Nondestructive testing and evaluation of composites by non-invasive IR imaging techniques

Ravibabu Mulaveesalaa,c*, Juned.A.Siddiquic, Vanita aroraa, V.S.Ghalib,c, Amarnath.Muniyappac and

Masahiro Takeid a Department of Electrical Engineering., Indian Institute of Technology Ropar, India;

b Signal Processing Research Group, K L University, Green Fields, Vaddeswaram, Guntur (Dist.), Andhra Pradesh, 522 502, India.;

c InfraRed Imaging Laboratory (IRIL), Electronics and Communication Engineering Research Group, PDPM-Indian Institute of Information Technology Design and Manufacturing, Jabalpur,

Airport road, Khamaria (P.O), Jabalpur, India-482005; d Graduate School of Chiba University, Artificial System Science, 1-33 Yayoi Inage Chiba #263-

8522 Japan.

ABSTRACT

InfraRed Thermography (IRT) is one of the promising technique for non-destructive testing method for characterization of materials. This technique relies on evaluation of the surface temperature variations to detect the presence of surface and subsurface anomalies within the material. Due to its whole field and remote testing capabilities, IRT has gained significant importance in testing of Glass Fiber Reinforced Plastic (GFRP) materials. A GFRP sample with defects of various sizes at a given depth was inspected using non-stationary thermographic techniques. In order to highlight the defect detection capabilities of the proposed non-stationary schemes, a comparison has been made using matched excitation energy in frequency domain by taking signal to noise ratio into consideration.

Keywords: Fourier transform, non-destructive testing, matched excitation, correlation phase images, glass fiber reinforced polymer.

1. INTRODUCTION The use of Fiber Reinforced Polymer (FRP) composites continue to increase in aeronautical, mechanical and building industries due to their superior strength to weight ratio and high shock absorbing properties. But the integrity of composite materials may be impaired in several ways during manufacturing and application stage, reducing their in-service performance. It is therefore necessary to identify a reliable non-destructive testing method to inspect the composites without influencing their usefulness. Infrared thermography is one of the several NDT techniques which can be used to accomplish this task as it is non-contact, quick and can examine over a relatively large area. The principle of IRT is to apply thermal stimulus to the sample being inspected and to monitor the resulting temperature changes over the surface of the sample using an infrared camera. This captured temporal temperature response is processed for the detection of subsurface features 1-6.

Pulse thermography (PT) and Lock-in thermography (LT) are the well known active thermographic NDT techniques1-9. In PT1, the surface under inspection is heated with high peak power short duration pulse and the captured temporal thermal response is used for the assessment of subsurface defects. It is a fast and simple technique, but the requirement of high peak power sources for deeper subsurface analysis and influence of non-uniform emissivity and heating over the sample limits its applicability. LT3 uses a continuous mono-frequency sinusoidal heat stimulus with low peak power rather than pulse based thermographic methods3. In order to detect subsurface defects, phase analysis is carried out instead of magnitude as it allows deeper depth of probing and is less sensitive to surface artifacts (e.g. non-uniform heating or surface emissivity variations). The resolution for detection of defects lying at different depths with enough contrast demands repetition of the test at different frequencies, making it a time-consuming process. Pulsed phase thermography2 (PPT) makes use of similar experimental procedure as that of PT but the data analysis is performed using phase information obtained from Fast Fourier Transform (FFT) of the recorded thermal profile 2,4,5,6.

Thermosense: Thermal Infrared Applications XXXV, edited by Gregory R. Stockton, Fred P. Colbert, Proc. of SPIE Vol. 8705, 87050Y · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2018461

Proc. of SPIE Vol. 8705 87050Y-1

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/23/2013 Terms of Use: http://spiedl.org/terms

However, recently introduced non-stationary thermal wave imaging techniques7-9, 11-14 (Frequency Modulated Thermal Wave Imaging (FMTWI)5, digitized counterpart of FMTWI6 and Barker Coded Thermal Wave Imaging (BCTWI)7 overcome the limitations of traditional thermographic techniques. This paper presents the defect detection capabilities of these techniques based on the phase information obtained from correlation based pulse compression processing of mean removed recorded temporal thermal response over the sample11-13. In order to make a comparison of these techniques, the excitation energy in each scheme is matched.

