Ersan stndag

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DANSEEngineering Diffraction

• Ersan Üstündag
• Iowa State University

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Engineering Diffraction Scope

• Main objective Predict lifetime and performance
• Needed
• Accurate in-situ constitutive laws ? f(?)
• Measurement of service conditions residual and
  internal stress

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Engineering Diffraction Typical Experiment
Eng. Diffractometers SMARTS (LANSCE) ENGIN X
(ISIS) VULCAN (SNS)

• Typical engineering studies
• Deformation studies
• Residual stress mapping
• Texture analysis
• Phase transformations
• Challenges
• Small strains (0.1)
• Quick and accurate setup
• Efficient experiment design and execution
• Realistic pattern simulation
• Real time data analysis
• Realistic error propagation
• Comparison to mechanics models
• Microstructure simulation

Braggs law ? 2dsin?
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Engineering Diffraction Vision for DANSE

• Objectives
• Enable new science ( enhance the value of EngND
  output)
• Utilize beam time more efficiently
• Help enlarge user community
• Approach
• Experiment planning and setup (Task 7.1)
• Experiment design
• Optimum sample handling (SScanSS)
• Error analysis
• Mechanics modeling (FEA, SCM) (Task 7.2)
• Multiscale (continuum to mesoscale)
• Constitutive laws ? f(?)
• Experiment simulation (Task 7.3)
• Instrument simulation (pyre-mcstas)
• Microstructure simulation (forward / inverse
  analysis)
• Impact
• Re-definition of diffraction stress analysis

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Use Case Engineering Diffraction
ltincludegt
Reduce
NeXus file
User
ltincludegt
ABAQUS
?(?)
ltincludegt
I(TOF)
pyre-mcstas
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Activity Diagram FEA (Finite Element Analysis)
SNS
Laptop
Linux cluster
?(P)
NeXus
Archive
E1, Y1, E2, Y2
Rietveld
ABAQUS
a1(P), a2(P)
?1c, ?2c
?1(a1), ?2(a2)
Compare (fmin) Optimize (E1,Y1)
?1(?1), ?2(?2)
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Example BMG-W fiber composite

• Residual stresses
• Compression loading at SMARTS
• Experiments on 20 to 80 volume fraction of W
• Unit cell finite element model
• GSAS output for average elastic strain in W in
  the longitudinal direction

20 W/BMG
80 W/BMG
Reference B. Clausen et al., Scripta Mater. 49
(2003) p. 123
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Activity Diagram FEA (Finite Element Analysis)
?(P)
ltincludegt
E1, Y1, E2, Y2
ltincludegt
ABAQUS
experimental data
?1c, ?2c
?1(a1), ?2(a2)
Compare Optimize
ltincludegt
ltincludegt
leastsq
fmin
?1(?1), ?2(?2)
Easy utilization of various software components
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Constitutive Laws for W
Voce
Power-law
s1
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Constitutive Laws for W

• Voce plasticity more suitable
• Unrealistic power-law coefficient (47)
• Unequal weighting of data

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Optimization Results FEA

• Also studied
• Stability of algorithms
• Effects of initial values -gt neural network
  algorithms

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Optimization Results FEA
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Use Case Engineering Diffraction
ltincludegt
Reduce
NeXus file
User
ltincludegt
EPSC
?(?)
ltincludegt
I(TOF)
pyre-mcstas

• Self-consistent modeling (SCM)
• Estimate of lattice strain (hkl dependent)
• Study of deformation mechanisms

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SCM Code Flow
Post Process
Main Process
Pre Process
Set parameters
EPSC Run
Input Files
Output Files
Set data
Plot
appEpsc.py
collectData.py
setParameters.py
inputGenerate.py
plotEngine.py
runEpsc.py
parameters.py
materialsInput.py
readExpOutput.py
readModelOutput.py
textureInput.py
getDataModule.py
diffractionInput.py
interpolateFunction.py
processInput.py
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Optimizer
PlotController
interrupt()
plot()
Optimization Process
OptController
select() boolean set() float
Data
DataControl
ParameterControl
s, e total s, e hkl
select() boolean weigh() array
select() boolean set() float
collect() array
Pre Process
Post Process
ExpData
EpscOutput
EpscInput
smooth() float
interpolate() float
collect() file
EpscBlackBox
- P Parameters
Main Process
- main() epsc1 11.out
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Mechanical Loading of BaTiO3

• Time-of-flight neutron diffraction data from ISIS
• Complete diffraction patterns in one setting
• Simultaneous measurement of two strain directions

• Different data analysis approaches
• Single peak fitting natural candidate but some
  peaks vanish as the corresponding domain is
  depleted
• Rietveld crystallographic model fit to all
  peaks but results are ambiguous
• Constrained Rietveld multi-peak fitting, but
  accounting for strain anisotropy (rsca) most
  promising

M. Motahari et al. 2006
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Strain Anisotropy Analysis

• Desirable to perform multi-peak fitting (e.g. via
  Rietveld analysis) to improve counting
  statistics.
• Question How to account for strain anisotropy
  (hkl-dependent) due to elastic constants and
  inelastic deformation (e.g., domain switching)?
• Current approach for cubic crystals (in GSAS)
• ? is called rsca and is a refined parameter for
  some peak profiles.
• Works reasonably well in the elastic regime, but
  not beyond.
• Need to develop a rigorous approach to allow
  multi-peak fitting with peak weighting and peak
  shift dictated by mechanics modeling.

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Neural Network Analysis
Schematic Representation
Output Vector
Target Vector
Input Vector
H. Ceylan et al.
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Constitutive Laws for W and BMG
Voce
Power-law
s1
W
BMG
Input parameters (s0)BMG, nBMG, (s0)W, (s1)W,
(?0)W, (?1)W and ?T
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Neural Network Analysis
Approach

• 1200 runs of ABAQUS with random input parameters
• Training of ANN algorithms with 1100 datasets
• Use of 100 datasets as test case
• Use of experimental data for inverse analysis
• Prediction of optimum values of input parameters

• Successful training of ANN
• Strong influence for this parameter

L. Li et al.
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Neural Network Analysis
Sensitivity Studies

• Strong influence by parameters (s0)BMG, (s0)W,
  (s1)W and (?0)W
• Weak/no influence by parameters nBMG, (?1)W and
  ?T

L. Li et al.
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Neural Network Analysis
Result

• Use of experimental data for inverse analysis
• Prediction of optimum values of all 7 input
  parameters
• Previous analyses optimized only 3 parameters

L. Li et al.
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Engineering Diffraction Microstructure

• Si single crystals (0.7 and 20 mm thick)
• SMARTS data
• Double peaks due to dynamical diffraction

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Engineering Diffraction Microstructure

• Si single crystal (20 mm thick)
• ENGIN-X depth scan
• Data originates from surface layers

Critical question Transition between a single
crystal and polycrystal?
E. Ustundag et al., Appl. Phys. Lett. (2006), in
print
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Engineering Diffraction Team

• E. Üstündag, S. Y. Lee, S. M. Motahari (ISU)
• X. L. Wang (SNS) - VULCAN
• C. Noyan, L. Li (Columbia) microstructure
• M. Daymond (Queens U., ISIS) ENGIN X, SCM
• L. Edwards and J. James (Open U., U.K.) -
  SScanSS
• C. Aydiner, B. Clausen, D. Brown, M. Bourke
  (LANSCE) - SMARTS
• J. Richardson (IPNS)
• P. Dawson (Cornell) 3-D FEA
• H. Ceylan (ISU) - optimization

Member of EngND Executive Committee