Weld defect detection method

Abstract

The invention relates to the technical field of weak magnetic detection, and provides a weld defect detection method which comprises the following steps: stacking six weak magnetic sensors in pairs to form an L shape, and positioning a second weak magnetic sensor and a fifth weak magnetic sensor which are stacked into a first group right above a weld, the first weak magnetic sensor and the fourth weak magnetic sensor which are stacked into the second group are located right behind the first group, and the third weak magnetic sensor and the sixth weak magnetic sensor which are stacked into the third group are arranged side by side and aligned at one side of the first group, namely, difference calculation is carried out on two magnetic induction intensity signal values of the same group; setting at least two conditions for judging defects according to the characteristics of the welding seam and a weak magnetic detection principle, substituting the magnetic induction intensity data after the background field is removed into the conditions for judging the defects by utilizing a redundancy detection principle to carry out defect feature recognition, and carrying out defect feature recognition according to a probability statistics principle. And the correct probability of defect judgment is calculated, so that the accuracy of a weld defect judgment result can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of weak magnetic detection, and in particular to a weld defect detection method. Background Art

[0002] As an indispensable technical means in modern industry, the importance of non-destructive testing is self-evident.

[0003] As an emerging passive non-destructive testing method, weak magnetic detection technology has the following advantages: (1) No need for additional excitation source: it works in the geomagnetic field environment and does not require a special magnetic excitation device; (2) Non-contact detection: it can detect coatings without special treatment of the surface of the workpiece being tested; (3) No special requirements for shape: there are no special requirements for the shape of the workpiece being tested, and it has strong adaptability; (4) Fast detection speed and simple operation: it uses high-precision fluxgate sensors for scanning and rapid data processing.

[0004] Its working principle is based on the stress changes and atomic structure changes caused by material defects, which lead to the spontaneous generation of abnormal magnetic fields. This abnormal signal is detected by a high-precision magnetic sensor to characterize the defects of the material. Assuming that the magnetic permeability of the material being tested is μ, and the magnetic permeability of the discontinuous area inside the workpiece is μ', if the relative magnetic permeability of the discontinuous area is greater than the relative magnetic permeability of the material being tested, that is, μ'>μ, then when the magnetic sensor passes through this area, the magnetic induction intensity curve will appear convex; if the relative magnetic permeability of the discontinuous area is less than the relative magnetic permeability of the material being tested, that is, μ'<μ, then when the magnetic sensor passes through this area, the magnetic induction intensity curve will appear concave. Weak magnetic detection technology uses high-precision magnetic sensors to detect abnormal signals of this sudden change in magnetic induction intensity, thereby characterizing the defects of the material.

[0005] At present, in the weld defect detection based on the weak magnetic principle, the sensor is mainly used to directly scan and detect the weld surface. It is difficult to suppress external interference, resulting in large quantitative errors in defect detection. In addition, single data will also cause a single defect judgment condition, resulting in errors in the final defect judgment result. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a weld defect detection method, which can improve the accuracy of weld defect judgment results.

[0007] The present invention provides a weld defect detection method, comprising: stacking six weak magnetic sensors in pairs to form an L shape, wherein the second weak magnetic sensor and the fifth weak magnetic sensor stacked in the first group are located directly above the weld, the first weak magnetic sensor and the fourth weak magnetic sensor stacked in the second group are located directly behind the first group, and the third weak magnetic sensor and the sixth weak magnetic sensor stacked in the third group are arranged side by side and aligned on one side of the first group, so as to obtain multiple magnetic induction intensity signal values ​​of the weld and the base material, preprocessing the magnetic induction intensity signal values, that is, performing difference calculation on two magnetic induction intensity signal values ​​of the same group to obtain magnetic induction intensity data after removing the background field, setting at least two conditions for judging defects according to the characteristics of the weld and the weak magnetic detection principle, using the redundant detection principle, substituting the magnetic induction intensity data after removing the background field into the conditions for judging defects to respectively identify defect characteristics, and calculating the correct probability of defect judgment according to the probability statistics principle.

[0008] Optionally, the conditions for determining a defect include: condition 1, condition 2, and condition 3; wherein,

[0009] Condition 1 includes: determining whether there is an abnormality in the difference between the magnetic induction intensity signal value of the second weak magnetic sensor after the background field is removed and the magnetic induction intensity signal value of the first weak magnetic sensor after the background field is removed;

[0010] Condition 2 includes: judging whether there is an abnormality in the difference between the magnetic induction intensity signal value of the second weak magnetic sensor after the background field is removed and the magnetic induction intensity signal value of the third weak magnetic sensor after the background field is removed;

[0011] Condition 3 includes: judging whether there is an abnormality in the magnetic induction intensity signal value of the second weak magnetic sensor after the background field is removed.

[0012] Optionally, the preprocessing includes: connecting the points of the magnetic induction intensity signal values ​​detected by the same weak magnetic sensor to form a line to obtain six corresponding real-time curves, and performing differential calculation on the real-time curves corresponding to two weak magnetic sensors stacked as a group to obtain three curves with the background field removed, wherein the curve for removing the background field corresponding to the weak magnetic sensors stacked as the first group is recorded as Rebf1, the curve for removing the background field corresponding to the weak magnetic sensors stacked as the second group is recorded as Rebf2, and the curve for removing the background field corresponding to the weak magnetic sensors stacked as the third group is recorded as Rebf3.