2. THEORY Thermal waves generated for a given incident heat flux propagate into the solid material by diffusion and cause the temperature variations. The presence of subsurface defects modifies the heat flow resulting in a temperature contrast over the sample. The temperature response at any location over the sample surface for a given modulated excitation heat flux is obtained from Fourier one-dimensional heat conduction equation in the absence of any heat source and sink inside the test sample as follows10:

( , ) = ( , )

(1)

where T(x,t) is the temperature at a given spatial location x, at a time t and α is the thermal diffusivity of the sample. For a chosen thermographic technique, incident heat flux generates a similar thermal wave on the surface of the object. The resultant temperature distribution can be computed for a semi infinite sample from the boundary conditions (x = 0, T - stimulus based on the thermographic technique and x→∞, T - ambient temperature) as follows for the non-stationary modulation schemes.

2.1 FMTWI

In this technique, frequency modulated thermal waves with a suitable band of frequencies having equal energies are probed into the test sample in order to detect defects located at different depths with enough sensitivity. This further leads to enhance the depth resolution in a single experimental cycle.

The thermal response for FMTWI is obtained by solving equation (1) with the stimulus given by8: = (2)

where T0 is the peak temperature, f is the initial frequency, B is the bandwidth. τ is the total duration of excitation. The obtained solution for T(x,t) is as8:

( , ) = + + + 22 (3)

2.2 DFMTWI

This technique makes use of digital form of linear frequency modulated signal. The digitized chirp signal can be represented as6: = ∑ (−1) (4)

where = 2 (2 + 1)( + ) Temperature evolution over the surface of the object for the digitized chirp is obtained as7:

Proc. of SPIE Vol. 8705 87050Y-2

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/23/2013 Terms of Use: http://spiedl.org/terms

( , ) = ( ⁄ )∑ ( ) ( ) ⁄ ( ) ∑(−1) ⁄ (5)

where ( ) = ∑(−1) and = ( ⁄ )( ) ∑(−1) ( ) ⁄

2.3 BCTWI

In this technique, 7-bit Barker coded excitation is employed for detection of defects. This binary phase code provides minimum compression side lobes and is given as7: = ∑ (−1) ( − ) (6)

where ni = 0, 1, 2, 3; ai = 0, 3, 5, 6.

The temperature response over the surface of the object is expressed as7:

( , ) = √√ × ∑ (−1) ( − ) / ( ) (7)

3. CORRELATION BASED PULSE COMPRESSION ANALYSIS Pulse compression approach concentrates the applied energy into a narrow time slot and provides better resolution for detection of subsurface defects. This can be obtained by computing the cross-correlation coefficient between the temporal temperature distribution of each pixel with that of a chosen reference non-defective pixel over the surface of the sample [4]. By considering the thermal response of the reference non-defective pixel as Tref and the delayed response from each defective pixel as T(t-τ), the cross-correlation coefficient can be computed as11-14: ( ) = ( ) ( − ) (8)

This cross-correlation produces sinc shaped compressed pulses which may contribute in enhancing the contrast of the correlation images and provide advantages similar to that obtained with high peak power short duration heat stimulus.

3.1 TEST SAMPLE

The test sample employed in all the aforementioned techniques is a GFRP sample containing artificial square-shaped Teflon inserts as defects formed by a set of 3, 5, 7, 10 and 15 mm lateral dimensions, located at depths ranging from 0.1 to 0.9 mm in 0.2 mm increments from the front surface of the sample of 2 mm thickness, as shown in Figure 1. The size of the sample is 300×300 mm.

