- Email: [email protected]
A comparative study between magnetic field distortion and magnetic flux leakage techniques for surface defect shape reconstruction in steel plates
Sensors and Actuators A 288 (2019) 10–20
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
A comparative study between magnetic field distortion and magnetic flux leakage techniques for surface defect shape reconstruction in steel plates Zhang Jiaying a , Liu Xiucheng a,∗ , Xiao Junwu a , Yang Zekun b , Wu Bin a , He Cunfu a a b
College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, People’s Republic of China Beijing Institute of Control Engineering, People’s Republic of China
a r t i c l e
i n f o
Article history: Received 27 June 2018 Received in revised form 8 January 2019 Accepted 21 January 2019 Available online 21 January 2019 Keywords: Magnetic field distortion Magnetic flux leakage Magnetic scanning imaging Defect shape reconstruction
a b s t r a c t Both magnetic flux leakage (MFL) and magnetic field distortion (MFD) techniques are promising methods for surface defect detection in ferromagnetic components. The two techniques are based on different physical principles. However, their defect sizing and shape reconstruction abilities are seldom comparatively explored. In this study, finite element simulations and experiments were performed to achieve magnetic scanning imaging of a defective plate with both MFD and MFL techniques. First, the curves of voltage extracted from the lines crossing the center of cylindrical-hole defect (CHD) were used to evaluate the performance of several feature parameters in sizing the diameter and the depth of CHD. Second, the profiles of the normalized magnetic scanning imaging results were analyzed with a threshold plane to evaluate the abilities of MFD and MFL on reconstructing the shape of 8-shaped and cross-type through wall defects. Under the condition that the Hall device for measuring both the normal component of MFL and MFD has same lift-off distance as 1 mm, the MFD technique showed the advantages in defect shape reconstruction at the cost of available detection depth as compared with MFL techniques. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Nondestructive detection of imperfections (such as surface pits, embedded voids and inclusion defects) is important in the product quality control in steel industry [1]. It is well known that in pre-magnetized ferromagnetic material leakage of magnetic flux happens at the position of any imperfection according to the magnetic refraction law [2]. The leakage field propagates into the background air where a defect exists, so the measurement of the leakage field can be used in defect detection. The defect detection way is the magnetic flux leakage (MFL) technique and has been widely applied in the non-destructive testing (NDT) [3–9]. In recent years, the techniques toward magnetic imaging of suspicious regions in a tested specimen attract wide attention due to the visualization of imperfections in the imaging results. As a result, MFL techniques with miniaturized GMR and TMR devices and arrays have been developed as new tools for high-resolution magnetic imaging of the tested components [10–13].
∗ Corresponding author. E-mail address: [email protected] (X. Liu). https://doi.org/10.1016/j.sna.2019.01.019 0924-4247/© 2019 Elsevier B.V. All rights reserved.
Ferromagnetic materials in a static magnetic field cause the distortion of magnetic lines of force in the space. The imperfections in the materials disturb the distortion effect on the static magnetic field. Hence, the observation of the static magnetic field distortion (MFD) allows the evaluation of the imperfections in the materials. Sun et al. [14,15] first reported a MFD sensor, which was made by directly winding sensing coils onto a cylindrical permanent magnet, for defect detection in steel strip, thread and rail. However, the moving speed affected the output signal of the sensing coil and the spatial resolution was limited by the bulky sensing coil. To ˜ et al. [16] used a single GMR solve these limitations, Aguila-Munoz device beneath the permanent magnet to sense the MFD caused by the cracks in tested specimens. The dependency of the parameters extracted from the line-scanning MFD signal on the crack features was investigated to obtain proper parameters for crack feature characterization. Xiao et al. [17] employed bi-reverse-connection Hall sensors to measure the normal components of the distorted magnetic field and realize magnetic scanning imaging of defects to provide shape information on the defects. Both MFL and MFD techniques are promising methods for imperfection detection in ferromagnetic components. It is difficult to easily recognize the exact shape of the defect from the mag-
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
11
Fig. 1. Simulation results of the defect-induced (a) magnetic flux leakage and (b) magnetic field distortion in the steel plate.
netic scanning imaging results obtained by MFL method due to the complex diffusion of flux leakage field in the space around the defect. Machine learning techniques [18,19] and advanced search algorithm [20] are introduced to achieve inversion for the defect shape reconstruction even sizing of 3-D arbitrary defect [21]. However, the training of the mathematical model, which establishes the relationship between the specific characteristics of the MFL signal and the geometry of the defect, requires large amounts of data and the shape reconstruction accuracy varies depending on the selected models. The work reported by Youssef et al. [22] suggested that compared with MFL, MFD scanning method can provide visually distinguishable images for 2D shape reconstruction without any complicated model training process. However, strict comparative study between MFD and MFL techniques for surface defects imaging was seldom reported. In this study, high-resolution magnetic scanning imaging of a defective plate was achieved by both MFD and MFL techniques. The comparative study between MFD and MFL techniques was performed to quantitatively evaluate their abilities of reconstructing the defect shapes. The comparison results indicated that compared with MFL techniques, the MFD techniques showed the advantages in defect shape reconstruction at the cost of available detection depth. The rest of the paper is organized as follows. In Section 2, finite element simulations were conducted in a steel plate with a cylindrical hole to investigate the spatial distribution of the magnetic flux leakage field or the distorted static magnetic field around the cylindrical hole. Both the normal and tangential components of the magnetic flux leakage field demonstrated the diffusion phenomena around the defect edge, thus leading to inaccuracy in defect shape reconstruction. The map representing the normal components of the distorted magnetic field well reflected the defect shape. In Section 3, the experimental set-up for both MFL and MFD inspection were given in detail and magnetic scanning imaging of cylindrical holes with different depth were derived from the experimental results. The performances of MFL and MFD on defect shape reconstruction and available depth were evaluated in Section 4. Finally, the findings of this study are summarized in Section 5.
2. Finite element simulation Two three-dimensional finite element models were established in COMSOL software to respectively simulate the physical phenomena of the defect-induced magnetic flux leakage and magnetic field distortion in steel plate. The dimension of the steel plate is
240 × 40 × 5 mm3 and there is a through-wall cylindrical hole with a diameter of 2 mm at the center of the plate to simulate a defect. As shown in Fig. 1a, a single U-shaped permanent magnetizer composed of rectangular magnet and yoke is employed for plate magnetization. The length of the magnetizer is 180 mm, which is sufficient to make sure that the central zone of the plate is evenly magnetized in the applied magnetic field. Tetrahedron free mesh was assigned to the entire model and the mesh refinement strategy was applied to both the permanent magnet and the area around the defect. A cut plane (15 mm × 15 mm) with a lift-off distance of 0.5 mm from the steel plate employed superfine mesh for extracting the distribution of the flux leakage field. The inset in the lower left of Fig. 1a presents the tangential component (along x-axis) of the magnetic field, Hx , along the center line of the plate without any defect. In the range from -45 mm to +45 mm, the value of Hx is nearly a constant. Defects are located at this particular range (±45 mm) in the evenly distributed magnetic field. The location is conducive to the quantitative evaluation of the performances of MFL techniques in magnetic scanning imaging of defects. After checking the distribution of the magnetic field in the entire model, we found that the discontinuity in the material around the through-wall cylindrical-hole defect (CHD) resulted in the magnetic flux leakage, which could be observed in the enlarged chart in the upper right of Fig. 1a. Searching the normal (along z-axis) or tangential components of the flux leakage field can help to locate the defect and quantitatively evaluate the sizes of the defects. The distribution of magnetic field in the space between a cylindrical permanent magnet (2 mm in height and 10 mm in diameter) and a steel plate was simulated. Compared to the case of a healthy plate, the existence of a CHD in the plate beneath the magnet causes the distortion in the magnetic lines of force in the space above the defect (see Fig. 1b). Monitoring the variations of the normal component of the distorted magnetic field during the movement of the permanent magnet is the key step of MFD method for defect detection. Fig. 2a and b respectively show the normal and tangential components of the leakage field around the defect. The normal component of the leakage field changes its sign when crossing the center of the defect along x-axis, whereas the tangential component always has the positive value. In top view of Fig. 2d and e, the central anomalous regions are the indicators of the defect and the outer contour of anomalous regions may reflect the shape and size of the defect. However, simple visual inspection can find that the anomalous regions have the non-circular outline and their areas are
12
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 2. Simulation results of the defect-induced magnetic flux leakage and magnetic field distortion. (a) and (b) respectively show the spatial distributions of the normal and tangential components of the leakage field. (c) shows the distorted field above the defect. (d), (e) and (f) are the respectively the top view of (a), (b) and (c).
