PanPrecipitation for Precipitation Modeling of Multi ...Precipitation Modeling: strategy...
Transcript of PanPrecipitation for Precipitation Modeling of Multi ...Precipitation Modeling: strategy...
Weisheng Cao, Fan Zhang, Shuanglin Chen
Chuan Zhang, Jun Zhu, Duchao Lv
PanPrecipitation for Precipitation Modeling of Multi-Component Alloys
by
April 08, 2020
CompuTherm, LLC8401 Greenway Blvd, Middleton, WI, USA
http://www.computherm.com
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Outline of Presentation
I. Introduction Materials Design by CALPHAD & ICME
Precipitation modeling and software design
Kinetic and strengthening models
II. Applications to multi-component Ni and Al alloys Precipitation behavior of Ni-based superalloys
Age hardening behavior of Al alloys
III. Discussion and software tutorial
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Materials Design by CALPHAD & Integrated Computational Materials Engineering (ICME)
Material Properties and Performance
Microstructure
Chemistry
ProcessingConditions
Thermodynamic Calculation
Kinetic Simulation
0 30 60 90 120 1500
30
60
90
120
150
180 1080oC 1050oC 1020oC 990oC 960oC
Ave
rage
Siz
e (n
m)
Time (minute)
Microstructural Evolution Model Mechanical Property Model
Model Simulation+Key ExperimentsModern Materials Design
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Vol
ume
Fra
ctio
n
log(Time)
Precipitation hardening
α’→ α +βα’: matrix phase (supersaturated )β: precipitate phaseα : stable solid solution
α’
L
α β
α +βTem
pera
ture
CB →A B
Concurrent nucleation, growth & coarsening advanced kinetic model Reliable thermodynamic data and mobility data smooth integration
Coa
rsen
ing
Nucleation
+Growth
Incu
batio
n
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Precipitation modeling
Integrated Modeling Tools(PanPrecipitation)
Calphad Modeling(PanEngine)
Thermodynamic & mobility database
Problem To Be Solved
Alloy Chemistry &Processing Condition
Microstructure
Precipitationdatabase
Microstructure Modeling
driving forcephase equilibriummobility data
Modeling ofAge Hardening
volume fractionaverage sizePSD
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Precipitation Modeling: strategy Precipitation Modeling: strategy
Precipitation process is very complicated and wedon’t have an universal model valid for everycases;
The model or model parameters may need to beadjusted case by case;
Extendibility and flexibility is the key (this is howPanPrecipitation is different);
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User-Defined
System
Matrix Phase
KWN Fast Acting
Built-in Phase Models
Virtual Precipitate Phase
Top-Level IntegrationPanPrecipitation: Generic Data Structure
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Built-in Phase Models
Growth
Nucleation Coarsening
Built-in Nucleation, Growth and Coarsening Equations
Can be replaced by user-defined equations
Low-Level IntegrationPanPrecipitation: Generic Data Structure
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All calculation engines automatically inherit these features from Pandat Software Architecture
8
Low-Level IntegrationPanPrecipitation: Strategic Software Design
ModularDesign
Well suited to current PANDAT architecture Allows for integration with other applications
(i.e., integration with ESI ProCast)
GenericData Structure
Flexible to expand and easy to maintain User-friendly interface and KDB structure Simplifies integration with user-defined models
3-LayerArchitecture
Parallel development of software and database More efficient with better quality Minimizes the maintenance workload Easy to migrate and commercialize
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Inputs/Outputs of PanPrecipitation
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Precipitation models: the KWN model
Concurrent nucleation, growth and coarsening
Evolution of average quantities: volume fraction, number density, particle size
Evolution of PSD: Particle Size Distribution
Many size classes
The KWN (Kampmann-Wagner Numerical) Model:
R
N
Risize classes
* R. Kampmann and R. Wagner, Decomposition of Alloys: the early stages, pp. 91-103 (1984)
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Precipitation models: nucleation
Classical nucleation theory:
Nv : Number of nucleation sites per unit volumeZ : Zeldovich factor accounting for decay of supercritical particlesβ* : Rate of solute atoms joining the critical nucleusτ : Incubation time∆G*: Activation energy for nucleation
*
*G
kT tvJ N Z e e
τ
β∆ −−
=
[1] Svoboda, J., et al., Materials Science and Engineering A, 2004. 385(1-2): p. 166-174.
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Precipitation models: Heterogeneous Nucleation
Heterogeneous nucleation can be considered by adjusting Nv and activation energy ∆G*.