[0013] Optionally, according to the principle of redundant detection, the three curves with the background field removed are differentially operated to generate two judgment curves, wherein Rebf1 and Rebf2 are differentially obtained to obtain the judgment curve Decide1, and Rebf2 and Rebf3 are differentially obtained to obtain the judgment curve Decide2. The gradient curve of the second weak magnetic sensor 2 is recorded as Decide3, and the point values ​​in the judgment curve Decide1, the judgment curve Decide2 and the gradient curve Decide3 are arrays of judgment defect data.

[0014] Optionally, it is necessary to combine two or more conditions for judging defects and calculate the probability of being a defect according to the formula before judging whether there is a defect at that location; when a certain condition for judging defects shows that there is no defect here or the calculation result shows that the probability of a defect here is small, then it is judged that there is no defect here. The method for judging defects is: a. If there are two conditions, both conditions must be met to judge that there is a defect here; b. If there are three conditions, under the premise that condition 1 is judged as a defect, one of condition 2 and condition 3 is judged as a defect, then this place can be judged as a defect.

[0015] Optionally, the probability of defect is calculated as:

[0016] Probability=A1*0.6+A2*0.2+A3*0.2

[0017] Among them, A1 corresponds to condition 1, A2 corresponds to condition 2, and A3 corresponds to condition 3. The values ​​of A1, A2, and A3 are 0 or 1, 0 represents the absence of an abnormality, and 1 represents the presence of an abnormality. When Probability ≥ 0.8, it can be determined that there is a defect here.

[0018] Optionally, the method for judging the abnormality between the magnetic induction intensity signal values ​​is a 3σ method, wherein the method for judging the signal abnormality under conditions 1 and 2 is the same, and the 3σ method includes:

[0019] First, the magnetic field gradient values ​​of the two judgment curves are extracted. The magnetic field gradient value is the first derivative value of each point value corresponding to the two judgment curves. The obtained magnetic field gradient value is stored in the array Decide-f[i,j]. The formula is as follows:

[0020] Decide-f[i,j]=Decide[i,j+1]-Decide[i,j]

[0021] Among them, Decide-f[i,j] is the weld magnetic induction intensity gradient data array, i is the channel number, j is the data sequence number, the channel number corresponding to the first weak magnetic sensor is channel 0, the data sequence number corresponding to the first weak magnetic sensor is 1, and so on;

[0022] After obtaining the magnetic field gradient value curve, the threshold range of each judgment curve is further obtained. in, is the average value of the magnetic field gradient obtained, and σ is the standard deviation of the magnetic field gradient obtained.

[0023] Optional, The calculation methods of and σ are as follows:

[0024]

[0025] Wherein, n is the total number of data points of the judgment curve, ΔB(i) is the magnetic field gradient value of the i-th data point of the judgment curve, when K is 3, the magnetic field gradient value is calculated to obtain a confidence probability interval threshold line of 99.73%, and the magnetic field gradient value corresponding to the judgment curve is compared with the threshold line. The sampling point position corresponding to the magnetic field gradient value exceeding the threshold range is determined to be an abnormal area.

[0026] The technical solution provided by the embodiment of the present invention has the following advantages compared with the prior art:

[0027] The weld defect detection method provided by the embodiment of the present invention arranges six weak magnetic sensors in pairs into two groups of L-shaped positions, thereby performing fine detection of the weld. Specifically, the second weak magnetic sensor and the fifth weak magnetic sensor of the first group are located directly above the weld and are used to detect the magnetic induction intensity signal value directly above the weld. The first weak magnetic sensor and the fourth weak magnetic sensor of the second group are located directly behind the first group and are also used to detect the magnetic induction intensity signal value directly above the weld. The third weak magnetic sensor and the sixth weak magnetic sensor of the third group are arranged side by side and aligned on one side of the first group to detect the magnetic induction intensity signal value of the base material. The detection error can be effectively reduced by performing difference calculation on two magnetic induction intensity signal values ​​of the same group. Then, according to the characteristics of the weld and the weak magnetic detection principle, at least two conditions for judging defects are set. In combination with the redundant detection principle, the magnetic induction intensity data after removing the background field is substituted into the conditions for judging defects to identify the defect characteristics respectively. According to the principle of probability statistics, the correct probability of defect judgment is calculated. An array weak magnetic sensor is adopted and its layout is optimized. Data differential technology is used to suppress external interference. Multiple conditions for judging defects can be set through multiple groups of data, thereby improving the reliability of the detection results and further improving the accuracy of the weld defect judgment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A structural schematic diagram of the relative position relationship of multiple weak magnetic sensors provided in an embodiment of the present invention;

[0029] Figure 2 A flowchart provided for an embodiment of the present invention;

[0030] Figure 3 The original curve simulation diagram provided by the embodiment of the present invention;

[0031] Figure 4 A simulation diagram of a background field removal curve provided by an embodiment of the present invention;

[0032] Figure 5 A judgment curve simulation diagram provided by an embodiment of the present invention;

[0033] Figure 6 A schematic diagram of a weak magnetic field detection structure provided by an embodiment of the present invention;

[0034] Figure 7 A conceptual diagram of a weak magnetic detection tooling provided in an embodiment of the present invention;

[0035] Figure 8 A cross-sectional view of a weak magnetic sensor installation slot provided in an embodiment of the present invention;

[0036] Fig. 9 A schematic diagram of a probe detection moving path provided in an embodiment of the present invention;

[0037] Fig.10 The original curve of the weld seam scanned provided by the embodiment of the present invention;

[0038] Fig.11 A background field removal curve provided by an embodiment of the present invention;

[0039] Fig.12 A defect judgment curve provided by an embodiment of the present invention;

[0040] Fig.13 Judgment condition 1 provided in the embodiment of the present invention;

[0041] Fig.14 Judgment condition 2 provided by the embodiment of the present invention;

[0042] Fig.15 Judgment condition 3 provided by the embodiment of the present invention;

[0043] Fig.16 A defect judgment diagram provided by an embodiment of the present invention.