much larger than that of the actual defect. The result means that the scanning imaging method of leakage field may lose the precision in defect shape reconstruction. In MFD method, the distribution of the distorted field around the CHD depends on the location of the magnet against the defect center. To precisely evaluate the performance of MFD method on defect sizing, the distorted field beneath the magnet needs be continuously restored while the magnet passes through the defect region. In other word, a large number of finite element models are required to simulate the MFD scanning process. Taking into account that the precise simulation of MFD is very time consuming, simulations are only conducted by moving the magnet along the center line of the defect in x-axis. The case in which the magnet locates above the center of the defect is recalled to show the defect-induced magnetic field distortion (Fig. 2c and f) for comparison reasons. The dark spot in Fig. 2f exhibits an approximate circular shape. Compared with the results in Fig. 2d and e, the diameter of the dark spot in Fig. 2f is closer to that of the CHD. Therefore, the MFD method may have the better performance on defect shape reconstruction than the MFL method. 3. Experimental 3.1. Experimental set-up The prototypes of sensors for MFL and MFD methods were constructed in the laboratory to verify the findings obtained from finite element simulations. In MFL applications (Fig. 3a), a U-shaped magnetizer was employed to provide a static magnetic field for specimen magnetization. The geometrical sizes of the yokes and permanent magnet are the same as those marked in Fig. 1a. A Hall device (EQ-730, produced by AKM Corporation) was attached at the end of a plastic cantilever, which was mounted onto the beam of a two-axis scanner. The linearity range of the selected Hall device is about ±130 Gs.
The arrangement of the Hall device was adjusted to alternatively measure the normal (Fig. 3b) and tangential (Fig. 3c) components of the leakage field, respectively. During the measurements of normal (or tangential) component of the leakage field, the lift-off distance of the Hall device was fixed as 1 mm (or 2 mm). Fig. 4a shows the configuration of the sensor for MFD application. All the parts of the sensor were assembled into the plastic frame which was fixed at the beam of the scanner with the holder. A cylindrical magnet of 2200 Gs with the identical size as its finite element model was placed into the plastic support to provide a static magnetic field, which was perpendicular to the surface specimen. To avoid the saturation of the Hall device and control the output voltage of the Hall device, the distance between the magnet and the Hall device was adjusted by changing the number of thin filler pieces. In this study, the distance between the magnet and the Hall device (or the specimen surface) was fixed as 5 mm (or 6 mm). At the bottom of the sensor frame, a groove was designed to hold the Hall device bracket and the sensitive direction of the Hall device was normal to the specimen surface. In both MFL and MFD applications, the sensors moved at a step of 0.1 mm under the driving force from the scanning beam. The step motors of the two-axis scanner were controlled by NI PXI7340 Motion Control Card. NI PXIe-6376 acquisition card was used to sample the output voltage of the Hall device. To evaluate the performances of both MFL and MFD sensors in the defect sizing and shape reconstruction, the experiments were performed on a specimen of medium carbon steel with defects. The sizes of the specimen and the defects are given in Fig. 5. Four cylindrical-hole defects have the identical diameter of 2 mm, but the depth ranges from 2 mm to 5 mm with a step of 1 mm. Two 8-shaped defects (ESDs) and two cross-type defects (CTDs) were prepared to quantitatively investigate the performances of MFL and MFD techniques in defect shape reconstruction. The symmetric axis of the defects of ESD-1 and ESD-2 are respectively parallel and perpendicular to the applied static magnetic field. The shapes of the
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
13
Fig. 3. Picture of the entire MFL sensor (a) and the arrangement of the Hall device for measuring the normal (b) and tangential components (c) of the leakage field.
Fig. 4. (a) configuration and (b) the picture of the sensor for MFD application.
CTD-1 and CTD-2 defects are formed by two intersecting ellipses and round-corner rectangles, respectively. 4. Results and discussion As the first step, the magnetic scanning imaging based on MFL or MFD was conducted at the top surface of the specimen. During the course of the experiments, we have noticed that the residual magnetic field can affect the performance of MFD in defect detection. As sketched in Fig. 6, when the measurement of MFD was undertaken after MFL test, the obtained curve of voltage is inconsistent with the results (marked as ’before MFL’) obtained from the as-received unmagnetized specimens. However, when we conduct demagnetization to the tested specimen before MFD test, the obtained curve of voltage recovers to have similar shape with the results obtained before MFL test. To suppress the effect of residual magnetic field on the performance of the sensor, a commercial demagnetization device was employed for specimen demagnetization before each test of MFD or MFL. The defective zone in the specimen was divided into five regions and one defect is located at the central of each region. Then, the five regions were alternatively scanned with the MFL and MFD sensors. Fig. 7a and b respectively show the spatial distributions of normal and tangential components of the leakage field around the defects. The scanning imaging results of the normal component of the distorted field induced by the defects are shown in Fig. 7c. It is found that the magnetic imaging results obtained by MFL method (Fig. 7a and b) fail to reflect the circular- or eight-shape
of the defect. As a result, it is hard to directly conduct performance comparison between the MFL and MFD method based on the results in Fig. 7. The results of CHDs are taken as examples to quantitatively evaluate the ability of MFL and MFD method on estimating the defect size. The curves of the amplitude extracted from the lines crossing the center of CHDs in x-y plane are plotted in Fig. 8. Then the results in Fig. 8 are used to estimate the diameter of the defects in both directions (parallel and perpendicular to the applied magnetic field). For the purpose of comparison, the amplitude curves extracted from the simulation results in Fig. 2 are scaled and plotted as dotted lines in Fig. 8. The measured curves of the amplitude obtained with both MFL and MFD methods have similar shapes with those of simulation results, indicating the good performance of the experimental set-up in magnetic scanning imaging. Along the magnetization direction (x-axis), the normal component of MFL changes its direction near the center of the CHD to form the amplitude curve with sinusoidal shape. The axial distance between the maximum and minimum amplitudes, Dm , which is frequently used as the feature parameter for defect sizing is employed for defect diameter estimation [23]. When the case comes to the direction perpendicular to the magnetization direction (y-axis), the intensity of normal component of MFL along the line crossing the center of the CHD maintains its negative sign. The measured curves shown in the left of Fig. 8b demonstrate minor differences compared to the simulation results. It is inferred that the differences originates from the inevitable error in geometrical sizes of the actual CHD against that of the simulation model. To achieve the
14
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 5. Geometrical sizes of defects in the (a) specimen 1# and (b) specimen 2#.
Fig. 6. MFD results obtained under different situations.
defect diameter estimation in y-axis direction, the full width at half maximum amplitude, Wh , is employed as the indicator [24,25]. The performances of the MFL and MFD on estimating the defect size depend on the lift-off distance of the Hall device from the tested surface and also the selected indicator. Therefore, it is better to conduct performances comparison between MFL and MFD methods under the same conditions. In this study, the Hall device for measuring both the normal component of MFL and MFD has same lift-off distance as 1 mm. As for the detection results of both the tangential component of MFL and the normal component of MFD, same parameter of Wh is employed as the indicator for defect diameter estimation in both x-axis and y-axis directions. As an example, the values of Dm and Wh estimated from the results of CHD-4 are shown in Fig. 9a. For normal component of MFL, both indicators (Wh and Dm ) can be applied for defect diameter estimation in x-axis. However, the Dm overestimates the defect diameter by around 20% and the value of Wh is 45% larger than that of the defect diameter. To improve the diameter estimation accuracy, it is suggested to reduce the lift-off distance to be lower than 1 mm if Wh and Dm are used as indicators. As indicated in Figs. 2e and 7 b, the dark region in the imaging
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
15
Fig. 7. Scanning imaging of (a) normal and (b) tangential components of the leakage field and (c) the distorted field in defective regions.
Fig. 8. Curves of amplitude extracted from the lines crossing the center of CHDs along (a) x-axis and (b) y-axis.
results of the tangential component of MFL elongates itself along y-axis, thus losing the resolution in defect diameter estimation. As a consequence, the value of Wh along y-axis estimated from the middle of Fig. 8b is 2.6 times larger than that of the actual size,
whereas the Wh along x-axis underestimates the defect diameter by around 20%. In MFD method, the parameter of Wh can well reflect the size of the defect in both directions with a maximum error of 15%. If the full width at a threshold lower than 0.5 is used as indictor,
16
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 9. (a) Performance of the indictors in sizing the defect along x-axis and y-axis and the dependency of the normalized peak voltage on the depth of CHD along x-axis (b) and y-axis (c), respectively.
