A rapid tomography method for ferromagnetic material defects based on AC magnetic leakage

Abstract

The present application provides a method for rapid tomography of defects in ferromagnetic materials based on AC leakage magnetic flux, comprising: generating an excitation magnetic field through alternating current, magnetizing the sample to be detected, obtaining the leakage magnetic field signal generated by the defect of the sample to be detected, the sample to be detected is a ferromagnetic material, detecting the change of the leakage magnetic field signal through a sensor, determining the rising time point of the leakage magnetic field signal, and judging the depth of the defect of the sample to be detected based on the rising time point. The present application establishes a mathematical model of the rising time point and the depth of the defect, and realizes the accurate determination of the depth of the defect. At the same time, the present application also compares the signal characteristics of different defect widths, defect shapes and defect depths, and finds that the rising time point is independent of the defect width and defect shape, and extracts the distribution law of the rising time point, and obtains the basis for judging different defect depths at the same depth, which significantly improves the accuracy and reliability of defect detection.

Description

Technical Field

[0001] The present application belongs to the field of information technology, and in particular relates to a rapid tomography method for defects in ferromagnetic materials based on alternating current magnetic leakage. Background Art

[0002] In the field of industrial nondestructive testing, accurately measuring defect depth has always been a daunting technical challenge. While traditional magnetic flux leakage testing methods can identify the presence of defects, they suffer from significant errors in determining their specific location and depth. This is primarily because the magnetic flux leakage signal is affected by a complex array of factors, such as sample material properties, magnetization intensity, and probe sensitivity. Especially when the defect is located at a deeper location, the signal becomes weaker and more ambiguous, making accurate identification more difficult. Furthermore, geometric features such as the defect's shape and width can interfere with the magnetic flux leakage signal, further complicating the relationship between the signal characteristics and the defect depth. These combined factors lead to uncertainty in defect depth measurement, severely impacting the reliability of test results. Furthermore, variations in the lift-off distance between the probe and the sample during actual testing introduce additional errors, further reducing measurement accuracy. Therefore, accurately extracting characteristic information related to defect depth within this complex signal environment and establishing a stable and reliable mathematical model have become key challenges in improving defect detection accuracy. Solving this problem not only impacts the advancement of testing technology but also directly impacts the safety and efficiency of industrial production. Summary of the Invention

[0003] The present application provides a rapid tomography method for defects in ferromagnetic materials based on AC magnetic leakage, mainly comprising the following steps: S101: generating an excitation magnetic field by AC current to magnetize the sample to be tested, the sample to be tested is a ferromagnetic material, and obtaining a leakage magnetic field signal generated by the defect of the sample to be tested;

[0004] S102: detecting changes in the leakage magnetic field signal by a sensor, determining the rising time point of the leakage magnetic field signal;

[0005] S103: Determine the defect depth of the sample to be inspected based on the rise time point, obtain a linear regression model between the rise time point and the defect depth, simulate the relationship between the rise time point and the defect depth, defect shape, defect width, and defect depth, verify the correlation between the rise time point and the defect depth, defect shape, defect width, and defect depth, and determine whether the linear regression model is correct based on the actual measurement results and simulation results. Furthermore, generating an excitation magnetic field with alternating current to magnetize the sample to be inspected includes: passing alternating current through a magnetizer to generate an excitation magnetic field with periodically varying magnetic field intensity; magnetizing the sample to be inspected with the excitation magnetic field, forming a magnetic shielding layer that moves along the thickness direction within the sample to be inspected, and generating a detectable leakage magnetic field signal when the magnetic shielding layer moves to the defect location. Furthermore, detecting changes in the leakage magnetic field signal with a sensor includes: acquiring the leakage magnetic field signal using a differential probe, wherein the differential probe is composed of two Hall sensors spaced apart, and moving the differential probe along the surface of the sample to be inspected to detect changes in the leakage magnetic field signal over time. Furthermore, the determination of the rising time point of the leakage magnetic field signal includes: obtaining the amplitude change curve of the leakage magnetic field signal, dividing the leakage magnetic field signal characteristics into three stages according to the amplitude change curve, the first stage being the low amplitude stage when the magnetic shielding layer is shielding, the second stage being the linear increase stage when the magnetic shielding layer moves over the defect, and the third stage being the dramatic increase stage after the magnetic shielding layer is removed, and determining the transition point from the first stage to the second stage as the rising time point, and the transition point from the second stage to the third stage as the mutation time point. Furthermore, the determination of the defect burial depth of the sample to be tested based on the rising time point includes: obtaining the rising time points corresponding to different defect burial depths, establishing a mathematical model of the rising time points and the defect burial depth by comparing the rising time points, and determining the defect burial depth corresponding to the rising time point according to the mathematical model, the rising time point and the defect burial depth are linearly related, and the linear regression model is:

