- Email: [email protected]
Naked eye visualization of defects in ferromagnetic materials and components
NDT&E International 60 (2013) 100–109
Contents lists available at ScienceDirect
NDT&E International journal homepage: www.elsevier.com/locate/ndteint
Naked eye visualization of defects in ferromagnetic materials and components V. Mahendran, John Philip n SMARTS, Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamilnadu, India
art ic l e i nf o
a b s t r a c t
Article history: Received 13 May 2013 Received in revised form 23 July 2013 Accepted 26 July 2013 Available online 13 August 2013
We report a methodology to visualize defects by naked eye using magnetically polarizable nanoemulsions stabilized with different surface active species. The response of the nanoemulsions to the leaked magnetic flux from a defective region is exploited to locate the defect. In the presence of leaked magnetic flux, the nanofluid shows a visually perceivable color change due to the changes in the interparticle spacing within the self-assembled nano-arrays. We discuss the methodologies to detect defect morphologies and the underlying physics. The detection methodologies to quantify the defect shape, location and dimensions are validated in specimens with simulated defects of different geometries. The notable advantage of this technique is that it is simple, user friendly (does not need any processing of electronic data), fast and ideal for inspection of large area surfaces rapidly. This technique is ideal for the detection of surface and subsurface defects such as voids, cracks and inclusions in ferromagnetic materials. & 2013 Elsevier Ltd. All rights reserved.
Keywords: MFL Crack Analytical model Sizing Nanofluid
1. Introduction Detection of defects in materials and components in a nondestructive manner, popularly known as Nondestructive testing (NDT), is very important for many industries in ensuring safety of machines and components, productivity and unexpected shutdowns. Several traditional NDT techniques have been established, yet new ones that are user friendly, rapid, compact, non-contact, highly sensitive and cost effective are being developed. Among magnetic testing of components, magnetic flux leakage (MFL) has been one of the popular techniques for defect detection in ferromagnetic materials, due to their simplicity and ease in usage [1–7]. The presence of a defect or crack or inclusion causes a sudden local change in the magnetic permeability that results in a flux leakage around the defective region. To detect such defects and their features such as morphology, dimension and location within the material, the leakage field near the defect is detected using a suitable magnetic field sensor such as Hall probe, flux gate sensor, magneto-diode, search coil, magnetic particle and Forester micro probe [8,9]. Recently, several new magnetic sensors such as SQUID [10], Giant magneto resistance (GMR) [9], giant magneto impedance (GMI) [11], anisotropic magneto resistance (AMR), [12] magnetic fluids, [13] etc. have been developed to detect defects
n
Corresponding author. Tel.: +91 44 27450356; fax: +91 44 27480232. E-mail address: [email protected] (J. Philip).
0963-8695/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ndteint.2013.07.011
with high sensitivity. Magneto optic (MO) sensors, works on the Faraday′s rotation principle [14,15]. In MFL techniques, the raw data needs to be processed along with computationally intensive numerical modeling to obtain accurate information on the defect shapes, dimension and the location within the test components. Analytical models are fast and simple but less accurate because of many approximations used during the calculation, whereas numerical models are accurate but they are computationally expensive [16]. MFL technique is extensively used for detection of corrosion pits in the oil and gas industries where the pipe wall should be magnetized to saturation for reliable and accurate detection of defects. Similarly, austenitic stainless steel is widely used in nuclear power plants due to their excellent corrosion resistance and high-temperature tensile and creep strength [17]. The partial δ ferrite structure formed in this material, due to imperfect heat treatments and mechanical stress are often wrongly diagnosed as a crack and sometimes real cracks are misjudged as δ-ferrite, which can lead to catastrophic failures of engineering components. Lack of magnetization and over-saturation of materials under study can lead to a poor flux leakage signal and overlap of signals with large background, respectively. For better visualization of the defects, the excitation flux should be sufficient to penetrate the defect depth and homogenous throughout the specimen. The lift off distance of the sensor should be adjusted such that the defect signal is strong enough to detect distinctly from the background noise. These problems associated with conventional MFL techniques warrant the need to have more
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
reliable, simple, cost effective and sensitive techniques for detection of leakage magnetic flux in ferromagnetic components and structures. Towards realizing this goal, we have developed a magnetically polarizable nanoemulsions for visual detection of defective region by naked eye and demonstrated its working principle [18,19]. In this paper, we develop methodologies to quantify defect morphologies, location and dimensions. Our approach is tested and validated in specimens with simulated defects of different geometries and dimensions.
2. Theoretical background A prior knowledge about the MFL profile of a defective region is useful for interpreting the defect features. There are several analytical [2,5,20–28] and numerical models [16,29–38] to predict the magnetic flux leakage from a defective region. Although Zatsepin and Shcherbinin model, based on magnetic line and surface dipoles, yields reasonably good results with respect to the experiments, it cannot be readily adapted for overlapping defects and defects of different morphologies [39]. In such cases, finite element approaches are ideal for the analysis of defects of different morphologies [27,40]. We use the simple analytical approach of Uetake-Saito to obtain the MFL profiles for testing our experimental results [24]. The magnetic flux leakage originates from the magnetic reluctance of the defective region where the magnetic permeability is much lower than that of the sound region. According to a simple dipole model, the tangential and normal components of the leakage flux from rectangular defect with depth Y 0 and width lg are given by [18,39], ! Hg ðx þ lg =2ÞY 0 ðxlg =2ÞY 0 1 1 tan tan Hx ¼ π ðx þ lg =2Þ2 þ yðy þ Y 0 Þ ðxlg =2Þ2 þ yðy þ Y 0 Þ ð1Þ Hy ¼
Hg ðx þ lg =2Þ2 þ ðy þ Y 0 Þ2 ln 2π ðxlg =2Þ2 þ ðy þ Y 0 Þ2
!
ðxlg =2Þ2 þ y2 2
ðx þ lg =2Þ þ
! ð2Þ
y2
where ‘Hg’ is the field inside the defect for an applied field ‘H a ’ and is given by Hg ¼
2Y 0 =lg þ 1 Ha ð1=μÞ2Y 0 =lg þ 1
ð3Þ
Here, the tangential and normal components of the leakage fluxes are calculated with an assumption that the surface charge density on the faces of the defect is a constant. The origin of the x–y coordinate axes is at the center of the top surface of the defect. It should be noted that the tangential component of leakage flux peaks at the center of the crack and falls to zero at the defect edges. On the contrary, the normal component shows a maxima at
101
the edges of the crack walls and becomes zero at the center of the defect. Zhang et al. [23], have developed analytical expressions for magnetic leakage field of two types of internal defects by taking into consideration magnetic image effects from the spatial boundary and the defect boundary using the modified dipole model and image theory. We use the modified dipole model for adjacent parallel surface slots to obtain the leakage field profile. The depths and widths of slot1, slot2 and the separation between the two adjacent slots are y1, y2, l1, l2, and 2lg, respectively. The schematic representation of the two adjacent defects geometry is shown in Fig. 1(a). According to the modified dipole model, the normal and tangential components of the two leakage fields from two parallel surface slots are given by [24], ( H g tan 1 l1 ðxþlg Þ tan 1 y1 ðxþl1 þlg Þ Bx ¼ ðxþlg Þ2 þyðyþl1 Þ ðxþl1 þlg Þ2 þyðyþy1 Þ π ) y2 ðxl2lg Þ l2 ðxlg Þ 1 tan þ tan 1 ðxl2 lg Þ2 þ yðy þ y2 Þ ðxlg Þ2 þ yðy þ l2 Þ ð4Þ By ¼
Hg 2π
(
þln
ln
ðxþlg Þ2 þðyþy1 Þ2 ðxþl1 þlg Þ2 þðyþy1 Þ2 ln ðxþl 2 2 ðxþlg Þ2 þy2 1 þlg Þ þy
ðxl2 lg Þ2 þ ðy þ y2 Þ2 ðxl12 lg Þ2 þ y2
ln
ðxlg Þ2 þ ðy þ y2 Þ2 ðxlg Þ2 þ y2
) ð5Þ
Fig. 1(b) shows the calculated normal component of leakage field profile for two adjacent cylindrical slots S1 by using Eq. (5).
3. Materials and methods The magnetically polarizable nanoemulsion consists of an octane-based ferrimagnetic nanoparticle dispersion with a particle size of about 10 nm, an ionic surfactant of sodium dodecyl sulfate (CH3 (CH2)10CH2SO4 Na+) and water. The iron oxide (Fe3O4) nanoparticles used in our study were synthesized by a simple coprecipitation technique [41,42]. The oil-in-water (O/W) ferrofluid emulsion was prepared using a simple emulsification procedure [43]. The octane-based ferrimagnetic nanoparticle dispersion was sheared in the presence of water containing sodium dodecyl sulfate. The first step leads to the formation of a water-in-oil (W/O) emulsion with a very large size distribution, which is then inverted to an O/W emulsion using a colloidal mixer. The resultant polydisperse emulsion is converted to a fairly monodisperse one with a narrow droplet size distribution using a fractionation technique that exploits the depletion flocculation under added surfactant
20
y
15
x y2
y1
10 Hy (mT)
2lg
5 0 -5
l2
-10 -15
l1
0.0
0.5
1.0 1.5 2.0 Position (cm)
2.5
Fig. 1. (a) Schematic representation of adjacent cylindrical slots (defects). (b) Calculated normal component of leakage field profile for two adjacent cylindrical slots (S1) by using eq. (5) with Hg ¼ 30.0 mT and lift off distance 1 mm.
102
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
leakage flux around the defect were measured at a constant lift-off distance of 1 mm, using hall probes. The specimen was first magnetized with an electromagnetic yoke and then the thin film sensor was placed on the rear surface, at a constant lift of distance of 1 mm. The color pattern on the sensor was recorded by using a digital camera or observed with naked eye. The regions where color changes observed in the sensor were analyzed more carefully to assess the severity of the defects. The sensor is scanned across the specimen surface to map the MFL profile across the entire specimen. To understand the color pattern and the defect location and morphology, leakage field from different specimens were measured using a Hall probe.
micelles [44]. The size selected emulsions are washed with an anionic surfactant (SDS) and triblock polymers (pluronics-F108). To stabilize the emulsion with SDS, it was washed with 2.8 mM SDS solution three times and then diluted with the same concentration of SDS and equilibrated for 72 h. For F108 stabilization, the emulsion is washed three times with desired concentration of F108 and then incubated for 72 h. SDS stabilizes emulsion droplets electrostatically and F108 stabilizes emulsion sterically. The diffraction wavelength depends on the emulsion droplet size and zeta potential determines the stability of the emulsion system. The size distribution and zeta potential of the final emulsions has been measured by using a Malvern Zetasizer (ZS) that works on the principle of dynamic light scattering (DLS). Fig. 2a shows the size distribution of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM. Fig. 2b shows the zeta potential of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM. The average size (diameter) and zeta potential of the emulsion droplets stabilized with SDS and F108 are 190 nm and 65 mV, and 220 nm and 10 mV, respectively. To fabricate the sensor, the nanoemulsion is sandwiched between two optically transparent (microscopic) glass slides. A spacer of 300 mm thickness is used to achieve desired uniform gap. The sides of the cells are sealed to avoid seepages, moisture trapping and contamination of emulsion with foreign particles from outside.