Fig. 10. (a) the normalized MFD line-scan profiles and (b) the estimated defect diameter for different lift-off distances.
the accuracy of MFD on defect diameter estimation would be higher than the performance of Wh . The ability of the peak voltage in defect depth evaluation was also investigated. For each case shown in Fig. 8b, the peak voltages obtained at the CHDs with different depths are normalized with the maximum peak voltage. Fig. 9b demonstrates the dependency of the normalized peak voltage on the depth of CHD when the analyzed direction is parallel to the static magnetic field (along x-axis). The variation in the normalized peak voltage measured along y-axis with the defect depth is shown in Fig. 9c. In both directions parallel and perpendicular to the static magnetic field, the peak voltage induced by the tangential component of MFL demonstrates monotonous growth trend as the defect depth increases. Therefore, the imaging results of the tangential component of MFL may be applicable to depth profile reconstruction and the higher peak amplitude in the imaging results indicate the greater depth of the defect. It is found that in the direction parallel to the static magnetic field the peak voltage induced by the normal component of MFL also monotonously increases as the defect depth increases. However, in the direction perpendicular to the static magnetic field, the variation in defect depth causes a slight decrease in the peak amplitude. Hence, the technique of measuring the normal component of MFL can only be applied for partial reconstruction of the depth profile. Unfortunately, the peak voltage induced by MFD nearly remains unchanged under the varying depth of CHD. This means that the MFD method fails to evaluate the defect depth even though it has the excellent performance in reconstructing the shape of the defect in x-y plane.
In the above investigations, the parameter of Wh is used as indicator for defect diameter estimation. The lift-off distance between the specimen and the Hall sensor affects the performance of Wh . MFD line-scan crossing the center of CHD-4 along x-axis was performed by adjusting the lift-off distance from 0.25 mm to 1.50 mm with a step of 0.25 mm. The normalized profiles of the normal component of the magnetic field are plotted in Fig.10a. The full width at half maximum amplitude (Wh ) increases as the lift-off distance increases. Therefore, the defect sizing accuracy using indicator of Wh depends on the lift-off distance. The performance of the full width at different amplitude or threshold of voltage, Vth , on defect diameter estimation is examined under the situations of different lift-off distances. It is shown in Fig. 10b that as the increasing of lift-off distance, approximate linear upward trend can be observed from the results of the estimated defect diameter. When the value of Vth is selected as 0.2, the full width of the profile always overestimates the defect diameter in the investigated range of lift-off distance. For a given lift-off distance, the accuracy of the full width on defect diameter estimation is related with the selected value of Vth . To retain the diameter estimation accuracy, a higher threshold of voltage is needed for the case with larger lift-off distance. The magnetic imaging results obtained from the ESD-1 and ESD2 are analyzed to assess the performances of MFL and MFD methods in shape reconstruction. The imaging results are respectively normalized by their maximum values of the voltage. A cut plane with a proper threshold of voltage (Vth ) is used to intersect with the profile of the normalized voltages and consequently the edges of the
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
17
Fig. 11. Shape reconstruction of ESD. (a) and (d) are based on normal components of MFL method. (b) and (e) are based on tangential components of MFL method. (c) and (f) are based on MFD method.
Fig. 12. The shape reconstruction results of CTD-1. (a) and (b) respectively show the imaging results of the normal and tangential components of MFL. (c) and (d) show the results obtained by MFD method.
intersection areas are extracted. The value of Vth for the imaging results of the tangential component of MFL is selected as 0.5 and the value of peak-to-peak for the imaging results of normal component of MFL is selected. The edges of the intersection areas for both MFL and MFD methods are plotted in Fig. 11. The normalized profile of the normal component of MFL (Fig. 2a) demonstrates a valley around the center of the defect. Hence, the intersection lines between the cut plane and the normalized profile of the voltage induced by the normal component of MFL fail to form a closed area. It is obvious that the two curves in Fig. 11a and d cannot be used to reconstruct the shape of ESD even though the distance between the two intersection lines is very close to the actual length of the defect in x-axis. The edge extracted from the imaging results of the tangential component of MFL exhibits an shoe-pad outline, as shown in Fig. 11b and e. It is worth noting that for both ESD-1 and ESD-2 the main axis of the reconstructed shapes are perpendicular to the applied static magnetic field. In the case of ESD-1, the reconstructed shoe-pad outline using tangential components of MFL (Fig. 11b) does not reflect the exact shape in both x-axis and y-axis. As for the case of ESD-2, the reconstructed
shoe-pad outline in Fig. 11e roughly reflect the actual shape along x-axis while in y-axis direction the length of the main axis of the defect are overestimated. This is caused by the nature that the technique of measuring the tangential component of MFL has different abilities in defect sizing in the directions parallel and perpendicular to the magnetization direction. Fig. 11c and f shows the typical edges extracted from the imaging results of MFD. It is obvious that the results of MFD can more precisely reconstruct the shapes of both ESD-1 and ESD-2 compared with the results obtained in MFL method. To doublecheck the superior performance of MFD method in defect shape reconstruction against the MFL method, additional experiments are performed for the two CTDs. The magnetic scanning results obtained by MFL and MFD are shown in Figs.12 and 13. It is easy to find that the magnetic imaging results using MFL method fails to reflect the actual shape of the cross-type defect. The images of the MFD results indicate the cross-type defect. As discussed in previous, the defect shape reconstruction accuracy of the MFD depends on the selected threshold of the voltage in the analysis process. The accuracy of MFD on reconstructing the
18
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 13. The shape reconstruction results of CTD-2. (a) and (b) respectively show the imaging results of the normal and tangential components of MFL. (c) and (d) show the results obtained by MFD method.
Table 1 Errors of shape reconstruction for the cases with different thresholds of the voltage. ESD-1
Actual defect Estimated (Vth = 0.2) Estimated (Vth = 0.3) Estimated (Vth = 0.4) Estimated (Vth = 0.5)
ESD-2
CTD-1
error(%)
Area(mm2 )
error(%)
Area(mm2)
error(%)
Area(mm2)
error(%)
9.71 12.15 9.64 7.69 6.06
– +25.0 −0.8 −20.8 −37.6
9.71 12.82 10.29 8.28 6.52
– +32.0 +6.0 −14.7 −32.9
16.38 16.28 12.44 9.63 7.36
– +0.61 −24.1 −41.2 −55.1
13.82 17.24 13.15 10.05 7.56
– +24.7 −4.8 −27.3 −45.3
shapes of ESD and CTD are investigated within different threshold of the voltage. When the value of Vth is alternatively selected as 0.2, 0.3, 0.4, and 0.5, the shape reconstruction results for ESD-1, ESD-2 are respectively plotted in Fig. 11c and f. The shape reconstruction results of CTD-1 and CTD-2 are shown in Figs. 12d and 13 d, respectively. The difference between the areas that are respectively enclosed by the reconstructed and actual defect shapes is estimated and treated as a criterion for selecting the optimal threshold of the voltage. The area of the defect shape, S, is calculated as: 1 (xk yk+1 − xk+1 yk ), 2 n
S=
CTD-2
Area(mm2 )
(1)
k=1
where xk and yk are the coordinates of the kth point at the extracted edge. The extracted edge is discretized into n points. The estimated areas of the reconstructed ESDs and CTDs obtained under the conditions of different values of Vth are listed in Table 1. When the value of Vth is equal to 0.2, the area of the reconstructed shape is larger than the area of the actual ESDs and CTDs. The MFD technique will underestimate the area of the ESDs and CTDs if the value of Vth is selected to be higher than 0.3. In the
direction perpendicular to the static magnetic field, the width of the CTD-1 smoothly reduces to from a narrow tip at both ends of the ellipse. The MFD sensor may lose sensitivity to the smoothly changed narrow tip, causing underestimation of the length of the CTD-1 in y-axis. Therefore, if the value of Vth is selected as 0.3, the MFD will underestimate the area of the CTD-1 by around 24.1%. For the cases of ESD-1, ESD-2 and CTD-2, the minimum absolute estimation error is achieved when the value of Vth is selected as 0.3 and the estimation error is in the range of ±6%. The results in Fig. 9b and c indicate that the MFD imaging result is not sensitive to the variation in the depth of surface CHDs. It is inferred that the available detection depth of the MFD method is limited and the MFD cannot reflect defect depth information in the material. To verify such speculation, MFD imaging for the four CHDs were performed from the bottom surface of the specimen (Fig. 5). All the four CHDs can be treated as embedded defects whose bottom surface has different distances away from the specimen surface to be scanned. Unfortunately, the MFD method cannot detect any of the four CHDs. As an example, Fig. 14a gives the MFD imaging result for the CHD-3 whose bottom surface has a distance of 1 mm away from the scanned surface of the specimen. This means that the MFD sensor used in this study cannot detect the discontinuity
Fig. 14. Magnetic imaging results of the CHD whose bottom surface is 1 mm away from the specimen surface to be scanned with the techniques of MFD (a) and MFL with normal (b) and tangential (c) components.
