My presentation at 3DMS2014 (Annecy, June 2014) about 3D segmentation of lamellar Ti alloy. This presents CHG filtering for lamellar orientation classification as well as grain boundary and crack segmentation. Unique info to characterise type of cracking occurring in such complex titanium microstructure! Download the presentation to get access to the animations!

1. 3D quantification
of trans- and inter-lamellar
fatigue crack in Ti alloy
L. Babout1, L. Jopek1, M. Preuss2
1Institute of Applied Computer Science, Lodz University of
Technology, Poland
2School of Materials, University of Manchester, UK
laurent.babout@p.lodz.pl, http://lbabout.iis.p.lodz.pl

2. Outline
• Introduction
• Experimental set-up
• Image processing steps
• Results
• Conclusion

3. Introduction (1/4)
• Myriads of applications of Ti alloy
• Different complex microstructures
• Need to understand short fatigue
crack-microstructure interaction
• X-ray microtomography: technique of
choice for mechanistic studies of
crack propagation

4. Introduction (2/4)
• Lamellar microstructure of (α+β) Ti alloy
• X-ray CT +EBSD study[1] shown crack
propagation influenced by
– β-gb misorientation
– α-lamellae/colonies favorably oriented for <a>
basal slip and <a> prismatic slip
[1] Birosca et al. Acta Mater., 2009, 57: 5834-5847

5. Introduction (3/4)
• α plates growth in the β phase Burgers
relationship: (100)β || (0002)α and [1-11]β || [11-20]α
α plates
(1-100)
(0002)
TL crack
IL crack

6. Introduction(4/4)
• What about proportion of trans-/inter-
lamellar cracking?
X-ray μCT / in situ fatigue
Image processing:
crack segmentation
α-lamellar/colony segmentation
(β-gb segmentation)
Local orientation calculation

7. Experimental set-up
• ME1230 (ID19 ESRF, back to 2006!)
– X-ray μCT: 0.7μm, 40 keV, phase contrast
– fatigue: 50 Hz, 0.5σ0.2, R=0.1
• 2 samples of Ti-6246 with notch
notch
β-gb
α-colony
β grain 1
β grain 2
crack1
crack2
27 kcycles

8. Image processing: α-colony
segmentation
• Existing method: local orientation map
based on image gradient (eigenvector
calculation)[1,2]
• Our method: directional filter bank (DFB)
using special structuring element
sensitive to surface-like objects[3]
[1] D. Jeulin, M. Moreaud, Im. Anal. Stereol., 2008, 27: 183-192.
[2] N. Vanderesse et al., Scripta Mater., 2008,58: 512-515.
[3] L. Babout, L. Jopek, M. Janaszewski, In 13th IAPR International Conference on Machine Vision
Applications. Kyoto. 2013.

9. CHG filter(1/2)
• Complementary of HourGlass
– tunable (default: r=5, θ=22.5°)
– Epanechnikov profile
– Default: 13 directions in <100>,<110> and
<111> directions
θ
n
r
[1 0 0]
[0 1 0]
[0 0 1]
[-1 1 1]
[1 1 1]
[1 -1 1]
[1 1 -1]
[1 0 -1]
[1 0 1]
[1 1 0]
[1 -1 0]
[0 1 1]
[ 0 1 -1]
•y
•x
•z

10. CHG filter (2/2)
• Lamellar classification (largest
response to DFB)
[1 1 1]
[1 1 0]
[1 1 -1]
[1 0 1]
[1 0 0]
[1 0 -1]
[1 -1 1]
[1 -1 0]
[-1 1 1]
[0 1 1]
[0 1 0]
[0 1 -1]
[0 0 1]
x
y
z

11. Image processing: β-gb
segmentation
• Challenging task
– local similarity of
α-layer/α-lamellae
– phase contrast “leaks”
• Multiple step
approach

12. Step 1: edge preserving smoothing
• Goal: vanish as much as α-lamellae as possible
while keeping sharp β-gb
• Possible methods:
– non linear diffusion
filtering (used in Amira)
– Mean shift smoothing[1]
• Does not fully solve
the problem
[1] Comaniciu et al., IEEE Trans.Pattern Anal.Mach.Intell., 2002, 17: 790-799.
NC>2950

