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Weld cracks nondestructive testing based on magneto-optical imaging under alternating magnetic field excitation
Sensors and Actuators A 285 (2019) 289–299
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Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Weld cracks nondestructive testing based on magneto-optical imaging under alternating magnetic field excitation Yanfeng Li a , Xiangdong Gao a,∗ , Qiaoqiao Zheng a , Perry P. Gao b , Nanfeng Zhang a a Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, No. 100 West Waihuan Road, Higher Education Mega Center, Panyu District, Guangzhou, 510006, China b US-China Youth Education Solutions Foundation, New York, NY, 10019, USA
a r t i c l e
i n f o
Article history: Received 12 October 2018 Received in revised form 9 November 2018 Accepted 10 November 2018 Available online 22 November 2018 Keywords: Magneto-optic systems Weld cracks Faraday MO effect Nondestructive testing
a b s t r a c t This paper researches the magneto-optical (MO) imaging law of weld cracks under alternating magnetic field excitation. Weld surface and subsurface cracks are detected by a MO sensor, and the relationship between the MO images’ characteristics and the magnetic field strength is analyzed based on the Faraday MO effect. A magnetic dipole model is proposed to study the magnetic field distribution over the weld crack. A finite element analysis (FEA) model of the weld crack is established, and the relationship between the magnetic flux leakage signal and the crack width is analyzed, which is useful for identifying cracks either on the surface or on the subsurface of the weld. A MO imaging nondestructive testing (NDT) experiment is carried out to detect weld cracks under alternating magnetic field excitation, and the difference among weld cracks is obtained by analyzing the gray values of the weld cracks’ MO images. Research results show that the magnetic flux leakage signals of weld surface and subsurface cracks can be clearly distinguished, the magnetic field intensity of the surface cracks is larger than that of the subsurface cracks at the same width, and the MO image of the weld cracks can reflect the intensity of the magnetic field through varied brightness, that is, the gray value of the MO image can match the corresponding magnetic field intensity. © 2018 Elsevier B.V. All rights reserved.
1. Introduction As one of the most important material coupling method in various industrial fields such as the aircraft industry, automobile manufacturing, and shipbuilding [1,2], laser welding technology has obvious advantages such as high density, great depth-to-width ratio, and the formation of high-quality weld with little deformation [3,4]. During laser welding, the welding process tends to be unstable due to the influence of laser power, welding speed, weld surface condition, and so on, which leads to weld defects and affects welding quality directly [5]. Therefore, it is key to accurately detect the position and width of the weld defects in real time, and the surface and subsurface crack detection is the most important. As a major test technology in industrial manufacturing, NDT plays a key role in the detection of weld defects and is usually used to monitor and ensure structural integrity [6]. There is a growing tendency to integrate nondestructive testing technologies such as ultrasonic testing (UT) [7,8], radiographic testing (RT) [9,10], mag-
∗ Corresponding author. E-mail address: [email protected] (X. Gao). https://doi.org/10.1016/j.sna.2018.11.017 0924-4247/© 2018 Elsevier B.V. All rights reserved.
netic flux leakage (MFL) [11–13], and eddy-current testing (ECT) [14] with machine vision to detect weld defects. However, each of these methods has drawbacks. For example, UT does not identify near-surface defects and requires a coupling medium during application; RT is harmful to the human body due to radiation and the experimental equipment is costly; MFL is suitable only for surface and subsurface inspection of magnetic materials, and it is difficult to detect the position and size of the welding defect; while ECT requires special signal processing techniques, and the results cannot be visualized. MO imaging is a technology to realize the visualization of the defect by detecting the amplitude of polarized light based on Faraday magneto optical effect. This technology has been developed for inspection of rivet cracks in aircraft [15], as it has high sensitivity and can be used to detect micro cracks on the surface and subsurface of aeronautical materials. Compared with traditional ECT, a novel NDT method based on MO imaging can achieve the visualization of weld cracks without complicated signal processing. Nowadays, MO imaging detection of weld cracks is based on the constant magnetic field excitation which with constant magnetic field magnitude and direction [16]. However, it is difficult for a MO sensor to detect multi-directional weld cracks under constant mag-
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netic field excitation and is easily saturated. In recent years, there has been increasing interest in the research of MO imaging induced by alternating magnetic fields. Some research teams have begun to study MO imaging characteristics under alternating magnetic field excitation [17,18]. It has been proved that MO imaging detection method has a broad application prospect in micro-weld tracking [19,20], and MO image characteristics of weld defects under alternating magnetic field excitation have been investigated [18]. Based on the above research, this paper aims to explore an innovative and effective method based on MO imaging for the detection of weld surface and subsurface cracks. The proposed method has the ability to detect different types of weld cracks based on characteristic signals of the leakage magnetic field and the gray value of the MO image. However, the MO imaging mechanism of weld cracks under alternating magnetic field is still under research, and the relationship between crack characteristics and the corresponding MO image is not clear, which limits the application of this NDT method in weld crack detection. In this paper, the effect of the external magnetic field on the MO imaging of the weld crack is studied based on the Faraday MO effect, and the imaging mechanism of this method is further analyzed. A magnetic dipole model is used to simulate the magnetic field distribution over the weld crack. The distribution characteristics of the magnetic flux leakage signal are analyzed by finite element modeling. Weld surface cracks and subsurface cracks are subjected to MO imaging NDT testing to analyze imaging characteristics under alternating magnetic field excitation. This paper proposes a magnetic distribution model of weld cracks according to the magnetic charge theory. The validity of the model is verified by both computer simulation and actual experiment contrastive analysis, and the characteristics of weld cracks’ magnetic field distribution are summarized. Combined with the gray distribution law of an MO image, the characteristics of MO images of different weld cracks are studied, which lays a foundation for improving the accuracy of weld crack classification. The paper is organized as follows. Section 2 introduces the mechanism of MO imaging and analyzes the MO imaging feature of weld defect. Section 3 presents the magnetic field distribution of weld crack by established theoretical model of crack leakage magnetic field. Section 4 analyses the magnetic field distribution of surface and subsurface cracks using the FEA method. Section 5 validates the simulation results through MO imaging detection experiments of weld cracks. Section 6 draws conclusions. 2. Weld crack detection based on MO imaging 2.1. Faraday MO effect
of light, and the propagation of light in a magnetic field is used to detect weld cracks. The MO film is composed of a mirror coating and a MO medium, which is placed on the weld. The light from a light-emitting diode (LED) source passes through the polarizer to become a linearly polarized light. The polarized light propagates through the MO medium and is reflected by the mirror coating, which contains crack information under the medium. Therefore, the linearly polarized light may carry weld crack information and propagate through an analyzer, before finally being captured by a complementary metal oxide semiconductor (CMOS) camera and then forming a 2D-real-time visualization image. As seen from Fig. 2(a), when the linearly polarized light passes through the MO film where a magnetic field does not exist, the projection of the amplitude of the linearly polarized light on the analyzer can be described as: l0 = Ecosϕ
(2)
where E is the amplitude of the linearly polarized light, and ϕ is the angle between the vibrating plane of the light and the analyzer. When the weldment is magnetized, if there is a crack defect on the surface of the weldment, both north (N) and south (S) pole magnetic fields will be formed on the defect edge due to the magnetic domain [25]. According to the Faraday MO effect, under the applied force of the N pole magnetic field, the vibrating plane of the polarized light will rotate in clockwise direction, while the applied force of the S pole pushes the polarized light to rotate counterclockwise by angle . The projections of the light amplitude on the analyzer are:
l1 = Ecos ϕ −
MO imaging technology is based on the Faraday MO effect [21,22]. When incident linearly polarized light passes through a MO film, if an external magnetic field is applied in the direction of the propagation of the incident linearly polarized light, the vibrating plane of the polarized light will rotate an angle . The Faraday rotation angle can be described by [23]: = VBL
Fig. 1. Principle diagram of MO imaging.
