Magnetic-optical combined detection method, system and device for surface defects of test piece

Abstract

The application discloses a kind of magnetooptical combined detection method, system, device, computer equipment and storage medium of test piece surface defect.The method includes the target area of test piece surface is 3D modeling, and the surface topography digital model of test piece is generated;The simulation area of the surface topography digital model of test piece is carried out magnetic flux leakage field finite element calculation, and the background magnetic flux leakage signal in preset time period is acquired, simulation area corresponds with target area, and the background magnetic flux leakage signal is the simulation value of the magnetic flux leakage signal of target area;Detection magnetic flux leakage signal in preset time period is acquired, and detection magnetic flux leakage signal is the measured value of the magnetic flux leakage signal of target area;From detection magnetic flux leakage signal, background magnetic flux leakage signal is removed, and detection signal after de-noising in preset time period is acquired;According to detection signal after de-noising in preset time period, it is determined whether target area has defect.The defect detection of the surface of test piece with complex surface topography can be realized by the method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nondestructive testing, and more particularly to a magnetooptical combined detection method, system and device for surface defects of a test piece, a computer device and a storage medium. BACKGROUND

[0002] Steel is an important industrial raw material and is widely used in construction, machinery, transportation and other fields. However, during the production and processing of steel, it is easily affected by adverse factors in the environment, thereby forming defects such as fine cracks on the surface of the steel. The alternating current magnetic flux leakage detection method is a commonly used nondestructive testing method, which can detect the test piece without damaging it.

[0003] However, during the alternating current magnetic flux leakage detection research, it is found that the surface morphology of the measured steel has a great influence on the signal-to-noise ratio of the defect signal. The fluctuation of the rough surface changes the magnetic field distribution near the surface of the measured steel. With the increase of the surface roughness, the background magnetic field noise becomes larger, which interferes with the defect signal. Since the background magnetic field noise and the defect signal have similar frequencies, it is difficult to filter out the background magnetic field noise. Therefore, this alternating current magnetic flux leakage detection method still has the problem of being unable to detect defects on the surface of the steel with complex surface morphology. SUMMARY

[0004] In view of at least one defect or improvement demand of the prior art, the present application provides a magnetooptical combined detection method, system and device for surface defects of a test piece, a computer device and a storage medium, which can realize defect detection on the surface of the test piece with complex surface morphology and improve the accuracy of defect detection.

[0005] To achieve the above-mentioned purpose, according to a first aspect of the present application, a magnetooptical combined detection method for surface defects of a test piece is provided, which comprises:

[0006] 3D modeling the target region on the surface of the test piece to generate a surface morphology digital model of the test piece;

[0007] Performing magnetic flux leakage field finite element calculation on a simulation region of the surface morphology digital model of the test piece to obtain a background magnetic flux leakage signal in a preset time period, the simulation region corresponding to the target region, and the background magnetic flux leakage signal being a simulation value of the magnetic flux leakage signal of the target region;

[0008] Obtaining a detection magnetic flux leakage signal in the preset time period, the detection magnetic flux leakage signal being a measured value of the magnetic flux leakage signal of the target region;

[0009] Removing the background magnetic flux leakage signal from the detection magnetic flux leakage signal to obtain a denoised detection signal in the preset time period;

[0010] Determining whether the target region has defects according to the denoised detection signal in the preset time period.

[0011] Further, according to the detection signal after noise elimination in the preset time period, it is determined whether the target region has defects, comprising extracting the peak value of each signal corresponding to each time in the detection signal after noise elimination in the preset time period; in the case that at least one signal peak value is greater than the threshold value, it is determined that the target region has defects; otherwise, it is determined that the target region has no defects.

[0012] Further, the target region of the test piece surface is 3D modeled to generate a digital model of the surface topography of the test piece, comprising obtaining point cloud data of the target region of the test piece surface; according to the point cloud data, the target region of the test piece surface is 3D modeled to generate a digital model of the surface topography of the test piece.

[0013] Further, the point cloud data of the target region of the test piece surface is obtained, comprising scanning the target region of the test piece surface according to a preset scanning step distance through a 3D laser imager to obtain the point cloud data of the target region of the test piece surface.

