Magnetic Nanofluid Based Approach for Imaging Defects

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LECopyright © 2013 by American Scientific Publishers

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Printed in the United States of America

Journal of NanofluidsVol. 2, pp. 165–174, 2013(www.aspbs.com/jon)

Magnetic Nanofluid Based Approach forImaging DefectsV. Mahendran, R. Ancy Beautlin†, and John Philip∗

SMARTS, Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam 603102,Tamilnadu, India

Magnetic flux leakage is one of the most widely used electromagnetic nondestructive techniques to locate andassess discontinuities such as fatigue, cracks, corrosion, erosion, non metallic inclusion and abrasive wear inmagnetic components. Here, we report a new optical technique for imaging internal defects in materials usinga magnetically polarizable nanofluid (ferrofluid). The gradient in the magnetic flux lines around the defectiveregion induces the formation of one-dimensional array of nanoparticles along the field direction in the nanofluid,which produces a discernible gray level contrast in the ferrofluid. The grey level contrast produced by the leakedmagnetic flux is exploited to detect defects buried inside the sample. The normal and tangential components ofleakage magnetic fluxes have been modeled using a dipole approach for rectangular and cylindrical defects ofdifferent dimensions. The simulated results are validated experimentally. Various image processing techniquessuch as contrast stretching, image gradient, contrast limited adaptive histogram equalization and image profilingare used to obtain better detection sensitivity.

KEYWORDS: Nanofluids, Ferrofluid, Nondestructive Testing, Magnetic Flux Leakage, Sensor.

1. INTRODUCTION

Magnetic nanofluids are colloidal suspensions of super-

paramagnetic particles in a suitable carrier fluid.1 Mag-

netic nanofluids have attracted enormous interest in recent

years because of their field driven self-assembly and

tunable physical properties.2�3 The unique field driven

properties of magnetic nanofluids have been exploited

for several interesting applications such as electronics

cooling,4�5 sealant, dampers,1 optical filter,2 defect6�7 and

ion sensors8�9 etc. They have also been used as a

model system for fundamental studies such as struc-

tural transition under external stimuli,10–15 magneto rheo-

logical properties,16–26 tunable thermal conductivity4�27–34

etc. Using the magnetically tunable optical properties

of nanoemulsions, we have developed an optical probe

for defect detection in ferromagnetic materials.6�7 In this

work, we report a methodology to image defects in

ferromagnetic materials and components using magnetic

∗Author to whom correspondence should be addressed.

Email: [email protected]†Present address: Department of Electronics and Communication

Engineering, Ponjesly College of Engineering, Nagercoil 629003,

Tamilnadu, India.

Received: 13 April 2013

Accepted: 25 April 2013

nanofluids and the defect feature extraction and its quan-

tification using image enhancing methods.

Magnetic flux leakage (MFL) is one of the most

commonly used electromagnetic nondestructive techniques

(NDT).35–55 This technique has been extensively used as

an inspection technique in the petrochemical engineer-

ing, transportation, and energy industries.56 It has been

employed in on-line inspection of various components

such as inspection of rail tracks, pipelines, tubes, etc in

oil, gas transportation industries, nuclear power plants and

metal industries, to locate and assess the discontinuities

such as fatigue, cracks, corrosion, erosion, non metallic

inclusion and abrasive wear in ferro magnetic structures.

MFL technique is found to have a high degree of cer-

tainty in the detection of defects with better sensitivity.57

In MFL, the leakage magnetic field from a disconti-

nuity, due to magnetic permeability variations, is mea-

sured by various techniques such as Hall probes, flux

gate sensors, magnetodiodes, search coils, magnetic par-

ticles and Forester micro probes. New magnetic sensors

like SQUID, Giant magneto resistance (GMR), anisotropic

magneto resistance (AMR) etc. can detect even very weak

leakage flux resulting from the magnetic inhomogene-

ity, with high sensitivity.58�59 Laborious data processing

and complicated operating procedures are the major draw-

backs of conventional MFL techniques. Here, we demon-

strate a simple flux leakage sensor, using a magnetic

J. Nanofluids 2013, Vol. 2, No. 3 2169-432X/2013/2/165/010 doi:10.1166/jon.2013.1060 165

Magnetic Nanofluid Based Approach for Imaging Defects Mahendran et al.