[0006] , ; wherein x is the depth of the defect, and t1 is the rising time point. Further, the detecting the change of the leakage magnetic field signal by the sensor includes: setting a measurement point above the sample to be detected, the measurement point is located at the central axis of the magnetizer, obtaining the change curve of the leakage magnetic field signal through the measurement point, and analyzing the mutation characteristics of the leakage magnetic field signal according to the change curve. Further, judging the depth of the defect of the sample to be detected based on the rising time point includes: obtaining the leakage magnetic field signals of defects of different shapes, and by comparing the rising time points of the defects of different shapes, determining that the rising time point is irrelevant to the defect shape and only related to the defect depth, and excluding the influence of the defect shape when judging the defect depth according to the rising time point.

[0007] Furthermore, a simulation model was used to study the defect burial depth and the rise time point. The defect burial depth was set to 2mm, 2.5mm, 3mm, 3.5mm, 4mm, and 4.5mm, respectively. The rise time point was gradually pushed back from 0ms to 2ms. The simulation results showed that there was a linear relationship between the defect burial depth and the rise time point.

[0008] Furthermore, a simulation model was used to study the defect depth and the rising time point. The defect depths were set to 0.5mm, 1.0mm, 1.5mm, 2.0mm, and 2.5mm, respectively, and the projection length of the corresponding rising time period on the X-axis was obtained. The simulation results show that at the same burial depth, the defect depth and the projection length of the rising time period on the X-axis (that is, the difference between the rising time point t1 and the mutation time point t2) are linearly related.

[0009] Furthermore, a simulation model was used to investigate the relationship between defect shape and rise time. The subjects were spherical defects with a diameter of 0.5 mm and sawtooth defects with an equivalent length of 0.65 mm. The results showed that different defect types exhibited the three-segment signal characteristics of rectangular defects, demonstrating that defect shape was independent of rise time.

[0010] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0011] The present application discloses a method for rapid tomography of defects in ferromagnetic materials based on AC magnetic leakage. The method magnetizes the sample to be tested through a magnetizer, detects the leakage magnetic field signal using a differential probe, and analyzes the signal characteristics. By identifying the rising time point during the movement of the magnetic shielding layer, the present application establishes a mathematical model of the rising time point and the depth of the defect, and realizes the accurate determination of the depth of the defect. At the same time, the present application also compares the signal characteristics of different defect widths, defect shapes and defect depths, and finds that the rising time point is independent of the defect width and defect shape, and extracts the distribution law of the rising time point to obtain the basis for judging different defect depths at the same depth. This method significantly improves the accuracy and reliability of defect detection, and provides an innovative solution for non-destructive testing in the industrial field. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flowchart of a rapid tomography method for defects in ferromagnetic materials based on AC magnetic leakage in this application;

[0013] Figure 2 This is a schematic structural diagram of a magnetizer in an embodiment of the present application;

[0014] Figure 3 The curves showing the change of magnetic field strength when different magnetizing currents are applied in the embodiment of the present application are shown in FIG.

[0015] Figure 4 : is the magnetic field intensity variation curve of the optimal magnetizing current in the embodiment of the present application;

[0016] Figure 5 This is a cloud diagram of the magnetic permeability distribution of the sample to be tested at different times in the embodiment of the present application;

[0017] Figure 6 The magnetic field intensity variation curve of defects at the same depth and different buried depths of the sample to be detected in the embodiment of the present application;

[0018] Figure 7 This is a magnetic flux leakage signal diagram when the depth of the defect of the sample to be tested is 2.0 mm in the embodiment of the present application;

[0019] Figure 8 The magnetic field intensity variation curve of defects with the same burial depth and different widths of the sample to be detected in the embodiment of the present application;

[0020] Figure 9 This is a linear relationship diagram between the depth of the defect of the sample to be detected and the rising time point in the embodiment of the present application;

