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SelfScan: Sample studies, systems
development and data collection
Alex Haig
19th April 2012
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SelfScan
• Neural Net based defect detection system
using Long Range Ultrasonic Testing
(LRUT) technology for Aircraft Structure
Health Monitoring
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Tasks
• Analysis of ultrasonic waves in target structures
• Transducer selection
• Systems development/automation
• Data collection
• Neural network development
• Neural network assessment
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Aircraft Components
• Two main classes of critical structure
component identified
– Aluminium skin panel
• Complex due to rivets and layers
• Approximately 2 mm to 5 mm thick
– Load bearing components
• 10 to 20 mm thick
• Aluminium or steel
• Complex shape
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Fuselage Panel
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Load baring structure
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Example Schematic
• Simplified geometry (as provided by
NDTE)
• Defect to monitor – crack growth in the
curved region
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Modelling
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Modelling Indications
• Defect sensitivity expected to be possible
• Success was most likely in the region of
500 kHz for the thick samples
– This increased the demand on the systems
development
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Transducer Testing
• Compression Elements
• Smart Materials
– 13 PZT composite
elements
– Macro Fibre Composites
• Plant Integrity Shear
Transducers
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Transducer Evaluation
Test location
Measurements
made
Performance by:
• Wave mode
• Frequency
Aluminium
plate
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Example Result
• 13 PZT composite
• Thickness resonant
at 100 kHz
• Force coupled
• 100kHz narrow band
signal
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Evaluation on samples
Defect detection
region of
interest
Monolithic
piezoceramic in-
plane shear
transducer
10 mm thick
aluminium
sample without
defect
Spray
deposited thin
matt white
powder coating
Photograph of Structural Plate Sample
View From Vibrometer Head Position
Photograph of Scanning
Vibrometer
1-3
composite
compression
transducer
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Thick Sample Trials
Point Disturbance In the Horizontal Axis
1-3 Composite
Transducer
Side Wall
Surface Wave
Defect Detection
Region Of Interest
Finite Element Analysis
(In-plane Stress Magnitude)
Vibrometry Experiment
(Out-of-plane Surface Velocity)
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Transducer Summary
• PI shear transducers and
13 Composites used to
generate desired surface
waves on thick sample at
wide range of
frequencies
• MFC transducers used to
generate plate waves in
fuselage panel
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Required Systems
• Large data was required
– Automated data collection
was achieved
– A systems were developed
to collect data in the range
of 20 kHz to 700 kHz
• System components
– Computer
– DAC & ADC
– Amplification
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Temperature Monitoring
• Four identical samples produced
• Sensors installed the same way on each
• Samples placed in thermal-cycling
chamber
• Samples exposed to a program of
temperature changes
• Data collected over time
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Variability with Temperature
Example
Data for
100kHz
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Component 2: Variability with
Temperature
Data for
100kHz
(Envelopes)
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Defect location
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Defect detection target
• End-user defined target for defect
detection – 5 mm crack (see fig)
• Ideally, 2 mm
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Lab tests
- Transducers coupled
in allocated
accessible region
- Desired ultrasonic
waves confirmed in
region of interest
Tx
1
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Lab tests
• Data collection
from four samples
over many weeks
• Defects introduced
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Pulse-echo tests
• Signals captured
were complex
• Variation was
observed with the
presence of a
defect
• Difficult to
differentiate from
other changes
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Change in amplitude at t = 1 ms
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Variability with temperature
30 °C 40 °C
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Case 2
Tx
Rx2
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Case 2 : Variability with Temperature
Signal
responses for
a range of
frequencies at
receiver
location 2
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Defect introduction
• Addition of defect changes the profile of
the received signal
• Since it isn’t easy to identify the individual
modes, neural network is being used.
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Case 2: transmission efficiency tests
Example: signal
responses for a
range of
frequencies at
receiver location
2
Further work
required for high
frequency
solution
20
-1
20
kH
Z
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Fatigue Experiment
• Fuselage sample and thick structural part
tested with artificial saw cut defects
• Requirement for “real” defect
• Fatigue experiment devised
• Sample fatigued
– Monitoring conducted with developed system
– NDT used to verify defect progress
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Thick Sample Trials
• Fatigue crack growth up to 5mm Pairs 1 & 2
Pair 4
Region For
Potential Crack
Pair 3
Monolithic in-plane
shear transducer
Receiver
Monolithic in-plane
shear transducer
Transmitter
Monolithic in-plane
shear transducer
Receiver
Monolithic in-plane
shear transducer
Transmitter
Co-located 1-3
composite transmitter
and receivers
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Thick Sample Trials
Region For
Potential Crack
With 1mm Notch
Pair 3 Pair 2 Pair 4 Pair 1
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Thick Sample Trials
Fatigue machine with three point bending setup
Sample with crack
initiation notch and
fixed transducer
Low frequency
transmitter/receiver
and controlling
laptop
High frequency control,
arbitrary wave form
generator and received
signal digitiser
High
Frequency
Receiver
Amplifier
High Frequency
Transmitter
Amplifier
High Frequency
Power Supply
Board
Power
Source
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Thick Sample Trials
• A crack defect was slowly grown in a sample
• The defect size was monitored with manual
NDT
• Meanwhile, ultrasonic data was automatically
collected at regular intervals
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Thick Sample Trials
Notch
Die indicating
crack
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Thick Sample Trials
0
1
2
3
4
5
6
0 500 1000 1500 2000
Su
rfa
ce
Cra
ck
Len
gth
(m
m)
Fatigue, thousand cycles
Long Range Ultrasonic Data Collection Over Fatigue Test
Ultrasonic Testing
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Signal Bank Collected
0mm Crack 5mm Crack
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Technique Development
• Neural Network system – Pavlos Stavrou, CERETETH
– Deliverable document received
– Lab trial for beginning of March
• What is an neural network ? – An artificial neural network is an information
processing system whose structure and functionality is inspired by biological nervous systems. Its key structural element is the neuron which is defined by its inputs, output and activation function.
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Technique Development
• What are the advantages of using neural networks ? – Distributed/Parallel information processing
– Robustness
– Training
• How can NNs aid in defect detection ? – Since LRU signals acquired from structures with
complex geometry are very complex, we need the processing and training capability of neural networks to detect even the finest differences in signals in order to classify them accurately.
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Technique Development
• NN for detecting defects in fatigued sample
– Feature Vector : (Correlation with reference non-
defective signal, Covariance with reference non-
defective signal, Dominant Pulse Power)
– Accuracy
• 93 % when with 0-2mm cracks render the sample
defective
• 100% when a sample is considered defective when
crack is over 2.5mm
• Conclusion
– The developed NN can successfully detect
defective specimens with crack sizes over 2.5mm
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Thank You