[0014] Further, the detection magnetic leakage signal in the preset time period is obtained, comprising applying an alternating magnetization field to the test piece surface in the preset time period through the excitation coil in the magnetic leakage detection probe to form a real magnetic leakage field, the magnetic field distribution of the real magnetic leakage field is the same as that of a simulation magnetic leakage field, the simulation magnetic leakage field is the magnetic leakage field when the simulation region is subjected to magnetic leakage field finite element calculation; the magnetic leakage signal of the target region in the preset time period is detected through the receiving coil in the magnetic leakage detection probe to obtain the detection magnetic leakage signal in the preset time period.

[0015] Further, the magnetic leakage detection probe has a flexible characteristic.

[0016] According to the second aspect of the present application, a magnetic-optical combined detection system for surface defects of a test piece is also provided, comprising: a test piece fixing device, a 3D laser imager, a magnetic leakage detection probe and a computer device, the magnetic leakage detection probe is integrated with an excitation coil and a receiving coil, the 3D laser imager and the magnetic leakage detection probe are both fixed on the back plate of the test piece and are respectively connected in communication with the computer device through a network;

[0017] The test piece fixing device is configured to fix the test piece;

[0018] The 3D laser imager is configured to scan the target region of the test piece surface according to a preset scanning step distance, obtain the point cloud data of the target region of the test piece surface, and transmit the point cloud data to the computer device;

[0019] The excitation coil in the magnetic flux leakage detection probe is configured to apply an alternating current magnetizing field to the surface of the test piece within a preset time period to form a true magnetic flux leakage field, the magnetic field distribution of the true magnetic flux leakage field being the same as that of a simulated magnetic flux leakage field, the simulated magnetic flux leakage field being a magnetic flux leakage field in the finite element calculation of the simulated region;

[0020] The receiving coil in the magnetic flux leakage detection probe is configured to detect the magnetic flux leakage signal of the target region within the preset time period, acquire the detected magnetic flux leakage signal within the preset time period, and transmit the detected magnetic flux leakage signal within the preset time period to the computer device;

[0021] The computer device is configured to perform the steps of any of the above methods.

[0022] According to a third aspect of the present application, a magnetic-optical combined detection device for surface defects of a test piece is also provided, which comprises:

[0023] The generating module is configured to perform 3D modeling on the target region of the surface of the test piece to generate a digital model of the surface topography of the test piece;

[0024] The acquiring module is configured to perform finite element calculation of the magnetic flux leakage field on a simulated region of the digital model of the surface topography of the test piece to acquire a background magnetic flux leakage signal within a preset time period, the simulated region corresponding to the target region, and the background magnetic flux leakage signal being a simulated value of the magnetic flux leakage signal of the target region;

[0025] The acquiring module is further configured to acquire a detected magnetic flux leakage signal within the preset time period, the detected magnetic flux leakage signal being a measured value of the magnetic flux leakage signal of the target region;

[0026] The acquiring module is further configured to remove the background magnetic flux leakage signal from the detected magnetic flux leakage signal to acquire a denoised detection signal within the preset time period;

[0027] The determining module is configured to determine whether the target region has defects according to the denoised detection signal within the preset time period.

[0028] According to a fourth aspect of the present application, a computer device is also provided, which comprises at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit performs the steps of any of the above methods.

[0029] According to a fifth aspect of the present application, a storage medium is also provided, which stores a computer program executed by a computer device, and when the computer program runs on the computer device, the computer device performs the steps of any of the above methods.

[0030] Overall, the above technical solutions conceived by the present application can achieve the following beneficial effects compared with the prior art:

[0031] (1) The magnetooptical combined detection method for surface defects of a test piece provided by the application can obtain a denoised detection signal in a preset time period by respectively acquiring a background magnetic leakage signal and a detection magnetic leakage signal of the surface of the test piece in the preset time period, removing the background magnetic leakage signal from the detection magnetic leakage signal, and mainly reflecting the micro-cracks and narrow recessed areas in the target area, thereby reducing or eliminating the interference of the roughness of the surface of the test piece on the detection magnetic leakage signal, achieving the purpose of detecting the defects of the test piece with complex surface topography, solving the problem of complex noise caused by complex surface topography, and improving the micro-crack detection capability on the surface of the complex topography ferromagnetic material.