13. Step 2: hole closing correction
Manual
segmentation
• Undersegmentation of β-gb leaves holes
• Can be filled using
Hole Closing Algorithm[1,2]
• Successfully used for
IGSCC in stainless steel[3]
[1] Z. Aktouf et al., Pattern Recogn. Lett., 2002, 23: 523-531.
[2] M. Janaszewski, et al., Pattern Recogn. Lett., 2011, 32: 2231-2238.
[3] L. Babout et al., Scripta Mater., 2011, 65: 131-134.
20 μm
crack
bridge

14. Step 3: CHG filtering +topological
criterion
• Numerous surface-like defects can be distinguished from
β-gb using CHG-DFB
• Size criterion and topological
criterion helps at removing
them
– based on topological numbers
– usually defects have more border
pts than 2D junction pts
i
s
t
h
m
u
Defect
After CHG-DFB

15. Image processing: crack
segmentation and image registration
• Crack segmented
from tomo. image at
t1 …
• … Superimposed
with microstructural
features from tomo.
image at t0
x
y
z
[1 1 1]
[1 1 0]
[1 1 -1]
[1 0 1]
[1 0 0]
[1 0 -1]
[1 -1 1]
[1 -1 0]
[-1 1 1]
[0 1 1]
[0 1 0]
[0 1 -1]
[0 0 1]
crack
notch
β-gb

16. Results (1/4)
• 2 samples – 2 scenarios (notch position)
• Crack orientation w.r.t. fatigue loading (z-axis)
– CHG classification + MV=max{λi}i=1,2,3V
Sample A
30°-40°
20°-30°
10°-20°
0°-10°
80°-90°
70°-80°
60°-70°
50°-60°
40°-50°
x
y
z
crack #2
crack #1
Sample B
30°-40°
20°-30°
10°-20°
0°-10°
80°-90°
70°-80°
60°-70°
50°-60°
40°-50°
z
x
y
β-gb1
β-gb2
β-gb3

17. Results (2/4)
• Cracks crossing colonies of ≠ orientations
– sA: crack1 not deflected by numerous colonies
– sB: strong deflection in same colony ([001]) near notch
x
y
z
[1 1 1]
[1 1 0]
[1 1 -1]
[1 0 1]
[1 0 0]
[1 0 -1]
[1 -1 1]
[1 -1 0]
[-1 1 1]
[0 1 1]
[0 1 0]
[0 1 -1]
[0 0 1]
[0 1 -1]
[-1 1 1] [1 1 1]
[1 1 -1]
[1 -1 1]
x
y
z
[0 1 1]
[0 0 1]
[0 1 1]
β-gb1
β-gb2
β-gb3

18. Results (3/4)
• Angle between crack and lamellar orientation
– lamellar orientation: 3D gradient map + MV=max{λi}i=1,2,3V
– inter- lamellar: angle < 30°
x
y
z
80°-90°
70°-80°
60°-70°
50°-60°
40°-50°
30°-40°
0°-30°
x
y
z
β-gb1
β-gb2
β-gb3

19. Results (4/4)
• Trans-lamellar cracking predominant
– ~60% larger than 70°
– colonies favorably oriented for basal <a> slip
• Non negligible
inter-lamellar
– 10-20%
– prismatic <a> slip
• Samples show
similar trends
• Comfort Birosca
et al. EBSD observations

20. Conclusions
 First 3D quantitative analysis of cracking type in
lamellar Ti Alloy using well-suited image
processing strategy
 Short fatigue crack propagation strongly driven
by the crystallographic nature of the colonies
when favorably oriented (i.e. basal/prismatic slip)
 Possible future work
 Test method on Birosca et al. tomography data
 DCT (above β transus) + IP + known variants  3D
crystallographic orientation of α phase
 Microstructure Faithful Modeling

21. Acknowledgements
• Polish National Research Centre (grant no:
6522/B/T02/2011/40)
• ME1230 team
– J.Y Buffiere (Quezac support )
– M. Karadge
– F. Garcia-Pastor