CN119470616A - A method for detecting weld defects - Google Patents
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Abstract
本发明涉及弱磁检测技术领域，提出了一种焊缝缺陷检测方法，包括：将六个弱磁传感器两两叠摞布置为L形，叠摞为第一组的第二弱磁传感器和第五弱磁传感器位于焊缝的正上方，叠摞为第二组的第一弱磁传感器和第四弱磁传感器位于第一组的正后方，叠摞为第三组的第三弱磁传感器和第六弱磁传感器并排且对齐布置在第一组的一侧，即对同一组的两个磁感应强度信号值进行差值计算，得到去除背景场后的磁感应强度数据,根据焊缝的特点和弱磁检测原理，设定至少两个判断缺陷的条件,利用冗余检测原理，将去除背景场后的磁感应强度数据带入判断缺陷的条件进行缺陷特征识别，并根据概率统计原则，计算缺陷判断的正确概率，能够提高焊缝缺陷判断结果的准确率。
The invention relates to the technical field of weak magnetic detection, and proposes 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 stack, 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, 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 defect judgment conditions according to the characteristics of the weld and the weak magnetic detection principle, using the redundant detection principle, bringing the magnetic induction intensity data after removing the background field into the defect judgment conditions to perform defect feature recognition, and calculating the correct probability of defect judgment according to the probability statistics principle, so as to improve the accuracy of the weld defect judgment result.
Description
Technical Field
The invention relates to the technical field of weak magnetic detection, in particular to a weld defect detection method.
Background
The importance of non-destructive testing as an indispensable technical means in the modern industry is self-evident.
The weak magnetic detection technology is used as an emerging passive nondestructive detection means, and has the advantages of no need of an additional excitation source, no need of a special magnetic excitation device, no need of coating detection and no need of special treatment on the surface of a detected workpiece, no special requirement on the shape of the detected workpiece, high adaptability, high detection speed, simplicity in operation, scanning by adopting a high-precision fluxgate sensor and rapid data processing.
The working principle is based on stress change and atomic structure change caused by material defects, and abnormal magnetic fields are generated spontaneously. The abnormal signal is detected by a high-precision magnetic sensor, so that the defect of the material is characterized. Assuming that the magnetic permeability of the body of the detected material is μ, and the magnetic permeability of the discontinuous region inside the workpiece is μ ', if the relative magnetic permeability of the discontinuous region is greater than the relative magnetic permeability of the detected material, i.e., μ ' > μ, then the magnetic induction intensity curve will appear to be convex when the magnetic sensor passes through the region, and if the relative magnetic permeability of the discontinuous region is less than the relative magnetic permeability of the detected material, i.e., μ ' < μ, then the magnetic induction intensity curve will appear to be concave when the magnetic sensor passes through the region. The weak magnetic detection technology is to detect the abnormal signal of the abrupt change of the magnetic induction intensity by using a high-precision magnetic sensor, thereby representing the defect of the material.
In the existing weld defect detection based on the field weakening principle, a sensor is mainly used for directly scanning and detecting the surface of a weld, external interference is difficult to restrain, so that the quantitative error of defect detection is large, and single data can cause single defect judging conditions, so that the final defect judging result has errors.
Disclosure of Invention
In order to solve the technical problems, the invention provides a weld defect detection method which can improve the accuracy of a weld defect judgment result.
The invention provides a weld defect detection method, which comprises the steps of arranging six weak magnetic sensors in an L shape in a stacked manner, wherein a second weak magnetic sensor and a fifth weak magnetic sensor which are stacked into a first group are positioned right above a weld, the first weak magnetic sensor and the fourth weak magnetic sensor which are stacked into a second group are positioned right behind the first group, the third weak magnetic sensor and the sixth weak magnetic sensor which are stacked into a third group are arranged side by side and aligned to one side of the first group, so as to obtain a plurality of magnetic induction intensity signal values of the weld and a substrate material, preprocessing the magnetic induction intensity signal values, namely performing difference value calculation on two magnetic induction intensity signal values of the same group, obtaining magnetic induction intensity data after removing a background field, setting at least two conditions for judging defects according to the characteristics of the weld and the weak magnetic detection principle, substituting the magnetic induction intensity data after removing the background field into the conditions for judging defects to respectively perform defect characteristic identification by using the redundancy detection principle, and calculating correct probability of defect judgment according to the probability statistics principle.