5. Results and discussions Fig. 4a–c shows the measured normal component of MFL signals across the defect length on samples S1, S2, and S3 a–d, respectively. It can be seen that the normal component shows maxima at the edges of the defect walls and zero at the center of the defect. Here, the low magnetic permeability of slot region (air) compared to that of the specimen leads to leakage of magnetic flux around the defective region. In general, the experimental MFL signal is less that the predicted value because of magnetic refraction, magnetic diffusion, and magnetic compression [8]. However, the theoretical model is very useful in predicting the MFL profiles from specimens, especially the one with multiple defects. The observed flux profiles were in reasonably good agreement with the locations where the color changes are observed on the sensor. The offset values of the magnetic flux at the edges of the samples are due to the background noise from yoke. Fig. 5a–i shows the schematics of the specimen S1, S2, S3(a–d) and the corresponding photographic images of the nanofluid sensor. The sensor exhibits a natural color of brown (without MFL) due to the presence of iron oxide nanoparticles in the oil droplets.
4. Experimental setup Mild steel plates of 50 mm length, 25 mm width and thickness 10 mm with several well defined defects dimensions and shapes were fabricated for the test. The different defect geometries used for the study and their dimensions are listed in Table 1. Fig. 3 shows schematic representation of a typical experimental setup for leakage field measurement and defect imaging. The specimen was magnetized using a DC magnetic yoke. The normal and tangential components of
250
25 SDS washed F108 washed
15 10 5 0 10
SDS washed F108 Washed
200 Counts (a.u)
Number (%)
20
150 100 50 0 -150 -100
100 1000 Size (nm)
-50 0 50 100 Zeta potential (mV)
150
Fig. 2. (a) Size distribution of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM. (b) zeta potential of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM.
Table 1 Details of the specimens and the simulated defect dimensions. Specimen
S1 (two cylindrical) S2 (rectangular, cylindrical) S3a (rectangular) S3b (rectangular) S3c (rectangular) S3d (rectangular)
Width/Diameter (mm)
Depth (mm)
Slot1
Slot2
Slot1
Slot2
5 3 0.5 0.5 0.5 0.5
10.5 6.5 – – – –
8 4 2 3 4 5
7.5 8 – – – –
Gap between the slots 2lg (mm)
Specimen dimension (L B T) (cm)
5 8 – – – –
21 2.5 1 21 2.5 1 21 4.5 1 21 4.5 1 21 4.5 1 21 4.5 1
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
103
Power supply
RS 232
RS 232
Gauss meter
Computer A/D X-Y driver Y-Stage
CCD Detector
Spectrograph
Halogen Lamp Variable attenuator
X -Stage Hall probe Focusing optics
Specimen
Emulsion film Electromagnet
BS
Optical fiber Fig. 3. Schematic representation of typical experimental setup used for magnetic leakage field measurement and defect imaging with nanoemulsion.
The defective region of each sample is encircled for clarity. At the center of the defect, no color pattern is observed because the leakage field of the normal component is zero at the center of the defect, as seen in Figs. 4a and b. The contribution from the tangential component is almost negligible due to the finite thickness of the sensor and the relatively large lift of distances used. On both sides of the defect, a color spectrum is observed due to the leakage of the magnetic flux around the defects. The center line on the color pattern was straight for the rectangular slot and semicircular for the cylindrical slot. Fig. 5e shows the photograph of the specimen S3(a–d) and the corresponding sensor images are shown in Fig. 5f–i. The defects are discernible from the images, though color contrasts were not very good. This is mainly due to the lower leakage flux, owing to the smaller defects present and the larger remanent thickness of the specimens. However, the defect center was very clearly discernible from the image. It is possible to obtain the color changes in the above cases by increasing the magnetization value, which was not possible with the magnetic yoke used in our experiments. The measured values of normal component of the leakage field plotted across the specimen (S3) length (Fig. 4c) shows that the locations of the defect, determined from the minimum value of MFL signal, are in good agreement with the measured values. The leakage flux values for S3.1, S3.2, S3.3 and S3.5 are 0.58, 0.7, 0.8 and 0.9 mT, respectively. For the calculations of the normal component of leakage field, we have used y¼1 mm and Hg ¼30 mT. The defect locations are indicated by dashed lines. To test the response and reversibility of the color appearance of the nanofluid sensor, it is tested under different magnetization conditions. Without external magnetic field, emulsion droplets are randomly oriented because of the thermal energy and appear as brown color, which is the intrinsic color of Fe3O4. As the specimen
is magnetized to a lower magnetic field, the leakage field intensity around the defect region causes the formation of colors, which become more intense when the magnetization field is increased to 35 mT. Fig. 6(a–c) shows the sensor response at different magnetization conditions for the specimen S1 with increasing magnetizing value from zero to a maximum. Fig. 6(c–e) shows the sensor response with decreasing magnetizing field from the maximum to zero. From the color pattern, the shape and location of the defects on the far side of scanning surface are discernible. Upon removal of the magnetic field, the color pattern almost disappears. The pattern persists for some time after the removal of field, because of the remanent field present on the samples surface. This demonstrates the re-usability of the flux sensor after the use and the reversible formation of one dimensional (1D) arrays in the fluid. The time (tc) required for droplets to form 1D arrays at a given field strength, depends on the competition between the magnetic force and viscous force experienced by the oil droplets in the carrier liquid. It is given by t c ¼ ða=5Þ 6πaη=F chain ððr=2aÞ5 1Þ where ‘η’ is the viscosity of the medium, ‘a’ droplet radius, ‘r’ distance between the droplets and ‘Fchain’ is the magnetic force acting between the droplets [45]. The typical response time for SDS and F108 stabilized droplets of diameters 172 nm and 220 nm, at an r value of 50 nm is around 0.8 ms and 1.5 ms respectively. From the color pattern observed on the sensor, along with the theoretical knowledge of the MFL profiles across defects of different geometries, the shape and location of the defects is clearly identifiable. For quantification of defect size and to compare the observed color pattern with MFL values, we have recorded the reflected Bragg peak from the sample at 1801 (back scattering) using a fiber-based reflection probe whose working principle was discussed earlier [46]. Fig. 7 shows the reflected Bragg peak from a typical nanofluid emulsion stabilized with
104
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
20
Hy (mT)
10 0 -10 -20 -40
-20
0
20
40
Position (mm)
Hy (mT)
20
0
-20
-30
-20
-10
0
10
20
Position (mm)
1.2
S3c
S3b S3a
0.6 Hy (mT)
S3d
0.0 -0.6 -1.2 0
30
60
90
120
Position (mm) Fig. 4. Measured normal component of the MFL signals across the samples length for (a) S1 (b) S2, and (c) S3a–d, respectively. The shape and locations of the defects are also indicated.
(a) an anionic surfactant (SDS) and (b) a triblock copolymer pluronics (F108), as a function of applied magnetic field strength. An increase in the magnetic field from 0 to 18 mT results in a diffraction peak at 890 nm for anionic surfactant stabilized emulsion and 840 nm for the pluronics (F108) stabilized emulsion. In both the cases, the Bragg peak (λmax) monotonically blue shifts (moves towards lower wavelength) with increasing magnetic field strength (shown by the arrow). In the absence of a magnetic field, droplets are under Brownian motion without any ordering, as evident from the fact that the individual magnetic grains inside the droplets are randomly oriented. The diffraction peaks are symmetric and narrow in all the cases, which indicate that the 1D ordering is stable. As the nanofluid droplets used in our studies
are super-paramagnetic in nature, an applied field induces a magnetic dipole in each drop, causing them to form chains. On applying an external field ‘H0’, the strength of the dipolar interaction between the droplets increases, which is described by the 3 coupling constant [47] Λ ¼ πμ0 d χ 2 H 20 =72kB T, where ‘d’ is the particle diameter, ‘χ’ and ‘kB T’ are the magnetic susceptibility and thermal energy, respectively. When Λ41, the emulsion forms linear chain-like structures along the field direction [47,48]. Typically for a droplet of 180 nm diameter, the required H0 value to see 1D structures is 5–10 mT. The magnitude of the magnetic dipole moment increases with the strength of the applied field until saturation is reached. At low concentration, one droplet thick chains are well separated and oriented along the field direction. Due to the presence of the onedimensional ordered structure, a Bragg peak can be observed, from which the interdroplet separation is estimated precisely. The condition for forming a linear chain is that the repulsive force between the droplets must exactly balance the attractive force between the droplets induced by the applied magnetic field. The dominant force for the field induced droplet chain is the dipole– dipole attraction. In addition, the van der Waals attraction becomes significant at short distances. The attractive dipole force within an infinitely long chain is given by [49] F chain ¼ ∑1 n¼1 nð6m2 =ðndÞ4 Þ, ‘m’ is the induced magnetic moment of each drop, which depends on the intrinsic susceptibility of the nanofluid drops. m ¼ μ0 4πa3 χ s H T =3. Here,‘μ0’ is the magnetic permeability of free space, ‘HT’ is the total magnetic field acting on each drop and ‘χs’ is the susceptibility of a spherical drop. Due to strong surface tension, the elongation of the nanometer-sized droplet is very small. The multipole contributes less than 10 3 Fm and hence can be neglected. The spacing between the droplets is directly measured from the determination of the spectral distribution of the scattered light at a constant angle. For perfectly aligned particles with a separation ‘d’, the first order Bragg condition leads to 2d ¼ λ0 =n, where ‘n’ is the refractive index of the suspending medium (n ¼1.33 for water) and ‘λ0’ is the wavelength of the light Bragg scattered at an angle of 180 degrees. Because the droplets are monodispersed and negligibly deformable owing to their large capillary pressure, the corresponding interfacial separation is h ¼ d2a. Because of angle dependant diffraction peak, for quantitative evaluation of the defects using the color pattern, it is important to collect the Bragg peak (color pattern) at a given angle of incidence throughout the experiments. Fig. 8 shows the diffraction peak wavelength (λmax) as a function of magnetic field for electrostatically (SDS stabilized) and sterically stabilized (F108 stabilized) nanoemulsions. With increasing magnetic field, λmax shifts to lower wavelength and falls exponentially. The exponents for SDS stabilized emulsion with concentration of 2.67 mM is 0.04 and F108 stabilized emulsion with 0.1, 0.2, 0.3, 0.5 and 0.75 mM concentrations were, 0.037, 0.057, 0.083, 0.088 and 0.05, respectively. From Fig. 8 it is evident that the emulsion stabilized with SDS respond at a minimum leakage field of 20 mT and the emulsion stabilized with 0.35 mM F108 respond even at a lower field of 10 mT. These results suggest that the steric stabilization of emulsion droplets with appropriate polymers such as F108 provides better sensitivity to defect detection compared to charge stabilization. The force distance profile measured by using magnetic chaining technique and the details are discussed in an earlier publication [50]. Fig. 9 shows the repulsive force distance profile, for SDS stabilized and F108 stabilized magnetic nanoemulsion. In both the cases, the force profiles were repulsive in nature and decay exponentially. For emulsion stabilized with 2.67 mM SDS, the force profile (for κa o5), is fitted with the classical electrostatic force profile equation F r ðrÞ ¼ 4πεψ 20 a2 ½κ=r þ 1=r 2 exp½κðr2aÞ, where 0 a0 is the droplet radius, 0 r 0 is the droplet separation
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
105
Fig. 5. (Color online) Schematics of the specimens S1–S3a–d (a, c and e) and the corresponding photographic images of nanofluid sensor (b, d and f–i), respectively. (f–i ) are S3a, S3b, S3c, S3d, respectively. The defective regions of each sample are encircled.