4. MATCHED ENERGY EXCITATION Non-stationary thermal wave excitation distributes the incident energy over a desired band of frequencies decided by the sample thermal properties and its thickness. In order to test the defect detection capabilities of the proposed schemes (FMTWI, DFMTWI and BCTWI) under matched energy excitation, a particular frequency component (as shown in Figure 2.) is extracted from the correlation profile obtained from the mean removed temporal temperature data. Energy at that frequency component is equated by adjusting the incident excitation power. The magnitude profiles obtained for the proposed excitation techniques are shown in Figure 2.

Proc. of SPIE Vol. 8705 87050Y-3

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/23/2013 Terms of Use: http://spiedl.org/terms

Wend na D:

Incredeing depth30cm

Front surface

o

0

O

Smm

5 m

7 m

lo.o

15 ma

.

O

o

O

a

o

o

Arb

b

b

O

o

O C

o TScm

o 1F5 cmi

1 mm

0.8 mm

0.2 mm

0.4 mm

0.8 mm

A

2mm

150

100

50

so

0

50

-100

Magnitude Response (dB)

-+- Barker-8-FM-e-DFM

71-p-.+"+.9F

ht+ 'Y4'++4-1.-1\*

+.+

-150o 0.01 0.02 0.03 0.04 0.05 0.06

Frequency (Hz)0.07 0.08 0.09 01

For the assessment of underlying defects, the mean rise in the temporal thermal profile of each pixel is removed using first-order polynomial fit and pulse compression using correlation function is performed on these mean removed profiles. For phase angle measurement, FFT is then employed.

Figure 1. Schematic layout of the experimental GFRP sample.

Figure 2. Frequency response of FM, DFM and Barker coded thermography.

The application of 1-dimensional FFT on the temporal thermal profile f(x) (where x is the index in the image sequence) of each pixel can be represented as2: ( ) = ∑ ( ) = ( ) + ( ) (9)

Where R(u) and I(u) are the real and imaginary components of F(u) respectively. Then the phase for each frequency component is computed as follows2: ( ) = tan ( )( ) (6)

Proc. of SPIE Vol. 8705 87050Y-4

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/23/2013 Terms of Use: http://spiedl.org/terms

200

180

160

5nT 140

enc 120

100

80

60

40

20

50 100 150Pixel number along breadth

200

180

160

5nT 140

enc 120

R

á 100E

80

60

40

20

50 100 150Pixel number along breadth

The corresponding phasegram number at the matched frequency for the chosen scheme (FMTWI, DFMTWI and Barker) is calculated from the relation: = (7)

where n = Number of the phasegram Fn= Frequency of the nth component in frequency domain N = Total number of samples Fs = Sampling frequency

5. RESULTS The correlation-phase images of the experimental GFRP sample acquired at matched frequency with different excitations are shown in Figure 3(a)-(c). The gray scale in each image has been adjusted to maximize the contrast between defective and sound area. It is clear from the obtained phase images that the contrast over the patches (detectability) at least depth (0.2 mm) is significantly higher than that located at deeper depths.

(a) FMTWI

(B) DFMTWI

Proc. of SPIE Vol. 8705 87050Y-5

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/23/2013 Terms of Use: http://spiedl.org/terms

200

180

160

80

60

40

20

50 100 150Pixel number along breadth

(c) BCTWI

Figure 3. Correlation-phase images obtained with FM, DFM and Barker excitation schemes at a matched frequency.

In order to compare the defect detection capability in correlation-phase images at matched frequency for different excitation schemes, signal to noise ratio of the defects is considered as a quantitative measure which can be obtained from the relation as follows13-14: ( ) = 20 log (9)

Figure 4. shows the measured SNR values for the defects lying at a depth of 0.2 mm from the front surface with lateral sizes: a-15 mm, b-10 mm, c-7 mm, d-5 mm and e-3 mm. These values are obtained using correlation-phase images at matched frequency for FMTWI, DFMTWI and BCTWI techniques. The results illustrate that for all of the defects under analysis, DFMTWI exhibits higher SNR than other two techniques in correlation-phase based analysis. The maximum difference in SNR values among all the techniques is found to be 20 dB.