Optionally, the conditions for judging the defects comprise a condition 1, a condition 2 and a condition 3, wherein,
The condition 1 includes judging whether the difference between the magnetic induction intensity signal value of the second weak magnetic sensor after removing the background field and the magnetic induction intensity signal value of the first weak magnetic sensor after removing the background field is abnormal;
judging whether 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 is abnormal or not;
Condition 3 includes determining whether there is an abnormality in the magnetic induction intensity signal value of the second weak magnetic sensor after the background field is removed.
Optionally, the preprocessing includes connecting the magnetic induction intensity signal values detected by the same weak magnetic sensor into a line to obtain six corresponding real-time curves, and performing differential calculation on the real-time curves corresponding to the two weak magnetic sensors stacked into one group to obtain three curves for removing the background field, wherein the curve for removing the background field corresponding to the weak magnetic sensor stacked into the first group is Rebf1, the curve for removing the background field corresponding to the weak magnetic sensor stacked into the second group is Rebf, and the curve for removing the background field corresponding to the weak magnetic sensor stacked into the third group is Rebf3.
Optionally, performing differential operation on the three curves from which the background field is removed according to the redundancy detection principle to generate two judging curves, wherein Rebf and Rebf2 are differentiated to obtain a judging curve Decide1, rebf2 and Rebf3 to obtain a judging curve Decide2, the gradient curve of the second weak magnetic sensor 2 is recorded as Decide3, and the point values in the judging curve Decide1, the judging curve Decide and the gradient curve Decide3 are the array of judging defect data.
Optionally, two or more conditions for judging the defect are combined and the probability of the defect is calculated according to a formula to judge whether the defect exists at the position, when a certain condition for judging the defect shows that the defect exists at the position or the calculated result is that the probability of the defect exists at the position is smaller, the defect is judged at the position, the defect judging method comprises the steps that a, if the defect judging method is that the two conditions are two, the defect can be judged at the position only by all the two conditions, and b, if the defect judging method is that the defect judging method is three conditions, and if the defect judging method is the condition, the defect judging method is the defect judging method.
Optionally, the probability calculation formula of the defect is:
Probability=A1*0.6+A2*0.2+A3*0.2
Wherein, the value of A 1 corresponds to condition 1, A 2 corresponds to condition 2, A 3 corresponds to condition 3, A 1、A2、A3 is 0 or 1,0 represents no abnormality, 1 represents abnormality, and when the Probability is more than or equal to 0.8, the defect can be judged to exist here.
Optionally, the method for determining abnormality between magnetic induction intensity signal values is a 3σ method, wherein the method for determining abnormality of signals under condition 1 and condition 2 is the same, and the 3σ method includes:
Firstly, extracting magnetic field gradient values of two judging curves, wherein the magnetic field gradient values are one-time derivative values of each corresponding point value on the two judging curves, and storing the obtained magnetic field gradient values into an array Decide-f [ i, j ] with the following formula:
Decide-f[i,j]=Decide[i,j+1]-Decide[i,j]
Wherein Decide-f [ i, j ] is a weld magnetic induction intensity gradient data array, i is the number of channels, j is a data serial number, wherein the number of channels corresponding to the first weak magnetic sensor is channel 0, the data serial number corresponding to the first weak magnetic sensor is 1, and so on;
After obtaining the magnetic field gradient value curves, further obtaining the threshold range of each judgment curve Wherein, The average value of the magnetic field gradient values is calculated, and σ is the standard deviation of the magnetic field gradient values.
Alternatively to this, the method may comprise,The method for calculating sigma is as follows:
Wherein n is the total number of data points of the judging curve, deltaB (i) is the magnetic field gradient value of the ith data point of the judging curve, when K is 3, the magnetic field gradient value is calculated to obtain a threshold line with the confidence probability of 99.73 percent, the magnetic field gradient value corresponding to the judging curve is compared with the threshold line, and the position of the sampling point corresponding to the magnetic field gradient value exceeding the threshold range is judged to be an abnormal region.