Fig. 6. (Color online) Sensor response at different magnetization conditions for the specimen S1 (a)–(c) sensor response with increasing magnetizing value from zero to a maximum (35 mT) and (c)–(e) sensor response with decreasing magnetizing field from maximum to zero.
distance, 0 ε0 is the dielectric permittivity of the suspending medium, 0 ψ 0 0 is the electrical surface potential and 0 κ 0 is the inverse Debye length that essentially depends on the electrolyte concentration ðC s Þ and can be represented as [51] κ1 ¼ ð1=4πÞ½2L2B C s 0:5 , where ‘LB ’ is the Bjerrum length. The solid line in Fig. 9 shows the theoretical curve and the symbols represent the experimental data points. These results shows a perfect agreement of experimental data with the electrostatic theory. The force profiles for sterically stabilized system can be represented by a simple exponential function FðhÞ ¼ kexpðh=λÞ, where ‘h’ is the interdroplet spacing and ‘λ’ is the decay length [52]. Here, the decay length is comparable to the radius of gyration of the adsorbed polymer. For sterically stabilized systems, theoretical approach of mean field and scaling approach distinguishes the loops and tail sections of the adsorbed chains that involves three length scales. The adsorbed layer thickness ‘λ’ and an adsorption length zn that separates the regions where the monomer concentration is dominated by loops and by tails and a microscopic length ‘b’ that is inversely proportional to the adsorption strength. At distances larger than λ, the concentration is dominated by the tails, and the force is always repulsive and decays exponentially with the distance. By using the Derjaguin approximation, in the scaling theory, we obtain the expression for force between two spherical droplets of radius R, FðhÞ ¼ ðkb TπR=λ2 Þexpðh=λÞ where kb is the Boltzmann constant and T is
the temperature. The above expression is valid only when the adsorbed polymer amount is close to the salutation value. The decay length obtained for the F108 of 0.1, 0.2, 0.3, 0.5 and 0.75 mM concentrations are 24, 21, 18, 16 and 19.5 nm, respectively. The magnitude of the pre-factor obtained from our experimental values in the above concentration range were 3.2103 10 11, 5.3341 10 11, 1.7023 10 10, 1.5033 10 10 and 1.8984 10 11, respectively, which were in agreement with the theoretically calculated values. The schematic representation of anionic surfactant and F108 stabilized nanoemulsions are shown in Fig. 10. Fig. 10a represents the emulsion droplet stabilized with SDS where the charged head group adsorbed at the oil–water interface and non polar chain extended in the oil phase. Fig. 10b schematically shows triblock copolymer stabilized emulsion droplet with randomly oriented magnetic nanoparticle. The hydrophobic PPO (blue chain) extended into the oil phase while, the hydrophilic PEO extends into the water and provide steric hindrance to oil droplet flocculation. The extended hydrophilic polymeric chains seems to provide better sensitivity (response at lower magnetic field) through interactions at larger interdroplet spacing and the charged head groups of the surfactant molecules provides electrostatic stabilization in presence of ionic surfactants. This was evident in Fig. 8 where pluronic stabilized emulsion droplets showed a better response compared to the SDS stabilized emulsion.
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
10
37.8 mT 36.0 mT 34.0 mT 32.4 mT 30.6 mT 28.8 mT 27.0 mT 25.2 mT 23.4 mT 21.6 mT 19.8 mT 18.0 mT
Increasing field
Refelectance (%)
8 6 4
10-11
Force (N)
106
2.67 mM SDS 0.1 mM F108 0.2 mM F108 0.3 mM F108 0.5 mM F108 0.75 mM F108
10-12
2 10-13 0 600
700 800 Wavelength (nm)
40
900
60
80 100 h (nm)
120
140
Fig. 9. Force profile as a function of inter-droplet spacing for SDS and F108 stabilized emulsions of different concentrations.
33.3 mT 30.6 mT 27.0 mT 23.4 mT 20.7 mT 19.9 mT 18.9 mT 18.0mT 17.0 mT 16.2 mT
6 Reflectance (%)
Increasing field
4
2
0
700
750 800 Wavelength (nm)
850
Fig. 7. The reflected Bragg peak from a typical nanofluid emulsion stabilized with (a) an anionic surfactant and (b) a triblock copolymer pluronics (F108) as a function of applied magnetic field strength.
λ max
900
2.67 mM SDS 0.1 mM F108 0.2 mM F108 0.3 mM F108 0.5 mM F108 0.75 mM F108
800
700
10
15
20 25 30 Field (mT)
35
40
Fig. 8. Bragg Peak wavelength as a function of applied magnetic field strength for nanoemulsion with 2.6 mM concentration of sodium dodecyl sulfate (SDS) and with F108 of different concentrations varying concentration from 0.1 to 0.75 mM.
6. Quantification of defects from the color images To further quantify the defect dimensions and morphologies precisely from the color images seen on the sensor, the Red– Green–Blue (RGB) profiles of the images are obtained. Fig. 11(a and b) shows the color images seen on the sensor and the RGB
Fig. 10. Schematic representation of emulsion droplet stabilized with a. anionic surfactant and b. triblock copolymer-F108. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).
intensity profiles across the pixels, respectively for the specimen S1. The line profile obtained along the center line is shown by the dotted line. For a larger cylindrical slot, all the three intensity profiles (RGB) peak at the center of the defect because the MFL is zero at this point where no 1D ordering occurs. The blue lines peak at the edges of the cylinder where the flux lines are maximum and hence the interdroplets spacing is at minimum. Therefore, from the pixel calibration and measurement of the lowest wavelength maximum (blue in the present case), it is possible to precisely calculate the dimensions of the cylindrical slot. Since the slot on the right side is smaller compared to the left, the MFL intensity was lower and hence the green profiles peak at the edges. In the case of adjacent defects, leakage fields from the two cracks overlap and the maximum leakage field at the edge of smaller crack is influenced by the leakage field from larger crack. Due to this overlap, blue intensity is observed at the defect center. In the case of near surface crack, leakage field is larger and hence the color pattern will be intense. An increase in the thickness of the sensor or lift off distance could decrease the intensity of the MFL signal. In such cases, the Bragg peaks (color) would shift towards the higher wavelength (i.e. red shift). Again, by measuring the distance between the peaks and by use of proper pixel calibration, it is possible to precisely measure the far side defect dimensions. Fig. 12a shows the sensor response for specimen S2 and Fig. 12b shows the corresponding RGB intensity profiles as a function of pixel number. The line profiles are obtained along the dotted line shown in the image. Fig. 13a shows the color image seen on the
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
107
250 200
150 Intensity
Intensity
200
150
100
100
50 50
0
100
200
300
400
500
600
0
Pixels
100
200
300
400
Pixels
sensor for a rectangular slot and Fig. 13b shows RGB intensity profiles as a function of pixels. In all the above three cases, the defect center line and the boundaries are very clearly discernible from the RGB profiles. Therefore, color images under magnetization along with the RGB profiles enable us to precisely estimate the location, morphology and dimensions of the defect using the new sensor. The dimensions measured from the RGB profiles fell within 5% error from the actual defect dimensions. It should be noted that the pixel values are different in the above cases because of the imaging conditions (zoom factor and camera to sensor distance). Therefore, each image is taken with meter scale on the side (as in Fig. 5) for pixel calibration. Fig. 14a shows the schematic representation of two adjacent cylindrical slots, Fig. 14b shows the normal component of leakage field along with the schematic representation of droplet confirmation and Fig. 14c the photographic image of the sensor under magnetic excitation. It can be seen that the droplet conformation at any point in the sensor (random or one dimensional array with distinct interdroplet spacing) depends on the MFL flux profile at that point. Finally, the sensor is also capable of detecting hidden magnets or magnetic materials. The photograph of the sensor response when it was kept on a nonmagnetic material with a hidden ring magnet inside is shown in Fig. 15. The boundaries of the magnets is clearly discernible from the colors seen in the nanofluid sensor. The temperature dependant studies show that the present sensor can work in the temperature range of 5–60 1C. The smallest defect we have detected using this sensor (with 0.3 mM F018 stabilized emulsion) is 500 μm width at a lift off distance 1 mm below the surface. There are possibilities to further improve the sensitivity by using emulsions with improved stabilization and also with enhanced magnetization fields.
7. Conclusion In the conclusion, a methodology to image and visualize defects buried inside ferromagnetic components by using magnetically
Fig. 12. (Color online) (a) Observed sensor response for S2 and (b) RGB intensity profiles as a function of pixel. The line profiles are obtained along the center line shown by a dotted line.
200
150 Intensity
Fig. 11. (Color online) (a) Magnetic nanoemulsion sensor response for S1 and (b) RGB intensity profiles as a function of pixels. The line profiles are obtained along the center line shown by a dotted line. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).