[0032] (2) The magnetooptical combined detection system for surface defects of a test piece provided by the application can effectively solve the problem of low signal-to-noise ratio of the detection signal caused by the complex surface topography of the test piece by combining 3D laser imaging technology and alternating current magnetic leakage detection technology (i.e. magnetooptical combination), improve the detection capability of micro-cracks on the surface of the test piece with complex surface topography, and has high detection efficiency and high detection precision. Moreover, an automatic operation process is adopted to generate a denoised detection signal, and the data is analyzed and processed by the upper computer system without manual intervention, thereby improving the detection efficiency and detection precision, and reducing the labor cost. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0034] Figure 1 A flowchart of a magnetooptical combined detection method for surface defects of a test piece provided by an embodiment of the present application;

[0035] Figure 2 A structural schematic diagram of a magnetooptical combined detection system for surface defects of a test piece provided by an embodiment of the present application;

[0036] Figure 3 A flowchart of a magnetooptical combined detection method for surface defects of a test piece provided by another embodiment of the present application;

[0037] Figure 4 A schematic diagram of 3D profile of a local steel surface and a curve diagram of a certain section provided by an embodiment of the present application;

[0038] Figure 5 A curve diagram of a detection magnetic leakage signal, a background magnetic leakage signal and a denoised detection signal provided by an embodiment of the present application;

[0039] Figure 6 A structural block diagram of a magnetic-optical combined detection device for surface defects of a test piece is provided for the embodiments of the present application.

[0040] Figure 7 An internal structure diagram of a computer device is provided for the embodiments of the present application.

[0041] Explanation of reference signs:

[0042] 1 - test piece fixing device, 2 - test piece, 3 - 3D laser imager, 4 - magnetic flux leakage detection probe, 5 - computer device. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present application clearer and more comprehensible, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0044] The terms "comprise" and "have" and any variations thereof in the specification and claims of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to these processes, methods, products or devices.

[0045] The embodiments of the present application provide a magnetic-optical combined detection method for surface defects of a test piece, which is used for detecting defects such as micro-cracks, narrow recessed areas, etc. on the surface of the test piece. The micro-cracks can be fine cracks with a depth of microns (for example, a depth of tens of microns or several tens of microns). The test piece can be a ferromagnetic material with a complex surface topography, including steel and the like.

[0046] As shown in Figure 1 A magnetic-optical combined detection method for surface defects of a test piece is provided, which is executed by a computer device and includes the following steps:

[0047] Step 101, 3D modeling is performed on a target area on the surface of the test piece to generate a surface topography digital model of the test piece.

[0048] The target area is a to-be-detected area on the surface of the test piece. Since the surface of the test piece usually has a large area, it needs to be divided into multiple areas for detection in sequence. For example, if the length of the surface of the test piece is 10 meters, the length of the target area can be 1 meter. At this time, in order to complete the detection of the surface defects of the test piece, 10 detections are required.

[0049] The surface topography digital model is a three-dimensional (3D) model of the target region of the surface of the test piece, which shows the 3D profile of the target region of the surface of the test piece.

[0050] Illustratively, the computer device acquires point cloud data of the target region of the surface of the test piece; and according to the point cloud data, uses a pre-installed 3D modeling software to perform 3D modeling on the target region of the surface of the test piece, to generate the surface topography digital model of the test piece.

[0051] The point cloud data is 3D point cloud, and at least includes three-dimensional coordinates (XYZ) of a plurality of sampling points in the target region, and is obtained by scanning the target region of the surface of the test piece according to a preset scanning step distance by a 3D laser imager. The 3D laser imager obtains the 3D point cloud according to a 3D laser imaging technology. The preset scanning step distance is a distance that can be covered by a single scanning of the 3D laser imager.

[0052] It should be noted that, due to the uncertainty principle, laser is not sensitive to the area of micro-cracks and narrow depressions, so it will not scan out the small defects. In addition, due to the high precision of laser, it can accurately scan out the rough topography of the uneven surface of the test piece. Therefore, the sampling points involved in the point cloud data include the uneven areas in the target region, but do not include the micro-cracks and narrow depressions in the target region.

[0053] In step 102, the magnetic leakage field finite element calculation is performed on the simulation region of the surface topography digital model of the test piece to obtain the background magnetic leakage signal in the preset time period. The simulation region corresponds to the target region, and the background magnetic leakage signal is a simulation value of the magnetic leakage signal of the target region.

[0054] The magnetic leakage signal of the target region at least includes the magnetic induction intensity of each sampling point in the target region under the magnetic leakage field. The relative positions of the 3D laser imager and the magnetic leakage detection probe are calibrated in advance, so that the simulation region corresponds to the target region.