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
of the material with a depth of 1 mm beneath the specimen surface. In addition, the imaging results of both the normal (Fig. 14b) and tangential (Fig. 14c) components clearly reflect the existence of the defect. Hence, the detection depth of the MFL method is much greater than that of the MFD method. 5. Conclusion Finite element simulations and experimental tests were performed to investigate the performances of both MFL and MFD methods in reconstructing the shape of defects. The magnetic imaging results of the normal component of MFL reflect threedimensional information of the defect. However, the intersection lines between the cut plane with different thresholds of the voltage and the normalized profile of the voltage induced by the normal component of MFL fail to reconstruct the shape of ESDs and CTDs. As for the curves of the voltage extracted at the lines crossing the defect center of CHD, the axial distance between the maximum and minimum amplitudes (Dm ) and the full width at half maximum amplitude (Wh ) are good feature parameters to respectively estimate the sizes of CHD in the directions parallel and perpendicular to the applied magnetic field. The peak voltage induced by the normal component of MFL around the CHD demonstrates an approximately linear increasing or decreasing tendency with the increase in the depth of CHD. The magnetic imaging results of the tangential component of MFL measured by the sensor proposed in Fig. 3c overestimate the diameter of the CHD by several times in the direction perpendicular to the applied magnetic field and underestimate the size of CHD in the direction parallel to the applied magnetic field. This causes the poor performance of MFL method in ESDs’ and CTDs’ shape reconstruction. The peak voltage induced by the tangential component of MFL around the CHD demonstrates an approximately linear increasing tendency with the increase in the depth of CHD. The residual magnetic field can affect the performance of MFD in defect detection. Specimen demagnetization before each test of MFD can help suppress the effect of residual magnetic field on the performance of the sensor. When the Hall device has a lift-off distance of 1 mm from the tested demagnetization specimen, the MFD technique of measuring the normal component of the distorted magnetic field can accurately evaluate the sizes of CHD at the cost of available detection depth. The experimental results show that the MFD technique cannot detect the subsurface defect with 1-mm liftoff distance to the scanned specimen surface. By selecting a proper threshold of voltage for the magnetic imaging result analysis, precise shape reconstruction of 8-shaped and cross-type through-wall defects can be achieved. In the future, applications of MFD method for reconstructing two-dimensional shape of complex defects will be investigated and advanced shape reconstruction algorithms will be developed. Acknowledgement This study was supported by the National Natural Science Foundation of China (Project Nos. 11527801, and 11402008). References [1] E. Hristoforou, D. Niarchos, H. Chiriac, et al., Non-destructive evaluation distribution sensors based on magnetostrictive delay lines, Sens. Actuators A Phys. 92 (1-3) (2001) 132–136. [2] M. Pelkner, M. Blome, V. Reimund, et al., Flux Leakage Measurements for Defect Characterization Using A High Precision 3-Axial GMR Magnetic Sensor, American Institute of Physics, 2011, pp. 380–387. [3] M. Afzal, S. Udpa, Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline, Ndt E Int. 35 (7) (2002) 449–457.
19
[4] Z. Zakaria, M.S.B. Mansor, A.H. Jahidin, et al., Simulation of magnetic flux leakage (MFL) analysis using FEMM software, in: Industrial Electronics & Applications (ISIEA), 2010 IEEE Symposium on IEEE, 2010, pp. 481–486. [5] G. Gao, Y. Qin, M. Lian, et al., Detecting typical defects in wire ropes through wavelet analysis, Insight-Non-Destr. Test. Condition Monit. 57 (2) (2015) 98–105. [6] M. Pelkner, R. Pohl, M. Kreutzbruck, et al., Development of adapted GMR-probes for automated detection of hidden defects in thin steel sheets, AIP Conf. Proc. 1706 (1) (2016), 020018, AIP Publishing. [7] R.K. Amineh, S. Koziel, N.K. Nikolova, et al., A space mapping methodology for defect characterization from magnetic flux leakage measurements, IEEE Trans. Magn. 44 (8) (2008) 2058–2065. [8] X.C. Liu, Y.J. Wang, B. Wu, et al., Design of tunnel magnetoresistive-based circular MFL sensor array for the detection of flaws in steel wire rope, J. Sens. 2016 (2016). [9] K. Sakai, K. Morita, Y. Haga, et al., Automatic scanning system for back-side defect of steel structure using magnetic flux leakage method, IEEE Trans. Magn. 51 (11) (2015) 1–3. [10] M. Pelkner, A. Neubauer, V. Reimund, et al., Local magnetization unit for GMR array based magnetic flux leakage inspection, AIP Conf. Proc. AIP 1430 (1) (2012) 1005–1012. [11] W. Sharatchandra Singh, B.P.C. Rao, C.K. Mukhopadhyay, et al., GMR-based magnetic flux leakage technique for condition monitoring of steel track rope, Insight-Non-Destr. Test. Condition Monit. 53 (7) (2011) 377–381. [12] B. Wu, Y.J. Wang, X.C. Liu, et al., A novel TMR-based MFL sensor for steel wire rope inspection using the orthogonal test method, Smart Mater. Struct. 24 (7) (2015), 075007. [13] X.C. Liu, J.W. Xiao, B. Wu, et al., A novel sensor to measure the biased pulse magnetic response in steel stay cable for the detection of surface and internal flaws, Sens. Actuators A Phys. 269 (2018) 218–226. [14] Y. Sun, Y. Kang, C. Qiu, A permanent magnetic perturbation testing sensor, Sens. Actuators A Phys. 155 (2) (2009) 226–232. [15] Y. Sun, Y. Kang, C. Qiu, A new NDT method based on permanent magnetic field perturbation, NDT E Int. 44 (1) (2011) 1–7. ˜ [16] J. Aguila-Munoz, J.H. Espina-Hernández, J.A. Pérez-Benítez, et al., A magnetic perturbation GMR-based probe for the nondestructive evaluation of surface cracks in ferromagnetic steels, NDT E Int. 79 (2016) 132–141. [17] C. Xiao, Y. Zhang, A method of magnetic scanning imaging for detecting defects in ferromagnetic materials, Meas. Sci. Technol. 22 (2) (2011), 025503. [18] Y. Li, J. Wilson, G.Y. Tian, Experiment and simulation study of 3D magnetic field sensing for magnetic flux leakage defect characterisation, NDT E Int. 40 (2) (2007) 179–184. [19] R.H. Priewald, C. Magele, P.D. Ledger, et al., Fast magnetic flux leakage signal inversion for the reconstruction of arbitrary defect profiles in steel using finite elements, IEEE Trans. Magn. 49 (1) (2013) 506–516. [20] F. Li, J. Feng, H. Zhang, et al., Quick reconstruction of arbitrary pipeline defect profiles from mfl measurements employing modified harmony search algorithm, IEEE Trans. Instrum. Meas. 67 (9) (2018) 2200–2213. [21] W. Han, J. Xu, P. Wang, et al., Defect profile estimation from magnetic flux leakage signal via efficient managing particle swarm optimization, Sensors 14 (6) (2014) 10361–10380. [22] S. Youssef, K. Szielasko, A. Sourkov, et al., Surface inspection and remanence imaging with magnetic field distortion measurement, in: World Conference on Non-Destructive Testing(WCNDT), 2016. [23] A. Gupta, K. Chandrasekaran, Finite element modeling of magnetic flux leakage from metal loss defects in steel pipeline, J. Fail. Anal. Prev. 16 (2) (2016) 316–323. [24] B. Liu, L. He, H. Zhang, et al., The axial crack testing model for long distance oil-gas pipeline based on magnetic flux leakage internal inspection method, Measurement 103 (2017) 275–282. [25] A. Sophian, G.Y. Tian, S. Zairi, Pulsed magnetic flux leakage techniques for crack detection and characterisation, Sens. Actuators A Phys. 125 (2) (2006) 186–191.
Biographies Jiaying Zhang is currently working towards his Master’s degree in Instrument and Meter Engineering at College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. Her research interests focus on the magnetic imaging technique for detecting impurities in ferromagnetic materials. Xiucheng Liu received his PhD in Mechanical Engineering from Beijing University of Technology, China in June 2013. Currently, he is an associate professor at the NDT&E Laboratory, Beijing University of Technology, China. His research interest includes the structural health monitoring (SHM) technology and micromagnetic materials characterization technology, etc. Junwu Xiao is currently working towards his Master’s degree in Mechanical Engineering at College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. His research interest focuses on the sensor and instrument design for magnetic flux leakage technique. Zekun Yang is currently working as an engineer in Beijing Institute of Control Engineering, China. His research interest focuses on the smart sensor and instrument design.
20
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Bin Wu received his PhD in Solid Mechanics from Taiyuan University of Technology, China in October 1996. Currently, he is a Professor at the NDT&E Laboratory, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. His research areas include mechanism of wave motion and nondestructive testing and evaluation technology, etc. He is a member of CSTAM and CMES.
Cunfu He received his PhD in Engineering Mechanics from Tsinghua University, China in 1996. Currently, he is a Professor at the NDT&E Laboratory, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. His research interests are in the areas of structural health monitoring (SHM) technology and micromagnetic materials characterization technology, etc. He is a member of CICS, CSTAM and CMES.