[0044] Description of reference numerals:

[0045] 1. The first weak magnetic sensor; 2. The second weak magnetic sensor; 3. The third weak magnetic sensor; 4. The fourth weak magnetic sensor; 5. The fifth weak magnetic sensor; 6. The sixth weak magnetic sensor; 7. The workpiece; 8. The weld; 9. The weak magnetic probe; 10. The sensor mounting slot; 11. The adapter; 12. The wiring harness box; 13. The aviation socket. DETAILED DESCRIPTION

[0046] A specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation.

[0047] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the technical solutions of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0048] As an indispensable technical means in modern industry, the importance of non-destructive testing is self-evident. The following is a detailed introduction to the current mainstream non-destructive testing methods and their limitations, as well as weak magnetic detection technology:

[0049] Radiographic testing (RT): widely used in complex structural parts, but not suitable for porous materials and cannot detect sub-surface defects. In addition, radiographic testing has a long cycle and high cost, and the inspectors need to be protected from radiation. Ultrasonic testing (UT): widely used for near-surface defect detection, but it has requirements for material roughness and shape, and the material must be suitable for ultrasonic propagation. Magnetic particle testing (MT): suitable for layered, near-surface and surface defects, but has limited detection capabilities for sub-surface defects of non-ferromagnetic materials. Penetrant testing (PT): only suitable for surface defect detection, not suitable for porous materials, and improper operation can easily cause environmental pollution. Eddy current testing (ECT): a detection method based on the eddy current principle, which requires an excitation coil to generate magnetic lines of force, but there is interference from the induced magnetic field and a contradiction between the effective detection area and efficiency.

[0050] As an emerging passive non-destructive testing method, weak magnetic detection technology mainly uses sensors to directly scan and detect the weld surface. It is difficult to suppress external interference, resulting in large quantitative errors in defect detection. In addition, single data will also result in a single defect judgment condition, which will cause errors in the final defect judgment result.

[0051] To this end, an embodiment of the present invention provides a weld defect detection method, which can improve the accuracy of weld defect judgment results.

[0052] At least one embodiment of the present invention provides a weld defect detection method, comprising: stacking six weak magnetic sensors in pairs in an L shape, wherein the second weak magnetic sensor and the fifth weak magnetic sensor of the first stacked group are located directly above the weld, the first weak magnetic sensor and the fourth weak magnetic sensor of the second stacked group are located directly behind the first group, and the third weak magnetic sensor and the sixth weak magnetic sensor of the third stacked group are arranged side by side and aligned on one side of the first group, so as to obtain multiple magnetic induction intensity signal values ​​of the weld and the base material, preprocessing the magnetic induction intensity signal values, that is, performing a difference calculation on two magnetic induction intensity signal values ​​of the same group to obtain magnetic induction intensity data after removing the background field, setting at least two conditions for judging defects according to the characteristics of the weld and the weak magnetic detection principle, using the redundant detection principle, substituting the magnetic induction intensity data after removing the background field into the conditions for judging defects to respectively identify defect characteristics, and calculating the correct probability of defect judgment according to the principle of probability statistics.

[0053] In the weld defect detection method provided by the above-mentioned embodiment of the present invention, the reliability of the detection result is improved by placing weak magnetic sensors in multiple groups of L-shaped positions, combining the redundant detection principle, and fusing multiple judgment methods.

[0054] The present invention is described below by several specific embodiments. In order to keep the following description of the embodiments of the present invention clear and concise, the detailed description of known functions and known components may be omitted. When any component of the embodiments of the present invention appears in more than one accompanying drawings, the component may be represented by the same reference numeral in each of the accompanying drawings.

[0055] refer to Figure 1 , Figure 7 and Figure 8 , Figure 1 A schematic diagram of the relative position relationship of multiple weak magnetic sensors provided in an embodiment of the present invention, Figure 7 A conceptual diagram of a weak magnetic detection tooling provided in an embodiment of the present invention, Figure 8 A cross-sectional view of a weak magnetic sensor installation slot provided in an embodiment of the present invention, as shown in FIG. Figure 1 , Figure 7 and Figure 8As shown, an embodiment of the present invention provides a weld defect detection method, comprising: stacking six weak magnetic sensors in pairs in an L shape, wherein the second weak magnetic sensor and the fifth weak magnetic sensor of the first stacked group are located directly above the weld, the first weak magnetic sensor and the fourth weak magnetic sensor of the second stacked group are located directly behind the first group, and the third weak magnetic sensor and the sixth weak magnetic sensor of the third stacked group are arranged side by side and aligned on one side of the first group, so as to obtain multiple magnetic induction intensity signal values ​​of the weld and the base material, preprocess the magnetic induction intensity signal values, that is, perform difference calculation on two magnetic induction intensity signal values ​​of the same group to obtain magnetic induction intensity data after removing the background field, set at least two conditions for judging defects according to the characteristics of the weld and the weak magnetic detection principle, use the redundant detection principle to substitute the magnetic induction intensity data after removing the background field into the conditions for judging defects to respectively identify defect features, and calculate the correct probability of defect judgment according to the principle of probability statistics.