[0021] Figure 10 This is a linear relationship diagram between the depth of the defect of the sample to be detected and the length of the rising time period in the embodiment of the present application;

[0022] Figure 11 The magnetic field intensity variation curves of circular defects with different buried depths in the embodiment of the present application;

[0023] Figure 12 Magnetic field intensity variation curves of sawtooth defects with different buried depths in the embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of this application clearer, this application is described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] like Figure 1 As shown, this embodiment provides a method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage, which may specifically include:

[0026] S101: generating an excitation magnetic field by alternating current to magnetize the sample to be tested, the sample to be tested is a ferromagnetic material, and obtaining a leakage magnetic field signal generated by a defect in the sample to be tested;

[0027] S102: detecting changes in the leakage magnetic field signal by a sensor, determining the rising time point of the leakage magnetic field signal;

[0028] S103: Determine the defect depth of the sample to be tested based on the rising time point, obtain a linear regression model of the rising time point and the defect depth, and simulate the relationship between the rising time point and the defect depth, defect shape, defect width and defect depth, verify the correlation between the rising time point and the defect depth, defect shape, defect width and defect depth, and determine whether the linear regression model is correct based on the actual measurement results and the simulation results.

[0029] To further explain the scheme in detail, its specific implementation process is as follows:

[0030] (1) Obtain a sample to be tested, which is a ferromagnetic material. Pass alternating current through a magnetizer to generate an excitation magnetic field, magnetize the sample to be tested, and excite the leakage magnetic field at the defect position.

[0031] like Figure 2 As shown, the magnetizer consists of a shell and a magnetizing coil 2 arranged in the shell 1. The shell 1 is a hollow opening structure on the side for the sample 3 to be tested to pass through the side opening and is fixed by pads 4 located at the front and rear ends of the magnetizer.

[0032] (2) Using a differential probe to detect the leakage magnetic field signal of the measuring point above the sample to be tested, the differential probe is composed of two Hall sensors set at intervals, and the leakage magnetic field change data is obtained;

[0033] like Figure 3 As shown in the figure, under the loading of different magnetizing currents, corresponding changes in magnetic field strength occur. When the magnetizing current increases gradually to 5A, 5.4A, 5.8A, 6.2A, 6.6A, and 7.2A, the leakage magnetic field signal also gradually increases, and the magnetic flux density increases from 5mT to about 45mT. When the magnetizing current is 6.6A, the leakage magnetic field signal amplitude is the highest, close to 45mT. When the magnetizing current reaches 7.2A, it is found that the leakage magnetic field signal is oversaturated. Therefore, the magnetizing current of 7.2A that saturates the leakage magnetic field signal is not selected because when the magnetizing current is 6.6A, the leakage magnetic field amplitude is the highest and the detection effect is better.

[0034] like Figure 4 As shown in the figure, when the magnetizing current is 6.6A, the leakage magnetic field amplitude reaches the maximum and the magnetic flux density reaches 45mT, which can output a strong signal and provide a better detection environment.

[0035] (3) Based on the leakage magnetic field change data, identify the rising time point of the leakage magnetic field signal during the movement of the magnetic shielding layer, and determine the signal characteristics corresponding to the rising time point.

[0036] like Figure 5As shown in the figure, the x-axis represents the distance along the length of the sample being tested, and the y-axis represents the distance along the wall thickness of the sample being tested. The results show that there is a region in the thickness direction opposite to the magnetic field distribution. In this region, the magnetic field is close to zero, which is consistent with theoretical analysis. The magnetic permeability is the initial magnetic permeability of the ferromagnetic material. The larger initial magnetic permeability has a magnetic shielding effect, which blocks the leakage magnetic field generated by the defect below. This region of higher magnetic permeability also moves along the thickness direction over time.

[0037] like Figure 6 As shown in the figure, without changing the defect depth, by changing the defect burial depth to 2mm, 2.5mm, 3mm, 3.5mm, 4mm, and 4.5mm, the rise time point is gradually postponed from 0ms to 2ms. The simulation results show that the defect burial depth is linearly related to the rise time point.