(1)
where B (in teslas) is the external magnetic flux density, L (in meters) represents the length of the polarized light’s passing through the MO film, and V (in radians per tesla per meter) represents the Verdet constant for material. When the optical film material and thickness are given, the deflection direction of the Faraday rotation angle is related only to the direction of the applied magnetic field [24]. The principle diagram of the MO imaging device is shown in Fig. 1. Humans have been studying the characteristic of propagation
l2 = Ecos ϕ +
(3) (4)
The corresponding light intensity Ii (i = 0, 1, 2) can be expressed as: I0 = l02 = E 2 cos2 (ϕ) I1 = l12 = E 2 cos I2 = l22 = E 2 cos
2 2
ϕ− ϕ+
(5) (6) (7)
The corresponding MO imaging light intensity of Ii (i = 0, 1, 2) is shown in Fig. 2 (b). I0 for the middle one and has an orange look, I1 for the left one and has a bright-yellow look, while I2 for the right one and has a brown look. The gray value of I1 is the highest, which corresponds to the N pole light intensity, and the gray value of I2 is the lowest, which corresponds to the S pole light intensity. Therefore, the relationship between these light intensities is I2
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Fig. 2. Polarization angle versus light intensity of MO imaging.
MO film is very sensitive to a magnetic field. It is precisely because of this sensitivity that a MO film can be used to detect magnetic materials that are less sensitive to a magnetic field than itself. The magnification hysteresis loop of the MO film is shown in Fig. 3. The saturation magnetization of the MO film is ±2.5 m T under the magnetic field intensity of ± 2 kA/m, and the Faraday rotation angle of the linearly polarized light is at about ±7.5◦ . The symbol of “-” indicates that the Faraday rotation angle is clockwise, while “+” is counterclockwise.
2.3. MO imaging feature analysis of weld defect
Fig. 3. Hysteresis loops of Q235 and MO film.
2.2. Hysteresis loops of weldment and MO film MO film magnetism changes cause differences between MO images. Fig.3 shows the hysteresis loops of Q235 and the MO film. It is observed that the MO film rapidly reaches saturation magnetization while Q235 is still magnetized, which indicates that the MO film is more sensitive than Q235 in the same magnetic field. A
The physical image of a weld defect is shown in Fig. 4 (a). It is a natural defect formed at the weld seam. The surface of weldment was polished before inspection. The tiny weld defects of polished weldment are invisible to the naked eye, as shown in Fig. 4(b). The MO image of the weld defect is shown in Fig. 4(c). It can be seen that the size, shape, and position of the defect are consistent with the actual defect. With MO imaging, it is possible to observe tiny weld defects that are difficult for the naked eye to see. As seen from Fig. 4(c), there is a weld defect on the surface of the weldment, and both N pole and S pole magnetic fields are formed at the edge of the defect due to magnetic domains. The MO image shows the transition zone from light to dark, which contains the position information of the weld defect area. Light intensity I0 corresponds
Fig. 4. Image of weld defect.
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Fig. 6. Weldment and magnetic circuit distribution. Fig. 5. Gray distribution of weld defect MO image.
to the imaging effect in areas where the weldment is unaffected, light intensity I1 shows the imaging effect of the brighter part of the upper edge of the weld defect, while light intensity I2 shows the imaging effect of the darker part of the lower edge of the weld defect. The relationship between the light intensities of the MO image is I2
3. Magnetic field modeling of weld crack
Fig. 7. Schematic of modeling for a micro crack.
3.2. Theoretical model of crack leakage magnetic field Using a magnetic sensor to detect a leakage magnetic field can determine the existence and characteristics of the defect [26,27]. The surface crack can be considered as an infinitely long rectangle groove whose depth is h and width is 2b, using a magnetic double model to simulate the distribution of the magnetic flux leakage field of the microcrack [28,29], as shown in Fig. 7. When a horizontal magnetic field is applied, the magnetic field of the microcrack is equivalent to the magnetic charge band model with opposite polarity on both sides of the rectangle groove. The X axis of the coordinate is distributed along the surface of the weldment, and the Y axis is perpendicular to the center of the weld. The width of the magnetic charge plane is, and the magnetic charge plane density isms . The magnetic field strength generated by the magnetic charge with d width plane at P is dH1 and dH2 respectively [30],
3.1. Magnetic circuit distribution of weldment Fig. 6 shows the weldment and magnetic circuit distribution. The ferromagnetic weldment is magnetized and generates two magnetic fields Ф1 and Ф2 under alternating magnetic field excitation. Magnetic field Ф1 is generated on the weldment by the magnetic induction of an electromagnet. The magnetic field Ф1 is relatively far from the MO sensor, which is usually placed close to the weld. Although Ф1 has an effect on the formation of MO images, this effect is very small, and its influence is symmetrical to the Y axis. The magnetic field Ф2 is generated by the magnetic flux leakage formed when the main magnetic flux flows along the weldment and meets at the crack. The magnetic field Ф2 plays a dominant role in MO imaging.
dH1 =
ms d 20 r12
dH2 = −
(8)
r1
ms d 20 r22
(9)
r2
where r1 and r2 are the distance from the P point to the magnetic charge surface elements on both sides of the crack respectively, while 0 is the permeability of the vacuum. The component of dH1 and dH2 in the direction of x, y is: dH1x =
ms (b + x) 2
20 (x + b) + (y + )2
d
(10)
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Fig. 8. Distribution diagram of H above the weld crack.
dH1y =
ms (y + )
ms (b − x)
2
20 (x + b) + (y + )2
dH2x =
2
20 (x − b) + (y + )2
dH2y =
ms (y + ) 2
20 (x − b) + (y + )2
d
(11)
Defect type
d
(12) Surface crack Subsurface crack
d
Hx
h
=
=
ms 20
0
dH1x +
h 0
dH2x
arctan
h(x + b)
2
(x + b) + y (y + b)
h
dH1y + 0
Length (mm)
0.2, 0.1, 0.05, 0.01 0.2, 0.1, 0.05, 0.01
20 20
the right. The left region of the zero crossing corresponds to the N pole and the right region corresponds to the S pole.
4.1. FEA modeling of magnetic flux leakage on weld crack
dH2x 0
h(x − b)
− arctan
2
(x + b) + y (y + b)
Similarly, the total magnetic field vertical component Hy at the crack can be obtained by integrating to dHy . =
Width (mm)
1 1
4. FEA of weld crack magnetic field
(14)
Hy
Depth (mm)
h
dH1x + 0
h
Sizes
(13)
The total magnetic field horizontal component Hx at the crack can be obtained by integrating to dHx , which is shown in the following equation:Hx =
Table 1 Detailed values of surface and subsurface cracks.
h
dH2y 0
2
2
2
(x + b) + (y + b) (x − b) + y2 ms = ln 2 2 2 40 (x + b) + y2 (x − b) + (y + h)
(15)
As the magnetic flux density horizontal component Bx corresponding to Hx is perpendicular to the incident linearly polarized light, it has no rotation effect on the incident linearly polarized light; therefore, the distribution of the magnetic field Hx has no effect on the MO imaging. The magnetic flux density vertical component By corresponding to Hy is parallel to the incident linearly polarized light, so it has a rotation effect on the incident linearly polarized light. When the weld crack is considered as infinitely deep and long, and for convenience, the specific value ms /2ϕ0 in Eq. (14) is supposed to be 1, and the specific value ms /4ϕ0 in Eq. (15) is supposed to be 1. The weld crack width is 0.1 mm, and the distribution of the magnetic field intensity at the height of 0.5 mm above the weld crack is shown in Fig. 8. As seen from Fig. 8(a), the distribution of Hx is symmetrical to the Y axis. The maximum value of Hx at the center of the Y axis is obtained and gradually decreases along both sides. It can be seen from Fig. 8(b) that the magnetic field Hy is positive on the left of the zero crossing and negative on
A three-dimensional FEA model is built in ANSYS-Maxwell to analyze the magnetic flux leakage distribution and its corresponding testing signals under magnetization for cracks in a low carbon steel plate (Q235). The simulation model consists of a U-shaped inducer and excitation coils above the Q235 specimen, as shown in Fig. 9(a). Each coil is wound by 700 turns of 0.5 mm enameled copper wire. To simulate a real detection environment, the computational field of the FEA model is set to air. The amplitude of the alternating currents in each coil is 200 V and the excitation frequency is 50 Hz. The detecting position is about 0.5 mm high above the weld crack. The calculated permeability values are assigned to each element in the Q235 specimen model and alternating current is loaded into the excitation coil to generate an eddy-current magnetic field. The magnetic field distribution of the Q235 specimen is obtained. In order to obtain better results in the 3D model, fine meshes are generated in the region of interest. The entire 3D model is divided into 50,596 elements. Two representative crack models used for the simulation and experiments are shown in Fig. 9(b–c). In the 100-mm width × 2-mm depth × 100-mm length Q235 specimen, the external notch (0.05mm width × 1-mm depth × 20-mm length) represents a surface crack defect, as shown in Fig. 9(b). The internal notch (0.05-mm width × 1-mm depth × 20-mm length) acts as subsurface crack defect, as shown in Fig. 9(c). The detailed values of surface and subsurface cracks are shown in Table 1. 4.2. Analysis for surface crack width magnetic field Width detection accuracy is an important indicator of MO imaging detection of weld surface cracks. Under the same conditions
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Fig. 9. The FEA model of weld cracks and two representative crack models used for the simulation and experiments.