[0056] It should be understood that the two weak magnetic sensors in the same group must be adjacent to each other. It can be understood in this way that a group of stacked weak magnetic sensors detects the magnetic induction intensity signal in the vertical direction, and the magnetic induction intensity signal is generated by the test piece. Therefore, the signal of the stacked weak magnetic sensor located at the bottom will be stronger than the signal of the stacked weak magnetic sensor located at the top. As the distance between the weak magnetic sensor and the test piece below becomes larger and larger, the influence of the test piece on the sensor detection signal will become smaller and smaller.

[0057] The weld defect detection method provided by the embodiment of the present invention arranges six weak magnetic sensors in pairs into two groups of L-shaped positions, thereby performing fine detection of the weld. Specifically, the second weak magnetic sensor and the fifth weak magnetic sensor of the first group are located directly above the weld and are used to detect the magnetic induction intensity signal value directly above the weld. The first weak magnetic sensor and the fourth weak magnetic sensor of the second group are located directly behind the first group and are also used to detect the magnetic induction intensity signal value directly above the weld. The third weak magnetic sensor and the sixth weak magnetic sensor of the third group are arranged side by side and aligned on one side of the first group to detect the magnetic induction intensity signal value of the base material. The detection error can be effectively reduced by performing difference calculation on two magnetic induction intensity signal values ​​of the same group. Then, according to the characteristics of the weld and the weak magnetic detection principle, at least two conditions for judging defects are set. In combination with the redundant detection principle, the magnetic induction intensity data after removing the background field is substituted into the conditions for judging defects to identify the defect characteristics respectively. According to the principle of probability statistics, the correct probability of defect judgment is calculated. An array weak magnetic sensor is adopted and its layout is optimized. Data differential technology is used to suppress external interference. Multiple conditions for judging defects can be set through multiple groups of data, thereby improving the reliability of the detection results and further improving the accuracy of the weld defect judgment results.

[0058] Specifically, the gap between the two stacked weak magnetic sensors is less than or equal to 1 mm.

[0059] Specifically, the spacing between two adjacent groups of weak magnetic sensors is less than or equal to 4 mm. Considering that signal lines need to be led out from the weak magnetic sensors, the spacing range between groups is larger than the stacking range.

[0060] Optionally, the conditions for judging defects include: condition 1, condition 2 and condition 3; wherein condition 1 includes: judging whether there is an abnormality in the difference between the magnetic induction intensity signal value of the second weak magnetic sensor 2 after removing the background field and the magnetic induction intensity signal value of the first weak magnetic sensor 1 after removing the background field; condition 2 includes: judging whether there is an abnormality in the difference between the magnetic induction intensity signal value of the second weak magnetic sensor 2 after removing the background field and the magnetic induction intensity signal value of the third weak magnetic sensor 3 after removing the background field; condition 3 includes: judging whether there is an abnormality in the magnetic induction intensity signal value of the second weak magnetic sensor 2 after removing the background field. Here, conditions 1, 2 and 3 judge abnormalities based on the 3σ principle. The condition curve calculates the magnetic field gradient, and then calculates 3σ to see whether the gradient curve exceeds 3σ. Among them, condition 1 is the most important, so it is a veto condition for judging defects; conditions 2 and 3 are optional. There are other conditions, but their importance will decrease step by step.

[0061] Optionally, the preprocessing includes: connecting the points of the magnetic induction intensity signal values ​​detected by the same weak magnetic sensor to form a line to obtain six corresponding real-time curves, and performing differential calculation on the real-time curves corresponding to two weak magnetic sensors stacked as a group to obtain three curves with the background field removed, wherein the curve for removing the background field corresponding to the weak magnetic sensors stacked as the first group is recorded as Rebf1, the curve for removing the background field corresponding to the weak magnetic sensors stacked as the second group is recorded as Rebf2, and the curve for removing the background field corresponding to the weak magnetic sensors stacked as the third group is recorded as Rebf3.

[0062] Optionally, according to the principle of redundant detection, the three curves with the background field removed are differentially operated to generate two judgment curves, wherein Rebf1 and Rebf2 are differentially obtained to obtain the judgment curve Decide1, and Rebf2 and Rebf3 are differentially obtained to obtain the judgment curve Decide2. The gradient curve of the second weak magnetic sensor 2 is recorded as Decide3. The gradient curve is obtained by derivatizing the curve with the background field removed. The gradient value is connected into a line to obtain the gradient curve. The point values ​​in the judgment curve Decide1, the judgment curve Decide2 and the gradient curve Decide3 are arrays of judgment defect data.

[0063] In the existing weak magnetic detection technology, the defect judgment is generally based on the detection data and the 3σ principle. However, in actual detection, this relatively simple judgment method often leads to misjudgment.

[0064] Therefore, the present invention adopts array sensors and optimizes their layout, uses data differential technology to suppress external interference, uses intelligent detection algorithms to optimize defect judgment conditions, and other means to effectively reduce detection errors and improve the reliability of the detection system, providing strong support for the widespread application of weak magnetic detection technology in weld defect detection.

