Title: Nucleation
1Nucleation
- 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
2Growth
- 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
3Coarsening
Coarsening does not need to be modelled
separately but arises naturally from growth model
in later stages of precipitation
Early stages
Late stages
shrinking
growing
c
c
Concentration Zr
Concentration Zr
All particles growing
Large particles growing, small particles shrinking
4Testing 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
5Effect 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
6Including 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
7Predicting Across a Grain
Can the model reproduce the observed behaviour?
Edge
Centre
Mean radius
Zr in solution
Volume Fraction
8Effect 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
9Optimizing 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
10Model 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
11Optimizing Homogenization
- BUT Homogenization temperature for 7050 is
restricted
Need to dissolve these phases during
homogenization
Must avoid onset of melting
- Model suggests that best temperature for
precipitating dispersoids in low Zr regions lies
below this range
12Two 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
13Effect on Dispersoids
Standard Homogenization
14Comparison 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
15Summary
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
16Acknowledgements
- 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