Fig. 10. Vertical magnetic induction intensity versus the surface crack width.
of length, depth, and magnetization, this paper studied the effect of surface cracks with different widths on the magnetic induction intensity vertical component By . The surface crack is in the center between the two magnetic poles, with a length of 20 mm, depth of 1 mm, and a width of 0.2 mm, 0.1 mm, 0.05 mm, and 0.01 mm respectively, and x = 0.5 mm is the center of the crack. The simulation results are shown in Fig. 10. It can be seen that the peak-to-valley value of the magnetic induction intensity of the vertical component By increases with the expansion of the surface crack width. The peak-to-valley spacing distance Dsp-v of the verti-
Fig. 11. Surface crack width versus Dsp-v .
cal component By displays a good proportional linear relationship with the surface crack width, as shown in Fig. 11. Therefore, Dsp-v can be regarded as a characteristic to evaluate a surface crack width. 4.3. Analysis for subsurface crack width magnetic field Under the same conditions of length, depth, and magnetization, the subsurface crack is in the center between the two magnetic poles, with a width of 0.2 mm, 0.1 mm, 0.05 mm, and 0.01 mm
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sity is at the edge of the crack. The peak-to-peak value Byp-p of the vertical component By can describe the width of the subsurface crack, which can be used as a characteristic to evaluate a subsurface crack width.
4.4. Analysis for crack position magnetic field
Fig. 12. Vertical magnetic induction intensity versus the subsurface crack width.
respectively, and x = 0.5 mm is the center of the crack. The simulation calculation of the relationship between the subsurface crack width and the magnetic induction intensity vertical component By is shown in Fig. 12. The peak-to-peak value Byp-p of the vertical component By increases as the subsurface crack width expands. According to the magnetic induction intensity distribution of the subsurface crack in Fig. 12, the maximum magnetic induction inten-
Fig. 13. Magnetic induction intensity vertical component By versus the crack width.
Under the same conditions of depth and magnetization, when the length of the crack is a constant value (20 mm), the width changes from 0.01 mm to 0.2 mm. As shown in Fig. 13 (a–d), the vertical signals of the surface and subsurface cracks increase with the linear relationship of the crack width, and the curves in the figure basically coincide. It can be seen from the diagram that the type of crack is the same and the larger the width, the greater the intensity of the magnetic flux leakage. When the width of the crack is constant, the intensity of the magnetic field leakage of the surface crack is greater than that of the subsurface crack, which means that there is magnetic leakage. It can be seen from Fig. 13 that the By is equal to zero at the coordinate x = 0.5. According to Fig. 8, when the value of By is positive, the left region of the zero crossing corresponds to the N pole, and when the value of By is negative, the right region corresponds to the S pole. At the same interval on the X axis, the vertical component By increases as the crack width expands, and the magnetic field leakage strength of the surface crack is greater than that of the subsurface crack. Considering Eq. (1), the light rotation angle
(a) Width 0.01 mm (b) Width0.05 mm (c) Width 0.1 mm (c) Width 0.2 mm
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Fig. 14. Experimental setup of MO sensing system.
varies greatly. This means that the change of the light intensity I in the transition region corresponding to the MO image will be more obvious, and the corresponding distribution of the gray value f(x, y) will also change significantly. 5. Experimental study of weld cracks by MO imaging 5.1. Experiment setup An experimental system of MO image acquisition of weld cracks is shown in Fig.14, consisting of a YAG laser welding machine, a MO sensor, an electromagnet, shielding gas, a three-axis moving experimental platform equipped with servo motors and fixtures. The electromagnet was supplied by alternating current (AC) power and generated an alternating magnetic field. The motion control platform was controlled by an industrial computer to ensure that the entire weld seam was detected. The weldment material was a plate structure made of Q235, with length of 100 mm, thickness of 2 mm, and width of 50 mm. Surface and subsurface cracks were simulated by the YAG laser welding on the abutting steel plate, with widths of 0.2 mm, 0.1 mm, 0.05 mm and 0.01 mm respectively. The MO sensor was placed on the weldments to obtain MO images of the weld cracks with a frame rate of 75 frames per second (FPS). The lift-off height of the MO sensor was set at 0.5 mm. The camera resolution was set at 400 × 400 pixels. The correspondence relation between pixel size and distance was 102 pixel/mm. Fig. 15 shows the dynamic weld crack MO imaging test in lowcarbon steel under alternating magnetic field excitation. In this test, the excitation AC voltage was set at 200 V and 50 Hz. The magnetic field of the weldment changes periodically with the voltage curve. The MO sensor can capture a frame of a dynamic MO image at every 13.3 ms. The relationship between the frame rate and voltage frequency is illustrated below. Fig. 15 shows three dynamic images of frame 1, frame 2, and frame 3, arranged as (a), (b), and (c) respectively. Therefore, it represents the same crack induced by three different magnetization directions. The MO sensor is very sensitive to the magnetic field changes, and the MO images are brighter at the N pole than at the S pole. As analyzed in previous experiments, the midline of the three frames belongs to the crack. As shown in Fig. 15(a), the first frame of the MO image collected in the test shows the imaging effect of the N pole excitation above the weld crack and the imaging effect of the S pole below the crack. As seen
Fig. 15. Dynamic MO imaging of weld crack when alternating magnetic field was applied.
from Fig. 15(b), the second frame of the MO imaging effect is opposite to the first frame. The MO image characteristics in Fig. 15(c) are not obvious around the weld crack, as the magnetic field of the yoke at both ends tends to be the same. Different weld cracks under alternating magnetic field excitation have different leakage magnetic fields, and the resulting gray value of the MO image reflects the intensity of the leakage magnetic field. 5.2. MO imaging characteristics of weld surface cracks To replicate actual weld surface cracks, a series of grooves were simulated through YAG laser welding on the surface of the abutting steel plate. The dynamic MO images acquired under alternating magnetic field excitation, such as frame 1 (F1), frame 2 (F2), and frame 3 (F3), are shown in Table 2, which contains a schematic cross-section of the weld, a physical image of the weldment, the region of interest of the F1 image, and a 3D gray image of the region of interest. As seen from Table 2, the weld surface crack information can be clearly observed, and the detailed position of the crack is displayed. In a MO image, the N pole region is bright-yellow and the S pole region is brown. Therefore, both regions show obvious
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Table 2 MO images of weld surface cracks under alternating excitation.
width, the larger the bright region of the MO image, which proves that the greater the crack width, the stronger the leakage magnetic field. 5.3. MO imaging characteristics of weld subsurface cracks
Fig. 16. Gray value in column versus the surface crack width.