100
50
0
100
200
300
Pixels Fig. 13. (Color online) (a) Sensor response for a rectangular slot and (b) RGB intensity profiles across the as a function of pixel. The line profiles are obtained along the image center line shown by a dotted line. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article)
polarizable nanoemulsion has been demonstrated. The nanofluid shows a visually perceivable color change due to the changes in the interparticle spacing within the self-assembled nano-arrays in presence of a defect or leaked magnetic flux. Defect dimensions are quantitatively extracted from the Bragg peak shift and the RGB profile analysis. Extracted defect widths are in good agreement with the magnetic flux leakage measurement. As the color pattern in the sensor is reversible, the sensor is reusable and allows rapid inspection of large area specimens.
108
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
Fig. 14. (Color online) (a) Schematic representation of two adjacent cylindrical slots (b) the normal component of leakage field along with the schematic representation of droplet confirmation and (c) the photographic image of the sensor under magnetic excitation.
Fig. 15. (Color online) Sensor response to a ring magnet concealed inside a material. The dotted lines indicates the outer edges of the ring magnet.
Acknowledgment J.P. thanks BRNS for funding of a perspective research grant on development of advance nanofluids. Authors thank Dr. T. Jayakumar and Dr. P.R. Vasudeva Rao, for support and encouragements.
Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at 10.1016/j.ndteint.2013.07.011.
References [1] Blitz J. Electrical and magnetic methods of nondestructive testing. New York: Adam Hilger; 1991. [2] Forster F. New findings in the field of non-destructive magnetic leakage field inspection. NDT and E International 1986;19:3–14. [3] Wang ZD, Gu Y, Wang YS. A review of three magnetic NDT technologies. Journal of Magnetism and Magnetic Materials 2012;324:382–8.
[4] Gloria NBS, Areiza MCL, Miranda IVJ. Rebello JMA. Development of a magnetic sensor for detection and sizing of internal pipeline corrosion defects. NDT and E International 2009;42:669–77. [5] Kopp G, Willems H. Sizing limits of metal loss anomalies using tri-axial MFL measurements: a model study. NDT and E International 2013;55:75–81. [6] Le M, Lee J, Jun J, Kim J. Estimation of sizes of cracks on pipes in nuclear power plants using dipole moment and finite element methods. NDT and E International 2013 (In press). [7] Sun Y, Kang Y. A new MFL principle and method based on near-zero background magnetic field. NDT and E International 2010;43:348–53. [8] Sun Y, Kang Y, Qiu C. A new NDT method based on permanent magnetic field perturbation. NDT and E International 2011;44:1–7. [9] Tsukada K, Yoshioka M, Kiwa T, Hirano Y. A magnetic flux leakage method using a magnetoresistive sensor for nondestructive evaluation of spot welds. NDT and E International 2011;44:101–5. [10] Krause HJ, Kreutzbruck Mv. Recent development in SQUID NDE. Physica C 2002;368:70–9. [11] Tehranchi MM, Ranjbarana M, Eftekhari H. Double core giant magnetoimpedance sensors for the inspection of magnetic flux leakage from metal surface cracks. Sensors and Actuators A 2011;170:55–61. [12] Allweins K, von Kreutzbruck M., Gierelt G. Defect detection in aluminum laser welds using an anisotropic magnetoresistive sensor array. J Appl Phys 2005;97:10Q102 (3 pp.), http://dx.doi.org/10.1063/1.1852391. [13] Jun J, Lee J. Nondestructive evaluation of cracks in a paramagnetic specimen with low conductivity by penetration of magnetic fluid. NDT and E International 2009;42:297–303. [14] Tehranchi MM, Hamidi SM, Eftekhari H, Karbaschi M, Ranjbaran M. The inspection of magnetic flux leakage from metal surface cracks by magnetooptical sensors. Sensors and Actuators A 2011;172:365–8. [15] Klank M, Hagedorn O, Holthaus C, Shamonin M, Dotsch H. Characterization and optimization of magnetic garnet films for magneto-optical visualization of magnetic field distributions. NDT and E International 2003;36:375–81. [16] Ravan M, Amineh RK, Koziel S, Nikolova NK, Reilly JP. Sizing of multiple cracks using magnetic flux leakage measurements. IET Science, Measurement and Technology 2010;4:1–11. [17] Kikuchi H, Sato K, Shimizu I, Kamada Y, Kobayashi S. Feasibility study of application of MFL to monitoring of wall thinning under reinforcing plates in nuclear power plants. IEEE Transactions on Magnetics 2011;47:3963–6. [18] Philip J, Rao CB, Jayakumar T, Raj B. A new optical technique for detection of defects in ferromagnetic materials and components. NDT and E International 2000;33:289–95. [19] Mahendran V, Philip J. Nanofluid based optical sensor for rapid visual inspection of defects in ferromagnetic materials. Applied Physics Letters 2012;100:073104. [20] Edwards C, Palmer SB. The magnetic leakage field of surface-breaking cracks. Journal of Physics D: Applied Physics 1986;19:657–74. [21] Pashagin AI, Bogdanovich BN, Shcherbinin VE. On correlation between the field and the flaw dimensions in magnetic nondestructive testing of hot-rolled pipes. Defektoskopiya 1988;3:91–3. [22] Mandal K, Atherton DL. A study of magnetic flux-leakage signals. Journal of Physics D: Applied Physics 1998;31:3211–8. [23] Zhang Y, Sekine K, Watanabe S. Magnetic leakage field due to sub-surface defects in ferromagnetic specimens. NDT and E International 1995;28:67–71. [24] Uetake I, Saito T. Magnetic flux leakage by adjacent parallel surface slots. NDT and E International 1997;30:371–6. [25] Minkov D, Shoji T. Method for sizing of 3-D surface breaking flaws by leakage flux. NDT and E International 1998;31:317–23. [26] Mandache C, Clapham L. A model for magnetic flux leakage signal predictions. Journal of Physics D: Applied Physics 2003;36:2427–32. [27] Lukyanets S, Snarskii A, Shamoninc M, Bakaevc V. Calculation of magnetic leakage field from a surface defect in a linear ferromagnetic material: an analytical approach. NDT and E International 2003;36:51–5. [28] Gobov YL, Loskutov VR, Reutov YY. A dipole model for magnetic leakage fields created by a circular butt-wellded joint. Defektoskopiya 2005;41:85–90. [29] Hwang JH, Lord W. Finite element modeling of magnetic field/defect interactions. Journal of Testing and Evaluation 1975;5:21–5. [30] Lord W, Hwang JH. Defect characterization from magnetic leakage fields. British Journal of Non-Destructive Testing 1977;19:14–8. [31] Schifini R, Bruno AC. Experimental verification of a finite element model used in a magnetic flux leakage inverse problem. Journal of Physics D: Applied Physics 2005;38:1875–81. [32] Al-Naemi FI, Hall JP, Moses AJ. FEM modeling techniques of magnetic flux leakage type NDT for ferromagnetic plate inspection. Journal of Magnetism and Magnetic Materials 2006;304:790–3. [33] Li Y, Tian GY, Ward S. Numerical simulation on magnetic flux leakage evaluation at high speed. NDT and E International 2006;39:367–73. [34] Zuoying H, Peiwen Q, Liang C. 3D FEM analysis in magnetic flux leakage method. NDT and E International 2006;39:61–6. [35] Amineh RK, Koziel S, Nikolova NK, Bandler JW, Reilly JP. A space mapping methodology for defect characterization from magnetic flux leakage measurements. IEEE Transactions on Magnetics 2008;44:2058–64. [36] Zhang Y, Ye Z, Wang C. A fast method for rectangular crack sizes reconstruction in magnetic flux leakage testing. NDT and E International 2009;42: 369–75.
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
[37] Chen Z, Yusa N, Miya K. Some advances in numerical analysis techniques for quantitative electromagnetic nondestructive evaluation. Nondestructive Test Evaluation 2009;24:35. [38] Yuan X, Wang C, Zuo X, Hou S. A method of 2D defect profile reconstruction from magnetic flux leakage signals based on improved particle filter. Insight 2011;53:152–5. [39] Zatsepin NN, Shcherbinin VE. Calculation of the magnetostatic field of surface defects, I. field topography of surface defect models. Defektoskopia 1966;5:500. [40] Katoh M, Masumoto N, Nishio K, Yamaguchi T. Modeling of the yokemagnetization in MFL-testing by finite elements. NDT and E International 2003;36:479–86. [41] Shima PD, Philip J, Raj B. Synthesis of aqueous and nonaqueous iron oxide nanofluids and study of temperature dependence on thermal conductivity and viscosity. Journal Physical Chemistry C 2010;114:18825–33. [42] Muthukumaran T, Gnanaprakash G, Philip J. Synthesis of stable magnetic nanofluids of different particle sizes. Journal of Nanofluids 2012;1:85–92. [43] Philip J, Mondain-Monval O, Leal-Calderon F, Bibette J. Colloidal force measurements in the presence of a polyelectrolyte. Journal of Physics D: Applied Physics 1997;30:2798. [44] Bibette J. Depletion interactions and fractionated crystallization for polydisperse emulsion purification. Journal Colloid Interface Science 1991;147:474–8.
109
[45] Baudry J, Rouzeau C, Goubault C, Robic C, Cohen-Tannoudji L, Koenig A, et al. Acceleration of the recognition rate between grafted ligands and receptors with magnetic forces. Proceedings of the National Academy of Sciences 2006;103:16076–8. [46] Mahendran V, Philip J. Sensing of biologically important cations such as Na+, K+, Ca2+, Cu2+, and Fe3+ using magnetic nanoemulsions. Langmuir 2013;29: 4252–8. [47] Liu J, Lawrence EM, Wu A, Ivey ML, Flores GA, Javier K, et al. Field-induced structures in ferrofluid emulsions. Physical Review Letters 1995;74:2828–31. [48] Ivey M, Liu J, Zhu Y, Cutillas S. Magnetic-field-induced structural transitions in a ferrofluid emulsion. Physical Review E 2000;63:011403. [49] Zhang H, Widom M. Field-induced forces in colloidal particle chains. Physical Review E 1995;51:2099–103. [50] Philip J, Jaykumar T, Kalyanasundaram P, Raj B, Monval OM. Effect of polymersurfactant association on colloidal force. Physical Review E 2002;66:011406. [51] Israelachvili JN. Intermolecular and Surface Forces.San Diego: Academic Press; 1985. [52] Mondain-Monval O, Espert A, Omarjee P, Bibette J, Leal-Calderon F, Philip J, et al. Polymer-induced repulsive forces: exponential scaling. Physical Review Letters 1998;80:1778–81.
