Transcript No Slide Title

Designing High Strength
Aluminium Alloys for
Aerospace Applications
H.Aourag
Aluminium Alloys in Aerospace
Airbus A340
Despite competition from other materials, Al alloys still
make up > 70% of structure of modern commercial airliner
Design Requirements
• Components must be
– Lightweight
– Damage tolerant
– Durable (corrosion resistant)
– Cost effective
• Requires careful balance of material
properties
Critical Material Properties
Aluminium Alloys
• Pure aluminium has
– Low density (rrelative Al=2.7, Fe=7.9)
– Readily available (Al is 3rd most abundant element
in Earth's crust)
– Highly formable (FCC crystal structure)
– Low strength and stiffness (EAl=70GPa,
EFe=211GPa)
– Low melting point (Tm=660oC)
• Alloy with other elements to improve strength
and stiffness - results in alloys with properties
well matched to aerospace requirements
Aerospace Al-Alloys
• Dominated by high strengthwrought
alloys
• Two main alloy series in particular
– 2xxx alloys (Al + Cu, Mg) UTS~500MPa
– 7xxx alloys B(Al + Mg, Zn, (Cu)) UTS~600MPa
C
E
A
H
G
D
F
Alloys used in typical wing structure
A) Slats - 2618
B) D-Nose Skins - 2024
C) Top Panel - 7150
D) Bottom Panel - 2024
E) Spars / Ribs - 7010
F) Flap Support - 7175
G) Flap Track - 7075
H) Landing Gear - 2024
Next Generation Aircraft
Bigger....
Airbus A380 > 950 seats
Boeing sonic cruiser > Mach.95
...Faster
Goals
• Next generation aircraft rely on advances
in materials and assembly methods
• Weight reduction is critical
– Alloy optimization
• Increase strength and stiffness and/or reduce
density whilst maintaining other properties
– Assembly optimization
• Reduce weight associated with joints between
components
Alloy Design
• Traditionally, alloy and process
development largely by trial and error
based on metallurgical experience
• Recently, emphasis has changed to
designing alloys and processes to meet
specific property goals
– Improved understanding of relationships between
processing, microstructure and properties
– Development of models to predict alloy
microstructure and performance
Applications of Modelling
• Models on a range of length scales
– Atomistic (nm)
• Limited application as currently capable of dealing with
only very small volumes of material
– Microstructural (nm-mm)
• Used to predict particle distributions, grain sizes etc.. as
function of alloy chemistry and processing conditions,
often coupled to microstructure-property models
– Macro-scale (>mm)
• Widely used to predict performance of components
during processing and service as a function of average
material properties and stress, strain, temperature....
Micro
• Finite element modelling to optimize
extrusion processing of aerospace Alalloys
• Thermodynamic modelling for the
development of weldable aerospace
aluminium alloys
• Precipitation kinetics modelling for
optimization of dispersoid particles in
7xxx alloys
Macro
Modelling Examples
Finite Element Modelling of Extrusion
The Extrusion Process
• Extrusion is widely used to produce
aerospace components
Ram
•
Billet
(Al alloy)
Direct
Extrusion
Direct
Die
Indirect
Extruded shapes are often complex design of die is critical
Die Design
• Die must be designed to ensure balanced
metal flow to avoid bending of extrusion
• Die shape influences metal temperature-aim to
avoid cold or hot spots
• Traditionally, die design based on past
experience and modifications of existing dies
• Alternative: Use finite element methods to
model extrusion process and identify and test
new die designs
The Finite Element Method
2D finite element mesh
for an extrusion
• Divide billet/extrusion into small, connected
elements
• Relate displacements/temperature changes in
one element to those in surrounding elements
using well established physical laws
Use of Finite Element Model
• Use commercially available FE package to
model metal flow and temperature during
extrusion
Modify design
New die design
Yes
Any problems?