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components. Further, we demonstrate some image pro-

cessing techniques that can provide better defect detection

sensitivity from the images.

2. MATERIALS AND METHODS

2.1. Magnetic Nanofluid Based Sensor

A fatty acid coated super paramagnetic magnetite (Fe3O4�nanoparticles of ∼10 nm dispersed in kerosene is used.

The magnetite nanoparticles used in our study are

synthesized by a simple co-precipitation technique.60–64

Without applied magnetic field, the particles are randomly

dispersed in the basefluid due to excess thermal energy.

Under applied field, the particles tend to align along the

field direction and form linear chains due to dipole–dipole

interaction attraction.

In a typical sensor head, magnetic nanofluid is sand-

wiched between two microscopic glass slides. A spacer

of 0.3 mm is used to maintain a uniform gap between

the slides. All the measurements were done at ambient

temperature. A mild steel plate of thickness 10 mm, with

rectangular and cylindrical slots of different dimensions

were used in this study. The specimens are magnetized by

an electromagnetic DC yoke. The thin film sensor head

is placed at a lift off distance and scanned on the sam-

ple surface. When the sensor head is over the defect-free

region, due to the uniform magnetic field, no magnetic flux

leakage occurs.

3. THEORETICAL CALCULATION OF MFLPROFILES FOR DIFFERENT DEFECTGEOMETRIES

Theoretical modeling of magnetic flux leakage signal

from various objects is essential for accurate detection

and sizing of defects. Zatsepin and Scherbinin65 mod-

eled the leakage fields of surface-breaking cracks by an

infinitely long strip of dipoles.42 Compared with the ana-

lytical approaches, the finite element method (FEM) is

a powerful tool for evaluating MFL signal because of

its flexibility in the simulations of defects with irregu-

lar geometrical shapes. The 2D and 3D FEM have been

widely used along with algorithms of wavelets, neutral net-

works and genetic algorithm to determine the defects from

MFL signals.56�66�67 The numerical models provide precise

results but are computationally expensive. On the contrary,

the analytical models and neutral networks are fast but less

precise due to various approximations used.68

In this work we have used analytical dipole model to

evaluate the experimental data. Zatsepin and Shcherbinin65

derived an expression for the leakage field of rectangu-

lar slots by assuming constant surface magnetic charge

density on the faces of the slot. The tangential (Hx� and

normal (Hy� components of leakage filed from rectangular

defect with depth Y0, width lg given by,6�65

Hx =Hg

�

(tan−1

�x+ lg/2�Y0

�x+ lg/2�2+y�y+Y0�

− tan−1�x− lg/2�Y0

�x− lg/2�2+y�y+Y0�

)(5)

Hy =Hg

2�ln

(�x+ lg/2�

2+ �y+Y0�2

�x− lg/2�2+ �y+Y0�

2

)(�x− lg/2�

2+y2

�x+ lg/2�2+y2

)

(6)

Where Hg is the field inside the defect for an applied field

Ha and is given by,

Hg =2Y0/lg +1

�1/��2Y0/lg +1Ha (7)

Here � is the permeability. Calculated tangential and nor-

mal components of leakage field by using Eqs. (5) and (6)

for a rectangular defect with 3 mm width, depth of 4 mm

is shown in Figure 1.

Figure 2 show the calculated tangential (Hx� and normal(Hy� components of leakage magnetic flux, for a cylindri-cal defect of diameter 10 mm and depth 6 mm. It can be

seen that the maximum leakage flux occurs at the center

of the defect for the tangential component and at the edges

for the normal component.