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
A comparative study between magnetic field distortion and magnetic flux leakage techniques for surface defect shape reconstruction in steel plates Zhang Jiaying a , Liu Xiucheng a,∗ , Xiao Junwu a , Yang Zekun b , Wu Bin a , He Cunfu a a b
College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, People’s Republic of China Beijing Institute of Control Engineering, People’s Republic of China
a r t i c l e
i n f o
Article history: Received 27 June 2018 Received in revised form 8 January 2019 Accepted 21 January 2019 Available online 21 January 2019 Keywords: Magnetic field distortion Magnetic flux leakage Magnetic scanning imaging Defect shape reconstruction
a b s t r a c t Both magnetic flux leakage (MFL) and magnetic field distortion (MFD) techniques are promising methods for surface defect detection in ferromagnetic components. The two techniques are based on different physical principles. However, their defect sizing and shape reconstruction abilities are seldom comparatively explored. In this study, finite element simulations and experiments were performed to achieve magnetic scanning imaging of a defective plate with both MFD and MFL techniques. First, the curves of voltage extracted from the lines crossing the center of cylindrical-hole defect (CHD) were used to evaluate the performance of several feature parameters in sizing the diameter and the depth of CHD. Second, the profiles of the normalized magnetic scanning imaging results were analyzed with a threshold plane to evaluate the abilities of MFD and MFL on reconstructing the shape of 8-shaped and cross-type through wall defects. Under the condition that the Hall device for measuring both the normal component of MFL and MFD has same lift-off distance as 1 mm, the MFD technique showed the advantages in defect shape reconstruction at the cost of available detection depth as compared with MFL techniques. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Nondestructive detection of imperfections (such as surface pits, embedded voids and inclusion defects) is important in the product quality control in steel industry [1]. It is well known that in pre-magnetized ferromagnetic material leakage of magnetic flux happens at the position of any imperfection according to the magnetic refraction law [2]. The leakage field propagates into the background air where a defect exists, so the measurement of the leakage field can be used in defect detection. The defect detection way is the magnetic flux leakage (MFL) technique and has been widely applied in the non-destructive testing (NDT) [3–9]. In recent years, the techniques toward magnetic imaging of suspicious regions in a tested specimen attract wide attention due to the visualization of imperfections in the imaging results. As a result, MFL techniques with miniaturized GMR and TMR devices and arrays have been developed as new tools for high-resolution magnetic imaging of the tested components [10–13].
∗ Corresponding author. E-mail address: [email protected] (X. Liu). https://doi.org/10.1016/j.sna.2019.01.019 0924-4247/© 2019 Elsevier B.V. All rights reserved.
Ferromagnetic materials in a static magnetic field cause the distortion of magnetic lines of force in the space. The imperfections in the materials disturb the distortion effect on the static magnetic field. Hence, the observation of the static magnetic field distortion (MFD) allows the evaluation of the imperfections in the materials. Sun et al. [14,15] first reported a MFD sensor, which was made by directly winding sensing coils onto a cylindrical permanent magnet, for defect detection in steel strip, thread and rail. However, the moving speed affected the output signal of the sensing coil and the spatial resolution was limited by the bulky sensing coil. To ˜ et al. [16] used a single GMR solve these limitations, Aguila-Munoz device beneath the permanent magnet to sense the MFD caused by the cracks in tested specimens. The dependency of the parameters extracted from the line-scanning MFD signal on the crack features was investigated to obtain proper parameters for crack feature characterization. Xiao et al. [17] employed bi-reverse-connection Hall sensors to measure the normal components of the distorted magnetic field and realize magnetic scanning imaging of defects to provide shape information on the defects. Both MFL and MFD techniques are promising methods for imperfection detection in ferromagnetic components. It is difficult to easily recognize the exact shape of the defect from the mag-
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
11
Fig. 1. Simulation results of the defect-induced (a) magnetic flux leakage and (b) magnetic field distortion in the steel plate.
netic scanning imaging results obtained by MFL method due to the complex diffusion of flux leakage field in the space around the defect. Machine learning techniques [18,19] and advanced search algorithm [20] are introduced to achieve inversion for the defect shape reconstruction even sizing of 3-D arbitrary defect [21]. However, the training of the mathematical model, which establishes the relationship between the specific characteristics of the MFL signal and the geometry of the defect, requires large amounts of data and the shape reconstruction accuracy varies depending on the selected models. The work reported by Youssef et al. [22] suggested that compared with MFL, MFD scanning method can provide visually distinguishable images for 2D shape reconstruction without any complicated model training process. However, strict comparative study between MFD and MFL techniques for surface defects imaging was seldom reported. In this study, high-resolution magnetic scanning imaging of a defective plate was achieved by both MFD and MFL techniques. The comparative study between MFD and MFL techniques was performed to quantitatively evaluate their abilities of reconstructing the defect shapes. The comparison results indicated that compared with MFL techniques, the MFD techniques showed the advantages in defect shape reconstruction at the cost of available detection depth. The rest of the paper is organized as follows. In Section 2, finite element simulations were conducted in a steel plate with a cylindrical hole to investigate the spatial distribution of the magnetic flux leakage field or the distorted static magnetic field around the cylindrical hole. Both the normal and tangential components of the magnetic flux leakage field demonstrated the diffusion phenomena around the defect edge, thus leading to inaccuracy in defect shape reconstruction. The map representing the normal components of the distorted magnetic field well reflected the defect shape. In Section 3, the experimental set-up for both MFL and MFD inspection were given in detail and magnetic scanning imaging of cylindrical holes with different depth were derived from the experimental results. The performances of MFL and MFD on defect shape reconstruction and available depth were evaluated in Section 4. Finally, the findings of this study are summarized in Section 5.
2. Finite element simulation Two three-dimensional finite element models were established in COMSOL software to respectively simulate the physical phenomena of the defect-induced magnetic flux leakage and magnetic field distortion in steel plate. The dimension of the steel plate is
240 × 40 × 5 mm3 and there is a through-wall cylindrical hole with a diameter of 2 mm at the center of the plate to simulate a defect. As shown in Fig. 1a, a single U-shaped permanent magnetizer composed of rectangular magnet and yoke is employed for plate magnetization. The length of the magnetizer is 180 mm, which is sufficient to make sure that the central zone of the plate is evenly magnetized in the applied magnetic field. Tetrahedron free mesh was assigned to the entire model and the mesh refinement strategy was applied to both the permanent magnet and the area around the defect. A cut plane (15 mm × 15 mm) with a lift-off distance of 0.5 mm from the steel plate employed superfine mesh for extracting the distribution of the flux leakage field. The inset in the lower left of Fig. 1a presents the tangential component (along x-axis) of the magnetic field, Hx , along the center line of the plate without any defect. In the range from -45 mm to +45 mm, the value of Hx is nearly a constant. Defects are located at this particular range (±45 mm) in the evenly distributed magnetic field. The location is conducive to the quantitative evaluation of the performances of MFL techniques in magnetic scanning imaging of defects. After checking the distribution of the magnetic field in the entire model, we found that the discontinuity in the material around the through-wall cylindrical-hole defect (CHD) resulted in the magnetic flux leakage, which could be observed in the enlarged chart in the upper right of Fig. 1a. Searching the normal (along z-axis) or tangential components of the flux leakage field can help to locate the defect and quantitatively evaluate the sizes of the defects. The distribution of magnetic field in the space between a cylindrical permanent magnet (2 mm in height and 10 mm in diameter) and a steel plate was simulated. Compared to the case of a healthy plate, the existence of a CHD in the plate beneath the magnet causes the distortion in the magnetic lines of force in the space above the defect (see Fig. 1b). Monitoring the variations of the normal component of the distorted magnetic field during the movement of the permanent magnet is the key step of MFD method for defect detection. Fig. 2a and b respectively show the normal and tangential components of the leakage field around the defect. The normal component of the leakage field changes its sign when crossing the center of the defect along x-axis, whereas the tangential component always has the positive value. In top view of Fig. 2d and e, the central anomalous regions are the indicators of the defect and the outer contour of anomalous regions may reflect the shape and size of the defect. However, simple visual inspection can find that the anomalous regions have the non-circular outline and their areas are
12
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 2. Simulation results of the defect-induced magnetic flux leakage and magnetic field distortion. (a) and (b) respectively show the spatial distributions of the normal and tangential components of the leakage field. (c) shows the distorted field above the defect. (d), (e) and (f) are the respectively the top view of (a), (b) and (c).
much larger than that of the actual defect. The result means that the scanning imaging method of leakage field may lose the precision in defect shape reconstruction. In MFD method, the distribution of the distorted field around the CHD depends on the location of the magnet against the defect center. To precisely evaluate the performance of MFD method on defect sizing, the distorted field beneath the magnet needs be continuously restored while the magnet passes through the defect region. In other word, a large number of finite element models are required to simulate the MFD scanning process. Taking into account that the precise simulation of MFD is very time consuming, simulations are only conducted by moving the magnet along the center line of the defect in x-axis. The case in which the magnet locates above the center of the defect is recalled to show the defect-induced magnetic field distortion (Fig. 2c and f) for comparison reasons. The dark spot in Fig. 2f exhibits an approximate circular shape. Compared with the results in Fig. 2d and e, the diameter of the dark spot in Fig. 2f is closer to that of the CHD. Therefore, the MFD method may have the better performance on defect shape reconstruction than the MFL method. 3. Experimental 3.1. Experimental set-up The prototypes of sensors for MFL and MFD methods were constructed in the laboratory to verify the findings obtained from finite element simulations. In MFL applications (Fig. 3a), a U-shaped magnetizer was employed to provide a static magnetic field for specimen magnetization. The geometrical sizes of the yokes and permanent magnet are the same as those marked in Fig. 1a. A Hall device (EQ-730, produced by AKM Corporation) was attached at the end of a plastic cantilever, which was mounted onto the beam of a two-axis scanner. The linearity range of the selected Hall device is about ±130 Gs.