chromatic aberrations. MO images can clearly reflect the distribution of the magnetic field strength. The gray value in the MO image can match the corresponding magnetic field intensity. According to the distribution of the gray value of the surface crack, it is possible to ascertain the position of the crack in the MO image without difficulty. In order to study the influence of the crack magnetic field intensity distribution on the MO imaging of cracks more easily, the regions of interest of F1 MO images were selected. As seen from Table 2, the brightness of the ROI of the F1 MO image is enhanced by the increase of the surface crack width. According to Eq. (3), if the light rotation angle increases gradually, it means that the welding magnetic induction intensity increases as the surface crack width expands. The ROI of the selected MO images are subjected to image processing to obtain a 3D distribution of the image gray values. It can be seen from Table 2 that the 3D distribution of the gray value increases with the expansion of the surface crack width. A graph is obtained from each set of images in Table 2, with different transition areas. In fact, there are various noises such as magnetic domain noise, background light noise and system noise during the detection of weld crack MO imaging. In order to reduce the effects of these noises, MO images could be processed by median filtering technique. The gray value of the image marked with a red box in the column is extracted, as shown in Fig. 16. For surface cracks, the gray value of the MO image in the column is linearly distributed. The width of 0.2 mm is the largest linear area, and 0.01 mm is the smallest. This indicates that the larger the crack
A weld subsurface crack was simulated on the upper and lower surfaces of the abutting plate by YAG laser welding, and the weld was not welded in the middle. The dynamic MO images obtained under alternating magnetic field excitation, such as F1, F2, and F3, are shown in Table 3, which contains a schematic cross-section of the weld, a physical image of the weldment, the ROI of the F1 image, and a 3D gray image of the ROI. When comparing Table 2 and Table 3, a large difference between the MO images of the weld surface crack and the subsurface crack can be noticed. The light intensity of the MO images on both sides of the weld subsurface is uniform and there is no obvious difference in brightness. As the width of the subsurface crack increases, the difference in the light intensity of the MO image also increases. Because there is no obvious difference in the brightness of the MO images on both sides of the weld crack, it is not conducive to extracting crack characteristics. Therefore, the MO image with an obvious difference between light and dark is selected in Table 3. For example, F1 with a width of 0.2 mm, F1 with a width of 0.1 mm, F1 with a width of 0.05 mm, and F1 with a width of 0.01 mm are chosen. The ROI of the selected MO images are subjected to image processing to obtain a 3D distribution of the image gray values. As it can be seen from Table 2, as the width of the crack increases, the 3D distribution of the gray value is more extensive. The gray value of the MO image marked with a red box in the column is extracted. The gray value in the column versus the subsurface crack width is shown in Fig. 17. The inflection point of the gray value means that the magnetic force line overflows at the boundary of the subsurface crack. When comparing Fig. 16 and Fig. 17, it can be seen that the gray value of subsurface crack image changes more smoothly. The gray value distribution of the weld subsurface crack is within the range of 67–179, and that of the weld surface crack is within the range of 35–192. This indicates that the leakage magnetic field intensity of the subsurface crack is smaller than that of the subsurface crack, which means that there is less leakage magnetic field. 5.4. Analysis of experimental results It can be seen from Table 2 and Table 3 that the MO imaging of surface and subsurface cracks has the same rule, that is, consecutive three-frame MO images of different width cracks include
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Table 3 MO images of weld subsurface cracks under alternating excitation.
Fig. 17. Gray value in column versus the subsurface crack width.
half-bright and half-dark images, full-bright and all-black images. It is proved that the location and width of the cracks do not affect the distribution of the area of bright and dark in the MO image, which changes only the brightness of the MO image. This will play a guiding role in analyzing the principle of MO imaging of subsurface and surface weld cracks.
In order to study the effect of the difference between the surface and subsurface crack magnetic flux leakage signals on the MO imaging of weld cracks in an alternating magnetic field environment, an alternating magnetic field simulation model and a weld crack MO imaging detection system are established as shown in Fig. 9(a) and Fig. 14. The obtained simulation result of the relationship between crack width and the vertical component of the magnetic flux leakage is shown in Fig. 18(a). The maximum value Bymax of the vertical component By of the magnetic flux leakage of the subsurface and surface weld cracks increases with the expansion of the crack width. The gray values of the subsurface and surface weld cracks’ MO images with different crack widths are shown in Fig. 18(b). The peak-to-valley gray value Np-v of the subsurface and surface of weld cracks’ MO images increases as the crack width expands. For the MO image gray value, the difference between the maximum and minimum values of the subsurface crack image is less than the surface crack image. This paper focuses on the characteristics of surface and subsurface crack leakage magnetic field under alternating magnetic fields, which are helpful in studying the characteristics of MO imaging of surface and subsurface cracks. The experimental results show that the larger the crack width, the greater the leakage magnetic field. The gray value Np-v of the surface crack’s MO image is larger than that of the subsurface crack, and the resulting gray value of the crack’s MO image reflects the intensity of the leakage mag-
Fig. 18. (a) Magnetic induction intensity By max versus the crack width (b) Gray value Np-v versus the crack width.
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netic field. Therefore, the MO image of a weld crack can reflect the intensity of the magnetic field through varied brightness. 6. Conclusions Based on the Faraday MO effect and the mechanism of bright and dark of a MO image, the MO imaging law of welding cracks is analyzed, which shows that the leakage magnetic field of cracks is the focus of research. A magnetic dipole model of weld crack leakage magnetic field is established by the magnetic charge theory, and the distribution law of the leakage magnetic field above the crack is analyzed. Through a finite element analysis simulation, it is verified that the crack width and position are the main factors affecting the magnetic field distribution above the weld crack. The crack width and position have an obvious influence on the vertical component of the leakage magnetic field, and the actual crack width can be calculated according to the vertical component. In this paper, a nondestructive testing platform for MO imaging of weld cracks under alternating magnetic field excitation was built. Weld cracks included surface cracks and subsurface cracks, whose width range was 0.2 mm, 0.1 mm, 0.05 mm, and 0.01 mm respectively. A MO imaging nondestructive testing was carried out under a 50 Hz alternating electromagnetic field excitation. Experimental results show that there are obvious linear boundary lines in the MO images of weld surface cracks—the larger the width, the larger the bright area of the MO image and the stronger the leakage magnetic field. Under the same width conditions, when the surface crack magnetic induction is the largest, the subsurface crack is the weakest. The induced magnetic field of a weld crack can be detected by a MO sensor. The MO image of a weld crack can reflect the magnitude of the magnetic field intensity through varied brightness, and the gray value of the MO image can match the corresponding magnetic field intensity. The greater the weld crack width, the stronger the leakage magnetic field and the larger the corresponding gray value of the MO image, which is consistent with the imaging rule of the weld crack. Under the same width conditions, the gray value Np-v of the MO image of the surface crack is larger than that of the subsurface crack. Combined with the above conclusions, the position and width of the crack can be detected. Acknowledgments This work was supported in part by the National Natural Science Foundation of China (51675104), the Science and Technology Planning Public Project of Guangdong Province, China (2016A010102015), and the Innovation Team Project, Department of Education of Guangdong Province, China (2017KCXTD010). References [1] G.V. Moskvitin, A.N. Polyakov, E.M. Birger, Application of laser welding methods in industrial production, Weld. Int. 27 (7) (2013) 572–580. [2] I. Miyamoto, Y. Okamoto, A. Hansen, J. Vihinen, T. Amberla, J. Kangastupa, High speed, high strength microwelding of Si/glass using ps-laser pulses, Opt. Express 23 (3) (2015) 3427–3439. [3] M. Riahi, A. Amini, Effect of different combinations of tailor-welded blank coupled with change in weld location on mechanical properties by laser welding, Int. J. Adv. Manuf. Technol. 67 (5-8) (2013) 1937–1945. [4] Z. Luo, J.S. Dai, C. Wang, F. Wang, Y. Tian, M. Zhao, Predictive seam tracking with iteratively learned feedforward compensation for high-precision robotic laser welding, Int. J. Ind. Manuf. Syst. Eng. 31 (1) (2012) 2–7. [5] O. Zahran, H. Kasban, M. EI-Kordy, F.E. Abd EI-Samie, Automatic weld defect identification from radiographic images, NDT&E Int. 57 (6) (2013) 26–35.
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Biography Xiangdong Gao received the B.E. degree in automation from Zhengzhou University, Zhengzhou, China, in 1985, the M.A. degree in automation from Central South University, Changsha, China, in 1988, and the Ph.D. degree in welding from South China University of Technology, Guangzhou, China, in 1998. He is currently the director of Guangdong Provincial Welding Engineering Technology Research Center and a Professor of School of Electromechanical Engineering, Guangdong University of Technology. His research interest is welding automation.