Figure 3 shows the tangential and normal components

of magnetic flux leakage for a rectangular defect with a

constant width of 3 mm and varying depths of 4, 8 and

12 mm. Figure 4 shows the tangential and normal compo-

nents of magnetic flux leakage for the rectangular defect

with a constant depth of 4 mm and varying widths 3, 6 and

8 mm. As the defect depth or width increases, the max-

imum value of the leakage magnetic flux (Hx, Hy� also

increases. Also, the peak position shifts with the defect

dimension. To map the leakage field along the specimen

surface, we have calculated the surface profiles of leakage

flux. The surface plots of leakage flux for a rectangular

(3×4 mm) and cylindrical defects are shown in Figures 5

and 6, respectively.

Figures 5 and 6 show the distinct MFL profiles for

defects of different morphologies and dimensions. Using

the surface plots, it is possible to locate the defects and

quantify the defect features and morphologies.

4. EXPERIMENTAL MEASUREMENT OF MFLPROFILES FROM DIFFERENT DEFECTGEOMETRIES

Two different Hall probes have been used for measuring

the tangential and normal components. For validation, a

mild steel plate, with cylindrical and rectangular slots of

different dimensions, is fabricated. After magnetizing the

sample, with a DC yoke, the MFL signal is measured using

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(b)(a)

Fig. 1. (a) Tangential and (b) normal components of leakage flux for a rectangular defect with 3 mm width, depth of 4 mm calculated using Zatsepin

and Scherbinin model.

(a) (b)

Fig. 2. (a) Tangential and (b) normal components of leakage flux for a cylindrical defect of diameter 10 mm, depth 6 mm calculated using Zatsepin

and Scherbinin model.

a Hall probe at a fixed lift-off distance of 1 mm. In order to

construct the surface leakage field profile, the Hall probe

is mounted on an automated xyz microcontroller based

scanner and the MFL data is collected by scanning the

surface at a constant lift off distance.

Figures 7(a) and 8(a) show the tangential components of

MFL signal from cylindrical and rectangular slots, respec-

tively where maximum occur at the centre of the defect.

On the contrary, the normal component of leakage field

shows zero value at the defect centre (Figs. 7(b) and 8(b))

and it peaks at the defect edges.

The theoretically calculated MFL profile is compared

with the experimental values (Fig. 9). The measured leak-

age profiles were in good agreement with the dipole model

calculations. The slight shift in the peak position observed

in the normal component is due to the larger size Hall

probe used in the measurement.

5. IMAGING OF DEFECT USING MAGNETICNANOFLUID

To image defects, specimen with artificial defect is first

magnetized with DC magnetic yoke. The magnetic fluid

based sensor is placed on the rear side of specimen at a

constant lift off distance of 1 mm. In the defective region,

leakage field will arise due to the change in magnetic per-

meability of the medium. Under the illumination with a

white light, images observed on the fluid sensor are cap-

tured using a CCD camera.

As mentioned earlier, application of magnetic field

causes ferrofluid to form one dimensional chain like struc-

tures. When the sensor is placed on the surface of the

magnetized specimen, the leakage fields from the defective

region causes a local ordering in the ferrofluid film. Since

the particle size is very small (smaller than the wavelength

of light used), no Bragg reflection is seen in the visible

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(a) (b)

Fig. 3. (a) Tangential and (b) normal components of magnetic flux leakage for a rectangular defect with a constant width of 3 mm and varying depths

of 4, 8 and 12 mm.

(a) (b)

Fig. 4. The (a) tangential and (b) normal components of magnetic flux leakage for the rectangular defects with a constant depth of 4 mm and varying

widths 3, 6 and 8 mm.

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Fig. 5. The surface profiles of (a) tangential and (b) normal components of magnetic flux leakage from the rectangular defect of dimensions 3×4 mm

(W×D).

(a) (b)

Fig. 6. The surface profiles of (a) tangential and (b) normal components of magnetic flux leakage from the cylindrical defect 10×6 mm (W×D).