Manually: the two variables can be assigned with a mathematical expression in KDB file
Theoretical estimation:
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Precipitation models: growth
Growth model for multi-component alloys:
1 Morral, J.E. and Purdy, G.R., Scripta Metallurgica et Materialia, 1994. 30(7): p. 905-9082 Svoboda, J., et al., Materials Science and Engineering A, 2004. 385(1-2): p. 166-174.3 Svoboda, J., et al., Acta Materialia 56 (2008) 4896–4904.
The Simplified Model: based on the growth model proposed by Morral and Purdy 1
The SFFK2 Model for complex systems – the principle of maximum entropy production – modified for shape evolution3
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Precipitation models: Shape Factors
Evolution of the aspect ratio of the precipitate stems from the anisotropic misfit strain of the precipitate and from the orientation dependence of the interface energy
σ = σ0 + σSS + σP
Depends on the mean solute concentration of each alloying element
= 3/2jjSS Ckσ
Does not change during ageing process
σI lattice resistance
σWH work hardening
σGB grain boundary hardening
Precipitation models: age hardening of Al alloys*
Yield Strength:Depends on the mean obstacle strength
M is the Taylor factorG is the shear modulus of the Al matrixβ Is a constant close to 0.5b is the magnitude of the burgers vectorR is mean particle size; Vf is volume fractionF is mean obstacle strength
2 1/2 3/ 23(2 )
2f
P
MFb
b
V
RGσ β
π−=
2
2
2 if ( )
2 if ( )
ii C
Ci
i C
RGb R R weak
RF
Gb R R strong
β
β
≤= ≥
ii
i
NF
N
F=
Hardness: HV = 0.33σ + 16.0
* Deschamps, A. et al., Acta Mater., 1998. 47(1): p. 293-305
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Outline of Presentation
I. Introduction Materials Design by ICME
Precipitation modeling and software design
Kinetic and strengthening models
II. Applications to multi-component Ni and Al alloys Precipitation behavior of Ni-based superalloys
Age hardening behavior of Al alloys
III. Discussion and software tutorial
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Multi-Step Heat Treatment: IN100
PanNi thermodynamic and mobility was used
Primary γ′ Secondary γ′ Tertiary γ′Size (nm) 1700 208 23
Volume fraction 20% 29% 11%
Alloy Chemistry: Ni-4.85Al-18.23Co-12.13Cr-3.22Mo-4.24Ti (wt%).
1149C for 2 hours
time, hour
Tem
per
atu
re981C for 1 hour
732C for 8 hours
By K. Maciejewski, H. Ghonem, Materials Science & Engineering A, 560 (10) (2013), 439-449.
Experimental Data:
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Multi-Step Heat Treatment: IN100
Exp data for T: 11%
Exp data for S: 29%
Exp data for P: 20%
Symbol for Exp Data
Effect of Cooling Rate on the Size Distribution of U720LI
The sample were soaked at 1180oC and then cooled continuously to 400oC at different speed
γ′ Solvus:
Calculated: 1157.5oCMeasured: 1160oC
Alloy Cr Co Ti Al Mo W Zr C B NiPM 16.26 14.73 5.05 2.50 3.01 1.27 0.036 0.023 0.018 Bal
C&W 16.06 14.52 5.04 2.54 3.08 1.20 0.047 0.013 0.018 Bal
78
3.25
0.0167
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U720LI: Cooling Effect on PrecipitationFast Cooling (78K/sec)
1nm 14nm
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U720LI: Cooling Effect on PrecipitationMedium Speed (3.25K/sec)
0.7nm 4nm 70nm
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U720LI: Cooling Effect on PrecipitationSlow Cooling (0.0167K/sec)
Simulated: 0.4nm 7nm 126nm 1260nm
Measured:790nm19nm3nm
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Outline of Presentation
I. Introduction Materials Design by CALPHAD & ICME
Precipitation modeling and software design
Kinetic and strengthening models
II. Applications to multi-component Ni and Al alloys Precipitation behavior of Ni-based superalloys
Age hardening behavior of Al alloys
III. Discussion and software tutorial
CompuTherm LLC – www.computherm.com 24
Age hardening of an AA6005 alloyComparison between measured and predicted responseof an AA6005 Aluminum Alloy to artificial ageing at 185°C
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.021.0