•Unbalanced metal flow
•Excess temperature variation
Simulate extrusion process
No
Make
prototype die
FE Model - Example Simulations
Example Simulations in 2D and 3D
Weldable Aerospace Al-Alloys
Joining Aerospace Al-Alloys
• Mechanical fasteners (rivets) are still the most
widely used method of joining airframe
components
• Riveted joints have a number of disadvantages
Riveted joint
Welded joint
Extra material required No extra material (less weight)
Labour intensive
Process readily automated
•
Problem: Most high strength Al-alloys suitable
for aerospace are considered unweldable
Difficulties with welding
• One of the major metallurgical problems
preventing the widespread application of
welding to aerospace Al-alloys is
solidification cracking
250 mm
7075 TIG Weld
Cracks arise when the
thermal stresses
generated during cooling
exceed the strength of the
almost solidified metal
Factors Influencing Solidification Cracking
1) Level of Thermal Stresses 2
2) Grain Structure of Fusion Zone 2
- columnar grains vs equiaxed grains
3) Absolute Freezing Range
?
- alloys with a wide freezing range are susceptible to cracking
4) Freezing Range for Dendrite Cohesion ?
- thought to occur at about 50-60% Solid (depend on grain structure)
5) Volume Fraction of Low Melting Point Eutectic Phases ?
- if there is sufficient liquid at the end of solidification to flow around
the dendrites, then any cracks might be healed
Thermodynamic Modelling
Thermodynamic Modelling
• For any alloy system, set of conditions and
configuration of the components there will be an
associated free energy
• Use computer models to calculate the free energy for
complex systems (lots of elements) from data for
simple systems (1,2 or 3 elements)
• Calculate the equilibrium (minimum free energy)
configuration and hence phase diagrams for complex
systems
– Can be useful in the interpretation of real microstructures
• Calculate phase fractions and compositions for certain
other well defined non-equilibrium problems
Simple Phase Diagrams
Even for simple 2xxx alloy (Al-Cu-Mg), need data for 3 binaries
and information about ternary phases
550 -Al
500
-Al 2Cu
-Al + -Al 2Cu
450
Liquid
550
Temperature (C)
Liquid + -Al
Liquid + -Al
500
-Al
450
-AlMg
400
-Al + -AlMg
350
0
10
20
30
wt.% Cu
40
50
60
L + -Cu
900
Liquid
800
700

Liquid +
Laves - C15
600
500
400
300
400
Cu-Mg System
1000
600
-AlMg
600
Temperature (C)
Liquid + -Al 2Cu
1100
Al-Mg System (Al-Rich)
650
-AlMg
Al-Cu System
(Al-Rich)
Liquid
650
Temperature (C)
1200
700
-Cu
+
Laves - C15
Liquid + Mg
L + CuMg2
Laves - C15
CuMg2
700
CuMg2 + Mg
300
0
10
20
30
wt.% Mg
40
50
60
0
20
40
60
80
100
wt.% Mg
Ternary Phases S - Al2CuMg, T - Mg32(Al,Cu)49, V - Al5Cu6Mg2, Q - Al7Cu3Mg6
MTDATA predicted phase diagrams
Real, commercial Al-alloys may contain > 10 alloying
elements!
Success of thermodynamic models relies on availability of
sufficient, high quality, thermodynamic data
Solidification Microstructures
Solidification occurs rapidly under non-equilibrium
conditions
However, given certain assumptions, thermodynamic
calculations and the equilibrium phase diagram can still
be used to predict solidification microstructure
Microstructure
Scheil Solidication Model - Assumptions:
(i) Local equilibrium exists at the
solid/liquid interface
(ii) No diffusion in the solid phases
(iii) Uniform liquid composition
(iv) No density difference between
C0
Liquid
Csol0sol1
C
Csol2
Csol3
T
Cliq1
Cliq2
Cliq3
Solid
solid and liquid
% Solute
Predictions for Binary Al-Cu Alloy
Freezing Range
Mass Phase Fraction
1.0
0.9
0.8
0.7
fcc -Al
0.6
0.5
0.4
0.3
0.2
Liquid
Eutectic
Reaction
 - Al2Cu
0.1
520 540 560 580 600 620 640 660 680 700
Temperature (C)
 - Al2Cu
fcc -Al
eutectic fcc -Al dendrites
eutectic
Predictions for Ternary Al-Cu-Mg alloy
Predictions for 2xxx (Al-4.5Cu-1.5wt%) Mg alloy
DT
TS
1.0
TL
Mass Phase Fraction
