A model approach to metals
A model approach to metals
Wei SHA
Professor of Materials Science
3 May 2006
Other materials
Metals
The for
Not
most
theimportant
same jobs
materials
Microstructure
Big
Small
Microstructure
Heat treatment: steel hardness and Al precipitation
60
Hardness, HRC
55
50
45
40
440°C
480°C
500°C
540°C
35
30
25
0.0625 0.125 0.25
0.5
1
2
4
8
16
Ageing Time, h
Guo, Sha, Vaumousse, Acta Materialia, 51, 2003, 101.
32
64
Mechanical processing
Rolling, forging, ball milling
Gasar
Gas-reinforced metals
Drenchev, Sobczak, Malinov, Sha, Materials Science and Technology, 22, 2006, in press.
Computer software systems
Predicting microstructures
Predicting properties
Predicting the effect of metal processing
Modelling methodologies
1) The Johnson-Mehl-Avrami method and its adaption to
continuous cooling and heating
2) Finite element method
3) Phase field method
4) Atomistic simulation
5) Neural network method
6) Surface engineering products
Neural network
Natual
3
Neuron
Artificial
Input
Layer
Hidden
Layer
Output
Layer
2
5
1
4
Neural Network
The human brain contains
1010 – 1011 neurons
X1
0.345
….
….
Training data set
INPUTS
OUTPUTS
X2
X3
X4
Y1
Y2
-0.701 1.000
0.002
0.678 -0.243
….
….
….
….
….
….
….
….
….
….
Malinov, Sha, McKeown, Computational Materials Science, 21, 2001, 375.
Titanium alloy
Phase transformation
970
5
5
10
15
20
25
35 30
40
50 45
910
55
70 60
65
85 75 80
90
95
880
T (oC)
940
5
10
15
20
25
30
35
40
50 45
55
60
70
75 65
85 80
90
95
10
15
20
25
35
50
70
85
850
820
10
T
Fr3
20
30
30
40
55
75
90
95
40
45
60
1
65
80
50
Cooling Rate (oC/min)
Malinov, Guo, Sha, Wilson, Metallurgical and Materials Transactions, 32A, 2001, 879.
Example of simulation and monitoring
Nucleation and growth of the phase in Ti6242
Furnace
Time-temperature path
In-situ monitoring
The Model
Malinov, Katzarov, Sha, Defect and Diffusion Forum, 237-40, 2005, 635.
SM_Monitoring.exe
Modules and graphical user interfaces
Simulation and modelling of different correlations
Composition-processing-temperature-mechanical properties
Fatigue life
CCT diagrams
Malinov, Sha, Computational Materials Science, 28, 2003, 179.
TTT diagrams
Microhardness profile
Gasar
Pores
• Heat conduction
• Gas diffusion
Drenchev, Sobczak, Sha, Malinov, Journal of Materials Science, 40, 2005, 2525.
1) Pass the 9 dots with
3 changes of direction
2) Plant 10 trees in 5 lines,
each line having 4 trees
Industrial applications
Optimization of the alloy composition
Find
Optimisation
Criteria
Alloy composition with
max strength at 420°C
Fix
Loops for
Heat Treatment
Trained
Neural
Network
Solution
Heat treatment = Annealing
T = 420°C
Sn, Cr, Fe, Si, Nb, Mn = 0; O = 0.12
Loops for
Temperature
Loops for
Alloy Composition
Vary
Al, Mo, Zr, V
Solution
Al = 5.8; Mo = 7.3; Zr = 5.2; V = 0
Tensile strength (420°C) = 932 MPa;
Yield strength = 665 MPa;
Elongation = 10%;
Modulus of elasticity = 94 GPa;
Fatigue strength = 448 MPa;
Fracture toughness = 101 MPa m 1/2
Malinov, Sha, McKeown, Computational Materials Science, 21, 2001, 375.
Industrial applications
Centrifugal casting process of composite
Drenchev, Sobczak, Malinov, Sha, Modelling Simul. Mater. Sci. Eng., 11, 2003, 635.
Industrial applications
SiC particle distribution during casting of Al alloy
radius r
0s
radius r
15 s
vol.
fraction
vol.
fraction
radius r
5s
radius r
20 s
vol.
fraction
radius r
vol.
fraction
radius r
10 s
vol.
fraction
vol.
fraction
25 s, completed
Drenchev, Sobczak, Malinov, Sha, Modelling Simul. Mater. Sci. Eng., 11, 2003, 651.
Industrial applications
Hot spot and gas cavity in centrifugal casting
Hot spot
Gas cavity
Drenchev, Sobczak, Malinov, Sha, Modelling Simul. Mater. Sci. Eng., 11, 2003, 651.