–20 –10 0 10 200

100

200

300

400

Position (mm)

Hx

(Gau

ss)

Hy

(Gau

ss)

–20 –10 0 10 20–300

–200

–100

0

100

200

300

Position (mm)

(a) (b)

Fig. 7. Measured values of (a) tangential and (b) normal components of MFL from a cylindrical defect of 10 mm diameter and 6 mm depth.

region like the one seen in nanoemulsion based sensor.6�7

However, the local ordering leads to changes in the number

density distribution within the sensor area, which causes

a grey level variation in the nanofluid. Figure 10 shows

the photographic images of the sensor for three specimens

with different defect geometries i.e., rectangular defects of

3 mm width and 4 mm depth, 5 mm width 3 mm depth

and a cylindrical defect of 10 mm diameter, 6 mm depth.

Dotted lines in the photographic images show the defect

locations.

6. IMAGE PROCESSING APPROACHES FORBETTER VISUALIZATION OF DEFECTS

Though the defects are faintly visible on the photographic

images on the sensor, it was not possible to obtain the

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–20 –15 –10 –5 0 5 10 15 20–100

0

100

200

300

400H

x (G

auss

)

Hy

(Gau

ss)

Position (mm)

–20 –15 –10 –5 0 5 10 15 20

–300

–200

–100

0

100

200

300

Position (mm)

(a) (b)

Fig. 8. Measured values of (a) tangential and (b) normal components of MFL for a rectangular defect of 3 mm width and 4 mm depth.

Fig. 9. (a) Tangential and (b) normal component of leakage field for rectangular crack of (3×4) mm. Scattered points correspond to experimentally

measured values and solid line represent calculated values.

(a) (b) (c)

Fig. 10. Sensor output image of a rectangular defect of (Width x Depth) (a) 3×4 mm (b) 5×3 mm and (c) a cylindrical defect of depth 6 mm and

diameter 10 mm.

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(d)(c)

(b)(a)

Fig. 11. (a) Original sensor output image of rectangular defect of 3×4 mm and (b) contrast enhanced image of a, (c) original sensor out-

put image of a cylindrical defect of 10× 6 mm (d) contrast enhanced

image of (c).

defect morphology and orientation precisely because of

poor resolution. For quantitative defect features, different

image processing tools are attempted.

6.1. Contrast Stretching (Full-Scale HistogramStretch)

The contrast stretch, which is often called as full scale his-togram stretch (FSHS), is a simple linear point operation

that expands the image histogram to fill the entire available

grayscale range.69�70 The definition of the multiplicative

scaling (P) and additive offset factors (L) in the full-scale

histogram stretch depend on the image f . For f that has acompressed histogram with maximum gray level value Band minimum value A,

A=minn�f �n�� and B =max

n�f �n�� (8)

A linear operation of the form g�n� = Pf �n�+L, whereg�n� is the modified image and f �n� is the original image,maps gray levels A and B in the original image to a gray

levels 0 and K− 1 in the transformed image.69 This can

be expressed in following two linear equations:

PA+L= 0 (9)

PB+L= K−1 (10)

The two unknowns (P , L), are given by

P =(K−1

B−A

)(11)

L=−A

(K−1

B−A

)(12)

The overall full-scale histogram stretch is given by

g�n�=(K−1

B−A

)f �n�−A (13)

Figure 11(a) shows the original sensor output image of

rectangular defect of 3×4 mm and (b) contrast enhanced

image of a. Figure 11(c) shows the original sensor output

image of a cylindrical defect of 10× 6 mm and (d) the

contrast enhanced image of c. It is clear that the contrast

stretching operation can produce dramatic improvements

in the quality of an image suffering from a poor grayscale

distribution.