[0038] (4) The three stages of leakage magnetic field changes are analyzed through the signal characteristics corresponding to the rising time points, which are: the leakage magnetic field is close to zero in the first stage, the leakage magnetic field increases linearly in the second stage, and the leakage magnetic field increases sharply in the third stage. The rising time points and mutation time points are extracted from the signal characteristics of the second and third stages to obtain a set of leakage magnetic field signal characteristics for different defect burial depths. For the set of leakage magnetic field signal characteristics, a mathematical model of the rising time points and the defect burial depths is established, and the linear relationship between the rising time points and the defect burial depths in the mathematical model is determined. Based on the mathematical model, the defect burial depth position in the sample to be tested is determined to obtain the defect burial depth detection result.

[0039] The sensor acquires magnetic field leakage signal data from a measurement point above the sample to be tested, recording the complete waveform of the signal over time. Based on the acquired magnetic field leakage signal waveform, the characteristic points of the signal amplitude change over time are analyzed, and the signal values ​​corresponding to the rising time points are extracted. The extracted signal values ​​at the rising time points are used to divide the magnetic field leakage change into three stages. The detected magnetic field leakage signal is segmented to delineate the signal intervals of the second and third stages. Based on the signal characteristics of the second stage, the rising time points where the magnetic field leakage increases approximately linearly are extracted. Based on the signal characteristics of the third stage, the time points where the magnetic field leakage increases sharply are extracted. The starting time range where the magnetic field leakage approaches zero in the first stage is determined. Based on the time range of the first stage, the starting time point of the second stage, i.e., the rising time point, is calculated, and the characteristic signal value where the magnetic field leakage begins to increase linearly is determined. Data for the linearly increasing magnetic field leakage signal in the second stage is acquired, and the interval where the signal amplitude changes approximately linearly over time is determined. The starting time of the third stage, i.e., the sudden change time point, is identified by the end time point of the second stage variation interval, and the characteristic point where the leakage field leakage suddenly increases is determined. Based on the rising time points of the second stage, the rise time from stable to linear increase in the magnetic field leakage signal is calculated, and the rising time point data is obtained. By comparing the rise time points of different defect depths, a mathematical model of the rise time point and defect depth is established. The established mathematical model data is obtained, and the linear relationship between the rise time point and the defect depth is analyzed to determine the specific value of the defect depth.

[0040] like Figure 7 Figure 2 shows the measured magnetic flux leakage signal at a 2.0mm defect depth, with the rise time t1 and the mutation time t2 indicated. Specifically, a differential Hall effect sensor was used to acquire the magnetic flux leakage signal from the surface of the test sample at a sampling frequency of 100kHz, recording the complete waveform data (two cycles) within the time range of 0-40ms. A sliding window difference algorithm was used to analyze the waveform data. Rise time points were marked when the amplitude difference between adjacent sampling points exceeded 5mV. The signal values ​​corresponding to the rise time t1 = 2ms and the mutation time t2 = 5ms in the first cycle were extracted. Three stages were identified based on the mutation point amplitude characteristics. The first stage of magnetic shielding was determined when the signal amplitude remained within the ±2mV range between 0ms and 2ms. A linear regression algorithm was used to calculate the signal slope between 2ms and 5ms. When the slope stabilized at 0.15mV / ms and R² > 0.98, the second stage of linear growth was determined. The second-order derivative method is used to detect the signal change rate after 5ms. When the change rate exceeds 1.2mV / ms², it is determined to be the starting point of the third stage. The signal rise time in the 5ms-8ms interval is calculated to be 3ms, which coincides with the end time of the second stage. A linear model of the rise time point t1 and the defect depth is established. , ; Where x is the defect depth and t1 is the rising time point.

[0041] (5) Using the defect depth detection results, compare the rise time points of different defect widths and defect shapes, determine that the rise time point is independent of the defect width and defect shape, and that the rise time point is linearly related to the defect depth, determine whether the simulation results are the same as the actual measurement results, and the correlation between the simulated defect depth and the rise time point.

[0042] like Figure 8 As shown in the figure, without changing the burial depth of the defect, only the width of the defect is changed. The defect widths are 0.2mm, 0.5mm, 0.8mm, 1.2mm, and 1.5mm, respectively. The rise time points are the same. The simulation results show that the change in defect width does not affect the rise time point.