[0065] To this end, an embodiment of the present invention provides an improved method. In this embodiment, it is necessary to combine two or more conditions for judging defects and calculate the probability of being a defect according to the formula before judging whether there is a defect at that location; when a condition for judging defects shows that there is no defect here or the calculation result shows that the probability of a defect at this location is small, it is judged that there is no defect here, and the method for judging defects is: a. If there are two conditions, both conditions must be met to judge that there is a defect here; b. If there are three conditions, under the premise that condition 1 is judged as a defect, one of condition 2 and condition 3 is judged as a defect, then this location can be judged as a defect, and the reliability of the detection result is improved by integrating multiple judgment methods.

[0066] Specifically, the probability calculation formula for defects is:

[0067] Probability=A1*0.6+A2*0.2+A3*0.2

[0068] Among them, A1 corresponds to condition 1, A2 corresponds to condition 2, and A3 corresponds to condition 3. The values ​​of A1, A2, and A3 are 0 or 1, 0 represents the absence of an abnormality, and 1 represents the presence of an abnormality. When Probability ≥ 0.8, it can be determined that there is a defect here.

[0069] Specifically, the method for judging the abnormality between the magnetic induction intensity signal values ​​is the 3σ method, wherein the method for judging the signal abnormality under conditions 1 and 2 is the same, and the 3σ method includes:

[0070] First, the magnetic field gradient values ​​of the two judgment curves are extracted. The magnetic field gradient value is the first derivative value of each point value corresponding to the two judgment curves. The obtained magnetic field gradient value is stored in the array Decide-f[i,j]. The formula is as follows:

[0071] Decide-f[i,j]=Decide[i,j+1]-Decide[i,j]

[0072] Among them, Decide-f[i,j] is the weld magnetic induction intensity gradient data array, i is the channel number, j is the data sequence number, among which the channel number corresponding to the first weak magnetic sensor 1 is channel 0, the data sequence number corresponding to the first weak magnetic sensor 1 is 1, and so on;

[0073] After obtaining the magnetic field gradient value curve, the threshold range of each judgment curve is further obtained. in, is the average value of the magnetic field gradient obtained, and σ is the standard deviation of the magnetic field gradient obtained.

[0074] Optional, The calculation methods of and σ are as follows:

[0075]

[0076] Wherein, n is the total number of data points of the judgment curve, ΔB(i) is the magnetic field gradient value of the i-th data point of the judgment curve, when K is 3, the magnetic field gradient value is calculated to obtain a confidence probability interval threshold line of 99.73%, and the magnetic field gradient value corresponding to the judgment curve is compared with the threshold line. The sampling point position corresponding to the magnetic field gradient value exceeding the threshold range is determined to be an abnormal area.

[0077] Using weak magnetic detection technology, multiple weak magnetic sensors placed in L-shaped positions are used to perform fine inspections on welds. Combined with data acquisition, preprocessing, defect feature recognition and two-dimensional imaging technologies, automatic defect judgment and visual display of weld defects are achieved. The signal obtained when the six sensors are detected is a voltage signal. During the detection, the areas detected by each sensor are: the first weak magnetic sensor 1 and the second weak magnetic sensor 2 detect directly above the weld, the second weak magnetic sensor 2 is in front, the first weak magnetic sensor 1 is in the back, the third weak magnetic sensor 3 detects the base material, the fourth weak magnetic sensor 4 detects the weld directly above the first weak magnetic sensor 1, the fifth weak magnetic sensor 5 detects the weld directly above the second weak magnetic sensor 2, and the sixth weak magnetic sensor 6 detects the base material directly above the third weak magnetic sensor 3.

[0078] like Figure 1 The figure shows the relative arrangement relationship of six weak magnetic sensors. The present invention determines whether there is a defect based on the corresponding calculation of the detection data between the sensors.

[0079] refer to Figure 2 , Figure 2 The flowchart provided by the embodiment of the present invention is as follows: Figure 2As shown, the weld workpiece 7 is scanned using a detection tool, and the magnetic induction intensity signal is collected by a weak magnetic sensor, the collected signal is stored, and the stored signal is read for preprocessing, that is, the collected magnetic induction intensity curve is subjected to an operation of removing the background field (eliminating environmental noise and magnetic field interference generated by non-defective areas of the workpiece itself). Finally, according to the characteristics of the weld and the principle of weak magnetic detection, two or more conditions for judging defects (such as sudden changes in magnetic induction intensity, abnormal distribution, etc.) are set, and the defect feature is identified for each judgment condition using the principle of redundant detection, and the correct probability of defect judgment is calculated according to the principle of probability statistics, so as to improve the accuracy and reliability of detection.

[0080] The specific steps are as follows

[0081] 1. Preprocessing of magnetic induction intensity curve

[0082] Figure 1 This is a simulation diagram of the original curve of six channel probes (CH1, CH2, CH3, CH4, CH5, CH6) scanning the defects of the prefabricated weld. The weak magnetic acquisition circuit board decodes the magnetic induction intensity data collected by the weak magnetic sensor, and then performs equivalent calculations based on the weak magnetic sensor calibration value to obtain the magnetic induction intensity collected by each weak magnetic sensor during the detection process. The upper computer software is programmed in C# language. The corresponding drawing interface of the software depicts the magnetic induction intensity value corresponding to each acquisition point, and connects the points into lines to obtain the following Figure 3 Real-time curves of the six probe channels are shown.