Contents lists available at ScienceDirect
NDT&E International journal homepage: www.elsevier.com/locate/ndteint
Naked eye visualization of defects in ferromagnetic materials and components V. Mahendran, John Philip n SMARTS, Metallurgy and Materials Group, Indira Gandhi Centre for Atomic Research, Kalpakkam 603102, Tamilnadu, India
art ic l e i nf o
a b s t r a c t
Article history: Received 13 May 2013 Received in revised form 23 July 2013 Accepted 26 July 2013 Available online 13 August 2013
We report a methodology to visualize defects by naked eye using magnetically polarizable nanoemulsions stabilized with different surface active species. The response of the nanoemulsions to the leaked magnetic flux from a defective region is exploited to locate the defect. In the presence of leaked magnetic flux, the nanofluid shows a visually perceivable color change due to the changes in the interparticle spacing within the self-assembled nano-arrays. We discuss the methodologies to detect defect morphologies and the underlying physics. The detection methodologies to quantify the defect shape, location and dimensions are validated in specimens with simulated defects of different geometries. The notable advantage of this technique is that it is simple, user friendly (does not need any processing of electronic data), fast and ideal for inspection of large area surfaces rapidly. This technique is ideal for the detection of surface and subsurface defects such as voids, cracks and inclusions in ferromagnetic materials. & 2013 Elsevier Ltd. All rights reserved.
Keywords: MFL Crack Analytical model Sizing Nanofluid
1. Introduction Detection of defects in materials and components in a nondestructive manner, popularly known as Nondestructive testing (NDT), is very important for many industries in ensuring safety of machines and components, productivity and unexpected shutdowns. Several traditional NDT techniques have been established, yet new ones that are user friendly, rapid, compact, non-contact, highly sensitive and cost effective are being developed. Among magnetic testing of components, magnetic flux leakage (MFL) has been one of the popular techniques for defect detection in ferromagnetic materials, due to their simplicity and ease in usage [1–7]. The presence of a defect or crack or inclusion causes a sudden local change in the magnetic permeability that results in a flux leakage around the defective region. To detect such defects and their features such as morphology, dimension and location within the material, the leakage field near the defect is detected using a suitable magnetic field sensor such as Hall probe, flux gate sensor, magneto-diode, search coil, magnetic particle and Forester micro probe [8,9]. Recently, several new magnetic sensors such as SQUID [10], Giant magneto resistance (GMR) [9], giant magneto impedance (GMI) [11], anisotropic magneto resistance (AMR), [12] magnetic fluids, [13] etc. have been developed to detect defects
n
Corresponding author. Tel.: +91 44 27450356; fax: +91 44 27480232. E-mail address: [email protected] (J. Philip).
0963-8695/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ndteint.2013.07.011
with high sensitivity. Magneto optic (MO) sensors, works on the Faraday′s rotation principle [14,15]. In MFL techniques, the raw data needs to be processed along with computationally intensive numerical modeling to obtain accurate information on the defect shapes, dimension and the location within the test components. Analytical models are fast and simple but less accurate because of many approximations used during the calculation, whereas numerical models are accurate but they are computationally expensive [16]. MFL technique is extensively used for detection of corrosion pits in the oil and gas industries where the pipe wall should be magnetized to saturation for reliable and accurate detection of defects. Similarly, austenitic stainless steel is widely used in nuclear power plants due to their excellent corrosion resistance and high-temperature tensile and creep strength [17]. The partial δ ferrite structure formed in this material, due to imperfect heat treatments and mechanical stress are often wrongly diagnosed as a crack and sometimes real cracks are misjudged as δ-ferrite, which can lead to catastrophic failures of engineering components. Lack of magnetization and over-saturation of materials under study can lead to a poor flux leakage signal and overlap of signals with large background, respectively. For better visualization of the defects, the excitation flux should be sufficient to penetrate the defect depth and homogenous throughout the specimen. The lift off distance of the sensor should be adjusted such that the defect signal is strong enough to detect distinctly from the background noise. These problems associated with conventional MFL techniques warrant the need to have more
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
reliable, simple, cost effective and sensitive techniques for detection of leakage magnetic flux in ferromagnetic components and structures. Towards realizing this goal, we have developed a magnetically polarizable nanoemulsions for visual detection of defective region by naked eye and demonstrated its working principle [18,19]. In this paper, we develop methodologies to quantify defect morphologies, location and dimensions. Our approach is tested and validated in specimens with simulated defects of different geometries and dimensions.
2. Theoretical background A prior knowledge about the MFL profile of a defective region is useful for interpreting the defect features. There are several analytical [2,5,20–28] and numerical models [16,29–38] to predict the magnetic flux leakage from a defective region. Although Zatsepin and Shcherbinin model, based on magnetic line and surface dipoles, yields reasonably good results with respect to the experiments, it cannot be readily adapted for overlapping defects and defects of different morphologies [39]. In such cases, finite element approaches are ideal for the analysis of defects of different morphologies [27,40]. We use the simple analytical approach of Uetake-Saito to obtain the MFL profiles for testing our experimental results [24]. The magnetic flux leakage originates from the magnetic reluctance of the defective region where the magnetic permeability is much lower than that of the sound region. According to a simple dipole model, the tangential and normal components of the leakage flux from rectangular defect with depth Y 0 and width lg are given by [18,39], ! Hg ðx þ lg =2ÞY 0 ðxlg =2ÞY 0 1 1 tan tan Hx ¼ π ðx þ lg =2Þ2 þ yðy þ Y 0 Þ ðxlg =2Þ2 þ yðy þ Y 0 Þ ð1Þ Hy ¼
Hg ðx þ lg =2Þ2 þ ðy þ Y 0 Þ2 ln 2π ðxlg =2Þ2 þ ðy þ Y 0 Þ2
!
ðxlg =2Þ2 þ y2 2
ðx þ lg =2Þ þ
! ð2Þ
y2
where ‘Hg’ is the field inside the defect for an applied field ‘H a ’ and is given by Hg ¼
2Y 0 =lg þ 1 Ha ð1=μÞ2Y 0 =lg þ 1
ð3Þ
Here, the tangential and normal components of the leakage fluxes are calculated with an assumption that the surface charge density on the faces of the defect is a constant. The origin of the x–y coordinate axes is at the center of the top surface of the defect. It should be noted that the tangential component of leakage flux peaks at the center of the crack and falls to zero at the defect edges. On the contrary, the normal component shows a maxima at
101
the edges of the crack walls and becomes zero at the center of the defect. Zhang et al. [23], have developed analytical expressions for magnetic leakage field of two types of internal defects by taking into consideration magnetic image effects from the spatial boundary and the defect boundary using the modified dipole model and image theory. We use the modified dipole model for adjacent parallel surface slots to obtain the leakage field profile. The depths and widths of slot1, slot2 and the separation between the two adjacent slots are y1, y2, l1, l2, and 2lg, respectively. The schematic representation of the two adjacent defects geometry is shown in Fig. 1(a). According to the modified dipole model, the normal and tangential components of the two leakage fields from two parallel surface slots are given by [24], ( H g tan 1 l1 ðxþlg Þ tan 1 y1 ðxþl1 þlg Þ Bx ¼ ðxþlg Þ2 þyðyþl1 Þ ðxþl1 þlg Þ2 þyðyþy1 Þ π ) y2 ðxl2lg Þ l2 ðxlg Þ 1 tan þ tan 1 ðxl2 lg Þ2 þ yðy þ y2 Þ ðxlg Þ2 þ yðy þ l2 Þ ð4Þ By ¼
Hg 2π
(
þln
ln
ðxþlg Þ2 þðyþy1 Þ2 ðxþl1 þlg Þ2 þðyþy1 Þ2 ln ðxþl 2 2 ðxþlg Þ2 þy2 1 þlg Þ þy
ðxl2 lg Þ2 þ ðy þ y2 Þ2 ðxl12 lg Þ2 þ y2
ln
ðxlg Þ2 þ ðy þ y2 Þ2 ðxlg Þ2 þ y2
) ð5Þ
Fig. 1(b) shows the calculated normal component of leakage field profile for two adjacent cylindrical slots S1 by using Eq. (5).
3. Materials and methods The magnetically polarizable nanoemulsion consists of an octane-based ferrimagnetic nanoparticle dispersion with a particle size of about 10 nm, an ionic surfactant of sodium dodecyl sulfate (CH3 (CH2)10CH2SO4 Na+) and water. The iron oxide (Fe3O4) nanoparticles used in our study were synthesized by a simple coprecipitation technique [41,42]. The oil-in-water (O/W) ferrofluid emulsion was prepared using a simple emulsification procedure [43]. The octane-based ferrimagnetic nanoparticle dispersion was sheared in the presence of water containing sodium dodecyl sulfate. The first step leads to the formation of a water-in-oil (W/O) emulsion with a very large size distribution, which is then inverted to an O/W emulsion using a colloidal mixer. The resultant polydisperse emulsion is converted to a fairly monodisperse one with a narrow droplet size distribution using a fractionation technique that exploits the depletion flocculation under added surfactant
20
y
15
x y2
y1
10 Hy (mT)
2lg
5 0 -5
l2
-10 -15
l1
0.0
0.5
1.0 1.5 2.0 Position (cm)
2.5
Fig. 1. (a) Schematic representation of adjacent cylindrical slots (defects). (b) Calculated normal component of leakage field profile for two adjacent cylindrical slots (S1) by using eq. (5) with Hg ¼ 30.0 mT and lift off distance 1 mm.
102
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
leakage flux around the defect were measured at a constant lift-off distance of 1 mm, using hall probes. The specimen was first magnetized with an electromagnetic yoke and then the thin film sensor was placed on the rear surface, at a constant lift of distance of 1 mm. The color pattern on the sensor was recorded by using a digital camera or observed with naked eye. The regions where color changes observed in the sensor were analyzed more carefully to assess the severity of the defects. The sensor is scanned across the specimen surface to map the MFL profile across the entire specimen. To understand the color pattern and the defect location and morphology, leakage field from different specimens were measured using a Hall probe.
micelles [44]. The size selected emulsions are washed with an anionic surfactant (SDS) and triblock polymers (pluronics-F108). To stabilize the emulsion with SDS, it was washed with 2.8 mM SDS solution three times and then diluted with the same concentration of SDS and equilibrated for 72 h. For F108 stabilization, the emulsion is washed three times with desired concentration of F108 and then incubated for 72 h. SDS stabilizes emulsion droplets electrostatically and F108 stabilizes emulsion sterically. The diffraction wavelength depends on the emulsion droplet size and zeta potential determines the stability of the emulsion system. The size distribution and zeta potential of the final emulsions has been measured by using a Malvern Zetasizer (ZS) that works on the principle of dynamic light scattering (DLS). Fig. 2a shows the size distribution of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM. Fig. 2b shows the zeta potential of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM. The average size (diameter) and zeta potential of the emulsion droplets stabilized with SDS and F108 are 190 nm and 65 mV, and 220 nm and 10 mV, respectively. To fabricate the sensor, the nanoemulsion is sandwiched between two optically transparent (microscopic) glass slides. A spacer of 300 mm thickness is used to achieve desired uniform gap. The sides of the cells are sealed to avoid seepages, moisture trapping and contamination of emulsion with foreign particles from outside.
