Numerical and Experimental Investigation of Laser-induced Optoacoustic Wave Propagation for Damage Detection

Student：Chen Liu

Dr. Yongming Liu, Chair

Arizona State University

July 15, 2016

Outline

Background

Experimental investigation of laser-induced optoacoustic wave propagation Setup

Experimental process

Parametric study

Numerical investigation using finite element method Finite element model

Laser-induced loading function

Results analysis and comparison

Wave mode study

Conclusions and future work

2

Background -1

• Cracks exist in various different kinds of materials

• Cracks in rail, aircraft and heavy-duty machines are

not easy to be detected

Damage detection methods

Traditional method

Non-destructive(NDE)

evaluation methods

Attaching sensors

Wave-based

methodLaser-

induced optoacoustic

wave

Lamb wave

Acoustic wave

…Laser---sensor

Sensor---laser

Laser---laser

Figure 1 Crack in aircraft component[1]

3[1] Holly Jordan, “Sensor Provide Real-Time Data on Aircraft Component Fatigue,”

http://www.wpafb.af.mil/news/story.asp?id=123306759.

crack

4

Laser-induced optoacoustic wave propagation for damage detection, it is applied

on laser scanning nondestructive testing(NDT), which is used to verify the

structural integrity of materials. Laser beam that generate ultrasound are

scanned across the testing surface.

Works by a mechanism called thermo-elastic expansion

-- directly shoot at the surface

-- laser beam is absorbed

-- create a localized heating

-- induce a stress wave

Figure 2 Laser scanning schemes for ultrasonic

wave-field image construction[2]

[2] Y.-K. An, B. Park, and H. Sohn, “Complete noncontact laser ultrasonic imaging for automated crack visualization in a plate,” Smart Mater. Struct., vol. 22, no. 2, p. 025022, 2013.

Background -2

• In most studies[2][3] YAG laser is used as emission terminal

• Few studies[4] set the emission terminal as high frequency

MOPA(Master Oscillator Power Amplifier) laser and receiving terminal is

attached piezo sensor

Laser type Power density Repetition rate

MOPA laser Low High(1.6kHz-1000kHz)

YAG laser high Low (10Hz-20Hz)

5

[2] Y.-K. An, B. Park, and H. Sohn, “Complete noncontact laser ultrasonic imaging for automated crack visualization in a plate,” Smart Mater. Struct., vol. 22, no. 2, p. 025022, 2013.[3] R. F. Anastasi, A. D. Friedman, and M. K. Hinders, “APPLICATION OF LASER BASED ULTRASOUND FOR NDE OF DAMAGE IN THICK STITCHED COMPOSITES,” 1997.[4] S. Yashiro, N. Toyama, J. Takatsubo, and T. Shiraishi, “Laser-Generation Based Imaging of Ultrasonic Wave Propagation on Welded Steel Plates and Its Application to Defect Detection,” Mater. Trans., vol. 51, no. 11, pp. 2069–2075, 2010.

Background -3

Numerical computational techniques have been developed for wave

propagation studies.

• Define the whole model as thermal-mechanical coupled model[5] , apply heat

flux on the laser hitting point.

• Define the laser hitting point as thermal-mechanical coupled and the rest of the

model as pure mechanical model, apply heat pulse on the coupled elements.

6[5] A. Soni and R. K. Patel, “Two Dimensional Finite Element Modeling Of Single Pulse Laser Drilling,” vol. 2, no. 3, pp. 389–396, 2013.

Background – numerical simulation

7

Motivation and novelty

• Development of a new laser source-induced acoustic wave

propagation

• Development of a new efficient multi-physics simulation

framework for mechanism investigation of the proposed

experimental setup

• Perform parametric study (both experimentally and

numerically) for optimized parameter determination

• Demonstration for structural components of local property

change detection

Experimental investigation of laser-induced

optoacoustic wave propagation

8

9

The objective of this test is to get the time of arrival between signals.

Laser beam is the emission terminal and sensor is the receiving terminal.