[0083] Figure 4 These are three curves (Rebf1, Rebf2, Rebf3) with the background field removed, which are obtained by differential operation of weak magnetic sensor data in six channels. The original curve data of the six channels are stored in the array Data[i,j], Data[0,j] represents the data of the CH1 channel, Data[1,j] represents the data of the CH2 channel, Data[2,j] represents the data of the CH3 channel, Data[3,j] represents the data of the CH4 channel, Data[4,j] represents the data of the CH5 channel, and Data[5,j] represents the data of the CH6 channel, and the selected channels are processed as follows: (The data value of each channel is the magnetic induction intensity value after decoding the voltage signal. The magnetic induction intensity value of each channel is stored in the array Data[i,j] for calculation, i is the channel number, and j is the data sequence number;

[0084] The magnetic induction intensity value of channel 1 minus channel 4 corresponds to sensor 1 and sensor 4;

[0085] The magnetic induction intensity value of channel 2 minus channel 5 corresponds to sensor 2 and sensor 5;

[0086] The magnetic induction intensity value of channel 3 minus channel 6 corresponds to sensor 3 and sensor 6. )

[0087] Rebf[i,j]=Data[i,j]-Data[i+3,j]

[0088] (4-1)

[0089] Rebf[i,j] is an array storing background field data (corresponding channels (1 and 4, 2 and 5, 3 and 6) have the same data change trend. Data subtraction can effectively extract the change amount and suppress noise, which can be considered as background field data removal). i is the channel number and j is the data sequence number. Draw points into lines in the drawing interface to get the following Figure 4 The three curves Rebf1, Rebf2, and Rebf3 with the background field removed are shown. The reason for arranging the positions of the six sensors in this way is that the second weak magnetic sensor 2 is selected as the core, and the conditions for defect judgment are 2-1, 2-2, and 2-3. If the third weak magnetic sensor 3 is aligned with the first weak magnetic sensor 1, there is a position difference between the second weak magnetic sensor 2 and the third weak magnetic sensor 3, and there will be a large error in the signal difference. Of course, this arrangement can also be based on the first weak magnetic sensor 1 as the core, and the judgment conditions should be 1-2, 1-1, and 1-3.

[0090] Figure 5 The three curves with the background field removed are based on the redundant detection principle, using data redundancy to improve the accuracy of judgment through different channel data, and performing differential operations to generate two judgment curves and the gradient curves Decide1, Decide2, and Decide3 of the sensor signal located directly above the weld. For conditions 1 and 2, the data Rebf[i,j] with the background field removed is processed as follows, where i is 0 and 1:

[0091] Decide[i,j]=Rebf[i,j]-Rebf[i+1,j]

[0092] (4-2)

[0093] For condition 3, the background field removed data Rebf[2,j] is processed as follows:

[0094] Decide[2,j]=Rebf[2,j+1]-Rebf[2,j]

[0095] (4-3)

[0096] Among them, Decide[i,j] is an array for storing defect judgment data, i is the channel number, and j is the data sequence number.

[0097] It is necessary to combine two or more judgment conditions and calculate the probability value of a defect according to the formula to determine whether there is a defect at that location. When one of the judgment conditions shows that there is no defect here or the calculation result shows that the probability of a defect here is small, it is determined that there is no defect here.

[0098] There are generally three conditions for determining whether a weld is defective: (1) whether there is an abnormality in the difference between the detection signals of the two sensors located directly above the weld; (2) whether there is an abnormality in the comparison signal between the weld and the base material; and (3) whether there is an abnormality in the detection signal gradient of the first sensor located directly above the weld.

[0099] The probability calculation method for judging defects is as follows: (1) If it is a two-condition judgment, both conditions must be met to judge that this is a defect; (2) If it is a three-condition judgment, under the premise that condition 1 is judged as a defect, if one of condition 2 and condition 3 is judged as a defect, then this can be judged as a defect.

[0100] The defect probability calculation formula is:

[0101] Probability=A1*0.6+A2*0.2+A3*0.2

[0102] (4-4)

[0103] The values ​​of A1, A2, and A3 are 0 or 1, indicating whether there is an abnormal signal. When Probability ≥ 0.8, it can be determined that there is a defect. After comprehensive calculation of multiple conditions, the reliability of the detection system is greatly improved.

[0104] 2. Conditional judgment

[0105] (1) Detection signal

[0106] The method for judging magnetic signal anomaly is the 3σ method, in which the method for judging signal anomaly under conditions 1 and 2 is the same.

[0107] First, extract the magnetic field gradient value of the "judgment curve" and store the obtained magnetic field gradient value in the array Decide-f[i,j]. The formula is as follows:

[0108] Decide-f[i,j]=Decide[i,j+1]-Decide[i,j]

[0109] (4-5)

[0110] Among them, Decide-f[i,j] is the weld magnetic induction intensity gradient data array, i is the channel number, and j is the data sequence number.

[0111] After obtaining the magnetic field gradient value curve, the threshold range of each judgment curve is further obtained through formulas 4-6 and 4-7. in, is the average value of the magnetic field gradient obtained, and σ is the standard deviation of the magnetic field gradient obtained. The calculation methods of and σ are as follows:

[0112]

[0113] Where n is the total number of data points of the judgment curve, and ΔB(i) is the magnetic field gradient value of the i-th data point of the judgment curve. When K is 3, the magnetic field gradient value is calculated to obtain a confidence probability of 99.73% interval threshold line (i.e., the upper and lower limits of the threshold range), and the magnetic field gradient value corresponding to the judgment curve is compared with the threshold line. The sampling point position corresponding to the magnetic field gradient value that exceeds the threshold range is determined as an abnormal area.