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Weld cracks nondestructive testing based on magneto-optical imaging under alternating magnetic field excitation Yanfeng Li a , Xiangdong Gao a,∗ , Qiaoqiao Zheng a , Perry P. Gao b , Nanfeng Zhang a a Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, No. 100 West Waihuan Road, Higher Education Mega Center, Panyu District, Guangzhou, 510006, China b US-China Youth Education Solutions Foundation, New York, NY, 10019, USA
a r t i c l e
i n f o
Article history: Received 12 October 2018 Received in revised form 9 November 2018 Accepted 10 November 2018 Available online 22 November 2018 Keywords: Magneto-optic systems Weld cracks Faraday MO effect Nondestructive testing
a b s t r a c t This paper researches the magneto-optical (MO) imaging law of weld cracks under alternating magnetic field excitation. Weld surface and subsurface cracks are detected by a MO sensor, and the relationship between the MO images’ characteristics and the magnetic field strength is analyzed based on the Faraday MO effect. A magnetic dipole model is proposed to study the magnetic field distribution over the weld crack. A finite element analysis (FEA) model of the weld crack is established, and the relationship between the magnetic flux leakage signal and the crack width is analyzed, which is useful for identifying cracks either on the surface or on the subsurface of the weld. A MO imaging nondestructive testing (NDT) experiment is carried out to detect weld cracks under alternating magnetic field excitation, and the difference among weld cracks is obtained by analyzing the gray values of the weld cracks’ MO images. Research results show that the magnetic flux leakage signals of weld surface and subsurface cracks can be clearly distinguished, the magnetic field intensity of the surface cracks is larger than that of the subsurface cracks at the same width, and the MO image of the weld cracks can reflect the intensity of the magnetic field through varied brightness, that is, the gray value of the MO image can match the corresponding magnetic field intensity. © 2018 Elsevier B.V. All rights reserved.
1. Introduction As one of the most important material coupling method in various industrial fields such as the aircraft industry, automobile manufacturing, and shipbuilding [1,2], laser welding technology has obvious advantages such as high density, great depth-to-width ratio, and the formation of high-quality weld with little deformation [3,4]. During laser welding, the welding process tends to be unstable due to the influence of laser power, welding speed, weld surface condition, and so on, which leads to weld defects and affects welding quality directly [5]. Therefore, it is key to accurately detect the position and width of the weld defects in real time, and the surface and subsurface crack detection is the most important. As a major test technology in industrial manufacturing, NDT plays a key role in the detection of weld defects and is usually used to monitor and ensure structural integrity [6]. There is a growing tendency to integrate nondestructive testing technologies such as ultrasonic testing (UT) [7,8], radiographic testing (RT) [9,10], mag-
∗ Corresponding author. E-mail address: [email protected] (X. Gao). https://doi.org/10.1016/j.sna.2018.11.017 0924-4247/© 2018 Elsevier B.V. All rights reserved.
netic flux leakage (MFL) [11–13], and eddy-current testing (ECT) [14] with machine vision to detect weld defects. However, each of these methods has drawbacks. For example, UT does not identify near-surface defects and requires a coupling medium during application; RT is harmful to the human body due to radiation and the experimental equipment is costly; MFL is suitable only for surface and subsurface inspection of magnetic materials, and it is difficult to detect the position and size of the welding defect; while ECT requires special signal processing techniques, and the results cannot be visualized. MO imaging is a technology to realize the visualization of the defect by detecting the amplitude of polarized light based on Faraday magneto optical effect. This technology has been developed for inspection of rivet cracks in aircraft [15], as it has high sensitivity and can be used to detect micro cracks on the surface and subsurface of aeronautical materials. Compared with traditional ECT, a novel NDT method based on MO imaging can achieve the visualization of weld cracks without complicated signal processing. Nowadays, MO imaging detection of weld cracks is based on the constant magnetic field excitation which with constant magnetic field magnitude and direction [16]. However, it is difficult for a MO sensor to detect multi-directional weld cracks under constant mag-
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netic field excitation and is easily saturated. In recent years, there has been increasing interest in the research of MO imaging induced by alternating magnetic fields. Some research teams have begun to study MO imaging characteristics under alternating magnetic field excitation [17,18]. It has been proved that MO imaging detection method has a broad application prospect in micro-weld tracking [19,20], and MO image characteristics of weld defects under alternating magnetic field excitation have been investigated [18]. Based on the above research, this paper aims to explore an innovative and effective method based on MO imaging for the detection of weld surface and subsurface cracks. The proposed method has the ability to detect different types of weld cracks based on characteristic signals of the leakage magnetic field and the gray value of the MO image. However, the MO imaging mechanism of weld cracks under alternating magnetic field is still under research, and the relationship between crack characteristics and the corresponding MO image is not clear, which limits the application of this NDT method in weld crack detection. In this paper, the effect of the external magnetic field on the MO imaging of the weld crack is studied based on the Faraday MO effect, and the imaging mechanism of this method is further analyzed. A magnetic dipole model is used to simulate the magnetic field distribution over the weld crack. The distribution characteristics of the magnetic flux leakage signal are analyzed by finite element modeling. Weld surface cracks and subsurface cracks are subjected to MO imaging NDT testing to analyze imaging characteristics under alternating magnetic field excitation. This paper proposes a magnetic distribution model of weld cracks according to the magnetic charge theory. The validity of the model is verified by both computer simulation and actual experiment contrastive analysis, and the characteristics of weld cracks’ magnetic field distribution are summarized. Combined with the gray distribution law of an MO image, the characteristics of MO images of different weld cracks are studied, which lays a foundation for improving the accuracy of weld crack classification. The paper is organized as follows. Section 2 introduces the mechanism of MO imaging and analyzes the MO imaging feature of weld defect. Section 3 presents the magnetic field distribution of weld crack by established theoretical model of crack leakage magnetic field. Section 4 analyses the magnetic field distribution of surface and subsurface cracks using the FEA method. Section 5 validates the simulation results through MO imaging detection experiments of weld cracks. Section 6 draws conclusions. 2. Weld crack detection based on MO imaging 2.1. Faraday MO effect
of light, and the propagation of light in a magnetic field is used to detect weld cracks. The MO film is composed of a mirror coating and a MO medium, which is placed on the weld. The light from a light-emitting diode (LED) source passes through the polarizer to become a linearly polarized light. The polarized light propagates through the MO medium and is reflected by the mirror coating, which contains crack information under the medium. Therefore, the linearly polarized light may carry weld crack information and propagate through an analyzer, before finally being captured by a complementary metal oxide semiconductor (CMOS) camera and then forming a 2D-real-time visualization image. As seen from Fig. 2(a), when the linearly polarized light passes through the MO film where a magnetic field does not exist, the projection of the amplitude of the linearly polarized light on the analyzer can be described as: l0 = Ecosϕ
(2)
where E is the amplitude of the linearly polarized light, and ϕ is the angle between the vibrating plane of the light and the analyzer. When the weldment is magnetized, if there is a crack defect on the surface of the weldment, both north (N) and south (S) pole magnetic fields will be formed on the defect edge due to the magnetic domain [25]. According to the Faraday MO effect, under the applied force of the N pole magnetic field, the vibrating plane of the polarized light will rotate in clockwise direction, while the applied force of the S pole pushes the polarized light to rotate counterclockwise by angle . The projections of the light amplitude on the analyzer are:
l1 = Ecos ϕ −
MO imaging technology is based on the Faraday MO effect [21,22]. When incident linearly polarized light passes through a MO film, if an external magnetic field is applied in the direction of the propagation of the incident linearly polarized light, the vibrating plane of the polarized light will rotate an angle . The Faraday rotation angle can be described by [23]: = VBL
Fig. 1. Principle diagram of MO imaging.