5. Results and discussions Fig. 4a–c shows the measured normal component of MFL signals across the defect length on samples S1, S2, and S3 a–d, respectively. It can be seen that the normal component shows maxima at the edges of the defect walls and zero at the center of the defect. Here, the low magnetic permeability of slot region (air) compared to that of the specimen leads to leakage of magnetic flux around the defective region. In general, the experimental MFL signal is less that the predicted value because of magnetic refraction, magnetic diffusion, and magnetic compression [8]. However, the theoretical model is very useful in predicting the MFL profiles from specimens, especially the one with multiple defects. The observed flux profiles were in reasonably good agreement with the locations where the color changes are observed on the sensor. The offset values of the magnetic flux at the edges of the samples are due to the background noise from yoke. Fig. 5a–i shows the schematics of the specimen S1, S2, S3(a–d) and the corresponding photographic images of the nanofluid sensor. The sensor exhibits a natural color of brown (without MFL) due to the presence of iron oxide nanoparticles in the oil droplets.
4. Experimental setup Mild steel plates of 50 mm length, 25 mm width and thickness 10 mm with several well defined defects dimensions and shapes were fabricated for the test. The different defect geometries used for the study and their dimensions are listed in Table 1. Fig. 3 shows schematic representation of a typical experimental setup for leakage field measurement and defect imaging. The specimen was magnetized using a DC magnetic yoke. The normal and tangential components of
250
25 SDS washed F108 washed
15 10 5 0 10
SDS washed F108 Washed
200 Counts (a.u)
Number (%)
20
150 100 50 0 -150 -100
100 1000 Size (nm)
-50 0 50 100 Zeta potential (mV)
150
Fig. 2. (a) Size distribution of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM. (b) zeta potential of the emulsion stabilized with SDS 2.67 mM and F108 0.5 mM.
Table 1 Details of the specimens and the simulated defect dimensions. Specimen
S1 (two cylindrical) S2 (rectangular, cylindrical) S3a (rectangular) S3b (rectangular) S3c (rectangular) S3d (rectangular)
Width/Diameter (mm)
Depth (mm)
Slot1
Slot2
Slot1
Slot2
5 3 0.5 0.5 0.5 0.5
10.5 6.5 – – – –
8 4 2 3 4 5
7.5 8 – – – –
Gap between the slots 2lg (mm)
Specimen dimension (L B T) (cm)
5 8 – – – –
21 2.5 1 21 2.5 1 21 4.5 1 21 4.5 1 21 4.5 1 21 4.5 1
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
103
Power supply
RS 232
RS 232
Gauss meter
Computer A/D X-Y driver Y-Stage
CCD Detector
Spectrograph
Halogen Lamp Variable attenuator
X -Stage Hall probe Focusing optics
Specimen
Emulsion film Electromagnet
BS
Optical fiber Fig. 3. Schematic representation of typical experimental setup used for magnetic leakage field measurement and defect imaging with nanoemulsion.
The defective region of each sample is encircled for clarity. At the center of the defect, no color pattern is observed because the leakage field of the normal component is zero at the center of the defect, as seen in Figs. 4a and b. The contribution from the tangential component is almost negligible due to the finite thickness of the sensor and the relatively large lift of distances used. On both sides of the defect, a color spectrum is observed due to the leakage of the magnetic flux around the defects. The center line on the color pattern was straight for the rectangular slot and semicircular for the cylindrical slot. Fig. 5e shows the photograph of the specimen S3(a–d) and the corresponding sensor images are shown in Fig. 5f–i. The defects are discernible from the images, though color contrasts were not very good. This is mainly due to the lower leakage flux, owing to the smaller defects present and the larger remanent thickness of the specimens. However, the defect center was very clearly discernible from the image. It is possible to obtain the color changes in the above cases by increasing the magnetization value, which was not possible with the magnetic yoke used in our experiments. The measured values of normal component of the leakage field plotted across the specimen (S3) length (Fig. 4c) shows that the locations of the defect, determined from the minimum value of MFL signal, are in good agreement with the measured values. The leakage flux values for S3.1, S3.2, S3.3 and S3.5 are 0.58, 0.7, 0.8 and 0.9 mT, respectively. For the calculations of the normal component of leakage field, we have used y¼1 mm and Hg ¼30 mT. The defect locations are indicated by dashed lines. To test the response and reversibility of the color appearance of the nanofluid sensor, it is tested under different magnetization conditions. Without external magnetic field, emulsion droplets are randomly oriented because of the thermal energy and appear as brown color, which is the intrinsic color of Fe3O4. As the specimen
is magnetized to a lower magnetic field, the leakage field intensity around the defect region causes the formation of colors, which become more intense when the magnetization field is increased to 35 mT. Fig. 6(a–c) shows the sensor response at different magnetization conditions for the specimen S1 with increasing magnetizing value from zero to a maximum. Fig. 6(c–e) shows the sensor response with decreasing magnetizing field from the maximum to zero. From the color pattern, the shape and location of the defects on the far side of scanning surface are discernible. Upon removal of the magnetic field, the color pattern almost disappears. The pattern persists for some time after the removal of field, because of the remanent field present on the samples surface. This demonstrates the re-usability of the flux sensor after the use and the reversible formation of one dimensional (1D) arrays in the fluid. The time (tc) required for droplets to form 1D arrays at a given field strength, depends on the competition between the magnetic force and viscous force experienced by the oil droplets in the carrier liquid. It is given by t c ¼ ða=5Þ 6πaη=F chain ððr=2aÞ5 1Þ where ‘η’ is the viscosity of the medium, ‘a’ droplet radius, ‘r’ distance between the droplets and ‘Fchain’ is the magnetic force acting between the droplets [45]. The typical response time for SDS and F108 stabilized droplets of diameters 172 nm and 220 nm, at an r value of 50 nm is around 0.8 ms and 1.5 ms respectively. From the color pattern observed on the sensor, along with the theoretical knowledge of the MFL profiles across defects of different geometries, the shape and location of the defects is clearly identifiable. For quantification of defect size and to compare the observed color pattern with MFL values, we have recorded the reflected Bragg peak from the sample at 1801 (back scattering) using a fiber-based reflection probe whose working principle was discussed earlier [46]. Fig. 7 shows the reflected Bragg peak from a typical nanofluid emulsion stabilized with
104
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
20
Hy (mT)
10 0 -10 -20 -40
-20
0
20
40
Position (mm)
Hy (mT)
20
0
-20
-30
-20
-10
0
10
20
Position (mm)
1.2
S3c
S3b S3a
0.6 Hy (mT)
S3d
0.0 -0.6 -1.2 0
30
60
90
120
Position (mm) Fig. 4. Measured normal component of the MFL signals across the samples length for (a) S1 (b) S2, and (c) S3a–d, respectively. The shape and locations of the defects are also indicated.
(a) an anionic surfactant (SDS) and (b) a triblock copolymer pluronics (F108), as a function of applied magnetic field strength. An increase in the magnetic field from 0 to 18 mT results in a diffraction peak at 890 nm for anionic surfactant stabilized emulsion and 840 nm for the pluronics (F108) stabilized emulsion. In both the cases, the Bragg peak (λmax) monotonically blue shifts (moves towards lower wavelength) with increasing magnetic field strength (shown by the arrow). In the absence of a magnetic field, droplets are under Brownian motion without any ordering, as evident from the fact that the individual magnetic grains inside the droplets are randomly oriented. The diffraction peaks are symmetric and narrow in all the cases, which indicate that the 1D ordering is stable. As the nanofluid droplets used in our studies
are super-paramagnetic in nature, an applied field induces a magnetic dipole in each drop, causing them to form chains. On applying an external field ‘H0’, the strength of the dipolar interaction between the droplets increases, which is described by the 3 coupling constant [47] Λ ¼ πμ0 d χ 2 H 20 =72kB T, where ‘d’ is the particle diameter, ‘χ’ and ‘kB T’ are the magnetic susceptibility and thermal energy, respectively. When Λ41, the emulsion forms linear chain-like structures along the field direction [47,48]. Typically for a droplet of 180 nm diameter, the required H0 value to see 1D structures is 5–10 mT. The magnitude of the magnetic dipole moment increases with the strength of the applied field until saturation is reached. At low concentration, one droplet thick chains are well separated and oriented along the field direction. Due to the presence of the onedimensional ordered structure, a Bragg peak can be observed, from which the interdroplet separation is estimated precisely. The condition for forming a linear chain is that the repulsive force between the droplets must exactly balance the attractive force between the droplets induced by the applied magnetic field. The dominant force for the field induced droplet chain is the dipole– dipole attraction. In addition, the van der Waals attraction becomes significant at short distances. The attractive dipole force within an infinitely long chain is given by [49] F chain ¼ ∑1 n¼1 nð6m2 =ðndÞ4 Þ, ‘m’ is the induced magnetic moment of each drop, which depends on the intrinsic susceptibility of the nanofluid drops. m ¼ μ0 4πa3 χ s H T =3. Here,‘μ0’ is the magnetic permeability of free space, ‘HT’ is the total magnetic field acting on each drop and ‘χs’ is the susceptibility of a spherical drop. Due to strong surface tension, the elongation of the nanometer-sized droplet is very small. The multipole contributes less than 10 3 Fm and hence can be neglected. The spacing between the droplets is directly measured from the determination of the spectral distribution of the scattered light at a constant angle. For perfectly aligned particles with a separation ‘d’, the first order Bragg condition leads to 2d ¼ λ0 =n, where ‘n’ is the refractive index of the suspending medium (n ¼1.33 for water) and ‘λ0’ is the wavelength of the light Bragg scattered at an angle of 180 degrees. Because the droplets are monodispersed and negligibly deformable owing to their large capillary pressure, the corresponding interfacial separation is h ¼ d2a. Because of angle dependant diffraction peak, for quantitative evaluation of the defects using the color pattern, it is important to collect the Bragg peak (color pattern) at a given angle of incidence throughout the experiments. Fig. 8 shows the diffraction peak wavelength (λmax) as a function of magnetic field for electrostatically (SDS stabilized) and sterically stabilized (F108 stabilized) nanoemulsions. With increasing magnetic field, λmax shifts to lower wavelength and falls exponentially. The exponents for SDS stabilized emulsion with concentration of 2.67 mM is 0.04 and F108 stabilized emulsion with 0.1, 0.2, 0.3, 0.5 and 0.75 mM concentrations were, 0.037, 0.057, 0.083, 0.088 and 0.05, respectively. From Fig. 8 it is evident that the emulsion stabilized with SDS respond at a minimum leakage field of 20 mT and the emulsion stabilized with 0.35 mM F108 respond even at a lower field of 10 mT. These results suggest that the steric stabilization of emulsion droplets with appropriate polymers such as F108 provides better sensitivity to defect detection compared to charge stabilization. The force distance profile measured by using magnetic chaining technique and the details are discussed in an earlier publication [50]. Fig. 9 shows the repulsive force distance profile, for SDS stabilized and F108 stabilized magnetic nanoemulsion. In both the cases, the force profiles were repulsive in nature and decay exponentially. For emulsion stabilized with 2.67 mM SDS, the force profile (for κa o5), is fitted with the classical electrostatic force profile equation F r ðrÞ ¼ 4πεψ 20 a2 ½κ=r þ 1=r 2 exp½κðr2aÞ, where 0 a0 is the droplet radius, 0 r 0 is the droplet separation
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
105
Fig. 5. (Color online) Schematics of the specimens S1–S3a–d (a, c and e) and the corresponding photographic images of nanofluid sensor (b, d and f–i), respectively. (f–i ) are S3a, S3b, S3c, S3d, respectively. The defective regions of each sample are encircled.
