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Applications of SRAS in NDE
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Obtaining Orientation
Calculated SAW velocity model: Nickel
Material elastic constants
SAW phase velocity
Crystallographic orientation
Literature
using SRAS
Orientation
information output
All linked
Obtaining Orientation
Obtain experimental data for many wave propagation directions
Obtaining Orientation
For each pixel compare the experimental data to the model of possible shapes and store the figure of merit.
(how well they match)
Obtaining Orientation
The location of highest figure of merit shows the crystallographic orientation for that pixel.
Repeat for all pixels in the image
This is a brute force search and is quite slow.
We use some tricks to speed this up in reality.
Orientation Images
Orientation properties determine material performance
Digital signal processing
Silicon solar cells
Orientation in 111 direction better for converting light into electricity hence the blue colour of the main cell
SRAS obtains similar values for orientation compared to gold standard technique electron back scatter diffraction (EBSD)
Digital signal processing
Also works for different crystal symmetries, in this case hexagonal.
EBSD has higher spatial resolution
Defect detection
Good Probe level
Good acoustic signal
No acoustic signal
Defect detection
Good Probe level
No acoustic signal
Good Probe level
‘modified’ acoustic signal
Defect detection for 3D printing
Powder-laser based methods useful for creating intricate geometry
Performance materials used, such as Titanium and Nickel-based alloys
Laser used to melt layers together to create parts
Weight saving and performance improvements possible
Figure: Lattice samples made with 3D printing
Defect detection
Previous layers are melted and affected during the following layers build
This can create defects away from the surface
If we only inspect the surface, then we need to scan every layer
Want to minimise inspection time
Inspected layers
Defect map
Defect detection
Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction, Zhao et al. Scientific & Reports, 7, (2017)
Defect detection
Design choices
Scan speed
Laser powder
Layer height
Hatch spacing
Processing choices
Contamination
Creep resistance
Refocus this to why AM is useful!
Video SLM and WAAM side by side
Defect detection
Initial studies highlighted shifts in velocity
Detecting mode converted ‘Lamb’ waves
Normal range for titanium
Exemplary detail
Defect detection
Polished samples: subsurface porosity quantified (validated by XCT)
SRAS optical
SRAS optical (magn.)
SRAS acoustic
SRAS acoustic (magn.)
XCT subsurface image
SEM surface image
Polished 1cm3 cube
Defect detection
Expected hatching pattern
Polished powder bed part with manufacturing defect (CM-247LC nickel super-alloy)
SRAS acoustic
Observed hatching pattern
SRAS optical
Digital signal processing
Grain boundary mapping
Allows statistics on the microstructure to be produced.
Mean size and standard deviation of grains
Distribution of grain sizes
Proportion of ‘fast’ and ‘slow’ grains etc.
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