Compared with the prior art, the technical scheme provided by the embodiment of the invention has the following advantages:
According to the weld defect detection method provided by the embodiment of the invention, the six weak magnetic sensors are arranged in a stacking manner to form two groups of L-shaped positions, so that the weld is subjected to fine detection, specifically, the second weak magnetic sensor and the fifth weak magnetic sensor of the first group are positioned right above the weld and are used for detecting magnetic induction intensity signal values right above the weld, the first weak magnetic sensor and the fourth weak magnetic sensor of the second group are positioned right behind the first group and are also used for detecting magnetic induction intensity signal values right 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 to one side of the first group and are used for detecting magnetic induction intensity signal values of a substrate material, detection errors are effectively reduced, and then the conditions for judging defects are set according to the characteristics of the weld and the weak magnetic detection principle, and the conditions for judging the defects are respectively substituted into by the magnetic induction intensity data after removing background fields by the redundancy detection principle, the correct probability of defect judgment is calculated according to the probability statistics principle, the array type weak magnetic sensors are adopted, the differential layout is optimized, the detection results are used for detecting the magnetic induction intensity signal values of the substrate material, and the defects are accurately judged by a plurality of groups of external defects, and the technical defect detection results are improved.
Drawings
FIG. 1 is a schematic diagram of a relative positional relationship of a plurality of weak magnetic sensors according to an embodiment of the present invention;
FIG. 2 is a flow chart provided by an embodiment of the present invention;
FIG. 3 is a simulation diagram of an original curve provided by an embodiment of the present invention;
FIG. 4 is a simulation diagram of a background field removal curve provided by an embodiment of the present invention;
FIG. 5 is a simulation diagram of a judgment curve provided in an embodiment of the present invention;
FIG. 6 is a schematic diagram of a weak magnetic detection structure according to an embodiment of the present invention;
FIG. 7 is a conceptual diagram of a weak magnetic detection tool provided by an embodiment of the invention;
FIG. 8 is a cross-sectional view of a weak magnetic sensor mounting groove provided by an embodiment of the present invention;
FIG. 9 is a schematic diagram of a probe detection movement path according to an embodiment of the present invention;
FIG. 10 is a plot of a raw scan weld provided by an embodiment of the present invention;
FIG. 11 is a graph of background field removal provided by an embodiment of the present invention;
FIG. 12 is a graph showing a judging defect according to an embodiment of the present invention;
FIG. 13 is a judgment condition 1 provided in the embodiment of the present invention;
FIG. 14 is a diagram showing a judgment condition 2 according to an embodiment of the present invention;
FIG. 15 is a diagram showing a judgment condition 3 according to an embodiment of the present invention;
Fig. 16 is a defect determining chart according to an embodiment of the present invention.
Reference numerals illustrate:
1. the device comprises a first weak magnetic sensor, a second weak magnetic sensor, a third weak magnetic sensor, a fourth weak magnetic sensor, a fifth weak magnetic sensor, a sixth weak magnetic sensor, a workpiece, a welding seam, a weak magnetic probe, a sensor mounting groove, a sensor 11, a switching device, a wire harness box, a aviation socket and an aviation socket.
Detailed Description
One embodiment of the present invention will be described in detail below with reference to the attached drawings, but it should be understood that the scope of the present invention is not limited by the embodiment.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the technical solutions of the present invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
The importance of non-destructive testing as an indispensable technical means in the modern industry is self-evident. The following is a detailed description of the current mainstream nondestructive testing means and limitations thereof, and the weak magnetic detection technology:
Radiation detection (RT) is widely applied to complex structural components, but is not suitable for porous materials, and subsurface defects cannot be detected. In addition, the radiation detection period is long, the cost is high, and the detection personnel need to perform radiation protection. Ultrasonic detection (UT) is widely used for near-surface defect detection, but has requirements on material roughness and shape, and needs materials suitable for ultrasonic propagation. Magnetic particle testing (MT) is suitable for lamellar, near-surface and surface defects, but has limited capability of detecting subsurface defects of non-ferromagnetic materials. Penetration Test (PT) is only suitable for surface defect test, is not suitable for porous materials, and is easy to cause environmental pollution due to improper operation. Eddy current detection (ECT) is a detection means based on the eddy current principle, and a coil needs to be excited to generate magnetic force lines, but the interference of an induced magnetic field and the contradiction between the effective detection area and efficiency exist.
The weak magnetic detection technology is used as an emerging passive nondestructive detection means, mainly uses a sensor to directly scan and detect the surface of a welding line, is difficult to inhibit external interference, causes large quantitative error of defect detection, and causes single defect judgment condition by single data, so that the final defect judgment result has error.
Therefore, the embodiment of the invention provides a weld defect detection method, which can improve the accuracy of a weld defect judgment result.
