SelfScan: Sample studies, systems development and data ... _Webinar_AH.pdf · Long Range Ultrasonic...

transcript

SelfScan: Sample studies, systems

development and data collection

Alex Haig

19th April 2012

SelfScan

• Neural Net based defect detection system

using Long Range Ultrasonic Testing

(LRUT) technology for Aircraft Structure

Health Monitoring

• Analysis of ultrasonic waves in target structures

• Transducer selection

• Systems development/automation

• Data collection

• Neural network development

• Neural network assessment

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

Fuselage Panel

Load baring structure

Example Schematic

• Simplified geometry (as provided by

• Defect to monitor – crack growth in the

curved region

Modelling

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

Transducer Testing

• Compression Elements

• Smart Materials

– 13 PZT composite

elements

– Macro Fibre Composites

• Plant Integrity Shear

Transducers

Transducer Evaluation

Test location

Measurements

Performance by:

• Wave mode

• Frequency

Aluminium

Example Result

• 13 PZT composite

• Thickness resonant

at 100 kHz

• Force coupled

• 100kHz narrow band

signal

Evaluation on samples

Defect detection

region of

interest

Monolithic

piezoceramic in-

plane shear

transducer

10 mm thick

aluminium

sample without

defect

deposited thin

matt white

powder coating

Photograph of Structural Plate Sample

View From Vibrometer Head Position

Photograph of Scanning

Vibrometer

composite

compression

transducer

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)

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

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

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

Variability with Temperature

Example

Data for

100kHz

Component 2: Variability with

Temperature

Data for

100kHz

(Envelopes)

Defect location

Defect detection target

• End-user defined target for defect

detection – 5 mm crack (see fig)

• Ideally, 2 mm

Lab tests

- Transducers coupled

in allocated

accessible region

- Desired ultrasonic

waves confirmed in

region of interest

Lab tests

• Data collection

from four samples

over many weeks

• Defects introduced

Pulse-echo tests

• Signals captured

were complex

• Variation was

observed with the

presence of a

defect

• Difficult to

differentiate from

other changes

Change in amplitude at t = 1 ms

Variability with temperature

30 °C 40 °C

Case 2

Case 2 : Variability with Temperature

Signal

responses for

a range of

frequencies at

receiver

location 2

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.

Case 2: transmission efficiency tests

Example: signal

responses for a

range of

frequencies at

receiver location

Further work

required for high

frequency

solution

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

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

Thick Sample Trials

Region For

Potential Crack

With 1mm Notch

Pair 3 Pair 2 Pair 4 Pair 1

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

Frequency

Receiver

Amplifier

High Frequency

Transmitter

Amplifier

High Frequency

Power Supply

Source

Thick Sample Trials

• A crack defect was slowly grown in a sample

• The defect size was monitored with manual

• Meanwhile, ultrasonic data was automatically

collected at regular intervals

Thick Sample Trials

Die indicating

Thick Sample Trials

0 500 1000 1500 2000

Fatigue, thousand cycles

Long Range Ultrasonic Data Collection Over Fatigue Test

Ultrasonic Testing

Signal Bank Collected

0mm Crack 5mm Crack

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.

• 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.

• 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

Thank You

SelfScan: Sample studies, systems development and data ... _Webinar_AH.pdf · Long Range Ultrasonic...

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