(1)
where B (in teslas) is the external magnetic flux density, L (in meters) represents the length of the polarized light’s passing through the MO film, and V (in radians per tesla per meter) represents the Verdet constant for material. When the optical film material and thickness are given, the deflection direction of the Faraday rotation angle is related only to the direction of the applied magnetic field [24]. The principle diagram of the MO imaging device is shown in Fig. 1. Humans have been studying the characteristic of propagation
l2 = Ecos ϕ +
(3) (4)
The corresponding light intensity Ii (i = 0, 1, 2) can be expressed as: I0 = l02 = E 2 cos2 (ϕ) I1 = l12 = E 2 cos I2 = l22 = E 2 cos
2 2
ϕ− ϕ+
(5) (6) (7)
The corresponding MO imaging light intensity of Ii (i = 0, 1, 2) is shown in Fig. 2 (b). I0 for the middle one and has an orange look, I1 for the left one and has a bright-yellow look, while I2 for the right one and has a brown look. The gray value of I1 is the highest, which corresponds to the N pole light intensity, and the gray value of I2 is the lowest, which corresponds to the S pole light intensity. Therefore, the relationship between these light intensities is I2
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Fig. 2. Polarization angle versus light intensity of MO imaging.
MO film is very sensitive to a magnetic field. It is precisely because of this sensitivity that a MO film can be used to detect magnetic materials that are less sensitive to a magnetic field than itself. The magnification hysteresis loop of the MO film is shown in Fig. 3. The saturation magnetization of the MO film is ±2.5 m T under the magnetic field intensity of ± 2 kA/m, and the Faraday rotation angle of the linearly polarized light is at about ±7.5◦ . The symbol of “-” indicates that the Faraday rotation angle is clockwise, while “+” is counterclockwise.
2.3. MO imaging feature analysis of weld defect
Fig. 3. Hysteresis loops of Q235 and MO film.
2.2. Hysteresis loops of weldment and MO film MO film magnetism changes cause differences between MO images. Fig.3 shows the hysteresis loops of Q235 and the MO film. It is observed that the MO film rapidly reaches saturation magnetization while Q235 is still magnetized, which indicates that the MO film is more sensitive than Q235 in the same magnetic field. A
The physical image of a weld defect is shown in Fig. 4 (a). It is a natural defect formed at the weld seam. The surface of weldment was polished before inspection. The tiny weld defects of polished weldment are invisible to the naked eye, as shown in Fig. 4(b). The MO image of the weld defect is shown in Fig. 4(c). It can be seen that the size, shape, and position of the defect are consistent with the actual defect. With MO imaging, it is possible to observe tiny weld defects that are difficult for the naked eye to see. As seen from Fig. 4(c), there is a weld defect on the surface of the weldment, and both N pole and S pole magnetic fields are formed at the edge of the defect due to magnetic domains. The MO image shows the transition zone from light to dark, which contains the position information of the weld defect area. Light intensity I0 corresponds
Fig. 4. Image of weld defect.
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Fig. 6. Weldment and magnetic circuit distribution. Fig. 5. Gray distribution of weld defect MO image.
to the imaging effect in areas where the weldment is unaffected, light intensity I1 shows the imaging effect of the brighter part of the upper edge of the weld defect, while light intensity I2 shows the imaging effect of the darker part of the lower edge of the weld defect. The relationship between the light intensities of the MO image is I2
3. Magnetic field modeling of weld crack
Fig. 7. Schematic of modeling for a micro crack.
3.2. Theoretical model of crack leakage magnetic field Using a magnetic sensor to detect a leakage magnetic field can determine the existence and characteristics of the defect [26,27]. The surface crack can be considered as an infinitely long rectangle groove whose depth is h and width is 2b, using a magnetic double model to simulate the distribution of the magnetic flux leakage field of the microcrack [28,29], as shown in Fig. 7. When a horizontal magnetic field is applied, the magnetic field of the microcrack is equivalent to the magnetic charge band model with opposite polarity on both sides of the rectangle groove. The X axis of the coordinate is distributed along the surface of the weldment, and the Y axis is perpendicular to the center of the weld. The width of the magnetic charge plane is, and the magnetic charge plane density isms . The magnetic field strength generated by the magnetic charge with d width plane at P is dH1 and dH2 respectively [30],
3.1. Magnetic circuit distribution of weldment Fig. 6 shows the weldment and magnetic circuit distribution. The ferromagnetic weldment is magnetized and generates two magnetic fields Ф1 and Ф2 under alternating magnetic field excitation. Magnetic field Ф1 is generated on the weldment by the magnetic induction of an electromagnet. The magnetic field Ф1 is relatively far from the MO sensor, which is usually placed close to the weld. Although Ф1 has an effect on the formation of MO images, this effect is very small, and its influence is symmetrical to the Y axis. The magnetic field Ф2 is generated by the magnetic flux leakage formed when the main magnetic flux flows along the weldment and meets at the crack. The magnetic field Ф2 plays a dominant role in MO imaging.
dH1 =
ms d 20 r12
dH2 = −
(8)
r1
ms d 20 r22
(9)
r2
where r1 and r2 are the distance from the P point to the magnetic charge surface elements on both sides of the crack respectively, while 0 is the permeability of the vacuum. The component of dH1 and dH2 in the direction of x, y is: dH1x =
ms (b + x) 2
20 (x + b) + (y + )2
d
(10)
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Fig. 8. Distribution diagram of H above the weld crack.
dH1y =
ms (y + )
ms (b − x)
2
20 (x + b) + (y + )2
dH2x =
2
20 (x − b) + (y + )2
dH2y =
ms (y + ) 2
20 (x − b) + (y + )2
d
(11)
Defect type
d
(12) Surface crack Subsurface crack
d
Hx
h
=
=
ms 20
0
dH1x +
h 0
dH2x
arctan
h(x + b)
2
(x + b) + y (y + b)
h
dH1y + 0
Length (mm)
0.2, 0.1, 0.05, 0.01 0.2, 0.1, 0.05, 0.01
20 20
the right. The left region of the zero crossing corresponds to the N pole and the right region corresponds to the S pole.
4.1. FEA modeling of magnetic flux leakage on weld crack
dH2x 0
h(x − b)
− arctan
2
(x + b) + y (y + b)
Similarly, the total magnetic field vertical component Hy at the crack can be obtained by integrating to dHy . =
Width (mm)
1 1
4. FEA of weld crack magnetic field
(14)
Hy
Depth (mm)
h
dH1x + 0
h
Sizes
(13)
The total magnetic field horizontal component Hx at the crack can be obtained by integrating to dHx , which is shown in the following equation:Hx =
Table 1 Detailed values of surface and subsurface cracks.
h
dH2y 0
2
2
2
(x + b) + (y + b) (x − b) + y2 ms = ln 2 2 2 40 (x + b) + y2 (x − b) + (y + h)
(15)
As the magnetic flux density horizontal component Bx corresponding to Hx is perpendicular to the incident linearly polarized light, it has no rotation effect on the incident linearly polarized light; therefore, the distribution of the magnetic field Hx has no effect on the MO imaging. The magnetic flux density vertical component By corresponding to Hy is parallel to the incident linearly polarized light, so it has a rotation effect on the incident linearly polarized light. When the weld crack is considered as infinitely deep and long, and for convenience, the specific value ms /2ϕ0 in Eq. (14) is supposed to be 1, and the specific value ms /4ϕ0 in Eq. (15) is supposed to be 1. The weld crack width is 0.1 mm, and the distribution of the magnetic field intensity at the height of 0.5 mm above the weld crack is shown in Fig. 8. As seen from Fig. 8(a), the distribution of Hx is symmetrical to the Y axis. The maximum value of Hx at the center of the Y axis is obtained and gradually decreases along both sides. It can be seen from Fig. 8(b) that the magnetic field Hy is positive on the left of the zero crossing and negative on
A three-dimensional FEA model is built in ANSYS-Maxwell to analyze the magnetic flux leakage distribution and its corresponding testing signals under magnetization for cracks in a low carbon steel plate (Q235). The simulation model consists of a U-shaped inducer and excitation coils above the Q235 specimen, as shown in Fig. 9(a). Each coil is wound by 700 turns of 0.5 mm enameled copper wire. To simulate a real detection environment, the computational field of the FEA model is set to air. The amplitude of the alternating currents in each coil is 200 V and the excitation frequency is 50 Hz. The detecting position is about 0.5 mm high above the weld crack. The calculated permeability values are assigned to each element in the Q235 specimen model and alternating current is loaded into the excitation coil to generate an eddy-current magnetic field. The magnetic field distribution of the Q235 specimen is obtained. In order to obtain better results in the 3D model, fine meshes are generated in the region of interest. The entire 3D model is divided into 50,596 elements. Two representative crack models used for the simulation and experiments are shown in Fig. 9(b–c). In the 100-mm width × 2-mm depth × 100-mm length Q235 specimen, the external notch (0.05mm width × 1-mm depth × 20-mm length) represents a surface crack defect, as shown in Fig. 9(b). The internal notch (0.05-mm width × 1-mm depth × 20-mm length) acts as subsurface crack defect, as shown in Fig. 9(c). The detailed values of surface and subsurface cracks are shown in Table 1. 4.2. Analysis for surface crack width magnetic field Width detection accuracy is an important indicator of MO imaging detection of weld surface cracks. Under the same conditions
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Fig. 9. The FEA model of weld cracks and two representative crack models used for the simulation and experiments.
