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ALUMINIUM SMELTING INDUSTRY
Casting and solidification
For fabricating aluminium alloys, it is useful
to understanding solidification during casting
and to predict the phases that are likely
to precipitate during cooling. A Scheil solidification
calculation of the alloy AA7075 was
performed by using the real alloy composition
(Al, 0.11 Si, 0.28 Fe, 1.36 Cu, 2.49 Mg, 0.19
Fig. 2: Calculated isothermal section of the Al-Cu-Mg-Zn quaternary
system at 600 °C and 6 wt.% Zn compared with experimental work of
Strawbridge [10]
Cr, 5.72 Zn, wt.%). The calculation predicts
that Al 45 Cr 7 solidifies primarily before the
formation of (Al) although it had not been
experimentally observed. Considering that it
is probably the only Cr-bearing phase in this
alloy, its formation would be almost certain.
Due to its small amount, however, its formation
can hardly be observed in the DTA trace
or in the solidified microstructures. The formation
of (Al) was followed by the Al 13 Fe 4 ,
Mg 2 Si, T and V (MgZn 2 ) phases, which agrees
well with the experimental results. Imposed
on the diagram shown in Fig. 1 (see previous
page) are the accumulated solid phase fractions
at different temperatures, which have
been evaluated from the experimental DTA
trace obtained by Bäckerud et al [8]. It should
be noted that DTA can only allow a qualitative
evaluation. Nevertheless, it is suggested by
the comparison that the real solidification significantly
deviates from the equilibrium solidifi-cation,
but can be reasonably approximated
by a Scheil solidification simulation.
Heat treatment
The controlled heat treatment of aluminium
alloys allows the metallurgist to optimise, control
and generate a reproducible and predictable
change in the microstructure of the alloy.
This serves to influence properties such as
strength, ductility, fracture toughness, thermal
stability, residual stress, dimensional stability
and resistance to corrosion and stress corrosion
cracking [9]. The main heat treatment
procedures for aluminium alloys are homogenisation
and annealing, as
well as precipitation hardening,
which involves the
three steps of solution heat
treatment, quenching and
aging. Computational modelling
tools, such as those
described here, can give
insight into each of these
stages. For example, the purpose
of solution heat treatment
of aluminium alloys is
to put the maximum practical
amount of the hardening
solutes, such as Cu, Mg, Si,
Zn or other elements, into a
state of solid solution in the
Al matrix. Multicomponent
phase diagrams calculated
using Thermo-Calc can aid
this type of analysis without
the need to perform timeconsuming
experiments.
The 7000-series alloys
are heat-treatable wrought aluminium alloys,
and it is useful to perform equilibrium calculations
at solution treating temperatures and
at aging temperatures in order to predict the
phase formations in these alloys. As an example,
Fig. 2 shows a calculated isothermal section
of the Al-Cu-Mg-Zn quaternary system
at the typical solution treating temperature of
460 °C, and at a Zn content of 6 wt.%; the calculation
was in very good agreement with the
experimental data from Strawbridge et al [10].
This diagram can be used to generally account
for the phase constitution at the solution temperature
for a number of 7000 series alloys,
e. g. AA7010, AA7050, AA7075, AA7175,
AA7475 and AA7178, etc. Andreatta [11] reported
that Al7Cu 2 Fe and Al 23 CuFe 4 are the
most abundant of the intermetallics in AA7075
and AA7475, together with traces of Mg 2 Si,
Al 6 Fe, S, T and Al 12 (Fe,Mn) 3 Si, after being
solution treated. In such cases, it is necessary
to perform equilibrium calculations using real
alloy compositions by including other minor
elements. Because of the high Fe content, the
calculation using TCAL1 shows that Al 7 Cu 2 Fe
forms in alloy AA7075 with an amount up to
1%, and Al 13 Fe 4 coexists in a small amount.
However, for alloy AA7475, Al 7 Cu 2 Fe is calculated
to be the only main intermetallic.
Summary
The materials community is increasingly using
computational modelling tools, and is applying
them more widely to material design and process
optimisation. For more than two decades,
CALPHAD- based software and databases
have been employed within the aluminium
industry and they have served to improve
the understanding of existing alloys, to accelerate
the development of new alloys and
also to model and understand better materials
processing routes. The quality of the predictions
depends on the quality of the thermodynamic
and kinetic databases that they use.
Some examples have been given here to illustrate
how these tools are being used within the
aluminium industry in the areas of casting and
solidification as well as heat treatment.
References
[1] N. Saunders, A.P. Miodownik, Calphad (Calculations
of Phase Diagrams): A Comprehensive
guide, Pergamon Materials Series, vol. 1, ed. R.W.
Cahn (Oxford, OX: Elsevier Science Ltd, 1998).
[2] National Research Council, Integrated Computational
Materials Engineering: A Transformational
Discipline for Improved Competitiveness and National
Security. Washington, DC: The National
Academies Press, 2008.
[3] http://www.whitehouse.gov/sites/default/files/
microsites/ostp/materials_genome_initiative-final.
pdf
[4] A.K. Gupta et al., 2006, Materials Science Forum,
519-521, 177
[5] H. Onda et al., 2007, Materials Science Forum,
561-565, 1967
[6] J. Senaneuch et al., 2002, Materials Science Forum,
396-402, 1697
[7] S.N. Samaras, G.N. Haidemenopoulos, 2007,
Journal of Materials Processing Technology, 63-73,
194
[8] L. Bäckerud, G.C. Chai, J. Tamminen, Solidification
Characteristics of Aluminium Alloys, Vol. 1 and
2. Sweden (1990)
[9] H. Moller, 2011, Heat Treatment of Al-7Si-Mg
casting alloys, Aluminium International Today, 16-
18, Vol 23, No 6
[10] D.J. Strawbridge, W. Hume-Rothery, A.T.
Little, The constitution of aluminium-copper-magnesium-zinc
alloys at 460 °C. J. Inst. Metals (London)
74 (1947) 191-225
[11] F. Andreatta, Local electrochemical behaviour
of 7xxx aluminium alloys, PhD thesis, 2004
Authors
Paul Mason is president of Thermo-Calc Software
Inc., based in McMurray, PA, USA.
Hai-Lin Chen is with Thermo-Calc Software AB,
based in Stockholm, Sweden.
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