[0055] Illustratively, the computer device builds a finite element simulation model of the target region under the magnetic leakage field based on the surface topography digital model of the test piece; and calculates the background magnetic leakage signal in the preset time period based on the finite element simulation model of the target region under the magnetic leakage field. The finite element simulation model of the target region under the magnetic leakage field is a simulation model established by performing finite element simulation analysis on the target region under the magnetic leakage field.

[0056] It should be noted that, since the sampling points involved in the point cloud data include uneven areas in the target region, but do not include micro-cracks and narrow recessed areas in the target region, the roughness of the target region on the surface of the test piece can be simulated by the finite element simulation model of the target region under the magnetic leakage field, but the micro-cracks, narrow recessed areas and the like cannot be simulated, therefore, the background magnetic leakage signal cannot reflect the depth information of the target region on the surface of the test piece, that is, the micro-cracks and narrow recessed areas in the target region cannot be detected by the background magnetic leakage signal.

[0057] In step 103, a detection magnetic leakage signal in a preset time period is obtained, and the detection magnetic leakage signal is a measured value of the magnetic leakage signal of the target region.

[0058] Exemplarily, the computer device obtains the detection magnetic leakage signal in the preset time period. The detection magnetic leakage signal is obtained by detecting defects of the target region on the surface of the test piece under the real magnetic leakage field by the magnetic leakage detection probe. The detection magnetic leakage signal can not only detect the micro-cracks and narrow recessed areas in the target region, but also detect the roughness of the target region.

[0059] In step 104, the background magnetic leakage signal is removed from the detection magnetic leakage signal to obtain a denoised detection signal in the preset time period.

[0060] Since the detection magnetic leakage signal can not only detect the micro-cracks and narrow recessed areas in the target region, but also detect the roughness of the target region, and the background magnetic leakage signal cannot detect the micro-cracks and narrow recessed areas in the target region, by removing the background magnetic leakage signal from the detection magnetic leakage signal, a detection magnetic leakage signal mainly reflecting the micro-cracks and narrow recessed areas in the target region can be obtained, that is, the denoised detection signal.

[0061] Exemplarily, the computer device differentiates the detection magnetic leakage signal and the background magnetic leakage signal, removes the background magnetic leakage signal from the detection magnetic leakage signal, and obtains the denoised detection signal in the preset time period.

[0062] In step 105, whether the target region has defects is determined according to the denoised detection signal in the preset time period.

[0063] Exemplarily, the computer device extracts a peak value of each signal corresponding to each time in the denoised detection signal in the preset time period; in a case that at least one peak value of the signals is greater than a threshold value, it is determined that the target region has defects; otherwise, in a case that the peak values of the signals are all not greater than the threshold value, it is determined that the target region has no defects. The threshold value is determined in advance by multiple calibrations before defect detection, which is not limited in the embodiment.

[0064] In the above method for magnetooptical combined detection of surface defects of a test piece, the background magnetic leakage signal and the detection magnetic leakage signal of the test piece surface in a preset time period are obtained respectively, the background magnetic leakage signal is a detection signal simulated based on a 3D digital model and can reflect the roughness of the target region, the detection magnetic leakage signal is a detection signal detected under a real magnetic leakage field and can reflect the micro-cracks and narrow recessed regions in the target region and the roughness of the target region, and the detection signal after noise reduction in the preset time period can be obtained by removing the background magnetic leakage signal from the detection magnetic leakage signal, the detection signal mainly reflects the micro-cracks and narrow recessed regions in the target region, so that the interference of the roughness of the test piece surface on the detection magnetic leakage signal can be reduced or eliminated, the purpose of detecting the defects of the test piece surface with complex surface topography is achieved, and the accuracy of defect detection can be improved.

[0065] In one embodiment, the step 103 of obtaining the detection magnetic leakage signal in the preset time period includes applying an alternating magnetizing field to the test piece surface in the preset time period by the excitation coil in the magnetic leakage detection probe to form a real magnetic leakage field, the magnetic field distribution of the real magnetic leakage field is the same as that of the simulation magnetic leakage field, and the simulation magnetic leakage field is a magnetic leakage field in the finite element calculation of the simulation region; and the magnetic leakage signal of the target region in the preset time period is detected by the receiving coil in the magnetic leakage detection probe to obtain the detection magnetic leakage signal in the preset time period.

[0066] In this embodiment, the magnetic leakage detection probe is an alternating magnetic leakage detection probe, which integrates the excitation coil and the receiving coil, applies the alternating magnetizing field to the test piece surface by the excitation coil to form the real magnetic leakage field, and then detects the magnetic induction intensity of the target region under the real magnetic leakage field by the receiving coil to achieve the purpose of obtaining the detection magnetic leakage signal in the preset time period.