0.9
0.8
fcc -Al
0.7
0.6
0.5
0.4
Liquid
0.3
0.2
0.1
S - Al2CuMg
 - Al2Cu
470
490
510
530 550 570 590
Temperature (C)
Ternary Eutectic Predicted at ~ 500ºC
610
630
650
Prediction of Freezing Range
To reduce tendency for solidification cracking, need to
minimize absolute freezing range
Use thermodynamic model to predict freezing range
for different alloy compositions
Effect of Mg
content on freezing
range of eutectic in
Al-4.5Cu-x Mg alloy
50
D T (Freezing Range of Eutectic)
45
40
Optimum
composition
range
35
30
25
20
Ternary Eutectic
[ + S]
15
Saddle Point
[ + S]
Binary Eutectic
[ + ]
10
5
0
0
0.5
1
1.5
wt.% Mg
2
2.5
3
Value of Calculations
• Thermodynamic calculations suggest
modifications to current alloy
compositions to improve weldability
• Focus experimental investigation on
promising compositions
– Save both development time and cost
• New weld filler wires have been
developed on the basis of these
calculations and are now being tested
Modelling Dispersoid Precipitation in
7xxx Aerospace Al Alloys
Prediction of Microstructure
• Thermodynamic calculations give an indication
of likely phases but give no information about
– How phase is distributed
• Particle size, spacing and location
– How microstructure changes as function of
time
• Transformation of metastable phases
• Evolution of volume fraction of phase and
particle size distribution
phase
transformation kinetics and are critical in
determining microstructure and hence
• These factors depend on
Kinetic Modelling
• Aim to predict key microstructural
parameters as a function of alloy
composition, temperature and time
• Difficult problem for aerospace Al-alloys
due to complex microstructures and
processing routes
– Large number of possible phases evolving
simultaneously
– Metal subjected to thermal cycling and
complex deformation during processing
7050 Plate
Focus on one alloy (7050) and product (thick
hot rolled plate)
Components machined from 7050
alloy thick plate are widely used in
load bearing applications e.g. wing
spars
7050 composition specification
Processing Sequence - 7050 Plate
Cast
Age
Direct chill
Solution treat
Homogenize
~475oC, 24h
475oC, 1h
spray quenched
Hot roll
~350-450oC
20+ passes
reduction~70%
Temperature
Microstructural Changes
Time
RD
50nm
Cast
Homogenized
Rolled
Solutionized
Aged
Dispersoids
Al3Zr dispersoid particles in
7050 after homogenization
• Fine Al3Zr dispersoid particles precipitate
during homogenization of 7050
• Dispersoid particles are important for the
control of grain structure during processing
– Act to pingrain boundaries
Modelling Dispersoid Precipitation
• Effectiveness of dispersoids depends on
their size, spacing and distribution
• Develop model for dispersoid
precipitation and use to optimize
homogenization treatment to give best
dispersoid distribution
• To model dispersoid precipitation must
account for both non-uniform distribution
of Zr due to microsegregation during
casting and Al3Zr precipitation kinetics
Schematic of Model
Start
Homogenization
temperature/time
profile
Average
zirconium
concentration
(depends on
position in slab)
Microsegregation
Model
(MTDATA Scheil
Model)
Local zirconium
concentration (as a
function of position
within grain)
Precipitation
Kinetics
Model
Dispersoid size,
number density,
spacing and size
distribution
Precipitation Kinetics
The precipitation of Al3Zr dispersoids is a diffusion
controlled phase transformation
Classically, precipitation of particles considered as
2-step process of nucleation and growth, followed
by coarsening
Nucleation
Time = t1
Clusters of Al, Zr atoms
form by random in matrix.
Stable clusters become
particle nuclei
Nucleation+growth
t2
Particles grow,
controlled by
diffusion of Zr
Coarsening
t3
Small particles dissolve
at the expense of large
particles to reduce total
interfacial area
Kinetics Model
?
?
?
?