[0114] The present invention arranges weak magnetic sensors in an L shape, and generates a detection curve containing multiple detection information through numerical calculations between multiple sets of sensor detection data. Multiple defect judgment information is obtained through abnormal identification of the detection curve, and the accuracy of defect judgment is improved through a comprehensive evaluation method of multiple judgment information.

[0115] The corresponding structural units of the weld detection method based on weak magnetic technology designed by the present invention are as follows: Figure 6 As shown, it is mainly composed of a host computer, a weak magnetic signal acquisition system, a probe and tooling.

[0116] The weak magnetic detection instrument uses a fluxgate sensor as a magnetic field measurement probe. The resolution of the sensor can reach 1nT, so it can detect the magnetic induction intensity signal on the surface of the test piece with high precision.

[0117] Probe tooling Figure 7 As shown, it includes: a sensor installation slot 10, a switching device 11, a wiring harness box 12, and an air socket 4. The interior of the sensor installation slot 10 is as follows Figure 8 As shown, 6 weak magnetic sensors are arranged in the installation slot. The first weak magnetic sensor 1, the second weak magnetic sensor 2, and the third weak magnetic sensor 3 form an "L" arrangement, and the fourth weak magnetic sensor 4, the fifth weak magnetic sensor 5, and the sixth weak magnetic sensor 6 form another "L" arrangement. The function of the adapter 11 is to connect the sensor installation slot 10 and the harness box 12. The harness box 12 plays a structural support role for the entire probe tooling, ensuring the stability of the tooling. The sensor can be easily replaced by disassembling and assembling the harness box 12. The function of the aviation plug 13 is to fix the aviation plug line to prevent the aviation plug line from shaking. In order to avoid the excessive magnetic permeability of the tooling material and interfere with the sensor signal acquisition, the tooling material selected in the design is a non-magnetic non-metallic material.

[0118] The schematic diagram of the probe detection path is as follows Fig. 9 As shown, the direction indicated by the arrow represents the moving direction of the probe. When detecting and scanning, the weld workpiece 7 is placed flat on the laboratory table, so that the tooling starts from the leftmost end of the test piece and scans the defects from right to left at a uniform speed. When detecting, the first weak magnetic sensor 1, the second weak magnetic sensor 2, the fourth weak magnetic sensor 4, and the fifth weak magnetic sensor 5 are located directly above the weld, and the third weak magnetic sensor 3 and the sixth weak magnetic sensor 6 are located on the substrate. Specific embodiments

[0120] 1. Experimental principle description:

[0121] Take the detection of a weld as an example, the surface of the workpiece 7 to be tested is regarded as a flat surface, and 6 weak magnetic sensors are selected according to Figure 8 The relative arrangement is placed in the sensor installation slot 10, and the weld 8 is scanned at a uniform speed. The distance between the probe and the weld 8 is as small as possible, and the relative position of the probe and the weld must be fixed.

[0122] After scanning the weld with the probe tooling, the system stores the continuous detection data of the six sensor channels and generates an original curve with the number of acquisition points as the X-axis and the magnetic induction intensity as the Y-axis. The background field of the original curve is removed using formula 4-1; three defect judgment conditions are obtained using formulas 4-2 and 4-3 combined with defect probability calculation formula 4-4.

[0123] The three methods of judging defects are the same: since the change of weak magnetic signal at the defect is relatively weak, it is difficult to judge the defect only by comparing the signal, so the gradient value is extracted from the curve after differentiation to obtain the gradient value curve reflecting the sudden change of weak magnetic signal intensity, and set a suitable threshold line. Compare the spatial magnetic field gradient value of each sampling point with the threshold line, and the sampling point area corresponding to the spatial magnetic field gradient value exceeding the threshold range is judged as a defect. The premise for the final judgment as a defect is that the first judgment condition judges the defect, and at least one of the second and third judgment conditions judges the defect, otherwise it is regarded as a misjudgment. After the defect position is determined, the defect position is represented by two-dimensional imaging of the detection results through the spline interpolation algorithm.

[0124] 2. Example description:

[0125] The workpiece 7 to be tested is a flat weld. The material of the workpiece is austenitic stainless steel. Three defects are made on the weld. Fig. 9 Schematic diagram of the probe detection moving path, scanning the weld at a constant speed 8. According to the experimental principle, the detected magnetic signal is preprocessed to obtain the judgment conditions for defect judgment and the result two-dimensional imaging display.

[0126] The original curve of the scanned weld is as follows:

[0127] Fig.10 , 11 12 is the original curve, background field removal curve, and defect judgment curve obtained by scanning the weld sample with a relative probe. According to the experimental principle, the background field of the original curve is removed, and the background field condition curve is removed to obtain two defect judgment curves according to the redundant detection principle, completing the preprocessing operation.

[0128] according to Fig.15 , and finally it was determined that there were three defective areas. Judgment condition 1 was shown in the four areas of sampling points 55-65, 13245, 200-210 and 26375. The first three areas met judgment condition 2 or condition 3, confirming the existence of defects; while in the fourth area, neither condition 2 nor condition 3 indicated defects, so this area was determined to be non-defective.