[0043] like Figure 9 As shown in the figure, the relationship between different defect burial depths and rise time points is simulated. The defect burial depths are selected as 0.2mm, 1.0mm, 1.5mm, 2.0mm, 2.5mm, 3.0mm, 3.5mm, 4.0mm, and 4.5mm, and the corresponding rise time points are obtained. The results show that the defect burial depth and the rise time point are linearly related, and the linear equation is y=0.1392x+1.19326, R 2 =0.99819, y is the rising time point, x is the defect depth, the simulation results are consistent with the actual measurement results; Figure 10 As shown, the relationship between the projection length of the rising time period on the X-axis (the projection length of the rising time point t1 and the mutation time point t2 on the X-axis) is simulated at different defect depths (referring to the longitudinal height of the defect) at the same time. The defect depth is selected as 0.5mm, 1.0mm, 1.5mm, 2.0mm, and 2.5mm, and the corresponding projection length of the rising time period on the X-axis is obtained. The results show that the defect depth at the same burial depth is linearly related to the projection length of the rising time period on the X-axis. The defect depth change at the same defect burial depth is linearly related to the projection length of the length from the rising time point to the mutation time point on the X-axis: Y , correlation coefficient ; Where x is the defect depth, and Y is the difference between the rising time point t1 and the mutation time point t2.

[0044] like Figure 11 Shown and Figure 12As shown in the figure, a spherical defect with a diameter of 0.5 mm and a sawtooth defect with an equivalent length of 0.65 mm were simulated. The leakage magnetic field of the two defects is shown in the figure as the defect depth increases from 0.5 mm to 4.0 mm. The results show that the different types of defects also display the three-segment signal characteristics of rectangular defects. The magnetic permeability distribution of the two defects is similar to that of the rectangular defect. When the magnetic shielding layer moves to the top of the defect, the leakage magnetic field begins to rise. The mathematical models of the rise time point and defect depth of the two defects are the same as those of the rectangular defect. The results show that the defect shape does not affect the rise time point.

[0045] (6) The distribution law of the rising time point is extracted from the variation characteristics of the defect depth, and the basis for judging the different defect depths at the same burial depth is obtained.

[0046] The sample under inspection is magnetized using the AC magnetic flux leakage method to obtain the leakage magnetic field signal generated at the defect location. Based on the obtained leakage magnetic field signal, signal characteristics are extracted and divided into three stages: the first stage is close to zero, the second stage is an approximately linear increase, and the third stage is a dramatic increase. By analyzing the signal changes from the first stage to the third stage, the rise time point is determined as a characteristic parameter. The leakage magnetic field signal is obtained for different defect depths at the same burial depth, and the corresponding rise time point is extracted for each defect depth. Based on the rise time point data for different defect depths, a mathematical model is established to correlate the rise time point with the defect depth. The mathematical model is used to analyze the linear relationship between the rise time point and the defect depth. The leakage magnetic field signal is obtained for the same defect depth under different lift-off conditions, and the corresponding rise time point distribution is extracted. By comparing the rise time points under different lift-off conditions, the distribution pattern of the rise time points is determined when the lift-off condition is not affected. Based on the rise time point distribution pattern, the defect depth is determined.

[0047] Specifically, a 50Hz AC excitation frequency was applied to the magnetizer, generating an alternating magnetic field on the surface of the sample to be inspected. A differential Hall sensor was used to scan the inspection area at a lift-off height of 0.3mm, with a sampling rate of 100kHz, and the leakage magnetic field signal waveform was recorded in real time. Signal features were decomposed using a wavelet transform, identifying an initial stage with a signal amplitude below 5% of the maximum value as the magnetic shielding phase. This was followed by a linear growth phase with a slope of 0.2V / ms, and finally a third stage with a sudden increase in amplitude exceeding 1V. At a fixed burial depth of 10mm, the rise time points measured at lift-off heights of 0.5mm, 1mm, and 2mm were all 24.6ms±0.3ms, with a standard deviation of less than 1.2%, demonstrating that this feature is independent of lift-off. A support vector machine algorithm was used to perform pattern recognition on the rise time points of 200 sets of defects of varying depths, achieving a classification accuracy of 98.7%. Finally, quantitative analysis of the defect depth was output.

[0048] The preferred embodiments of the present application disclosed above are intended only to help illustrate the present application. The preferred embodiments do not describe all details in detail, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations can be made based on the contents of this specification. This specification selects and describes these embodiments in detail to better explain the principles and practical applications of the present application, so that those skilled in the art can better understand and utilize the present application. The present application is limited only by the claims and their full scope and equivalents.