Time is divided into a large number of small
steps
Growth, nucleation and coarsening allowed to
occur concurrently governed by driving force
and concentration gradients
At each step new particles nucleate and
existing particles grow (or shrink) depending
on local interfacial compositions
After each step, solute supersaturation in the
matrix is recalculated and used for next step
Nucleation
• Nucleation rate (number of new particles
formed/s) depends on
Driving force
increasing but
diffusion rate
decreasing
Nucleation rate
I/f energy
Temperature
– Thermodynamic driving force for formation
of new phase
– Diffusion rate (temperature)
– Interfacial energy between nucleus and
matrix
Nucleation rate
Growth
• Growth rate for each particle depends on
– Concentration gradient ahead of particle
• Equilibrium compositions from phase diagram
• Particle size
Zr concentration
– Diffusion rate
Zr in particle
Concentration profiles
Small particle
Large particle
Zr in matrix at interface
(depends on particles size)
distance
Coarsening
Coarsening does not need to be modelled separately
but arises naturally from growth model in later stages
of precipitation
c
All particles growing
Late stages
Concentration Zr
Concentration Zr
Early stages
growing
shrinking
c
Large particles growing,
small particles shrinking
Testing the Model
Number
• First test model against experiment for a
single initial Zr concentration
Size
Evolution of size
distribution with time
Comparison
of model
prediction
and
experiment at
500oC
Effect of Zirconium Segregation
• In practice, Zr concentration varies across a
grain due to segregation during casting
• Leads to non-uniform dispersoid precipitation
during homogenization
Edge
Low Zr
High Zr
Centre
EDGE
CENTRE
Zr concentration after casting
Observed dispersoid distribution
after homogenization
Including Effect of Segregation
• To model Al3Zr distribution across a grain
– Divide the distance from grain edge to
centre into large number of elements
– Model dispersoid evolution in each element
– Allow zirconium redistribution by diffusion
between elements
Zr concentration
Zr diffusing out of element
Edge
Zr diffusing into element
Zr removed into
Al3Zr dispersoids
Centre
Predicting Across a Grain
Can the model reproduce
the observed behaviour?
Edge
Centre
Volume Fraction
Centre
Edge
Zr in solution
Centre
Mean radius
Edge Centre
Edge
Effect of Dispersoid Distribution
• Inhomogeneously distributed dispersoids
are not best for control of grain structure
• In regions where there are few
dispersoids, new grains can form
(recrystallization) - this is undesirable
Structure after processing
New grains have formed and
partially consumed original
grains - this structure does
not give best properties
Optimizing Dispersoid Distribution
• Use model to determine optimum
homogenization conditions to promote
dispersoid precipitation in low Zr regions
• Aim is to reduce the formation of new
(recrystallized) grains during processing
• For best recrystallization resistance, want
a large number of small dispersoid
particles, as uniformly distributed as
possible
Model Predictions
Growth
Nucleation
Temperature /oC
Temperature /oC
Use model to investigate kinetics in detail
Time /h
To promote dispersoid nucleation in low Zr regions
need to hold at ~425oC
Optimizing Homogenization
? BUT Homogenization temperature for 7050 is restricted
Must avoid
onset of melting
Homogenization range
Need to
dissolve these
phases during
homogenization
AA7050
? Model suggests that best temperature for precipitating
dispersoids in low Zr regions lies below this range
Two Step Practice
? Two step homogenization practice may
be of benefit
? Step 1: Hold at a temperature to precipitate
optimum dispersoid distribution
? Step 2: Hold at final homogenization
temperature
? Model used to determine best conditions
for step 1
? 5h Hold time at 430oC
? Test 2 step homogenization practice
Effect on Dispersoids
Standard Homogenization
Two step treatment
Comparison of Recrystallization
Standard Practice
Recrystallized Fraction = 30.4%
Hold + Homogenize Practice
Recrystallized Fraction = 14.0%
Two step homogenization practice, developed entirely by
computer modelling, is effective in significantly reducing the
fraction of recrystallization
Summary
Aerospace aluminium alloys are complex
materials, developed over a long period of time
by empirical experiment to meet industrial
needs
In recent years, the understanding of the
metallurgical processes governing the
microstructure and properties of these alloys
has greatly increased
This has led to the development of models that
have practical application in the design of new
alloys and processes
Acknowledgements
• For provision of data and examples of FE
and thermodynamic modelling
– Dr Qiang Li, Birmingham University
– Dr Andy Norman, Manchester Materials
Science Centre
• Luxfer and Alcoa for funding some of this
research