Acknowledgements
Postdoctoral RA, PhD students, visiting scientists
• Savko Malinov, School of Mechanical & Aerospace Engineering
• Ivaylo Katzarov, School of Mathematics & Physics
• Zhanli Guo, Sente Software, England
• Ludmil Drenchev, Bulgarian Academy of Sciences
• Jerzy Sobczak, Foundry Research Institute, Poland
Wei SHA
Professor of Materials Science
3 May 2006
Other materials
Metals
The for
Not
most
theimportant
same jobs
materials
Microstructure
Big
Small
Microstructure
Heat treatment: steel hardness and Al precipitation
60
Hardness, HRC
55
50
45
40
440°C
480°C
500°C
540°C
35
30
25
0.0625 0.125 0.25
0.5
1
2
4
8
16
Ageing Time, h
Guo, Sha, Vaumousse, Acta Materialia, 51, 2003, 101.
32
64
Mechanical processing
Rolling, forging, ball milling
Gasar
Gas-reinforced metals
Drenchev, Sobczak, Malinov, Sha, Materials Science and Technology, 22, 2006, in press.
Computer software systems
Predicting microstructures
Predicting properties
Predicting the effect of metal processing
Modelling methodologies
1) The Johnson-Mehl-Avrami method and its adaption to
continuous cooling and heating
2) Finite element method
3) Phase field method
4) Atomistic simulation
5) Neural network method
6) Surface engineering products
Neural network
Natual
3
Neuron
Artificial
Input
Layer
Hidden
Layer
Output
Layer
2
5
1
4
Neural Network
The human brain contains
1010 – 1011 neurons
X1
0.345
….
….
Training data set
INPUTS
OUTPUTS
X2
X3
X4
Y1
Y2
-0.701 1.000
0.002
0.678 -0.243
….
….
….
….
….
….
….
….
….
….
Malinov, Sha, McKeown, Computational Materials Science, 21, 2001, 375.
Titanium alloy
Phase transformation
970
5
5
10
15
20
25
35 30
40
50 45
910
55
70 60
65
85 75 80
90
95
880
T (oC)
940
5
10
15
20
25
30
35
40
50 45
55
60
70
75 65
85 80
90
95
10
15
20
25
35
50
70
85
850
820
10
T
Fr3
20
30
30
40
55
75
90
95
40
45
60
1
65
80
50
Cooling Rate (oC/min)
Malinov, Guo, Sha, Wilson, Metallurgical and Materials Transactions, 32A, 2001, 879.
Example of simulation and monitoring
Nucleation and growth of the phase in Ti6242
Furnace
Time-temperature path
In-situ monitoring
The Model
Malinov, Katzarov, Sha, Defect and Diffusion Forum, 237-40, 2005, 635.
SM_Monitoring.exe
Modules and graphical user interfaces
Simulation and modelling of different correlations
Composition-processing-temperature-mechanical properties
Fatigue life
CCT diagrams
Malinov, Sha, Computational Materials Science, 28, 2003, 179.
TTT diagrams
Microhardness profile
Gasar
Pores
• Heat conduction
• Gas diffusion
Drenchev, Sobczak, Sha, Malinov, Journal of Materials Science, 40, 2005, 2525.
1) Pass the 9 dots with
3 changes of direction
2) Plant 10 trees in 5 lines,
each line having 4 trees
Industrial applications
Optimization of the alloy composition
Find
Optimisation
Criteria
Alloy composition with
max strength at 420°C
Fix
Loops for
Heat Treatment
Trained
Neural
Network
Solution
Heat treatment = Annealing
T = 420°C
Sn, Cr, Fe, Si, Nb, Mn = 0; O = 0.12
Loops for
Temperature
Loops for
Alloy Composition
Vary
Al, Mo, Zr, V
Solution
Al = 5.8; Mo = 7.3; Zr = 5.2; V = 0
Tensile strength (420°C) = 932 MPa;
Yield strength = 665 MPa;
Elongation = 10%;
Modulus of elasticity = 94 GPa;
Fatigue strength = 448 MPa;
Fracture toughness = 101 MPa m 1/2
Malinov, Sha, McKeown, Computational Materials Science, 21, 2001, 375.
Industrial applications
Centrifugal casting process of composite
Drenchev, Sobczak, Malinov, Sha, Modelling Simul. Mater. Sci. Eng., 11, 2003, 635.
Industrial applications
SiC particle distribution during casting of Al alloy
radius r
0s
radius r
15 s
vol.
fraction
vol.
fraction
radius r
5s
radius r
20 s
vol.
fraction
radius r
vol.
fraction
radius r
10 s
vol.
fraction
vol.
fraction
25 s, completed
Drenchev, Sobczak, Malinov, Sha, Modelling Simul. Mater. Sci. Eng., 11, 2003, 651.
Industrial applications
Hot spot and gas cavity in centrifugal casting
Hot spot
Gas cavity
Drenchev, Sobczak, Malinov, Sha, Modelling Simul. Mater. Sci. Eng., 11, 2003, 651.
Acknowledgements
Postdoctoral RA, PhD students, visiting scientists
• Savko Malinov, School of Mechanical & Aerospace Engineering
• Ivaylo Katzarov, School of Mathematics & Physics
• Zhanli Guo, Sente Software, England
• Ludmil Drenchev, Bulgarian Academy of Sciences
• Jerzy Sobczak, Foundry Research Institute, Poland