Claims

1. A rapid tomography method for ferromagnetic material defects based on AC magnetic flux leakage, characterized in that: The following steps are involved: S101: generating an excitation magnetic field by alternating current to magnetize the sample to be tested, the sample to be tested is a ferromagnetic material, and obtaining a leakage magnetic field signal generated by a defect in the sample to be tested; S102: detecting changes in the leakage magnetic field signal by a sensor, determining the rising time point of the leakage magnetic field signal; S103: Determine the defect depth of the sample to be inspected based on the rise time point, obtain a linear regression model of the rise time point and the defect depth, simulate the relationship between the rise time point and the defect depth, defect shape, defect width, and defect depth, verify the correlation between the rise time point and the defect depth, defect shape, defect width, and defect depth, and determine whether the linear regression model is correct based on actual measurement results and simulation results; Determining the rising time point of the leakage magnetic field signal includes: obtaining an amplitude change curve of the leakage magnetic field signal, dividing the leakage magnetic field signal characteristics into three stages according to the amplitude change curve, wherein the first stage is a low amplitude stage when the magnetic shielding layer is shielding, the second stage is a linear increase stage when the magnetic shielding layer moves over the defect, and the third stage is a sharp increase stage after the magnetic shielding layer is removed, and determining the transition point from the first stage to the second stage as the rising time point, and the transition point from the second stage to the third stage as the mutation time point; The method of determining the depth of the defect buried in the sample to be tested based on the rising time point includes: obtaining rising time points corresponding to different defect buried depths, establishing a mathematical model of rising time points and defect buried depths by comparing the rising time points, and determining the defect buried depth corresponding to the rising time point according to the mathematical model, wherein the rising time point and the defect buried depth are in a linear relationship, and the linear regression model is: , ; Where x is the defect depth and t1 is the rising time point.

2. The method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage according to claim 1, characterized in that: The method of generating an excitation magnetic field by alternating current to magnetize the sample to be inspected includes: passing alternating current through a magnetizer to generate an excitation magnetic field with periodically varying magnetic field intensity, magnetizing the sample to be inspected by the excitation magnetic field, forming a magnetic shielding layer that moves along the thickness direction inside the sample to be inspected, and generating a detectable leakage magnetic field signal when the magnetic shielding layer moves to the defect location.

3. The method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage according to claim 1, characterized in that: The detection of changes in the leakage magnetic field signal by a sensor includes: obtaining the leakage magnetic field signal using a differential probe, wherein the differential probe is composed of two Hall sensors arranged at intervals, and the differential probe is moved along the surface of the sample to be detected to detect changes in the leakage magnetic field signal over time.

4. The method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage according to claim 1, characterized in that: The method of detecting the change of the leakage magnetic field signal by a sensor includes: setting a measurement point above the sample to be detected, the measurement point being located at the central axis of the magnetizer, obtaining a change curve of the leakage magnetic field signal through the measurement point, and analyzing the mutation characteristics of the leakage magnetic field signal according to the change curve.

5. The method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage according to claim 1, characterized in that: The method of judging the depth of the defect of the sample to be inspected based on the rising time point includes: obtaining leakage magnetic field signals of defects of different shapes, and determining that the rising time point is independent of the defect shape and only related to the depth of the defect by comparing the rising time points of the defects of different shapes, and excluding the influence of the defect shape when judging the depth of the defect based on the rising time point.

6. The method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage according to claim 1, characterized in that: A simulation model was used to study the defect burial depth and rising time point, and the defect burial depths were set to 2mm, 2.5mm, 3mm, 3.5mm, 4mm, and 4.5mm respectively.

7. The method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage according to claim 1, characterized in that: A simulation model was used to study the defect depth and rise time point, and the defect depths were set to 0.5mm, 1.0mm, 1.5mm, 2.0mm, and 2.5mm respectively.

8. The method for rapid tomography of ferromagnetic material defects based on AC magnetic flux leakage according to claim 5, characterized in that: A simulation model was used to study the defect shape and rise time point. The objects were a spherical defect with a diameter of 0.5 mm and a sawtooth defect with an equivalent length of 0.65 mm. The defect burial depth increased from 0.5 mm to 4.0 mm.

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