Fig. 10. Vertical magnetic induction intensity versus the surface crack width.
of length, depth, and magnetization, this paper studied the effect of surface cracks with different widths on the magnetic induction intensity vertical component By . The surface crack is in the center between the two magnetic poles, with a length of 20 mm, depth of 1 mm, and a width of 0.2 mm, 0.1 mm, 0.05 mm, and 0.01 mm respectively, and x = 0.5 mm is the center of the crack. The simulation results are shown in Fig. 10. It can be seen that the peak-to-valley value of the magnetic induction intensity of the vertical component By increases with the expansion of the surface crack width. The peak-to-valley spacing distance Dsp-v of the verti-
Fig. 11. Surface crack width versus Dsp-v .
cal component By displays a good proportional linear relationship with the surface crack width, as shown in Fig. 11. Therefore, Dsp-v can be regarded as a characteristic to evaluate a surface crack width. 4.3. Analysis for subsurface crack width magnetic field Under the same conditions of length, depth, and magnetization, the subsurface crack is in the center between the two magnetic poles, with a width of 0.2 mm, 0.1 mm, 0.05 mm, and 0.01 mm
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sity is at the edge of the crack. The peak-to-peak value Byp-p of the vertical component By can describe the width of the subsurface crack, which can be used as a characteristic to evaluate a subsurface crack width.
4.4. Analysis for crack position magnetic field
Fig. 12. Vertical magnetic induction intensity versus the subsurface crack width.
respectively, and x = 0.5 mm is the center of the crack. The simulation calculation of the relationship between the subsurface crack width and the magnetic induction intensity vertical component By is shown in Fig. 12. The peak-to-peak value Byp-p of the vertical component By increases as the subsurface crack width expands. According to the magnetic induction intensity distribution of the subsurface crack in Fig. 12, the maximum magnetic induction inten-
Fig. 13. Magnetic induction intensity vertical component By versus the crack width.
Under the same conditions of depth and magnetization, when the length of the crack is a constant value (20 mm), the width changes from 0.01 mm to 0.2 mm. As shown in Fig. 13 (a–d), the vertical signals of the surface and subsurface cracks increase with the linear relationship of the crack width, and the curves in the figure basically coincide. It can be seen from the diagram that the type of crack is the same and the larger the width, the greater the intensity of the magnetic flux leakage. When the width of the crack is constant, the intensity of the magnetic field leakage of the surface crack is greater than that of the subsurface crack, which means that there is magnetic leakage. It can be seen from Fig. 13 that the By is equal to zero at the coordinate x = 0.5. According to Fig. 8, when the value of By is positive, the left region of the zero crossing corresponds to the N pole, and when the value of By is negative, the right region corresponds to the S pole. At the same interval on the X axis, the vertical component By increases as the crack width expands, and the magnetic field leakage strength of the surface crack is greater than that of the subsurface crack. Considering Eq. (1), the light rotation angle
(a) Width 0.01 mm (b) Width0.05 mm (c) Width 0.1 mm (c) Width 0.2 mm
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Fig. 14. Experimental setup of MO sensing system.
varies greatly. This means that the change of the light intensity I in the transition region corresponding to the MO image will be more obvious, and the corresponding distribution of the gray value f(x, y) will also change significantly. 5. Experimental study of weld cracks by MO imaging 5.1. Experiment setup An experimental system of MO image acquisition of weld cracks is shown in Fig.14, consisting of a YAG laser welding machine, a MO sensor, an electromagnet, shielding gas, a three-axis moving experimental platform equipped with servo motors and fixtures. The electromagnet was supplied by alternating current (AC) power and generated an alternating magnetic field. The motion control platform was controlled by an industrial computer to ensure that the entire weld seam was detected. The weldment material was a plate structure made of Q235, with length of 100 mm, thickness of 2 mm, and width of 50 mm. Surface and subsurface cracks were simulated by the YAG laser welding on the abutting steel plate, with widths of 0.2 mm, 0.1 mm, 0.05 mm and 0.01 mm respectively. The MO sensor was placed on the weldments to obtain MO images of the weld cracks with a frame rate of 75 frames per second (FPS). The lift-off height of the MO sensor was set at 0.5 mm. The camera resolution was set at 400 × 400 pixels. The correspondence relation between pixel size and distance was 102 pixel/mm. Fig. 15 shows the dynamic weld crack MO imaging test in lowcarbon steel under alternating magnetic field excitation. In this test, the excitation AC voltage was set at 200 V and 50 Hz. The magnetic field of the weldment changes periodically with the voltage curve. The MO sensor can capture a frame of a dynamic MO image at every 13.3 ms. The relationship between the frame rate and voltage frequency is illustrated below. Fig. 15 shows three dynamic images of frame 1, frame 2, and frame 3, arranged as (a), (b), and (c) respectively. Therefore, it represents the same crack induced by three different magnetization directions. The MO sensor is very sensitive to the magnetic field changes, and the MO images are brighter at the N pole than at the S pole. As analyzed in previous experiments, the midline of the three frames belongs to the crack. As shown in Fig. 15(a), the first frame of the MO image collected in the test shows the imaging effect of the N pole excitation above the weld crack and the imaging effect of the S pole below the crack. As seen
Fig. 15. Dynamic MO imaging of weld crack when alternating magnetic field was applied.
from Fig. 15(b), the second frame of the MO imaging effect is opposite to the first frame. The MO image characteristics in Fig. 15(c) are not obvious around the weld crack, as the magnetic field of the yoke at both ends tends to be the same. Different weld cracks under alternating magnetic field excitation have different leakage magnetic fields, and the resulting gray value of the MO image reflects the intensity of the leakage magnetic field. 5.2. MO imaging characteristics of weld surface cracks To replicate actual weld surface cracks, a series of grooves were simulated through YAG laser welding on the surface of the abutting steel plate. The dynamic MO images acquired under alternating magnetic field excitation, such as frame 1 (F1), frame 2 (F2), and frame 3 (F3), are shown in Table 2, which contains a schematic cross-section of the weld, a physical image of the weldment, the region of interest of the F1 image, and a 3D gray image of the region of interest. As seen from Table 2, the weld surface crack information can be clearly observed, and the detailed position of the crack is displayed. In a MO image, the N pole region is bright-yellow and the S pole region is brown. Therefore, both regions show obvious
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Table 2 MO images of weld surface cracks under alternating excitation.
width, the larger the bright region of the MO image, which proves that the greater the crack width, the stronger the leakage magnetic field. 5.3. MO imaging characteristics of weld subsurface cracks
Fig. 16. Gray value in column versus the surface crack width.