6.2. Contrast Limited Adaptive HistogramEqualization (CLAHE)

In Contrast Limited Adaptive Histogram Equalization

(CLAHE) procedure, the image is first divided into sev-

eral non-overlapping regions of almost equal sizes and

then the histogram of each region is calculated. Based on

the contrast expansion requirement, a limit for clipping

histograms is obtained. Each histogram is redistributed in

such a way that its height does not exceed the clip limit

‘�’ given by71

�= MN

L

(1+ �

100�Smax−1�

)(14)

Where ‘�’ is a clip factor, which is equal to zero the clip

limit becomes exactly equal to ‘�MN/L�’ and for �= 100,

the maximum allowable slope is ‘Smax’. Finally, cumulativedistribution functions (CDF) of the resultant contrast lim-

ited histograms are determined for grayscale images. The

pixels are mapped by linearly combining the results from

the mappings of the four nearest regions.71 The results

obtained on the raw image, after applying CLAHE and

contrast stretching to the histogram equalized image is

shown in Figure 12.

The Matlab function “adapthisteq()” enhances the con-

trast of the grayscale image by transforming the values

using CLAHE. CLAHE operates on small regions in the

image, called tiles, rather than the entire image. Each

tile’s contrast is enhanced and the neighboring tiles are

then combined by using bilinear interpolation to eliminate

artificially induced boundaries. The contrast can be lim-

ited to avoid amplification of inherent noise in the image.

The Figure 12, (a) shows the photographic image of the

raw sensor output from a rectangular defect and (b) the

grayscale image of a. The adaptive histogram equalized

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

Fig. 12. CLAHE processed images for rectangular defect of 3×4 (width×depth) mm.

0 50 100 150 200 2500

10

20

30

40

50

Distance (Pixel)

Inte

nsity

(a.

u)

(a)

(b)

Fig. 13. (a) sensor output image of a rectangular defect of 3 mm

width and 4 mm depth (b) RGB Intensity Profile along the line segment

shown in (a).

and contrast stretched image are shown in (c) and (d),

respectively. The processed images show the defect bound-

aries very clearly.

For quantitative estimation of defect width, we have

used line profiling on the images. The intensity profile of

an image is the set of intensity values taken from regularly

spaced points along a line segment shown on the image.

It gives the intensity of Red, Green, Blue (RGB) profiles

corresponding to each pixel along a line segment. From

the contrast in the grey level values, the defect width is

measured precisely.

Figure 13(a) shows the raw sensor output image of a

rectangular specimen with a defect of width and depth 3

and 4 mm, respectively (b) RGB Intensity Profile along

0 50 100 150 200 2500

20

40

60

80

100

Inte

nsity

(a.

u)

Distance (Pixel)

(a)

(b)

Fig. 14. (a) Sensor output image of a rectangular defect of width 5 mm

and depth 3 mm (b) RGB Intensity profile along the line segment

shown in (a).

the line segment shown in a. Figure 14(a) shows the image

seen on the sensor from a specimen with a rectangular slot

of width and depth 4 and 3 mm, respectively and (b) the

RGB intensity profiles across the image is shown by the

dotted line.

In all the cases, the defect dimensions were distinguish-

able from the contrast in the intensity values. Though

the raw sensor output images show poor boundaries, the

RGB profiles enable estimation of the defect location,

morphology and its dimensions precisely. The measured

dimensions from the images were within 10% of its actual

values, which could be improved further. These results

show that the quantification of defect dimensions and the

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morphologies are possible using suitable image processing

tools.

7. CONCLUSION

In conclusion, a new MFL sensing methodology has been

developed to detect defects in ferromagnetic components

using magnetic nanofluids. The leakage field from defects

induces nanochain formation and a gradient in the par-

ticle distribution within the sensor. The redistribution of

nanochains across the defect location facilitates the identi-

fication of defect location and morphology. For quantitative

estimation of defect features, different image processing

tools have been employed. Extracted defect dimensions

were within 10% of the actual value. The advantage of the

nanofluid sensor is that it is simple, in-expensive and covers

large area inspection in a short period of time.

Acknowledgments: Authors thank Dr. T. Jayakumar,

Dr. Baldev Raj, Shri. S. C. Chetal and Dr. P. R. Vasudeva

Rao for support and encouragements. J. P thanks Board

of Research Nuclear Sciences (BRNS) for a perspec-

tive research grant for advanced nanofluid development

program.

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Magnetic Nanofluid Based Approach for Imaging Defects

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