Figure 4. Signal to Noise Ratios of the defects.

0

20

40

60

80

100

120

a b c d e

SNR

in d

B

Defect

Barker

FM

DFM

Proc. of SPIE Vol. 8705 87050Y-6

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/23/2013 Terms of Use: http://spiedl.org/terms

6. CONCLUSIONS This paper presents a comparative study on quantitative estimation of different non-stationary thermographic techniques with FM, DFM and Barker excitations schemes on GFRP sample containing Teflon square patches of varying sizes located at different depths. Capability of phase based detection scheme has been studied on recently proposed correlation based pulse compression under matched excitation. The performance of these techniques has been quantified using SNR parameter value for defects located at least depth.

REFERENCES

[1] Shepard, S. M., Lhota, J. R., Rubadeux, B. A., Wang, D. and Ahmed, T., "Reconstruction and enhancement of active thermographic image sequences," Opt. Eng., 42, 1337–1342 (2003).

[2] Maldague, X. and Marinetti, S., "Pulsed phase thermography," J. Appl. Phys., 79, 2694-2698 (1996). [3] Busse, G., Wu, D. and Karpen, W., "Thermal wave imaging with phase sensitive modulated thermography," J.

Appl. Phys., 71, 3962 (1992). [4] Pickering, S. and Almond, D., "Matched excitation energy comparison of the pulse and lock-in thermography

NDE techniques," NDT & E. Int., 41, (7), 501–509 (2008). [5] Mulaveesala, R. and Tuli, S, "Implementation of frequency-modulated thermal wave imaging for non-

destructive sub-surface defect detection," Insight: Non-Destructive Testing and Condition Monitoring, 47 (4), 206-208 (2005).

[6] Mulaveesala, R. and Tuli, S., "Digitized frequency modulated thermal wave imaging for non-destructive testing," Materials Evaluation., vol.63, No.10, 1046-50 (2005).

[7] Mulaveesala, R. and Ghali, V. S., "Coded Excitation for Infrared non-destructive testing of carbon fiber reinforced plastics," Rev. Sci. Instrum., 82, 054902; doi:10.1063/1.3594551 (2011).

[8] Mulaveesala, R., Panda, S.S.B., Mude, R.N. and Amarnath, M., "Non-destructive evaluation of concrete structures by non-stationary thermal wave imaging," Progress in Electromagnetics Research Letters., 32 , 39-48 (2012).

[9] Ghali, V. S. and Mulaveesala, R., "Quadratic frequency modulated thermal wave imaging for non-destructive testing," Progress In Electromagnetics Research M., 26, 11-22 (2012).

[10] Carslaw, H. S. and Jaeger, J. C., [Conduction of Heat in Solids], 2nd edition, Clarendon Press., Oxford (1986). [11] Mulaveesala, R., Jyani, Somayajulu, V. and Pushpraj, S., "Pulse compression approach to infrared

nondestructive characterization," Rev. Sci. Instrum., vol.79, No. 9 (2008). [12] Tabatabaei, N., Mandelis, A. and Amaechi, B. T., "Thermophotonic radar imaging: An emissivity-normalized

modality with advantages over phase lock-in thermography," Appl. Phys. Lett., 98, 163706 (2011). [13] Ghali, V. S. and Mulaveesala R., "Comparative data processing approaches for thermal wave imaging

techniques for non-destructive testing," Sensing and Imaging International., DOI 10.1007/s11220-011-0059-0 (2011).

[14] Mulaveesala, R., Ghali, V.S. and Arora, V., "Applications of non-stationary thermal wave imaging methods for characterization of fibre reinforced plastic materials," Electronics Letters., Vol. 49 (2) (2013).

Proc. of SPIE Vol. 8705 87050Y-7

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/23/2013 Terms of Use: http://spiedl.org/terms