1. Undergraduate
Texas A&M- Corpus Christi
10/12/13

2. Outline
 Introduction to Concepts
 Fatigue
 Imaging Technologies
 Motivation
 Goals
 Application
 Experiment
 Set-up
 Calculations
 Data acquisition/analysis methods
 Discussion
 Acknowledgements

3. What is Fatigue?
 In materials science, fatigue is the
progressive and localized structural
damage that occurs when a material is
subjected to cyclic loading.
 The nominal maximum stress values are
less than the ultimate tensile stress limit,
and may be below the yield stress limit of
the material.

4. Introduction to Imaging
 Defect detection technologies have a wide range
of variances (cost, data, etc.) and an even bigger
range of applications.
 Reliability (precision, accuracy)
 Versatility (applications, speed, sensitivity)
 Cost (system + training + cost of runtime)

5. Imaging Technologies: Digital Camera
 Digital color imagers allow the capture of objects
and scenes visible by the naked eye (400-700nm)
(color, shape, size, texture), with little to no
distortion.
 Remotely, an inspector can point out other obvious
features to make reasonable predictions about the
image content of a part.

6. Imaging Technologies: Thermal Imager
 Thermal systems are most commonly used for
night vision, or mold inspection due to their acute
ability to detect infrared wavelengths (750nm-
1mm) (heat), invisible to the human eye.
 Infrared radiation primarily acts to set molecules
into vibration. This allows the easy detection of
hot spots, which are usually problem areas.

7. Imaging Technologies: Hyperspectral Imager
 Hyperspectral imaging systems can take images
over a wide range of wavelengths (400-1000nm)
and fuse them together with spatial data.
 Relative brightness for each pixel of data at each
wavelength is recorded, creating a spectral profile
or “finger print”
 Varied by elevation, texture, as well as changes in
material properties.

8. Motivation
 Since little specifics are known about fatigue, we
will analyze aluminum castings with three vision
systems to find defects.
 Fatigue is the most common form of mechanical
failure, fatigue loading will be used to validate
break points

9. Goals
 To create a repeatable process for
accurately analyzing parts and predicting
life remaining
 To determine the most effective of the
three vision technologies to
nondestructively detect defects.

10. Application
 This Projects simulates the defect
included on an as-cast part.
 This is applicable to industry where little
machinating is needed from forging a part
to delivery
 In theory, this process could be used
analyze major components of mechanical
assemblies (crank shafts, turbine blades,
hydrolic systems, aircraft wings)

11. Calculations
 s=(M*c)/I, M=(s*I)/c
 C=D/2
 I=p(D^4)/64
 D=.355’’
 Assuming s=100MPa
 M=F*DL
 N=10^5 cycles at 180rpm
 About 9 hours of cycling
Fatigue-Life Method

12. Experiment

13. Experiments: Sample preparation & Data
Acquisition: Digital Color Image Acquisition
 Lay each casting horizontally on the countertop
 Turn off overhead light and flash to reduce bright
spots
 Keep the camera distance from the castings constant
for each measurement (casting should be in focus, at
about 10 feet)
 Take images of both sides of each casting

14. Experiments: Sample preparation &
Data Acquisition: Hyperspectral Image
Acquisition
 Lay a casting on the auto-moving stage and adjust
vertical stage till casting in focus and in camera view
 Set system to reflection mode and set the stage to scan in
330 micron step size increments
 Set scan length to 22 mm and the integration time to 50
ms

15. Experiments: Sample preparation & Data
Acquisition: Thermal Image Acquisition
 Put castings in an oven set at 200-215
o
F
 After the castings reach temperature equilibrium, remove
castings from the oven two at a time quickly to avoid excessive
heat loss
 Put hot castings on a black foam board to reduce noise caused
by the hot castings’ exposure to the environment, increasing
background temperature
 Take measurements of the both sides of each casting, with
camera lens three feet away from the castings

16. Data Analysis Methods: Digital
 Visually Inspect the part for signs of fatigue, and
obvious defects

17. Data Analysis Methods: Thermal
 temperature range of interest was adjusted using a software
tool to a specific range to reduce noise in the images.
 Hot spots are easily identifiable as these hot spots represent
brighter areas in the image.
 Specific points were analyzed and picked as the hottest and
most substantial hot spots on the samples as points of
defects.

18. Data Analysis Methods: Hyper-spectral
 These figures demonstrates the spectral profiles
obtained from the region where the Moment
applied will be at a Maximum.
 These spectral profiles have similar shape, but
significant variations in reflectance values are
most likely due to changing surface finish.

19. Discussion (Validation In Progress)
 Due to the Destructive nature of
Fatigue testing and the small number
of custom samples, defects have not
been completely validated.
 Would like to extract more data before
breaking.
 Current Hyperspectral capabilities do
not allow for integration of round
surfaces

20. Predictions
 I predict that overloading will become the
greatest influence.
 X=e*cos(w*t)
 X’=-e*w*sin(w*t)
 X’’=-e*w^2*cos(w*t)
 Added force of X’’*M(mass of weight)
 M*g vs. e*w^2 (1mm of deflection could
triple load)

21. References
 M. Sharifzadeh, S. Alirezaee, R.
Amirfattahi, and S. Sadri, “Detection of
steel defect using the image processing
algorithms,” Proc. of the 12th IEEE
International Multitopic Conference, 2008,
p. 125-127.
 R. C. Juvinall, K. M. Marshek,
Fundamentals of Machine Component
Design, third edition

22. Acknowledgements
 This material is based upon work
supported by the National Science
Foundation und Grant No. 0960000
 Would like to thank LSAMP for
continued support

23. Questions?