Fig. 6. (Color online) Sensor response at different magnetization conditions for the specimen S1 (a)–(c) sensor response with increasing magnetizing value from zero to a maximum (35 mT) and (c)–(e) sensor response with decreasing magnetizing field from maximum to zero.
distance, 0 ε0 is the dielectric permittivity of the suspending medium, 0 ψ 0 0 is the electrical surface potential and 0 κ 0 is the inverse Debye length that essentially depends on the electrolyte concentration ðC s Þ and can be represented as [51] κ1 ¼ ð1=4πÞ½2L2B C s 0:5 , where ‘LB ’ is the Bjerrum length. The solid line in Fig. 9 shows the theoretical curve and the symbols represent the experimental data points. These results shows a perfect agreement of experimental data with the electrostatic theory. The force profiles for sterically stabilized system can be represented by a simple exponential function FðhÞ ¼ kexpðh=λÞ, where ‘h’ is the interdroplet spacing and ‘λ’ is the decay length [52]. Here, the decay length is comparable to the radius of gyration of the adsorbed polymer. For sterically stabilized systems, theoretical approach of mean field and scaling approach distinguishes the loops and tail sections of the adsorbed chains that involves three length scales. The adsorbed layer thickness ‘λ’ and an adsorption length zn that separates the regions where the monomer concentration is dominated by loops and by tails and a microscopic length ‘b’ that is inversely proportional to the adsorption strength. At distances larger than λ, the concentration is dominated by the tails, and the force is always repulsive and decays exponentially with the distance. By using the Derjaguin approximation, in the scaling theory, we obtain the expression for force between two spherical droplets of radius R, FðhÞ ¼ ðkb TπR=λ2 Þexpðh=λÞ where kb is the Boltzmann constant and T is
the temperature. The above expression is valid only when the adsorbed polymer amount is close to the salutation value. The decay length obtained for the F108 of 0.1, 0.2, 0.3, 0.5 and 0.75 mM concentrations are 24, 21, 18, 16 and 19.5 nm, respectively. The magnitude of the pre-factor obtained from our experimental values in the above concentration range were 3.2103 10 11, 5.3341 10 11, 1.7023 10 10, 1.5033 10 10 and 1.8984 10 11, respectively, which were in agreement with the theoretically calculated values. The schematic representation of anionic surfactant and F108 stabilized nanoemulsions are shown in Fig. 10. Fig. 10a represents the emulsion droplet stabilized with SDS where the charged head group adsorbed at the oil–water interface and non polar chain extended in the oil phase. Fig. 10b schematically shows triblock copolymer stabilized emulsion droplet with randomly oriented magnetic nanoparticle. The hydrophobic PPO (blue chain) extended into the oil phase while, the hydrophilic PEO extends into the water and provide steric hindrance to oil droplet flocculation. The extended hydrophilic polymeric chains seems to provide better sensitivity (response at lower magnetic field) through interactions at larger interdroplet spacing and the charged head groups of the surfactant molecules provides electrostatic stabilization in presence of ionic surfactants. This was evident in Fig. 8 where pluronic stabilized emulsion droplets showed a better response compared to the SDS stabilized emulsion.
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
10
37.8 mT 36.0 mT 34.0 mT 32.4 mT 30.6 mT 28.8 mT 27.0 mT 25.2 mT 23.4 mT 21.6 mT 19.8 mT 18.0 mT
Increasing field
Refelectance (%)
8 6 4
10-11
Force (N)
106
2.67 mM SDS 0.1 mM F108 0.2 mM F108 0.3 mM F108 0.5 mM F108 0.75 mM F108
10-12
2 10-13 0 600
700 800 Wavelength (nm)
40
900
60
80 100 h (nm)
120
140
Fig. 9. Force profile as a function of inter-droplet spacing for SDS and F108 stabilized emulsions of different concentrations.
33.3 mT 30.6 mT 27.0 mT 23.4 mT 20.7 mT 19.9 mT 18.9 mT 18.0mT 17.0 mT 16.2 mT
6 Reflectance (%)
Increasing field
4
2
0
700
750 800 Wavelength (nm)
850
Fig. 7. The reflected Bragg peak from a typical nanofluid emulsion stabilized with (a) an anionic surfactant and (b) a triblock copolymer pluronics (F108) as a function of applied magnetic field strength.
λ max
900
2.67 mM SDS 0.1 mM F108 0.2 mM F108 0.3 mM F108 0.5 mM F108 0.75 mM F108
800
700
10
15
20 25 30 Field (mT)
35
40
Fig. 8. Bragg Peak wavelength as a function of applied magnetic field strength for nanoemulsion with 2.6 mM concentration of sodium dodecyl sulfate (SDS) and with F108 of different concentrations varying concentration from 0.1 to 0.75 mM.
6. Quantification of defects from the color images To further quantify the defect dimensions and morphologies precisely from the color images seen on the sensor, the Red– Green–Blue (RGB) profiles of the images are obtained. Fig. 11(a and b) shows the color images seen on the sensor and the RGB
Fig. 10. Schematic representation of emulsion droplet stabilized with a. anionic surfactant and b. triblock copolymer-F108. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).
intensity profiles across the pixels, respectively for the specimen S1. The line profile obtained along the center line is shown by the dotted line. For a larger cylindrical slot, all the three intensity profiles (RGB) peak at the center of the defect because the MFL is zero at this point where no 1D ordering occurs. The blue lines peak at the edges of the cylinder where the flux lines are maximum and hence the interdroplets spacing is at minimum. Therefore, from the pixel calibration and measurement of the lowest wavelength maximum (blue in the present case), it is possible to precisely calculate the dimensions of the cylindrical slot. Since the slot on the right side is smaller compared to the left, the MFL intensity was lower and hence the green profiles peak at the edges. In the case of adjacent defects, leakage fields from the two cracks overlap and the maximum leakage field at the edge of smaller crack is influenced by the leakage field from larger crack. Due to this overlap, blue intensity is observed at the defect center. In the case of near surface crack, leakage field is larger and hence the color pattern will be intense. An increase in the thickness of the sensor or lift off distance could decrease the intensity of the MFL signal. In such cases, the Bragg peaks (color) would shift towards the higher wavelength (i.e. red shift). Again, by measuring the distance between the peaks and by use of proper pixel calibration, it is possible to precisely measure the far side defect dimensions. Fig. 12a shows the sensor response for specimen S2 and Fig. 12b shows the corresponding RGB intensity profiles as a function of pixel number. The line profiles are obtained along the dotted line shown in the image. Fig. 13a shows the color image seen on the
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
107
250 200
150 Intensity
Intensity
200
150
100
100
50 50
0
100
200
300
400
500
600
0
Pixels
100
200
300
400
Pixels
sensor for a rectangular slot and Fig. 13b shows RGB intensity profiles as a function of pixels. In all the above three cases, the defect center line and the boundaries are very clearly discernible from the RGB profiles. Therefore, color images under magnetization along with the RGB profiles enable us to precisely estimate the location, morphology and dimensions of the defect using the new sensor. The dimensions measured from the RGB profiles fell within 5% error from the actual defect dimensions. It should be noted that the pixel values are different in the above cases because of the imaging conditions (zoom factor and camera to sensor distance). Therefore, each image is taken with meter scale on the side (as in Fig. 5) for pixel calibration. Fig. 14a shows the schematic representation of two adjacent cylindrical slots, Fig. 14b shows the normal component of leakage field along with the schematic representation of droplet confirmation and Fig. 14c the photographic image of the sensor under magnetic excitation. It can be seen that the droplet conformation at any point in the sensor (random or one dimensional array with distinct interdroplet spacing) depends on the MFL flux profile at that point. Finally, the sensor is also capable of detecting hidden magnets or magnetic materials. The photograph of the sensor response when it was kept on a nonmagnetic material with a hidden ring magnet inside is shown in Fig. 15. The boundaries of the magnets is clearly discernible from the colors seen in the nanofluid sensor. The temperature dependant studies show that the present sensor can work in the temperature range of 5–60 1C. The smallest defect we have detected using this sensor (with 0.3 mM F018 stabilized emulsion) is 500 μm width at a lift off distance 1 mm below the surface. There are possibilities to further improve the sensitivity by using emulsions with improved stabilization and also with enhanced magnetization fields.
7. Conclusion In the conclusion, a methodology to image and visualize defects buried inside ferromagnetic components by using magnetically
Fig. 12. (Color online) (a) Observed sensor response for S2 and (b) RGB intensity profiles as a function of pixel. The line profiles are obtained along the center line shown by a dotted line.
200
150 Intensity
Fig. 11. (Color online) (a) Magnetic nanoemulsion sensor response for S1 and (b) RGB intensity profiles as a function of pixels. The line profiles are obtained along the center line shown by a dotted line. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).