• PC controls function generator

and laser machine

• Oscilloscope collects the signals

received from sensor and

synchronized from function

generator

Figure 3 Schematic diagram for the test

Experimental objective

10

Setup

• Fiber Laser (IPG YLPM-1-4X200-20-

20)

• Laser head

• Aluminum plate (4.375” X 12” X

0.0625”)

• Oscilloscope (Tektronix DPO 2024B)

• Function generator (RIGOL

DG1022)

• PC

• Remote control (IPG YLP-RC-USB)

Function generator control laser

machine to form a pulse signal, and

set the pulse durationFigure 4 Experimental setup

Laser head

Fiber laser

Aluminum plate

Remote control

Oscilloscope

Function generator

PC

11

Experimental process

1. Horizontal path test

- change the horizontal position of the laser beam

2. Effect of number of magnets test

- change the number of magnets

3. Vertical path test

- change the vertical position of the laser beam

1

2

3

4

5

1 2 3 4 5

9.525cm9.525cm

Figure 5 (a) Specimen of single path test (b) Specimen of multiple path test

(a) (b)

Parametric study

Figure 6 (a)There is no signal received from the sensor (b) There is signal received but not very clear (c) There is signal received (d) There is signal received and it is the clearest signal

(a) (b) (c) (d)

12

13

• Number of pulse: 1• Laser pulse duration: 200ns• Laser pulse period: 10μs (100kHz)

• Laser fire duration: 0.5ms (1000Hz) • Sampling rate: 62.5 MHz

Figure 7 Parameter setup

Figure 8 Signal plotted by raw data

Parameter setup

Figure 9 Filtered signal

Butterworth Bandpass filter:

Laser signal: Highpass: 1500Hz

Lowpass: 500Hz

Received signal: Highpass: 120kHz

Lowpass: 80kHz

14

Signal processing - 1

15

Hilbert-Huang transformation

Hilbert-Huang transformation

Empirical mode decomposition

Hilbert spectral analysis

Intrinsic mode functions

Extract characteristics

Hilbert-Huang transformation is used to analyze nonlinear and non-stationary signals.

Widely used in damage detection fields:

• Damage in plate structures[6]

• Digital image splicing detection[7]

• Damage identification of a benchmark buildings[8]

[6] A. I. Zemmour, “The Hilbert-Huang Transform for Damage Detection in Plate Structures,” 2006.[7] D. Fu, Y. Q. Shi, and W. Su, “Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4283 LNCS, pp. 177–187, 2006.[8] S. Lin, J. N. Yang, and L. Zhou, “Damage identification of a benchmark building for structural health monitoring,” Smart Mater. Struct., vol. 14, pp. S162–S169, 2005.

16

Figure 10 Decomposed received signal

Hilbert-Huang transformation to decompose the filtered signal to different modes

Signal processing - 2

Numerical investigation using finite

element method (FEM)

17

Finite element model

Figure 11 (a) Finite element model in front view (b) 3D finite element (c) FE model in side view

(a) (b) (c)

Define the material properties of laser beam area and test plate separately:

Laser hitting point: Linear isotropic; Thermal expansion coefficient; Density

Rest part of the specimen: Linear isotropic; Density18

Magnets

Laser hittingpoints

Piezosensor

Magnets

Figure 12 Whole model after meshing Figure 13 Meshed model of laser hitting point and magnet

Element edge size:

Laser hitting point --- 0.0005m

Rest of the model --- 0.001m

19

Magnets

Laser hitting point

Meshed model

Laser-induced loading function

Temperature = 273.15𝐾 0 𝑚𝑠 < 𝑡 < 0.1𝑚𝑠274.35K 0.1𝑚𝑠 < 𝑡 < 0.6𝑚𝑠273.15𝐾 0.6𝑚𝑠 < 𝑡 < 1𝑚𝑠

Figure 14 Loading function

Hypothesis for this simplified loading function:

• Synchronize with laser firing profile and light

speed is ignored

• The opto-thermal conversion efficiency is

assumed to be a constant

Boundary condition:

Fix the upper side of the specimen

All DOFs = 0

Temperature applied on the specimen is estimated as 1.2K[5] during 0.5ms

273

273.2

273.4

273.6

273.8

274

274.2

274.4

274.6

0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012

Tem

pera

ture

(K)

Time(s)

20[9] S. Safdar, L. Li, M. A. Sheikh, and Zhu Liu, “Finite element simulation of laser tube bending: Effect of scanning schemes on bending angle, distortions and stress distribution,” Opt. Laser Technol., vol. 39, no. 6, pp. 1101–1110, 2007.