[0067] Preferably, the magnetic leakage detection probe has a flexible characteristic.

[0068] By using the flexible magnetic leakage detection probe integrated with the excitation coil and the receiving coil, the magnetic leakage detection probe can be attached to the test piece surface during magnetization and detection, so that the influence of the lift-off value on the detection signal in the detection process can be eliminated, and the problem of poor detection stability caused by the large lift-off value of the traditional rigid probe when detecting the rough surface can be solved.

[0069] As shown in Figure 2 A magnetooptical combined detection system for surface defects of a test piece is provided, which includes a test piece fixing device 1, a 3D laser imager 3, a magnetic leakage detection probe 4 and a computer device 5, the magnetic leakage detection probe 4 integrates an excitation coil and a receiving coil, the 3D laser imager 3 and the magnetic leakage detection probe 4 are both fixed on the back plate of the test piece 2 and are both in communication with the computer device 5 through network connection.

[0070] A test piece fixing device 1 configured to fix a test piece 2.

[0071] A 3D laser imager 3 configured to scan a target region on the surface of the test piece 2 according to a preset scanning step, obtain point cloud data of the target region on the surface of the test piece 2, and transmit the point cloud data to a computer device 5.

[0072] An excitation coil in the magnetic flux leakage detection probe 4 configured to apply an alternating current magnetizing field to the surface of the test piece 2 for a preset period of time to form a real magnetic flux leakage field, the magnetic field distribution of the real magnetic flux leakage field being the same as that of a simulated magnetic flux leakage field, the simulated magnetic flux leakage field being a magnetic flux leakage field in the simulation region during the finite element calculation of the magnetic flux leakage field; a receiving coil in the magnetic flux leakage detection probe 4 configured to detect a magnetic flux leakage signal of the target region in the preset period of time, obtain a detected magnetic flux leakage signal in the preset period of time, and transmit the detected magnetic flux leakage signal in the preset period of time to the computer device 5.

[0073] A computer device configured to perform steps 101-105 of the method described in the above embodiments.

[0074] As shown in Figure 3 Another embodiment provides a method for magnetooptical combined detection of defects on the surface of a test piece, comprising the following steps:

[0075] Step one: a 3D laser imager scans a target region on the surface of a test piece in 3D, obtains 3D point cloud data of the target region, and transmits the 3D point cloud data to an upper computer system (computer device in the above embodiment) through network communication, and generates a digital model of the surface topography of the test piece at the current scanning step.

[0076] Step two: the upper computer system performs finite element calculation and analysis of the magnetic flux leakage field using the digital model of the surface topography, calculates the distribution of the magnetic flux leakage signal in the absence of defects, and takes it as the background magnetic flux leakage signal.

[0077] Step three: simultaneously with the movement of the 3D laser imager, a magnetic flux leakage detection probe is used to magnetize and measure the magnetic flux leakage field of the target region, obtain a detected magnetic flux leakage signal, and transmit the detected magnetic flux leakage signal to the upper computer system through network communication.

[0078] Step four: the background magnetic flux leakage signal is removed from the detected magnetic flux leakage signal to obtain a denoised detection signal, the peak value of the detection signal is extracted, and if it exceeds a threshold value, it is determined to have a defect (crack), otherwise it is determined to have no defect.

[0079] Step five: move the 3D laser imager and the magnetic flux leakage detection probe to scan and detect the next scanning step.

[0080] Step six: repeat steps one to five until all positions on the test piece are detected.

[0081] Among them, the magnetic flux leakage detection probe uses a flexible probe, which can eliminate the effect of the rough surface of the specimen on the lift-off during the detection and magnetization process.

[0082] Preferably, the relative positions of the 3D laser imager and the magnetic flux leakage detection probe are pre-calibrated, with the 3D laser imager positioned in front of the magnetic flux leakage detection probe in the direction of specimen movement, so that the two do not overlap, and the position indices of the simulation signal and the detection signal are consistent (i.e., the simulation area corresponds to the target area).

