Designing High Strength Aluminium Alloys for Aerospace Applications
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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

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Designing High Strength Aluminium Alloys for Aerospace Applications

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Designing high strength aluminium alloys for aerospace applications

Designing High Strength Aluminium Alloys for Aerospace Applications

H.Aourag


Aluminium alloys in aerospace

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

Design Requirements

  • Components must be

    • Lightweight

    • Damage tolerant

    • Durable (corrosion resistant)

    • Cost effective

  • Requires careful balance of material properties


Critical material properties

Critical Material Properties


Aluminium alloys

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

B

C

E

A

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

H

F

G

D

Aerospace Al-Alloys

  • Dominated by “high strength” wrought alloys

  • Two main alloy series in particular

    • 2xxx alloys (Al + Cu, Mg) UTS~500MPa

    • 7xxx alloys (Al + Mg, Zn, (Cu)) UTS~600MPa

Alloys used in typical wing structure


Next generation aircraft

Next Generation Aircraft

Bigger....

Airbus A380 > 950 seats

...Faster

Boeing sonic cruiser > Mach.95


Goals

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

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

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....


Modelling examples

Modelling Examples

Macro

  • Finite element modelling to optimize extrusion processing of aerospace Al-alloys

  • Thermodynamic modelling for the development of weldable aerospace aluminium alloys

  • Precipitation kinetics modelling for optimization of dispersoid particles in 7xxx alloys

Micro


Finite element modelling of extrusion

Finite Element Modelling of Extrusion


The extrusion process

Direct

The Extrusion Process

  • Extrusion is widely used to produce aerospace components

Extrusion

Billet

Die

Ram

(Al alloy)

Indirect

Direct

  • Extruded shapes are often complex - design of die is critical


Die design

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

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 of Finite Element Model

  • Use commercially available FE package to model metal flow and temperature during extrusion

Modify design

Yes

  • Any problems?

  • Unbalanced metal flow

  • Excess temperature variation

New die design

No

Make prototype die

Simulate extrusion process


Fe model example simulations

FE Model - Example Simulations

Example Simulations in 2D and 3D


Weldable aerospace al alloys

Weldable Aerospace Al-Alloys


Joining aerospace al alloys

Riveted joint

Extra material required

Labour intensive

  • Problem: Most high strength Al-alloys suitable for aerospace are considered “unweldable”

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

Welded joint

No extra material (less weight)

Process readily automated


Difficulties with welding

250 mm

7075 TIG Weld

Difficulties with welding

  • One of the major metallurgical problems preventing the widespread application of welding to aerospace Al-alloys is solidification cracking

Cracks arise when the thermal stresses generated during cooling exceed the strength of the almost solidified metal


Factors influencing solidification cracking

2

2

2) Grain Structure of Fusion Zone

- 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

Factors Influencing Solidification Cracking

1) Level of Thermal Stresses


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

Al-Cu System (Al-Rich)

Al-Mg System (Al-Rich)

Cu-Mg System

Simple Phase Diagrams

Even for simple 2xxx alloy (Al-Cu-Mg), need data for 3 binaries and information about ternary phases

S - Al2CuMg, T - Mg32(Al,Cu)49, V - Al5Cu6Mg2, Q - Al7Cu3Mg6

Ternary Phases

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

Cliq1

Csol1

Cliq2

Csol2

Cliq3

Csol3

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:

C0

(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 solid and liquid

Liquid

Csol0

T

Solid

% Solute


Predictions for binary al cu alloy

Freezing Range

1.0

0.9

0.8

0.7

fcc a-Al

0.6

Mass Phase Fraction

Liquid

Eutectic

Reaction

0.5

0.4

0.3

0.2

q - Al2Cu

0.1

620

560

600

580

660

680

640

700

540

520

Temperature (C)

Predictions for Binary Al-Cu Alloy

q - Al2Cu eutectic

fcc a-Al dendrites

fcc a-Al eutectic


Predictions for ternary al cu mg alloy

DT

1.0

0.9

0.8

0.7

fcc a-Al

0.6

Mass Phase Fraction

0.5

0.4

Liquid

0.3

0.2

S - Al2CuMg

0.1

q - Al2Cu

570

510

550

530

590

610

630

650

490

470

Temperature (C)