[0129] The above inventions are only several specific embodiments of the present invention. However, the embodiments of the present invention are not limited thereto. Any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A weld defect detection method, characterized in that: include: The six weak magnetic sensors are stacked in pairs to form an L shape, wherein the second weak magnetic sensor (2) and the fifth weak magnetic sensor (5) of the first stack are located directly above the weld, the first weak magnetic sensor (1) and the fourth weak magnetic sensor (4) of the second stack are located directly behind the first stack, and the third weak magnetic sensor (3) and the sixth weak magnetic sensor (6) of the third stack are arranged side by side and aligned on one side of the first stack, so as to obtain multiple magnetic induction intensity signal values ​​of the weld and the base material; Preprocessing the magnetic induction intensity signal values, that is, performing difference calculation on two magnetic induction intensity signal values ​​of the same group to obtain magnetic induction intensity data after removing the background field; According to the characteristics of the weld and the principle of weak magnetic detection, at least two conditions for judging defects are set; By using the redundant detection principle, the magnetic induction intensity data after removing the background field is substituted into the defect judgment conditions to identify the defect characteristics respectively, and the correct probability of defect judgment is calculated according to the probability statistics principle.

2. The weld defect detection method according to claim 1, characterized in that: The conditions for determining defects include: condition 1, condition 2 and condition 3; wherein, Condition 1 includes: judging whether there is an abnormality in the difference between the magnetic induction intensity signal value of the second weak magnetic sensor (2) after the background field is removed and the magnetic induction intensity signal value of the first weak magnetic sensor (1) after the background field is removed; Condition 2 includes: judging whether there is an abnormality in the difference between the magnetic induction intensity signal value of the second weak magnetic sensor (2) after the background field is removed and the magnetic induction intensity signal value of the third weak magnetic sensor (3) after the background field is removed; Condition 3 includes: judging whether there is any abnormality in the magnetic induction intensity signal value of the second weak magnetic sensor (2) after the background field is removed.

3. The weld defect detection method according to claim 2, characterized in that: The preprocessing includes: connecting the points of the magnetic induction intensity signal values ​​detected by the same weak magnetic sensor into lines to obtain six corresponding real-time curves, and performing differential calculation on the real-time curves corresponding to two weak magnetic sensors stacked as a group to obtain three curves with the background field removed, wherein the curve for removing the background field corresponding to the weak magnetic sensors stacked as the first group is recorded as Rebf1, the curve for removing the background field corresponding to the weak magnetic sensors stacked as the second group is recorded as Rebf2, and the curve for removing the background field corresponding to the weak magnetic sensors stacked as the third group is recorded as Rebf3.

4. The weld defect detection method according to claim 3, characterized in that: According to the redundant detection principle, the three curves with the background field removed are subjected to differential operation to generate two judgment curves, wherein the judgment curve Decide1 is obtained by differential operation between Rebf1 and Rebf2, and the judgment curve Decide2 is obtained by differential operation between Rebf2 and Rebf3. The gradient curve of the second weak magnetic sensor (2) is recorded as Decide3. The point values ​​in the judgment curve Decide1, the judgment curve Decide2 and the gradient curve Decide3 are arrays of judgment defect data.

5. The weld defect detection method according to claim 4, characterized in that: It is necessary to combine two or more conditions for judging defects and calculate the probability of being a defect according to the formula before judging whether there is a defect at that location; when a condition for judging defects shows that there is no defect here or the calculation result shows that the probability of a defect here is small, then it is judged that there is no defect here. The method for judging defects is: a. If there are two conditions, both conditions must be met to judge that there is a defect here; b. If there are three conditions, under the premise that condition 1 is judged as a defect, one of condition 2 and condition 3 is judged as a defect, then this place can be judged as a defect.

6. The weld defect detection method according to claim 5, characterized in that: The probability calculation formula of the defect is: Probability=A1*0.6+A2*0.2+A3*0.2 Among them, A1 corresponds to condition 1, A2 corresponds to condition 2, and A3 corresponds to condition 3. The values ​​of A1, A2, and A3 are 0 or 1, 0 represents the absence of an abnormality, and 1 represents the presence of an abnormality. When Probability ≥ 0.8, it can be determined that there is a defect here.

7. The weld defect detection method according to claim 6, characterized in that: The method for judging the abnormality between the magnetic induction intensity signal values ​​is the 3σ method, wherein the method for judging the signal abnormality under conditions 1 and 2 is the same, and the 3σ method includes: First, the magnetic field gradient values ​​of the two judgment curves are extracted. The magnetic field gradient value is the first derivative value of each point value corresponding to the two judgment curves. The obtained magnetic field gradient value is stored in the array Decide-f[i,j]. The formula is as follows: Decide-f [ i,j ] =Decide [ i,j+1 ] -Decide [ i,j ] Wherein, Decide-f[i,j] is the weld magnetic induction intensity gradient data array, i is the channel number, j is the data sequence number, wherein the channel number corresponding to the first weak magnetic sensor (1) is channel 0, the data sequence number corresponding to the first weak magnetic sensor (1) is 1, and so on; After obtaining the magnetic field gradient value curve, the threshold range of each judgment curve is further obtained. in, is the average value of the magnetic field gradient obtained, and σ is the standard deviation of the magnetic field gradient obtained.

8. The weld defect detection method according to claim 7, characterized in that: Said The calculation methods of and σ are as follows: Wherein, n is the total number of data points of the judgment curve, ΔB(i) is the magnetic field gradient value of the i-th data point of the judgment curve, when K is 3, the magnetic field gradient value is calculated to obtain a confidence probability interval threshold line of 99.73%, and the magnetic field gradient value corresponding to the judgment curve is compared with the threshold line. The sampling point position corresponding to the magnetic field gradient value exceeding the threshold range is determined to be an abnormal area.

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