Figure 1.7, the sodification of the Al-4.5wt.%Cu alloy
at the cooling rate of 0.25K/s is simulated and compared with the
experimentally determined data [8]
Figure 1.7: Comparison of simulated and experimentally determined Cu composition profile
within the Fcc matrix for the Al-4.5wt.%Cu alloy at the cooling rate of 0.25K/s
In this simulation, the combined thermodynamic and mobility database of Al-
based alloys, PanAl_TH+MB, is used. More information regading to
solidification can be found in PanSolidification module under the Software
section.
1.7 References
1. Cui, S.L., et al., Assessment of Atomic Mobilities in fcc Al-Ag-Zn Alloys.
Journal of Phase Equilibria and Diffusion, 2011. 32(6): p. 512-524.
2. Chang, H., et al., Assessment of the atomic mobilities for ternary Al-Cu-Zn
fcc alloys. Calphad, 2010. 34: p. 68-74.
3. Zhang, W.B., et al., Assessment of the atomic mobility in fcc Al-Cu-Mg
alloys. Calphad, 2010. 34: p. 286-293.
4. Xin, J.H., et al., Prediction of diffusivities in fcc phase of the Al-Cu-Mg
system: First-principles calculations coupled with CALPHAD technique.
Computational Materials Science, 2014. 90: p. 32-43.
5. Yao, J.J., et al., Diffusional mobility for fcc phase of Al-Mg-Zn system and
its applications. Calphad, 2008. 32: p. 602-607.
6. Cao, W., et al., An Integrated Computational Tool for Precipitation
Simulation. JOM, 2011. 63(7): p. 29-34.
7. Tundal, U.H. and N. Ryum, Dissolution of particles in binary alloys: Part II.
experimenal investigation on an Al-Si alloy. Metallurgical and Materials
Transactions A, 1992. 23: p. 445-449.
8. Yan, X., Thermodynamic and Solidification Modeling Coupled with
Experimental Investigation of the Multicomponent Aluminum Alloys, in
Materials Science and Engineering. 2001, University of Wisconsin-
Madison: Madison, WI.