[0083] A 3D laser imager and a magnetic flux leakage (MF) detection probe simultaneously scan the specimen. The MF detection probe acquires the distribution of the MF leakage field signal on the specimen surface, while the 3D laser imager acquires the 3D contour of the specimen surface. A digital model is generated from the 3D contour and used as the model input for the finite element method (FEM) calculation of the MF leakage field. The parameters in the FEM calculation are consistent with the actual MF leakage field detection parameters. The MF leakage field at the location (simulation area) corresponding to the MF leakage probe path position (target area) is extracted to obtain the background MF leakage signal. The background MF leakage signal and the detected MF leakage signal are fused to perform differential processing on the detected MF leakage signal and the background MF leakage signal. The fusion processing can be a preprocessing operation before differential processing, such as filtering invalid signals or normalization, to ensure that the dimensions of the detected MF leakage signal and the background MF leakage signal are the same.

[0084] refer to Figure 4 , Figure 4 Let R be the surface roughness obtained from scanning with a 3D laser imager. a A schematic diagram and a curve of a section of a local steel surface, plotted from a 3D point cloud of a local area with a diameter of 400 μm. The horizontal axis, the left vertical axis, and the right vertical axis represent the X-axis, Z-axis, and Y-axis, respectively, all in millimeters (mm). Figure 4 As can be seen, the 3D contour is composed of many 3D point clouds, and a certain cross-section is... Figure 4 A thin, elongated curve in the diagram represents the XOZ plane.

[0085] refer to Figure 5 , Figure 5 This is a schematic diagram of the curves of the detected magnetic flux leakage signal, the background magnetic flux leakage signal, and the noise-reduced detection signal provided in the embodiments of this application. The horizontal axis of the figure represents the number of sampling points (in units), and the vertical axis represents the voltage value output by the receiving coil of the magnetic flux leakage detection probe (in volts V). This voltage value corresponds to the amplitude of the magnetic flux leakage detection signal. Figure 5The first figure shows the detection leakage magnetic signal curve obtained from actual leakage magnetic field detection. It can be seen that the peak value of the actual detection leakage magnetic signal is close to 400V, and the amplitude of the noise signal fluctuates around 200V. At this time, the signal-to-noise ratio of the actual detection leakage magnetic signal is close to 2, which is low. Figure 5 The second figure shows the background magnetic flux leakage signal curve when there are no defects, which is extracted by finite element calculation of the 3D model obtained by scanning. It can be seen that the amplitude of the background magnetic flux leakage signal is close to the amplitude of the actual detected magnetic flux leakage signal at the corresponding position, and the amplitude fluctuates around 200V. Therefore, the simulated noise signal can be used to replace the actual noise signal. Figure 5 The third figure shows the detection signal curve after removing the background leakage magnetic signal. It can be seen that the peak value of the denoised detection signal is close to 100V, and the amplitude of the noise signal is close to 0. At this time, the signal-to-noise ratio of the denoised detection signal can reach 50-100, which is high.

[0086] Therefore, from Figure 5 As can be seen, the signal-to-noise ratio of the actual detection magnetic flux leakage signal is low due to the complex surface morphology of the specimen, and the defect signal is not easy to identify. However, after removing the background magnetic flux leakage signal from the detection magnetic flux leakage signal, the signal-to-noise ratio is greatly improved, and it is easy to detect shallow cracks on rough surfaces.

[0087] In this embodiment, a magneto-optical joint detection method for surface defects of specimens with complex morphologies is provided. By combining 3D laser imaging technology and AC magnetic flux leakage detection technology (i.e., magneto-optical joint), the method effectively solves the problem of low signal-to-noise ratio caused by the complex surface morphology of the specimen, thus improving the detection capability of microcracks on the surface of specimens with complex morphologies. This method offers high detection efficiency and accuracy. Furthermore, the automated operation process generates a denoised detection signal, which is then analyzed and processed by a host computer system without manual intervention, improving detection efficiency and accuracy while reducing labor costs.

[0088] Based on the same inventive concept, this application also provides a magneto-optical joint detection device for specimen surface defects, used to implement the magneto-optical joint detection method for specimen surface defects described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the magneto-optical joint detection device for specimen surface defects provided below can be found in the limitations of the magneto-optical joint detection method for specimen surface defects described above, and will not be repeated here.

[0089] like Figure 6 As shown, this application also provides a magneto-optical joint detection device 600 for surface defects of a specimen, which includes a generation module 601, an acquisition module 602 and a determination module 603.

[0090] The generating module 601 is configured to perform 3D modeling on the target region of the test piece surface to generate a digital model of the surface topography of the test piece.