The arrangement of the Hall device was adjusted to alternatively measure the normal (Fig. 3b) and tangential (Fig. 3c) components of the leakage field, respectively. During the measurements of normal (or tangential) component of the leakage field, the lift-off distance of the Hall device was fixed as 1 mm (or 2 mm). Fig. 4a shows the configuration of the sensor for MFD application. All the parts of the sensor were assembled into the plastic frame which was fixed at the beam of the scanner with the holder. A cylindrical magnet of 2200 Gs with the identical size as its finite element model was placed into the plastic support to provide a static magnetic field, which was perpendicular to the surface specimen. To avoid the saturation of the Hall device and control the output voltage of the Hall device, the distance between the magnet and the Hall device was adjusted by changing the number of thin filler pieces. In this study, the distance between the magnet and the Hall device (or the specimen surface) was fixed as 5 mm (or 6 mm). At the bottom of the sensor frame, a groove was designed to hold the Hall device bracket and the sensitive direction of the Hall device was normal to the specimen surface. In both MFL and MFD applications, the sensors moved at a step of 0.1 mm under the driving force from the scanning beam. The step motors of the two-axis scanner were controlled by NI PXI7340 Motion Control Card. NI PXIe-6376 acquisition card was used to sample the output voltage of the Hall device. To evaluate the performances of both MFL and MFD sensors in the defect sizing and shape reconstruction, the experiments were performed on a specimen of medium carbon steel with defects. The sizes of the specimen and the defects are given in Fig. 5. Four cylindrical-hole defects have the identical diameter of 2 mm, but the depth ranges from 2 mm to 5 mm with a step of 1 mm. Two 8-shaped defects (ESDs) and two cross-type defects (CTDs) were prepared to quantitatively investigate the performances of MFL and MFD techniques in defect shape reconstruction. The symmetric axis of the defects of ESD-1 and ESD-2 are respectively parallel and perpendicular to the applied static magnetic field. The shapes of the
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
13
Fig. 3. Picture of the entire MFL sensor (a) and the arrangement of the Hall device for measuring the normal (b) and tangential components (c) of the leakage field.
Fig. 4. (a) configuration and (b) the picture of the sensor for MFD application.
CTD-1 and CTD-2 defects are formed by two intersecting ellipses and round-corner rectangles, respectively. 4. Results and discussion As the first step, the magnetic scanning imaging based on MFL or MFD was conducted at the top surface of the specimen. During the course of the experiments, we have noticed that the residual magnetic field can affect the performance of MFD in defect detection. As sketched in Fig. 6, when the measurement of MFD was undertaken after MFL test, the obtained curve of voltage is inconsistent with the results (marked as ’before MFL’) obtained from the as-received unmagnetized specimens. However, when we conduct demagnetization to the tested specimen before MFD test, the obtained curve of voltage recovers to have similar shape with the results obtained before MFL test. To suppress the effect of residual magnetic field on the performance of the sensor, a commercial demagnetization device was employed for specimen demagnetization before each test of MFD or MFL. The defective zone in the specimen was divided into five regions and one defect is located at the central of each region. Then, the five regions were alternatively scanned with the MFL and MFD sensors. Fig. 7a and b respectively show the spatial distributions of normal and tangential components of the leakage field around the defects. The scanning imaging results of the normal component of the distorted field induced by the defects are shown in Fig. 7c. It is found that the magnetic imaging results obtained by MFL method (Fig. 7a and b) fail to reflect the circular- or eight-shape
of the defect. As a result, it is hard to directly conduct performance comparison between the MFL and MFD method based on the results in Fig. 7. The results of CHDs are taken as examples to quantitatively evaluate the ability of MFL and MFD method on estimating the defect size. The curves of the amplitude extracted from the lines crossing the center of CHDs in x-y plane are plotted in Fig. 8. Then the results in Fig. 8 are used to estimate the diameter of the defects in both directions (parallel and perpendicular to the applied magnetic field). For the purpose of comparison, the amplitude curves extracted from the simulation results in Fig. 2 are scaled and plotted as dotted lines in Fig. 8. The measured curves of the amplitude obtained with both MFL and MFD methods have similar shapes with those of simulation results, indicating the good performance of the experimental set-up in magnetic scanning imaging. Along the magnetization direction (x-axis), the normal component of MFL changes its direction near the center of the CHD to form the amplitude curve with sinusoidal shape. The axial distance between the maximum and minimum amplitudes, Dm , which is frequently used as the feature parameter for defect sizing is employed for defect diameter estimation [23]. When the case comes to the direction perpendicular to the magnetization direction (y-axis), the intensity of normal component of MFL along the line crossing the center of the CHD maintains its negative sign. The measured curves shown in the left of Fig. 8b demonstrate minor differences compared to the simulation results. It is inferred that the differences originates from the inevitable error in geometrical sizes of the actual CHD against that of the simulation model. To achieve the
14
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 5. Geometrical sizes of defects in the (a) specimen 1# and (b) specimen 2#.
Fig. 6. MFD results obtained under different situations.
defect diameter estimation in y-axis direction, the full width at half maximum amplitude, Wh , is employed as the indicator [24,25]. The performances of the MFL and MFD on estimating the defect size depend on the lift-off distance of the Hall device from the tested surface and also the selected indicator. Therefore, it is better to conduct performances comparison between MFL and MFD methods under the same conditions. In this study, the Hall device for measuring both the normal component of MFL and MFD has same lift-off distance as 1 mm. As for the detection results of both the tangential component of MFL and the normal component of MFD, same parameter of Wh is employed as the indicator for defect diameter estimation in both x-axis and y-axis directions. As an example, the values of Dm and Wh estimated from the results of CHD-4 are shown in Fig. 9a. For normal component of MFL, both indicators (Wh and Dm ) can be applied for defect diameter estimation in x-axis. However, the Dm overestimates the defect diameter by around 20% and the value of Wh is 45% larger than that of the defect diameter. To improve the diameter estimation accuracy, it is suggested to reduce the lift-off distance to be lower than 1 mm if Wh and Dm are used as indicators. As indicated in Figs. 2e and 7 b, the dark region in the imaging
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
15
Fig. 7. Scanning imaging of (a) normal and (b) tangential components of the leakage field and (c) the distorted field in defective regions.
Fig. 8. Curves of amplitude extracted from the lines crossing the center of CHDs along (a) x-axis and (b) y-axis.
results of the tangential component of MFL elongates itself along y-axis, thus losing the resolution in defect diameter estimation. As a consequence, the value of Wh along y-axis estimated from the middle of Fig. 8b is 2.6 times larger than that of the actual size,
whereas the Wh along x-axis underestimates the defect diameter by around 20%. In MFD method, the parameter of Wh can well reflect the size of the defect in both directions with a maximum error of 15%. If the full width at a threshold lower than 0.5 is used as indictor,
16
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 9. (a) Performance of the indictors in sizing the defect along x-axis and y-axis and the dependency of the normalized peak voltage on the depth of CHD along x-axis (b) and y-axis (c), respectively.