The invention provides a weld defect detection method, which comprises the steps of arranging six weak magnetic sensors in an L shape in a stacked mode, wherein a second weak magnetic sensor and a fifth weak magnetic sensor which are stacked to form a first group are located right above a weld, the first weak magnetic sensor and the fourth weak magnetic sensor which are stacked to form a second group are located right behind the first group, the third weak magnetic sensor and the sixth weak magnetic sensor which are stacked to form a third group are arranged side by side and aligned to one side of the first group, so that a plurality of magnetic induction intensity signal values of the weld and a substrate material are obtained, preprocessing the magnetic induction intensity signal values, namely performing difference value calculation on two magnetic induction intensity signal values of the same group, obtaining magnetic induction intensity data after removing a background field, setting at least two conditions for judging defects according to the characteristics of the weld and the weak magnetic detection principle, substituting the magnetic induction intensity data after removing the background field into the conditions for judging defects to respectively conduct defect feature recognition by utilizing the redundancy detection principle, and calculating correct probability of defect judgment according to the probability statistics principle.
In the weld defect detection method provided by the embodiment of the invention, the reliability of the detection result is improved by adopting a method of combining multiple judgment modes by arranging a plurality of groups of L-shaped positions of the weak magnetic sensor and combining a redundancy detection principle.
The invention is illustrated by the following examples. Detailed descriptions of known functions and known components may be omitted as so as to not obscure the description of the embodiments of the present invention. When any element of an embodiment of the present invention appears in more than one drawing, the element may be referred to by the same reference numeral in each drawing.
Referring to fig. 1, 7 and 8, fig. 1 is a schematic structural diagram of a relative positional relationship of a plurality of weak magnetic sensors provided by an embodiment of the present invention, fig. 7 is a conceptual diagram of a weak magnetic detection tool provided by an embodiment of the present invention, and fig. 8 is a cross-sectional diagram of a weak magnetic sensor mounting groove provided by an embodiment of the present invention, as shown in fig. 1, 7 and 8, the embodiment of the present invention provides a method for detecting a weld defect, which includes arranging six weak magnetic sensors in an L-shape in a two-by-two stack manner, wherein the second weak magnetic sensor and the fifth weak magnetic sensor which are stacked in a first group are located directly above the weld, the first weak magnetic sensor and the fourth weak magnetic sensor which are stacked in a second group are located directly behind the first group, the third weak magnetic sensor and the sixth weak magnetic sensor which are stacked in a third group are arranged side by side and aligned on one side of the first group, thereby obtaining a plurality of magnetic induction intensity signal values of the weld and a base material, performing a pre-treatment on the two magnetic induction intensity signal values of the same group, that is, performing a difference calculation on the two magnetic induction intensity signal values of the same group, obtaining data after removing a background field, determining a probability of a defect by using a principle of a defect, and determining that the defect is determined by using a redundancy rule, and a defect is determined by a correct probability, respectively.
It should be understood that two weak magnetic sensors of the same group must be adjacent, and it should be understood that a group of weak magnetic sensors stacked together detects a magnetic induction intensity signal in the vertical direction, and the magnetic induction intensity signal is generated by the tested object, so that a weak magnetic sensor signal stacked together at the lower side is stronger than a weak magnetic sensor signal stacked together at the upper side, and as the distance between the weak magnetic sensors and the test piece below is larger, the influence of the test piece on the sensor detection signal is smaller.
According to the weld defect detection method provided by the embodiment of the invention, the six weak magnetic sensors are arranged in a stacking manner to form two groups of L-shaped positions, so that the weld is subjected to fine detection, specifically, the second weak magnetic sensor and the fifth weak magnetic sensor of the first group are positioned right above the weld and are used for detecting magnetic induction intensity signal values right above the weld, the first weak magnetic sensor and the fourth weak magnetic sensor of the second group are positioned right behind the first group and are also used for detecting magnetic induction intensity signal values right 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 to one side of the first group and are used for detecting magnetic induction intensity signal values of a substrate material, detection errors are effectively reduced, and then the conditions for judging defects are set according to the characteristics of the weld and the weak magnetic detection principle, and the conditions for judging the defects are respectively substituted into by the magnetic induction intensity data after removing background fields by the redundancy detection principle, the correct probability of defect judgment is calculated according to the probability statistics principle, the array type weak magnetic sensors are adopted, the differential layout is optimized, the detection results are used for detecting the magnetic induction intensity signal values of the substrate material, and the defects are accurately judged by a plurality of groups of external defects, and the technical defect detection results are improved.
Specifically, the gap between two weak magnetic sensors stacked is 1mm or less.