[0091] The acquiring module 602 is configured to perform magnetic leakage field finite element calculation on a simulation region of the digital model of the surface topography of the test piece to acquire a background magnetic leakage signal in a preset time period, the simulation region corresponding to the target region, and the background magnetic leakage signal being a simulation value of the magnetic leakage signal of the target region.

[0092] The acquiring module 602 is further configured to acquire a detection magnetic leakage signal in the preset time period, the detection magnetic leakage signal being a measured value of the magnetic leakage signal of the target region.

[0093] The acquiring module 602 is further configured to remove the background magnetic leakage signal from the detection magnetic leakage signal to acquire a denoised detection signal in the preset time period.

[0094] The determining module 603 is configured to determine whether the target region has a defect according to the denoised detection signal in the preset time period.

[0095] In one embodiment, the determining module 603 is further configured to extract a peak value of each signal corresponding to each time in the denoised detection signal in the preset time period; in a case where at least one peak value of the signals is greater than a threshold value, it is determined that the target region has a defect; otherwise, it is determined that the target region has no defect.

[0096] In one embodiment, the generating module 601 is further configured to acquire point cloud data of the target region of the test piece surface; and perform 3D modeling on the target region of the test piece surface according to the point cloud data to generate the digital model of the surface topography of the test piece.

[0097] The above various modules of the magnetic-optical combined detection device for test piece surface defects can be realized by software, hardware and combinations thereof, in whole or in part. The above various modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above various modules.

[0098] The present application also provides a computer device, and an internal structure diagram of the computer device can be as shown in Figure 7The computer device shown in the figure includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to realize a kind of magnetic-optical combined detection method of test piece surface defect. The display unit of the computer device is used to form visually visible picture, which can be display screen, projection device or virtual reality imaging device, and the input device of the computer device can be touch layer covered on display screen, or key, trackball or touchpad arranged on the shell of computer device, or external keyboard, touchpad or mouse etc.

[0099] Those skilled in the art can understand that, Figure 7 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0100] The present application also provides a computer device comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program which, when executed by the processing unit, causes the processing unit to perform the steps of each of the method embodiments described above.

[0101] The present application also provides a computer-readable storage medium storing a computer program executable by a computer device, which, when running on the computer device, causes the computer device to perform the steps of each of the method embodiments described above. The computer-readable storage medium can include but is not limited to any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, micro-drives and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0102] It should be noted that, for the foregoing method embodiments, the sequences of the described actions can be changed, and the actions can be performed in other sequences or concurrently. Additionally, it should be understood that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0103] In the above embodiments, the description of each embodiment is focused on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0104] In the several embodiments provided by the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the units is only a logical function division, and there can be another division manner in actual implementation; for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electric, mechanical or other forms.

[0105] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. In actual implementation, some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0106] In addition, each functional unit in the embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of a software functional unit.

[0107] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned memory includes: a U disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0108] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be instructed by a program to be completed by relevant hardware, and the program can be stored in a computer readable memory, which can include a flash disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0109] The above is only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of embodiments of the present disclosure after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0110] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present disclosure.

[0111] Those skilled in the art readily understand that the above only describes preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A magneto-optical joint detection method for surface defects of a specimen, characterized in that, include: Using a 3D laser imager, the target area on the surface of the specimen is scanned according to a preset scanning step distance to obtain point cloud data of the target area on the surface of the specimen. Based on the point cloud data, a 3D model of the target area on the surface of the specimen is performed to generate a digital model of the surface morphology of the specimen. The leakage magnetic field finite element method is used to calculate the leakage magnetic field of the simulation area of ​​the digital model of the surface morphology of the specimen, and the background leakage magnetic field signal within a preset time period is obtained. The simulation area corresponds to the target area, and the background leakage magnetic field signal is the simulation value of the leakage magnetic field signal of the target area. An alternating magnetizing field is applied to the surface of the specimen by the excitation coil in the leakage magnetic field detection probe within the preset time period to form a real leakage magnetic field. The magnetic field distribution of the real leakage magnetic field is the same as that of the simulated leakage magnetic field. The simulated leakage magnetic field is the leakage magnetic field when the leakage magnetic field of the simulated area is calculated by finite element method. The magnetic flux leakage signal in the target area is detected by the receiving coil in the magnetic flux leakage detection probe within the preset time period, and the detected magnetic flux leakage signal within the preset time period is obtained. The detected magnetic flux leakage signal is the measured value of the magnetic flux leakage signal in the target area. Remove the background magnetic leakage signal from the detected magnetic leakage signal to obtain the noise-reduced detection signal within a preset time period; Based on the noise-reduced detection signal within the preset time period, determine whether the target area has defects.