Fig. 10. (a) the normalized MFD line-scan profiles and (b) the estimated defect diameter for different lift-off distances.
the accuracy of MFD on defect diameter estimation would be higher than the performance of Wh . The ability of the peak voltage in defect depth evaluation was also investigated. For each case shown in Fig. 8b, the peak voltages obtained at the CHDs with different depths are normalized with the maximum peak voltage. Fig. 9b demonstrates the dependency of the normalized peak voltage on the depth of CHD when the analyzed direction is parallel to the static magnetic field (along x-axis). The variation in the normalized peak voltage measured along y-axis with the defect depth is shown in Fig. 9c. In both directions parallel and perpendicular to the static magnetic field, the peak voltage induced by the tangential component of MFL demonstrates monotonous growth trend as the defect depth increases. Therefore, the imaging results of the tangential component of MFL may be applicable to depth profile reconstruction and the higher peak amplitude in the imaging results indicate the greater depth of the defect. It is found that in the direction parallel to the static magnetic field the peak voltage induced by the normal component of MFL also monotonously increases as the defect depth increases. However, in the direction perpendicular to the static magnetic field, the variation in defect depth causes a slight decrease in the peak amplitude. Hence, the technique of measuring the normal component of MFL can only be applied for partial reconstruction of the depth profile. Unfortunately, the peak voltage induced by MFD nearly remains unchanged under the varying depth of CHD. This means that the MFD method fails to evaluate the defect depth even though it has the excellent performance in reconstructing the shape of the defect in x-y plane.
In the above investigations, the parameter of Wh is used as indicator for defect diameter estimation. The lift-off distance between the specimen and the Hall sensor affects the performance of Wh . MFD line-scan crossing the center of CHD-4 along x-axis was performed by adjusting the lift-off distance from 0.25 mm to 1.50 mm with a step of 0.25 mm. The normalized profiles of the normal component of the magnetic field are plotted in Fig.10a. The full width at half maximum amplitude (Wh ) increases as the lift-off distance increases. Therefore, the defect sizing accuracy using indicator of Wh depends on the lift-off distance. The performance of the full width at different amplitude or threshold of voltage, Vth , on defect diameter estimation is examined under the situations of different lift-off distances. It is shown in Fig. 10b that as the increasing of lift-off distance, approximate linear upward trend can be observed from the results of the estimated defect diameter. When the value of Vth is selected as 0.2, the full width of the profile always overestimates the defect diameter in the investigated range of lift-off distance. For a given lift-off distance, the accuracy of the full width on defect diameter estimation is related with the selected value of Vth . To retain the diameter estimation accuracy, a higher threshold of voltage is needed for the case with larger lift-off distance. The magnetic imaging results obtained from the ESD-1 and ESD2 are analyzed to assess the performances of MFL and MFD methods in shape reconstruction. The imaging results are respectively normalized by their maximum values of the voltage. A cut plane with a proper threshold of voltage (Vth ) is used to intersect with the profile of the normalized voltages and consequently the edges of the
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
17
Fig. 11. Shape reconstruction of ESD. (a) and (d) are based on normal components of MFL method. (b) and (e) are based on tangential components of MFL method. (c) and (f) are based on MFD method.
Fig. 12. The shape reconstruction results of CTD-1. (a) and (b) respectively show the imaging results of the normal and tangential components of MFL. (c) and (d) show the results obtained by MFD method.
intersection areas are extracted. The value of Vth for the imaging results of the tangential component of MFL is selected as 0.5 and the value of peak-to-peak for the imaging results of normal component of MFL is selected. The edges of the intersection areas for both MFL and MFD methods are plotted in Fig. 11. The normalized profile of the normal component of MFL (Fig. 2a) demonstrates a valley around the center of the defect. Hence, the intersection lines between the cut plane and the normalized profile of the voltage induced by the normal component of MFL fail to form a closed area. It is obvious that the two curves in Fig. 11a and d cannot be used to reconstruct the shape of ESD even though the distance between the two intersection lines is very close to the actual length of the defect in x-axis. The edge extracted from the imaging results of the tangential component of MFL exhibits an shoe-pad outline, as shown in Fig. 11b and e. It is worth noting that for both ESD-1 and ESD-2 the main axis of the reconstructed shapes are perpendicular to the applied static magnetic field. In the case of ESD-1, the reconstructed shoe-pad outline using tangential components of MFL (Fig. 11b) does not reflect the exact shape in both x-axis and y-axis. As for the case of ESD-2, the reconstructed
shoe-pad outline in Fig. 11e roughly reflect the actual shape along x-axis while in y-axis direction the length of the main axis of the defect are overestimated. This is caused by the nature that the technique of measuring the tangential component of MFL has different abilities in defect sizing in the directions parallel and perpendicular to the magnetization direction. Fig. 11c and f shows the typical edges extracted from the imaging results of MFD. It is obvious that the results of MFD can more precisely reconstruct the shapes of both ESD-1 and ESD-2 compared with the results obtained in MFL method. To doublecheck the superior performance of MFD method in defect shape reconstruction against the MFL method, additional experiments are performed for the two CTDs. The magnetic scanning results obtained by MFL and MFD are shown in Figs.12 and 13. It is easy to find that the magnetic imaging results using MFL method fails to reflect the actual shape of the cross-type defect. The images of the MFD results indicate the cross-type defect. As discussed in previous, the defect shape reconstruction accuracy of the MFD depends on the selected threshold of the voltage in the analysis process. The accuracy of MFD on reconstructing the
18
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Fig. 13. The shape reconstruction results of CTD-2. (a) and (b) respectively show the imaging results of the normal and tangential components of MFL. (c) and (d) show the results obtained by MFD method.
Table 1 Errors of shape reconstruction for the cases with different thresholds of the voltage. ESD-1
Actual defect Estimated (Vth = 0.2) Estimated (Vth = 0.3) Estimated (Vth = 0.4) Estimated (Vth = 0.5)
ESD-2
CTD-1
error(%)
Area(mm2 )
error(%)
Area(mm2)
error(%)
Area(mm2)
error(%)
9.71 12.15 9.64 7.69 6.06
– +25.0 −0.8 −20.8 −37.6
9.71 12.82 10.29 8.28 6.52
– +32.0 +6.0 −14.7 −32.9
16.38 16.28 12.44 9.63 7.36
– +0.61 −24.1 −41.2 −55.1
13.82 17.24 13.15 10.05 7.56
– +24.7 −4.8 −27.3 −45.3
shapes of ESD and CTD are investigated within different threshold of the voltage. When the value of Vth is alternatively selected as 0.2, 0.3, 0.4, and 0.5, the shape reconstruction results for ESD-1, ESD-2 are respectively plotted in Fig. 11c and f. The shape reconstruction results of CTD-1 and CTD-2 are shown in Figs. 12d and 13 d, respectively. The difference between the areas that are respectively enclosed by the reconstructed and actual defect shapes is estimated and treated as a criterion for selecting the optimal threshold of the voltage. The area of the defect shape, S, is calculated as: 1 (xk yk+1 − xk+1 yk ), 2 n
S=
CTD-2
Area(mm2 )
(1)
k=1
where xk and yk are the coordinates of the kth point at the extracted edge. The extracted edge is discretized into n points. The estimated areas of the reconstructed ESDs and CTDs obtained under the conditions of different values of Vth are listed in Table 1. When the value of Vth is equal to 0.2, the area of the reconstructed shape is larger than the area of the actual ESDs and CTDs. The MFD technique will underestimate the area of the ESDs and CTDs if the value of Vth is selected to be higher than 0.3. In the
direction perpendicular to the static magnetic field, the width of the CTD-1 smoothly reduces to from a narrow tip at both ends of the ellipse. The MFD sensor may lose sensitivity to the smoothly changed narrow tip, causing underestimation of the length of the CTD-1 in y-axis. Therefore, if the value of Vth is selected as 0.3, the MFD will underestimate the area of the CTD-1 by around 24.1%. For the cases of ESD-1, ESD-2 and CTD-2, the minimum absolute estimation error is achieved when the value of Vth is selected as 0.3 and the estimation error is in the range of ±6%. The results in Fig. 9b and c indicate that the MFD imaging result is not sensitive to the variation in the depth of surface CHDs. It is inferred that the available detection depth of the MFD method is limited and the MFD cannot reflect defect depth information in the material. To verify such speculation, MFD imaging for the four CHDs were performed from the bottom surface of the specimen (Fig. 5). All the four CHDs can be treated as embedded defects whose bottom surface has different distances away from the specimen surface to be scanned. Unfortunately, the MFD method cannot detect any of the four CHDs. As an example, Fig. 14a gives the MFD imaging result for the CHD-3 whose bottom surface has a distance of 1 mm away from the scanned surface of the specimen. This means that the MFD sensor used in this study cannot detect the discontinuity
Fig. 14. Magnetic imaging results of the CHD whose bottom surface is 1 mm away from the specimen surface to be scanned with the techniques of MFD (a) and MFL with normal (b) and tangential (c) components.