chromatic aberrations. MO images can clearly reflect the distribution of the magnetic field strength. The gray value in the MO image can match the corresponding magnetic field intensity. According to the distribution of the gray value of the surface crack, it is possible to ascertain the position of the crack in the MO image without difficulty. In order to study the influence of the crack magnetic field intensity distribution on the MO imaging of cracks more easily, the regions of interest of F1 MO images were selected. As seen from Table 2, the brightness of the ROI of the F1 MO image is enhanced by the increase of the surface crack width. According to Eq. (3), if the light rotation angle increases gradually, it means that the welding magnetic induction intensity increases as the surface crack width expands. The ROI of the selected MO images are subjected to image processing to obtain a 3D distribution of the image gray values. It can be seen from Table 2 that the 3D distribution of the gray value increases with the expansion of the surface crack width. A graph is obtained from each set of images in Table 2, with different transition areas. In fact, there are various noises such as magnetic domain noise, background light noise and system noise during the detection of weld crack MO imaging. In order to reduce the effects of these noises, MO images could be processed by median filtering technique. The gray value of the image marked with a red box in the column is extracted, as shown in Fig. 16. For surface cracks, the gray value of the MO image in the column is linearly distributed. The width of 0.2 mm is the largest linear area, and 0.01 mm is the smallest. This indicates that the larger the crack
A weld subsurface crack was simulated on the upper and lower surfaces of the abutting plate by YAG laser welding, and the weld was not welded in the middle. The dynamic MO images obtained under alternating magnetic field excitation, such as F1, F2, and F3, are shown in Table 3, which contains a schematic cross-section of the weld, a physical image of the weldment, the ROI of the F1 image, and a 3D gray image of the ROI. When comparing Table 2 and Table 3, a large difference between the MO images of the weld surface crack and the subsurface crack can be noticed. The light intensity of the MO images on both sides of the weld subsurface is uniform and there is no obvious difference in brightness. As the width of the subsurface crack increases, the difference in the light intensity of the MO image also increases. Because there is no obvious difference in the brightness of the MO images on both sides of the weld crack, it is not conducive to extracting crack characteristics. Therefore, the MO image with an obvious difference between light and dark is selected in Table 3. For example, F1 with a width of 0.2 mm, F1 with a width of 0.1 mm, F1 with a width of 0.05 mm, and F1 with a width of 0.01 mm are chosen. The ROI of the selected MO images are subjected to image processing to obtain a 3D distribution of the image gray values. As it can be seen from Table 2, as the width of the crack increases, the 3D distribution of the gray value is more extensive. The gray value of the MO image marked with a red box in the column is extracted. The gray value in the column versus the subsurface crack width is shown in Fig. 17. The inflection point of the gray value means that the magnetic force line overflows at the boundary of the subsurface crack. When comparing Fig. 16 and Fig. 17, it can be seen that the gray value of subsurface crack image changes more smoothly. The gray value distribution of the weld subsurface crack is within the range of 67–179, and that of the weld surface crack is within the range of 35–192. This indicates that the leakage magnetic field intensity of the subsurface crack is smaller than that of the subsurface crack, which means that there is less leakage magnetic field. 5.4. Analysis of experimental results It can be seen from Table 2 and Table 3 that the MO imaging of surface and subsurface cracks has the same rule, that is, consecutive three-frame MO images of different width cracks include
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Table 3 MO images of weld subsurface cracks under alternating excitation.
Fig. 17. Gray value in column versus the subsurface crack width.
half-bright and half-dark images, full-bright and all-black images. It is proved that the location and width of the cracks do not affect the distribution of the area of bright and dark in the MO image, which changes only the brightness of the MO image. This will play a guiding role in analyzing the principle of MO imaging of subsurface and surface weld cracks.
In order to study the effect of the difference between the surface and subsurface crack magnetic flux leakage signals on the MO imaging of weld cracks in an alternating magnetic field environment, an alternating magnetic field simulation model and a weld crack MO imaging detection system are established as shown in Fig. 9(a) and Fig. 14. The obtained simulation result of the relationship between crack width and the vertical component of the magnetic flux leakage is shown in Fig. 18(a). The maximum value Bymax of the vertical component By of the magnetic flux leakage of the subsurface and surface weld cracks increases with the expansion of the crack width. The gray values of the subsurface and surface weld cracks’ MO images with different crack widths are shown in Fig. 18(b). The peak-to-valley gray value Np-v of the subsurface and surface of weld cracks’ MO images increases as the crack width expands. For the MO image gray value, the difference between the maximum and minimum values of the subsurface crack image is less than the surface crack image. This paper focuses on the characteristics of surface and subsurface crack leakage magnetic field under alternating magnetic fields, which are helpful in studying the characteristics of MO imaging of surface and subsurface cracks. The experimental results show that the larger the crack width, the greater the leakage magnetic field. The gray value Np-v of the surface crack’s MO image is larger than that of the subsurface crack, and the resulting gray value of the crack’s MO image reflects the intensity of the leakage mag-
Fig. 18. (a) Magnetic induction intensity By max versus the crack width (b) Gray value Np-v versus the crack width.
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netic field. Therefore, the MO image of a weld crack can reflect the intensity of the magnetic field through varied brightness. 6. Conclusions Based on the Faraday MO effect and the mechanism of bright and dark of a MO image, the MO imaging law of welding cracks is analyzed, which shows that the leakage magnetic field of cracks is the focus of research. A magnetic dipole model of weld crack leakage magnetic field is established by the magnetic charge theory, and the distribution law of the leakage magnetic field above the crack is analyzed. Through a finite element analysis simulation, it is verified that the crack width and position are the main factors affecting the magnetic field distribution above the weld crack. The crack width and position have an obvious influence on the vertical component of the leakage magnetic field, and the actual crack width can be calculated according to the vertical component. In this paper, a nondestructive testing platform for MO imaging of weld cracks under alternating magnetic field excitation was built. Weld cracks included surface cracks and subsurface cracks, whose width range was 0.2 mm, 0.1 mm, 0.05 mm, and 0.01 mm respectively. A MO imaging nondestructive testing was carried out under a 50 Hz alternating electromagnetic field excitation. Experimental results show that there are obvious linear boundary lines in the MO images of weld surface cracks—the larger the width, the larger the bright area of the MO image and the stronger the leakage magnetic field. Under the same width conditions, when the surface crack magnetic induction is the largest, the subsurface crack is the weakest. The induced magnetic field of a weld crack can be detected by a MO sensor. The MO image of a weld crack can reflect the magnitude of the magnetic field intensity through varied brightness, and the gray value of the MO image can match the corresponding magnetic field intensity. The greater the weld crack width, the stronger the leakage magnetic field and the larger the corresponding gray value of the MO image, which is consistent with the imaging rule of the weld crack. Under the same width conditions, the gray value Np-v of the MO image of the surface crack is larger than that of the subsurface crack. Combined with the above conclusions, the position and width of the crack can be detected. Acknowledgments This work was supported in part by the National Natural Science Foundation of China (51675104), the Science and Technology Planning Public Project of Guangdong Province, China (2016A010102015), and the Innovation Team Project, Department of Education of Guangdong Province, China (2017KCXTD010). References [1] G.V. Moskvitin, A.N. Polyakov, E.M. Birger, Application of laser welding methods in industrial production, Weld. Int. 27 (7) (2013) 572–580. [2] I. Miyamoto, Y. Okamoto, A. Hansen, J. Vihinen, T. Amberla, J. Kangastupa, High speed, high strength microwelding of Si/glass using ps-laser pulses, Opt. Express 23 (3) (2015) 3427–3439. [3] M. Riahi, A. Amini, Effect of different combinations of tailor-welded blank coupled with change in weld location on mechanical properties by laser welding, Int. J. Adv. Manuf. Technol. 67 (5-8) (2013) 1937–1945. [4] Z. Luo, J.S. Dai, C. Wang, F. Wang, Y. Tian, M. Zhao, Predictive seam tracking with iteratively learned feedforward compensation for high-precision robotic laser welding, Int. J. Ind. Manuf. Syst. Eng. 31 (1) (2012) 2–7. [5] O. Zahran, H. Kasban, M. EI-Kordy, F.E. Abd EI-Samie, Automatic weld defect identification from radiographic images, NDT&E Int. 57 (6) (2013) 26–35.
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Biography Xiangdong Gao received the B.E. degree in automation from Zhengzhou University, Zhengzhou, China, in 1985, the M.A. degree in automation from Central South University, Changsha, China, in 1988, and the Ph.D. degree in welding from South China University of Technology, Guangzhou, China, in 1998. He is currently the director of Guangdong Provincial Welding Engineering Technology Research Center and a Professor of School of Electromechanical Engineering, Guangdong University of Technology. His research interest is welding automation.