2. The method as described in claim 1, characterized in that, The step of determining whether the target area has defects based on the noise-reduced detection signal within the preset time period includes: Extract the peak value of each signal at each moment in the denoised detection signal within the preset time period; If the peak value of at least one signal is greater than a threshold, the target region is determined to be defective; otherwise, the target region is determined to be defect-free.

3. The method as described in claim 1, characterized in that, The magnetic flux leakage detection probe has flexible characteristics.

4. A magneto-optical joint detection system for surface defects of a specimen, characterized in that, The system includes a specimen fixing device, a 3D laser imager, a magnetic flux leakage detection probe, and a computer device. The magnetic flux leakage detection probe integrates an excitation coil and a receiving coil. The 3D laser imager and the magnetic flux leakage detection probe are both fixed on the back plate of the specimen and are respectively connected to the computer device via a network for communication. The specimen fixing device is configured to fix the specimen; The 3D laser imager is configured to scan the target area on the surface of the specimen according to a preset scanning step distance, acquire point cloud data of the target area on the surface of the specimen, and transmit the point cloud data to the computer device. The excitation coil in the leakage magnetic field detection probe is configured to apply an alternating magnetization field to the surface of the specimen within a preset time period to form a real leakage magnetic field. The magnetic field distribution of the real leakage magnetic field is the same as that of the simulated leakage magnetic field. The simulated leakage magnetic field is the leakage magnetic field when performing finite element calculations on the leakage magnetic field in the simulation area. The receiving coil in the magnetic flux leakage detection probe is configured to detect the magnetic flux leakage signal of the target area within the preset time period, acquire the detected magnetic flux leakage signal within the preset time period, and transmit the detected magnetic flux leakage signal within the preset time period to the computer device. The computer device is configured to perform 3D modeling of the target area on the surface of the specimen based on the point cloud data, and generate a digital model of the surface morphology of the specimen; perform finite element calculation of the leakage magnetic field on the simulation area of ​​the digital model of the surface morphology of the specimen, and obtain the background leakage magnetic field signal within the preset time period, wherein the simulation area corresponds to the target area, and the background leakage magnetic field signal is the simulation value of the leakage magnetic field signal of the target area. Remove the background magnetic flux leakage signal from the detected magnetic flux leakage signal to obtain the noise-reduced detection signal within a preset time period; determine whether there is a defect in the target area based on the noise-reduced detection signal within the preset time period.

5. A magneto-optical combined detection device for surface defects of a specimen, characterized in that, include: The generation module is configured to scan the target area on the surface of the specimen using a 3D laser imager according to a preset scanning step distance, and obtain point cloud data of the target area on the surface of the specimen. Based on the point cloud data, a 3D model of the target area on the surface of the specimen is performed to generate a digital model of the surface morphology of the specimen. The acquisition module is configured to perform finite element calculation of leakage magnetic field on the simulation area of ​​the digital model of the surface morphology of the specimen, and acquire the background leakage magnetic field signal within a preset time period. The simulation area corresponds to the target area, and the background leakage magnetic field signal is the simulation value of the leakage magnetic field signal of the target area. The acquisition module is further configured to apply an alternating magnetization field to the surface of the specimen within the preset time period through the excitation coil in the leakage magnetic field detection probe to form a real leakage magnetic field. The magnetic field distribution of the real leakage magnetic field is the same as that of the simulated leakage magnetic field. The simulated leakage magnetic field is the leakage magnetic field when the leakage magnetic field finite element calculation is performed on the simulated area. The magnetic flux leakage signal in the target area is detected by the receiving coil in the magnetic flux leakage detection probe within the preset time period, and the detected magnetic flux leakage signal within the preset time period is obtained. The detected magnetic flux leakage signal is the measured value of the magnetic flux leakage signal in the target area. The acquisition module is further configured to remove the background magnetic leakage signal from the detected magnetic leakage signal and acquire the noise-reduced detection signal within a preset time period. The determination module is configured to determine whether there are defects in the target area based on the noise-reduced detection signal within the preset time period.

6. A computer device, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of the method of claim 1 or 2.

7. A storage medium, characterized in that, It stores a computer program that is executed by a computer device, which, when run on the computer device, causes the computer device to perform the steps of the method of claim 1 or 2.

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