Ternary Eutectic Predicted at ~ 500ºC

Predictions for Ternary Al-Cu-Mg alloy

Predictions for 2xxx (Al-4.5Cu-1.5wt%) Mg alloy

TS

TL


Prediction of freezing range

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

Optimum composition range


Value of calculations

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

Modelling Dispersoid Precipitation in 7xxx Aerospace Al Alloys


Prediction of microstructure

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

  • These factors depend on phase transformation kinetics and are critical in determining microstructure and hence properties


Kinetic modelling

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

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

Direct chill

Age

Solution treat

475oC, 1h

spray quenched

Homogenize

~475oC, 24h

Hot roll

~350-450oC

20+ passes

reduction~70%

Processing Sequence - 7050 Plate


Microstructural changes

Temperature

RD

50nm

Microstructural Changes

Time

Cast

Homogenized

Rolled

Solutionized

Aged


Dispersoids

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

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


Designing high strength aluminium alloys for aerospace applications

Schematic of Model

Start

Homogenization temperature/time profile

Average zirconium concentration (depends on position in slab)

Precipitation

Kinetics Model

Local zirconium concentration (as a function of position within grain)

Microsegregation Model

(MTDATA Scheil Model)

Dispersoid size, number density, spacing and size distribution


Precipitation kinetics

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

Nucleation+growth

Coarsening

Time = t1

t2

t3

Clusters of Al, Zr atoms form by random in matrix. Stable clusters become particle nuclei

Particles grow, controlled by diffusion of Zr

Small particles dissolve at the expense of large particles to reduce total interfacial area


Designing high strength aluminium alloys for aerospace applications

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

I/f energy

Nucleation rate

Nucleation

  • Nucleation rate (number of new particles formed/s) depends on

    • Thermodynamic driving force for formation of new phase

    • Diffusion rate (temperature)

    • Interfacial energy between nucleus and matrix

Driving force

increasing but diffusion rate

decreasing

Temperature

Nucleation rate


Growth

Growth

  • Growth rate for each particle depends on

    • Concentration gradient ahead of particle

      • Equilibrium compositions from phase diagram

      • Particle size

    • Diffusion rate

Concentration profiles

Zr in particle

Small particle

Large particle

Zr concentration

Zr in matrix at interface

(depends on particles size)

distance


Coarsening

Coarsening

Coarsening does not need to be modelled separately but arises naturally from growth model in later stages of precipitation

Early stages

Late stages

growing

shrinking

c

c

Concentration Zr

Concentration Zr

All particles growing

Large particles growing, small particles shrinking


Testing the model

Testing the Model

  • First test model against experiment for a single initial Zr concentration

Comparison of model prediction and experiment at 500oC

Number

Size

Evolution of size distribution with time


Effect of zirconium segregation

Edge

Low Zr

Dispersoid free zone

High Zr

Centre

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

CENTRE

Observed dispersoid distribution after homogenization

Zr concentration after casting


Including effect of segregation

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 diffusing out of element

Zr diffusing into element

Zr removed into Al3Zr dispersoids

Zr concentration

Centre

Edge


Predicting across a grain

Centre

Edge

Centre

Edge

Centre

Edge

Predicting Across a Grain

Can the model reproduce the observed behaviour?

Edge

Centre

Mean radius

Zr in solution

Volume Fraction


Effect of dispersoid distribution

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

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

Model Predictions

Use model to investigate kinetics in detail

Growth

Nucleation

Temperature /oC

Temperature /oC

Time /h

To promote dispersoid nucleation in low Zr regions need to hold at ~425oC


Optimizing homogenization

Homogenization range

AA7050

Optimizing Homogenization

Need to dissolve these phases during homogenization

  • BUT Homogenization temperature for 7050 is restricted

Must avoid onset of melting

  • Model suggests that best temperature for precipitating dispersoids in low Zr regions lies below this range


Two step practice

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

Two step treatment

Effect on Dispersoids

Standard Homogenization


Comparison of recrystallization

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

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

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


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