100
50
0
100
200
300
Pixels Fig. 13. (Color online) (a) Sensor response for a rectangular slot and (b) RGB intensity profiles across the as a function of pixel. The line profiles are obtained along the image center line shown by a dotted line. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article)
polarizable nanoemulsion has been demonstrated. The nanofluid shows a visually perceivable color change due to the changes in the interparticle spacing within the self-assembled nano-arrays in presence of a defect or leaked magnetic flux. Defect dimensions are quantitatively extracted from the Bragg peak shift and the RGB profile analysis. Extracted defect widths are in good agreement with the magnetic flux leakage measurement. As the color pattern in the sensor is reversible, the sensor is reusable and allows rapid inspection of large area specimens.
108
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
Fig. 14. (Color online) (a) Schematic representation of two adjacent cylindrical slots (b) the normal component of leakage field along with the schematic representation of droplet confirmation and (c) the photographic image of the sensor under magnetic excitation.
Fig. 15. (Color online) Sensor response to a ring magnet concealed inside a material. The dotted lines indicates the outer edges of the ring magnet.
Acknowledgment J.P. thanks BRNS for funding of a perspective research grant on development of advance nanofluids. Authors thank Dr. T. Jayakumar and Dr. P.R. Vasudeva Rao, for support and encouragements.
Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at 10.1016/j.ndteint.2013.07.011.
References [1] Blitz J. Electrical and magnetic methods of nondestructive testing. New York: Adam Hilger; 1991. [2] Forster F. New findings in the field of non-destructive magnetic leakage field inspection. NDT and E International 1986;19:3–14. [3] Wang ZD, Gu Y, Wang YS. A review of three magnetic NDT technologies. Journal of Magnetism and Magnetic Materials 2012;324:382–8.
[4] Gloria NBS, Areiza MCL, Miranda IVJ. Rebello JMA. Development of a magnetic sensor for detection and sizing of internal pipeline corrosion defects. NDT and E International 2009;42:669–77. [5] Kopp G, Willems H. Sizing limits of metal loss anomalies using tri-axial MFL measurements: a model study. NDT and E International 2013;55:75–81. [6] Le M, Lee J, Jun J, Kim J. Estimation of sizes of cracks on pipes in nuclear power plants using dipole moment and finite element methods. NDT and E International 2013 (In press). [7] Sun Y, Kang Y. A new MFL principle and method based on near-zero background magnetic field. NDT and E International 2010;43:348–53. [8] Sun Y, Kang Y, Qiu C. A new NDT method based on permanent magnetic field perturbation. NDT and E International 2011;44:1–7. [9] Tsukada K, Yoshioka M, Kiwa T, Hirano Y. A magnetic flux leakage method using a magnetoresistive sensor for nondestructive evaluation of spot welds. NDT and E International 2011;44:101–5. [10] Krause HJ, Kreutzbruck Mv. Recent development in SQUID NDE. Physica C 2002;368:70–9. [11] Tehranchi MM, Ranjbarana M, Eftekhari H. Double core giant magnetoimpedance sensors for the inspection of magnetic flux leakage from metal surface cracks. Sensors and Actuators A 2011;170:55–61. [12] Allweins K, von Kreutzbruck M., Gierelt G. Defect detection in aluminum laser welds using an anisotropic magnetoresistive sensor array. J Appl Phys 2005;97:10Q102 (3 pp.), http://dx.doi.org/10.1063/1.1852391. [13] Jun J, Lee J. Nondestructive evaluation of cracks in a paramagnetic specimen with low conductivity by penetration of magnetic fluid. NDT and E International 2009;42:297–303. [14] Tehranchi MM, Hamidi SM, Eftekhari H, Karbaschi M, Ranjbaran M. The inspection of magnetic flux leakage from metal surface cracks by magnetooptical sensors. Sensors and Actuators A 2011;172:365–8. [15] Klank M, Hagedorn O, Holthaus C, Shamonin M, Dotsch H. Characterization and optimization of magnetic garnet films for magneto-optical visualization of magnetic field distributions. NDT and E International 2003;36:375–81. [16] Ravan M, Amineh RK, Koziel S, Nikolova NK, Reilly JP. Sizing of multiple cracks using magnetic flux leakage measurements. IET Science, Measurement and Technology 2010;4:1–11. [17] Kikuchi H, Sato K, Shimizu I, Kamada Y, Kobayashi S. Feasibility study of application of MFL to monitoring of wall thinning under reinforcing plates in nuclear power plants. IEEE Transactions on Magnetics 2011;47:3963–6. [18] Philip J, Rao CB, Jayakumar T, Raj B. A new optical technique for detection of defects in ferromagnetic materials and components. NDT and E International 2000;33:289–95. [19] Mahendran V, Philip J. Nanofluid based optical sensor for rapid visual inspection of defects in ferromagnetic materials. Applied Physics Letters 2012;100:073104. [20] Edwards C, Palmer SB. The magnetic leakage field of surface-breaking cracks. Journal of Physics D: Applied Physics 1986;19:657–74. [21] Pashagin AI, Bogdanovich BN, Shcherbinin VE. On correlation between the field and the flaw dimensions in magnetic nondestructive testing of hot-rolled pipes. Defektoskopiya 1988;3:91–3. [22] Mandal K, Atherton DL. A study of magnetic flux-leakage signals. Journal of Physics D: Applied Physics 1998;31:3211–8. [23] Zhang Y, Sekine K, Watanabe S. Magnetic leakage field due to sub-surface defects in ferromagnetic specimens. NDT and E International 1995;28:67–71. [24] Uetake I, Saito T. Magnetic flux leakage by adjacent parallel surface slots. NDT and E International 1997;30:371–6. [25] Minkov D, Shoji T. Method for sizing of 3-D surface breaking flaws by leakage flux. NDT and E International 1998;31:317–23. [26] Mandache C, Clapham L. A model for magnetic flux leakage signal predictions. Journal of Physics D: Applied Physics 2003;36:2427–32. [27] Lukyanets S, Snarskii A, Shamoninc M, Bakaevc V. Calculation of magnetic leakage field from a surface defect in a linear ferromagnetic material: an analytical approach. NDT and E International 2003;36:51–5. [28] Gobov YL, Loskutov VR, Reutov YY. A dipole model for magnetic leakage fields created by a circular butt-wellded joint. Defektoskopiya 2005;41:85–90. [29] Hwang JH, Lord W. Finite element modeling of magnetic field/defect interactions. Journal of Testing and Evaluation 1975;5:21–5. [30] Lord W, Hwang JH. Defect characterization from magnetic leakage fields. British Journal of Non-Destructive Testing 1977;19:14–8. [31] Schifini R, Bruno AC. Experimental verification of a finite element model used in a magnetic flux leakage inverse problem. Journal of Physics D: Applied Physics 2005;38:1875–81. [32] Al-Naemi FI, Hall JP, Moses AJ. FEM modeling techniques of magnetic flux leakage type NDT for ferromagnetic plate inspection. Journal of Magnetism and Magnetic Materials 2006;304:790–3. [33] Li Y, Tian GY, Ward S. Numerical simulation on magnetic flux leakage evaluation at high speed. NDT and E International 2006;39:367–73. [34] Zuoying H, Peiwen Q, Liang C. 3D FEM analysis in magnetic flux leakage method. NDT and E International 2006;39:61–6. [35] Amineh RK, Koziel S, Nikolova NK, Bandler JW, Reilly JP. A space mapping methodology for defect characterization from magnetic flux leakage measurements. IEEE Transactions on Magnetics 2008;44:2058–64. [36] Zhang Y, Ye Z, Wang C. A fast method for rectangular crack sizes reconstruction in magnetic flux leakage testing. NDT and E International 2009;42: 369–75.
V. Mahendran, J. Philip / NDT&E International 60 (2013) 100–109
[37] Chen Z, Yusa N, Miya K. Some advances in numerical analysis techniques for quantitative electromagnetic nondestructive evaluation. Nondestructive Test Evaluation 2009;24:35. [38] Yuan X, Wang C, Zuo X, Hou S. A method of 2D defect profile reconstruction from magnetic flux leakage signals based on improved particle filter. Insight 2011;53:152–5. [39] Zatsepin NN, Shcherbinin VE. Calculation of the magnetostatic field of surface defects, I. field topography of surface defect models. Defektoskopia 1966;5:500. [40] Katoh M, Masumoto N, Nishio K, Yamaguchi T. Modeling of the yokemagnetization in MFL-testing by finite elements. NDT and E International 2003;36:479–86. [41] Shima PD, Philip J, Raj B. Synthesis of aqueous and nonaqueous iron oxide nanofluids and study of temperature dependence on thermal conductivity and viscosity. Journal Physical Chemistry C 2010;114:18825–33. [42] Muthukumaran T, Gnanaprakash G, Philip J. Synthesis of stable magnetic nanofluids of different particle sizes. Journal of Nanofluids 2012;1:85–92. [43] Philip J, Mondain-Monval O, Leal-Calderon F, Bibette J. Colloidal force measurements in the presence of a polyelectrolyte. Journal of Physics D: Applied Physics 1997;30:2798. [44] Bibette J. Depletion interactions and fractionated crystallization for polydisperse emulsion purification. Journal Colloid Interface Science 1991;147:474–8.
109
[45] Baudry J, Rouzeau C, Goubault C, Robic C, Cohen-Tannoudji L, Koenig A, et al. Acceleration of the recognition rate between grafted ligands and receptors with magnetic forces. Proceedings of the National Academy of Sciences 2006;103:16076–8. [46] Mahendran V, Philip J. Sensing of biologically important cations such as Na+, K+, Ca2+, Cu2+, and Fe3+ using magnetic nanoemulsions. Langmuir 2013;29: 4252–8. [47] Liu J, Lawrence EM, Wu A, Ivey ML, Flores GA, Javier K, et al. Field-induced structures in ferrofluid emulsions. Physical Review Letters 1995;74:2828–31. [48] Ivey M, Liu J, Zhu Y, Cutillas S. Magnetic-field-induced structural transitions in a ferrofluid emulsion. Physical Review E 2000;63:011403. [49] Zhang H, Widom M. Field-induced forces in colloidal particle chains. Physical Review E 1995;51:2099–103. [50] Philip J, Jaykumar T, Kalyanasundaram P, Raj B, Monval OM. Effect of polymersurfactant association on colloidal force. Physical Review E 2002;66:011406. [51] Israelachvili JN. Intermolecular and Surface Forces.San Diego: Academic Press; 1985. [52] Mondain-Monval O, Espert A, Omarjee P, Bibette J, Leal-Calderon F, Philip J, et al. Polymer-induced repulsive forces: exponential scaling. Physical Review Letters 1998;80:1778–81.