Figure 17 Time history profile for von Mises stress

Blue line ----laser signal which starts from 0.1ms to 0.6ms

Purple line ----receiving signal from the piezo sensor 21

Time of arrival

Time history profile for simulation

Results analysis and comparison

22

1.Vertical path test

2.00E-042.50E-04

3.00E-04

3.50E-044.00E-04

1.87E-04

2.86E-04

3.25E-04

3.24E-04

4.03E-04

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

4.00E-04

4.50E-04

0 1 2 3 4 5 6

TO

F(s

)

Vertical position

simulation test

4.76E+02

4.83E+02 4.87E+02

4.90E+02

4.92E+025.21E+02

4.91E+02

4.50E+02

5.31E+02

4.90E+02

0.00E+00

1.00E+02

2.00E+02

3.00E+02

4.00E+02

5.00E+02

6.00E+02

0 1 2 3 4 5 6

speed(m

/s)

Vertical postion

Simulation test

Figure 18 (a)Time of arrival in vertical move (b) Speed in vertical move

(b)(a)

23

Results and comparison - 1

2. Horizontal path test

2.00E-04

2.50E-04

2.00E-04

2.50E-04

1.50E-04

1.87E-04

1.69E-04

2.35E-04

2.41E-04

2.03E-04

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

0 2 4 6

Tim

e o

f Arr

ival(s)

Horizontal position

Simulation test

4.76E+02

3.94E+02

5.08E+02

4.32E+02

7.62E+02

5.21E+02

5.84E+02

4.33E+02

4.48E+02 6.28E+02

0.00E+00

1.00E+02

2.00E+02

3.00E+02

4.00E+02

5.00E+02

6.00E+02

7.00E+02

8.00E+02

9.00E+02

0 1 2 3 4 5 6

Speed(m

/s)

Horizontal position

Simulation test

Figure 19 (a)Time of arrival in horizontal move (b) Speed in horizontal move

(b)(a)

24

Results and comparison - 2

3. Effect of number of magnets test

3.00E-05

2.00E-04

2.50E-04

2.80E-04

3.00E-04

4.92E-051.87E-04

2.40E-04

3.02E-04

4.59E-04

0.00E+00

5.00E-05

1.00E-04

1.50E-04

2.00E-04

2.50E-04

3.00E-04

3.50E-04

4.00E-04

4.50E-04

5.00E-04

0 1 2 3 4 5 6

Tim

e o

f arr

ival(s)

Number of magnets

simulation test

3.18E+03

4.76E+02

3.81E+02 3.40E+02 3.18E+02

2.10E+03

5.21E+02

4.07E+023.18E+02 2.08E+020.00E+00

5.00E+02

1.00E+03

1.50E+03

2.00E+03

2.50E+03

3.00E+03

3.50E+03

0 1 2 3 4 5 6

Speed (

m/s

)

Number of magnets

simulation test

Figure 20 (a)Time of arrival with increasing magnets (b) Speed in horizontal move with increasing magnets

(b)(a)

25

Results and comparison - 3

26

Wave mode study

Figure 16 Extracted x, y displacement of

the finite element

(a) schematic representation of the Lamb

wave mode and

(b) the extracted x,y displacement[10]

[10] T. Peng, a. Saxena, K. Goebel, Y. Xiang, and Y. Liu, “Integrated experimental and numerical investigation for fatigue damage diagnosis in composite plates,” Struct. Heal. Monit., vol. 13, no. 5, pp. 537–547, 2014.

Bottom node ——Top node ——

Bottom node ——Top node ——

Figure 15 Extracted (a) x displacement (b) y displacement of the simulation

(a) (b)

Dominant wave mode —— S0 mode (symmetric mode)

27

Conclusions and future work - 1

Conclusions

This research developed an integrated experimental and simulation

framework for a novel optoacoustic wave propagation for damage detection.

• Using a low-power high frequency MOPA laser is feasible

• Local mass change has a large influence on wave propagation; implementation for

small damage detection

• Butterworth bandpass filter and Hilbert-Huang transformation can extract the time

of arrival information accurately

• Laser firing parameters have significant effect on the generated signals; A 100kHz

pulse frequency with 200ns pulse duration can generate clear acoustic wave signals

Future work

• Noise reduction is required for more accurate analysis

• Automated scanning for large area detection

• Inverse imaging reconstruction for the detection of damage shape and

location

• Laser-induced optoacoustic wave propagation for damage detection in

composite materials

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Conclusions and future work - 2

29

Acknowledgements

• My deepest gratitude is to my thesis advisor Dr. Yongming Liu for

the continuous support of my study and research throughout my

Master’s period. His patience and guidance helped me to overcome a

lot of difficulties and finish this thesis.

• I would also like to thank my committee members: Dr. Liping Wang

and Dr. Yang Jiao, for their insightful comments and questions.

• Last but not the least, my sincere thank goes to my fellow labmates

in Arizona State University, especially to Tishun Peng, for

enlightening me with the help of the experiment and simulation.

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Thank You !

Any questions?