Specifically, the distance between two adjacent groups of weak magnetic sensors is less than or equal to 4 mm, and the stacking range of the group-to-group interval Fan Bi is larger in consideration of the fact that signal wires are led out from the weak magnetic sensors.
Optionally, the defect judging conditions comprise a condition 1, a condition 2 and a condition 3, wherein the condition 1 comprises judging whether 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 is abnormal, the condition 2 comprises judging whether 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 is abnormal, and the condition 3 comprises judging whether the magnetic induction intensity signal value of the second weak magnetic sensor 2 after removing the background field is abnormal. Here, the condition 1, condition 2, and condition 3 are all determined according to the 3σ principle. The condition curve is used for solving the magnetic field gradient, then 3 sigma is calculated, and whether the gradient curve exceeds 3 sigma is judged, wherein the condition 1 has the highest importance degree, so that the condition 2 and the condition 3 are optional. There are other conditions, except that the importance thereof is gradually reduced.
Optionally, the preprocessing includes connecting the magnetic induction intensity signal values detected by the same weak magnetic sensor into a line to obtain six corresponding real-time curves, and performing differential calculation on the real-time curves corresponding to the two weak magnetic sensors stacked into one group to obtain three curves for removing the background field, wherein the curve for removing the background field corresponding to the weak magnetic sensor stacked into the first group is Rebf1, the curve for removing the background field corresponding to the weak magnetic sensor stacked into the second group is Rebf, and the curve for removing the background field corresponding to the weak magnetic sensor stacked into the third group is Rebf3.
Optionally, performing differential operation on the three curves from which the background field is removed according to a redundancy detection principle to generate two judging curves, wherein Rebf and Rebf2 are differentiated to obtain a judging curve Decide1, rebf and Rebf3 to obtain a judging curve Decide2, the gradient curve of the second weak magnetic sensor 2 is Decide3, the gradient curve is obtained by deriving the curve from which the background field is removed, the gradient curve is obtained by connecting points of gradient values into a line, and the point values in the judging curve Decide, the judging curve Decide and the gradient curve Decide are arrays for judging defect data.
In the existing weak magnetic detection technology, defect judgment is generally carried out by utilizing a3 sigma principle based on detection data. However, in practical detection, such a relatively simple judgment method often generates erroneous judgment.
Therefore, the invention adopts the array type sensor and optimizes the layout, utilizes the data differential technology to inhibit external interference, utilizes the intelligent detection algorithm to optimize the defect judging condition and the like, effectively reduces the detection error, improves the reliability of the detection system, and provides powerful support for the wide application of the weak magnetic detection technology in weld defect detection.
Therefore, the embodiment of the invention provides an improved mode. In the embodiment, two or more conditions for judging the defect are combined and the probability of the defect is calculated according to a formula to judge whether the defect exists or not, when a certain condition for judging the defect shows that the defect exists or the probability of the calculated result is that the defect exists is smaller, the defect is judged, the defect judging method comprises the steps that a, if the condition is two conditions, the two conditions are all established to judge the defect, b, if the condition is three conditions, on the premise that the condition 1 judges the defect, one of the condition 2 and the condition 3 judges the defect, the defect can be judged, and the reliability of the detection result is improved through a method of combining multiple judging modes.
Specifically, the probability calculation formula of the defect is:
Probability=A1*0.6+A2*0.2+A3*0.2
Wherein, the value of A 1 corresponds to condition 1, A 2 corresponds to condition 2, A 3 corresponds to condition 3, A 1、A2、A3 is 0 or 1,0 represents no abnormality, 1 represents abnormality, and when the Probability is more than or equal to 0.8, the defect can be judged to exist here.
Specifically, the method for judging abnormality between magnetic induction intensity signal values is a 3σ method, wherein the method for judging signal abnormality under condition 1 and condition 2 is the same, and the 3σ method comprises:
Firstly, extracting magnetic field gradient values of two judging curves, wherein the magnetic field gradient values are one-time derivative values of each corresponding point value on the two judging curves, and storing the obtained magnetic field gradient values into an array Decide-f [ i, j ] with the following formula:
Decide-f[i,j]=Decide[i,j+1]-Decide[i,j]
Wherein Decide-f [ i, j ] is a weld magnetic induction intensity gradient data array, i is the number of channels, j is a data serial number, wherein the number of channels corresponding to the first weak magnetic sensor 1 is channel 0, the data serial number corresponding to the first weak magnetic sensor 1 is 1, and so on;
After obtaining the magnetic field gradient value curves, further obtaining the threshold range of each judgment curve Wherein, The average value of the magnetic field gradient values is calculated, and σ is the standard deviation of the magnetic field gradient values.