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
of the material with a depth of 1 mm beneath the specimen surface. In addition, the imaging results of both the normal (Fig. 14b) and tangential (Fig. 14c) components clearly reflect the existence of the defect. Hence, the detection depth of the MFL method is much greater than that of the MFD method. 5. Conclusion Finite element simulations and experimental tests were performed to investigate the performances of both MFL and MFD methods in reconstructing the shape of defects. The magnetic imaging results of the normal component of MFL reflect threedimensional information of the defect. However, the intersection lines between the cut plane with different thresholds of the voltage and the normalized profile of the voltage induced by the normal component of MFL fail to reconstruct the shape of ESDs and CTDs. As for the curves of the voltage extracted at the lines crossing the defect center of CHD, the axial distance between the maximum and minimum amplitudes (Dm ) and the full width at half maximum amplitude (Wh ) are good feature parameters to respectively estimate the sizes of CHD in the directions parallel and perpendicular to the applied magnetic field. The peak voltage induced by the normal component of MFL around the CHD demonstrates an approximately linear increasing or decreasing tendency with the increase in the depth of CHD. The magnetic imaging results of the tangential component of MFL measured by the sensor proposed in Fig. 3c overestimate the diameter of the CHD by several times in the direction perpendicular to the applied magnetic field and underestimate the size of CHD in the direction parallel to the applied magnetic field. This causes the poor performance of MFL method in ESDs’ and CTDs’ shape reconstruction. The peak voltage induced by the tangential component of MFL around the CHD demonstrates an approximately linear increasing tendency with the increase in the depth of CHD. The residual magnetic field can affect the performance of MFD in defect detection. Specimen demagnetization before each test of MFD can help suppress the effect of residual magnetic field on the performance of the sensor. When the Hall device has a lift-off distance of 1 mm from the tested demagnetization specimen, the MFD technique of measuring the normal component of the distorted magnetic field can accurately evaluate the sizes of CHD at the cost of available detection depth. The experimental results show that the MFD technique cannot detect the subsurface defect with 1-mm liftoff distance to the scanned specimen surface. By selecting a proper threshold of voltage for the magnetic imaging result analysis, precise shape reconstruction of 8-shaped and cross-type through-wall defects can be achieved. In the future, applications of MFD method for reconstructing two-dimensional shape of complex defects will be investigated and advanced shape reconstruction algorithms will be developed. Acknowledgement This study was supported by the National Natural Science Foundation of China (Project Nos. 11527801, and 11402008). References [1] E. Hristoforou, D. Niarchos, H. Chiriac, et al., Non-destructive evaluation distribution sensors based on magnetostrictive delay lines, Sens. Actuators A Phys. 92 (1-3) (2001) 132–136. [2] M. Pelkner, M. Blome, V. Reimund, et al., Flux Leakage Measurements for Defect Characterization Using A High Precision 3-Axial GMR Magnetic Sensor, American Institute of Physics, 2011, pp. 380–387. [3] M. Afzal, S. Udpa, Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline, Ndt E Int. 35 (7) (2002) 449–457.
19
[4] Z. Zakaria, M.S.B. Mansor, A.H. Jahidin, et al., Simulation of magnetic flux leakage (MFL) analysis using FEMM software, in: Industrial Electronics & Applications (ISIEA), 2010 IEEE Symposium on IEEE, 2010, pp. 481–486. [5] G. Gao, Y. Qin, M. Lian, et al., Detecting typical defects in wire ropes through wavelet analysis, Insight-Non-Destr. Test. Condition Monit. 57 (2) (2015) 98–105. [6] M. Pelkner, R. Pohl, M. Kreutzbruck, et al., Development of adapted GMR-probes for automated detection of hidden defects in thin steel sheets, AIP Conf. Proc. 1706 (1) (2016), 020018, AIP Publishing. [7] R.K. Amineh, S. Koziel, N.K. Nikolova, et al., A space mapping methodology for defect characterization from magnetic flux leakage measurements, IEEE Trans. Magn. 44 (8) (2008) 2058–2065. [8] X.C. Liu, Y.J. Wang, B. Wu, et al., Design of tunnel magnetoresistive-based circular MFL sensor array for the detection of flaws in steel wire rope, J. Sens. 2016 (2016). [9] K. Sakai, K. Morita, Y. Haga, et al., Automatic scanning system for back-side defect of steel structure using magnetic flux leakage method, IEEE Trans. Magn. 51 (11) (2015) 1–3. [10] M. Pelkner, A. Neubauer, V. Reimund, et al., Local magnetization unit for GMR array based magnetic flux leakage inspection, AIP Conf. Proc. AIP 1430 (1) (2012) 1005–1012. [11] W. Sharatchandra Singh, B.P.C. Rao, C.K. Mukhopadhyay, et al., GMR-based magnetic flux leakage technique for condition monitoring of steel track rope, Insight-Non-Destr. Test. Condition Monit. 53 (7) (2011) 377–381. [12] B. Wu, Y.J. Wang, X.C. Liu, et al., A novel TMR-based MFL sensor for steel wire rope inspection using the orthogonal test method, Smart Mater. Struct. 24 (7) (2015), 075007. [13] X.C. Liu, J.W. Xiao, B. Wu, et al., A novel sensor to measure the biased pulse magnetic response in steel stay cable for the detection of surface and internal flaws, Sens. Actuators A Phys. 269 (2018) 218–226. [14] Y. Sun, Y. Kang, C. Qiu, A permanent magnetic perturbation testing sensor, Sens. Actuators A Phys. 155 (2) (2009) 226–232. [15] Y. Sun, Y. Kang, C. Qiu, A new NDT method based on permanent magnetic field perturbation, NDT E Int. 44 (1) (2011) 1–7. ˜ [16] J. Aguila-Munoz, J.H. Espina-Hernández, J.A. Pérez-Benítez, et al., A magnetic perturbation GMR-based probe for the nondestructive evaluation of surface cracks in ferromagnetic steels, NDT E Int. 79 (2016) 132–141. [17] C. Xiao, Y. Zhang, A method of magnetic scanning imaging for detecting defects in ferromagnetic materials, Meas. Sci. Technol. 22 (2) (2011), 025503. [18] Y. Li, J. Wilson, G.Y. Tian, Experiment and simulation study of 3D magnetic field sensing for magnetic flux leakage defect characterisation, NDT E Int. 40 (2) (2007) 179–184. [19] R.H. Priewald, C. Magele, P.D. Ledger, et al., Fast magnetic flux leakage signal inversion for the reconstruction of arbitrary defect profiles in steel using finite elements, IEEE Trans. Magn. 49 (1) (2013) 506–516. [20] F. Li, J. Feng, H. Zhang, et al., Quick reconstruction of arbitrary pipeline defect profiles from mfl measurements employing modified harmony search algorithm, IEEE Trans. Instrum. Meas. 67 (9) (2018) 2200–2213. [21] W. Han, J. Xu, P. Wang, et al., Defect profile estimation from magnetic flux leakage signal via efficient managing particle swarm optimization, Sensors 14 (6) (2014) 10361–10380. [22] S. Youssef, K. Szielasko, A. Sourkov, et al., Surface inspection and remanence imaging with magnetic field distortion measurement, in: World Conference on Non-Destructive Testing(WCNDT), 2016. [23] A. Gupta, K. Chandrasekaran, Finite element modeling of magnetic flux leakage from metal loss defects in steel pipeline, J. Fail. Anal. Prev. 16 (2) (2016) 316–323. [24] B. Liu, L. He, H. Zhang, et al., The axial crack testing model for long distance oil-gas pipeline based on magnetic flux leakage internal inspection method, Measurement 103 (2017) 275–282. [25] A. Sophian, G.Y. Tian, S. Zairi, Pulsed magnetic flux leakage techniques for crack detection and characterisation, Sens. Actuators A Phys. 125 (2) (2006) 186–191.
Biographies Jiaying Zhang is currently working towards his Master’s degree in Instrument and Meter Engineering at College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. Her research interests focus on the magnetic imaging technique for detecting impurities in ferromagnetic materials. Xiucheng Liu received his PhD in Mechanical Engineering from Beijing University of Technology, China in June 2013. Currently, he is an associate professor at the NDT&E Laboratory, Beijing University of Technology, China. His research interest includes the structural health monitoring (SHM) technology and micromagnetic materials characterization technology, etc. Junwu Xiao is currently working towards his Master’s degree in Mechanical Engineering at College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. His research interest focuses on the sensor and instrument design for magnetic flux leakage technique. Zekun Yang is currently working as an engineer in Beijing Institute of Control Engineering, China. His research interest focuses on the smart sensor and instrument design.
20
J. Zhang et al. / Sensors and Actuators A 288 (2019) 10–20
Bin Wu received his PhD in Solid Mechanics from Taiyuan University of Technology, China in October 1996. Currently, he is a Professor at the NDT&E Laboratory, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. His research areas include mechanism of wave motion and nondestructive testing and evaluation technology, etc. He is a member of CSTAM and CMES.
Cunfu He received his PhD in Engineering Mechanics from Tsinghua University, China in 1996. Currently, he is a Professor at the NDT&E Laboratory, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, China. His research interests are in the areas of structural health monitoring (SHM) technology and micromagnetic materials characterization technology, etc. He is a member of CICS, CSTAM and CMES.