21.5
22.0
22.5
23.0
0.6 hra)
01Myh this study
log(
Num
ber
Den
sity
/ m
-3)
log( t / sec )
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.01.0
1.5
2.0
2.5
3.0
2.5 hr, 4.7nm
0.6 hr, 2.6 nm
b)
log(
R /
Α)
log( t / sec )
01Myh this study
2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.040
50
60
70
80
90
100
2.5 hrc)
01Myh this study
Har
dnes
s (V
PN
)
log( t / sec )
Alloy Composition: Al-0.55Mg-0.82Si-0.16Cu-0.2Fe-0.5MnReference: Myhr, O.R., Grong, and Andersen, S.J., Acta Mater.,
2001. 49(1): p. 65-75
number density
average size
hardness
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Comparison between measured and predicted responseof an AA6005 Aluminum Alloy to reheating at 350°C
14000 14500 15000 15500 16000 1650018
19
20
21
22
23
a)
01Myh This Study
Time / sec
log(
Num
ber
Den
sity
/ m
-3)
14000 14500 15000 15500 16000 165001.0
1.5
2.0
2.5
3.0
Time / sec
log(
R /
A)
01Myh This Study
14000 14500 15000 15500 16000 1650020
30
40
50
60
70
80
90
100
01Myh This Study
Har
dnes
s (V
PN
)
Time / sec
Reheating of an AA6005 alloy
Alloy Composition: Al-0.55Mg-0.82Si-0.16Cu-0.2Fe-0.5MnReference: Myhr, O.R., Grong, and Andersen, S.J., Acta Mater.,
2001. 49(1): p. 65-75
hardnessnumber density
average size
reheat at 350C
4 hours at 185C
time, hour
Tem
per
atu
re
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Reheating of other alloys
0 1 2 3 4 50
10
20
30
40
50
60
70
80
90
100
a)
Har
dnes
s (V
PN
)
log(Time/sec)
250C 300C 350C
0 1 2 3 4 50
10
20
30
40
50
60
70
80
90
100
b)
Har
dnes
s (V
PN
)
log(Time/sec)
250C 300C 350C
0 1 2 3 4 50
10
20
30
40
50
60
70
80
90
100
c)
Har
dnes
s (V
PN
)
log(Time/sec)
250C 300C 350C
Experiment: Myhr, O.R., Grong, and Andersen, S.J., Acta Mater., 2001. 49(1): p. 65-75
a) AA6005:Al-0.54Mg-0.56Si-0.2Fe-0.0062Mn
b) AA6060 Alloy: Al-0.35Mg-0.56Si-0.21Fe-0.0064Mn
c) AA6063 Alloy: Al-0.74Mg-0.58Si-0.21Fe-0.0061Mn
reheat
3 hours at 195C
time, hour
Tem
per
atu
re
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Comparison with experiments
Comparison between measured and predicted number density of different
aluminum alloys
16 17 18 19 20 21 22 23 2416
17
18
19
20
21
22
23
24
Pre
dict
ed
Measured
01Myh: 75Asb:
20 30 40 50 60 70 80 90 100 11020
30
40
50
60
70
80
90
100
110
Alloy II reheatingAlloy III reheatingAlloy IV reheating
Experimental Data: 01Myh
Pre
dict
ed
Measured
Experimental Data: 01MyhAlloy IV ageing at 185oCAlloy IV reheating
Comparison between measured and predicted hardness of different
aluminum alloys
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Outline of Presentation
I. Introduction Materials Design by CALPHAD & ICME
Precipitation modeling and software design
Kinetic and strengthening models
II. Applications to multi-component Ni and Al alloys Precipitation behavior of Ni-based superalloys
Age hardening behavior of Al alloys
III. Discussion and software tutorial
CompuTherm LLC – www.computherm.com 29
PanPrecipitation Module
Databases Needed: Thermodynamic database, Mobility database, Kinetic database
What can be calculated? • Temporal evolution of average particle size and number density
• Temporal evolution of particle size distribution
• Temporal evolution of volume fraction and composition of precipitates
• Co-precipitation of more than one precipitates, such as γ′ and γ′′ in nickel 718
• Interfacial energy estimation based on the generalized broken bond method
• Models for heterogeneous nucleation at grain boundary/edge/corner or at dislocations
• Evolution of aspect ratio to describe the morphology evolution of precipitate
• Strengthen model to consider multiple particle size groups with weak/strong pair coupling or bowing mechanisms
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Workspace and Projects
Create a new workspace, or Add a new project Select the PanPrecipitation module
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Thank You for Your Attention!
http://www.computherm.com
This work was financially supported by the USAF through SBIR, STTR and MAI
projects