The document discusses the application of deep learning, particularly convolutional neural networks (CNN), for the automatic classification of fracture surfaces in scanning electron microscope (SEM) images. It addresses current challenges in failure analysis due to declining expertise, and presents a method that enables high-quality classification of fracture types (ductile, brittle, fatigue) using a machine-based system. The conclusion emphasizes the potential of this technology to assist engineers in detecting fracture mechanics more effectively, although it requires a significant amount of data and computational resources.

1. Application of deep learning for automatic
classification of fracture surface’s SEM image.
Kenta Yamagiwa (kenta@yamagiwa.org)
Ph.D., Senior Researcher
Mechanics and System Safety Group,
National Institute for Occupational Safety and Health,
Japan
ICEFA08@Budapest, Hungary

2. Problems in Japan
• Number of people
– who are interested in
failure analysis
– can understand the
fracture surface
is decreasing.
• Qualitative analysis
– Low quality of fractography
– Wrong results lead wrong
countermeasures.
• Unstable operation.
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3. Supporting system of fractography
Current status
Human based
Future status
Machine based
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Needless to work.
More beer and life elevated.

4. SEM (Scanning Electron Microscopy) of fracture surface
Ductile (Dimple) Brittle (Facet) Fatigue (Striation)
Feature of fracture surface depends on fracture mechanics.
They are quite different as you can see.
Is it possible to classify automatically?
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5. Classification of Images
Before classification After classification
DOGS
CATS
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6. Deep Learning
• Huge number of pictures
– Ex. More than 1 million.
• Deep layer of neural
network
• Supervised learning /
Unsupervised learning
• Needless to consider
characteristic
values/vectors like
roughness parameters for
the classification.
https://www.xenonstack.com/
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7. [Un] Supervised Learning
Supervised
• Need to train
• Text (previously classified)
for train is required.
Unsupervised
• Needless to train
• Text is not required.
Test data
Training data
Drawing the line
(Classification)
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8. Convolution Neural Network (CNN)
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Convolution process (filters)
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2
4
0
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8
3
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9
image
number

9. Convolution Neural Network (CNN)
Fatigue
Brittle
Ductile
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Convolution process (filters)
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Prob. = 0.2
Prob. = 0.1
Prob. = 0.7

10. Conditions
• Library: Tensorflow (https://www.tensorflow.org) by Google
• Machine: Mac mini (3GHz, Intel core i7, Memory 8G)
– Too poor…
• Method: CNN / Supervised Learning
– Hyper parameters: Learning rate=10-5, Image size = 32
• Number of classification: 3 (ductile, brittle, fatigue)
• Number of training images: 3132 (with augmentation)
– ductile: 972
– brittle: 572
– fatigue: 1588
• Number of test images: 214
– Ductile: 14
– Brittle: 150
– Fatigue: 50
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11. Calculation process
1. Training process by using training images
2. Obtaining Training Score
3. Testing process by using test images
4. Obtaining Testing Score
5. Return to the Training.
– Filter values in CNN is adjusted to reduce Error values.
Training
Score
Err val.
(Cross Entropy)
Testing
Score
CNN
CNN
CNN
CNN
CNN
CNN
D
D
F
F
B
D
Training Process
CNN
CNN
CNN
D
D
B
Testing Process
Changing filter values in CNN
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Next Step

12. 0.5
0.6
0.7
0.8
0.9
1
1 10 100 1000
Score
Number of training
Training
Testing
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Result
Perfect score about 2k steps.
Finally, testing score is more than 0.9.

13. Result (correct classification)
Blue: Prob. of Fatigue
Green: Prob. of Brittle
Purple: Prob. of Ductile
The network judged the pic as fatigue
from the beginning.
Correct fracture mechanics Fracture mechanics detected by the network
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14. Result (correct classification)
Blue: Prob. of Fatigue
Green: Prob. of Brittle
Purple: Prob. of Ductile
Fatigue is negative from the beginning.
At first, the network judged the pic as brittle.
As the learning progressed, the network judged the pic as ductile.
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15. Result (misclassification)
Blue: Prob. of Fatigue
Green: Prob. of Brittle
Purple: Prob. of Ductile
Brittle is negative. Dithering over whether fatigue or ductile.
Different result
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16. Confusion matrix
Step = 1 Ductile Brittle Fatigue Trans-granular
Ductile 0 28 0 0
Brittle 0 150 0 0
Fatigue 0 56 0 0
Transgranular 0 14 0 0
Step = 500 Ductile Brittle Fatigue Trans-granular
Ductile 5 21 0 0
Brittle 1 149 0 0
Fatigue 0 0 56 0
Transgranular 0 0 0 14
Label of classification result
CorrectLabel
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17. Next step
• Appending other fracture
mechanics. Creep and
SCC(Inter/Trans granular).
• Application to the surfaces which
are difficult to estimate the load
(cat iron/aluminum)
• Application to non-metallic
materials (carbon composite).
• Automation for taking SEM
pictures.
Fatigue fracture surface of cast iron (FCD450)
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18. Conclusion
• Deep learning for automatic
classification of fracture surface’s
SEM image is well worked.
• It is enabled to support the
beginner engineer to make
decision for the detection of
fracture mechanics.
• More images and more deep
network, more calculation
resource are required.
ICEFA08@Budapest, Hungary
0.5
0.6
0.7
0.8
0.9
1
1 10 100 1000
Score
Number of training
Training
Testing

19. Acknowledgement
• ELIONIX (SEM manufacturer)
• Kiguchi Technics (Specimen)
• TDU students (Fatigue test)
• Thank you for paying your
attention.
ICEFA08@Budapest, Hungary