Alternatively to this, the method may comprise,The method for calculating sigma is as follows:
Wherein n is the total number of data points of the judging curve, deltaB (i) is the magnetic field gradient value of the ith data point of the judging curve, when K is 3, the magnetic field gradient value is calculated to obtain a threshold line with the confidence probability of 99.73 percent, the magnetic field gradient value corresponding to the judging curve is compared with the threshold line, and the position of the sampling point corresponding to the magnetic field gradient value exceeding the threshold range is judged to be an abnormal region.
And the welding line is finely detected by a plurality of weak magnetic sensors which are arranged at a plurality of groups of L-shaped positions by using a weak magnetic detection technology. And the technologies of data acquisition, preprocessing, defect feature recognition, two-dimensional imaging and the like are combined, so that the automatic defect judgment and visual display of the weld defects are realized. The signals obtained when 6 sensors detect are voltage signals. The detection areas of the sensors during detection are that a first weak magnetic sensor 1 and a second weak magnetic sensor 2 detect the position right above a welding line, the second weak magnetic sensor 2 is in front, the first weak magnetic sensor 1 is behind, a third weak magnetic sensor 3 detects a substrate material, a fourth weak magnetic sensor 4 detects the position right above the first weak magnetic sensor 1, a fifth weak magnetic sensor 5 detects the position right above the second weak magnetic sensor 2, and a sixth weak magnetic sensor 6 detects the position right above the third weak magnetic sensor 3.
As shown in fig. 1, which shows the relative arrangement relationship of six weak magnetic sensors, the present invention determines whether a defect exists according to the corresponding calculation of the detection data between the sensors.
Referring to fig. 2, fig. 2 is a flowchart provided in an embodiment of the present invention, a detection operation technical flow is shown in fig. 2, a detection tool is used to scan a welded workpiece 7, a weak magnetic sensor is used to collect magnetic induction intensity signals, the collected signals are stored, the stored signals are read for preprocessing, that is, the collected magnetic induction intensity curve is subjected to an operation of removing a background field (eliminating environmental noise and magnetic field interference generated by a non-defective area of the workpiece itself), finally, two or more conditions for judging defects (such as mutation and abnormal distribution of magnetic induction intensity) are set according to the characteristics of the welded seam and the weak magnetic detection principle, and each judgment condition is respectively subjected to defect characteristic identification by using a redundancy detection principle, and the correct probability of defect judgment is calculated according to a probability statistics principle, so as to improve the accuracy and reliability of detection.
The method comprises the following steps of
1. Magnetic induction intensity curve pretreatment
FIG. 1 is a simulation of an original curve of six channel probes (CH 1, CH2, CH3, CH4, CH5, CH 6) scanning for preformed weld defects. The weak magnetic collection circuit board decodes the magnetic induction intensity data collected by the weak magnetic sensors, and then performs equivalent calculation based on the calibration values of the weak magnetic sensors to obtain the magnetic induction intensity collected by each weak magnetic sensor in the detection process. The upper computer software is programmed by C# language, and the corresponding drawing interface of the software draws magnetic induction intensity values corresponding to all the acquisition points, and the connection points form a line, so that the real-time curves of the six probe channels shown in figure 3 are obtained.
Fig. 4 is three curves (Rebf 1, rebf2, rebf 3) with background field removed, resulting from differential operation of weak magnetic sensor data in six channels. Storing original curve Data of six channels into an array Data [ i, j ], wherein Data [0, j ] represents Data of a CH1 channel, data [1, j ] represents Data of a CH2 channel, data [2, j ] represents Data of a CH3 channel, data [3, j ] represents Data of a CH4 channel, data [4,j ] represents Data of a CH5 channel, data [5,j ] represents Data of a CH6 channel, and carrying out the following treatment on the selected channels, wherein the Data value of each channel is a magnetic induction value after decoding a voltage signal, the magnetic induction value of each channel is stored into the array Data [ i, j ] for calculation, i is the number of channels, and j is a Data serial number;
Subtracting the magnetic induction intensity value of the channel 4 from the channel 1, and corresponding to the sensor 1 and the sensor 4;
subtracting the magnetic induction intensity value of the channel 5 from the channel 2, and corresponding to the sensor 2 and the sensor 5;
Channel 3 subtracts the magnetic induction value of channel 6, corresponding to sensor 3 and sensor 6. )
Rebf[i,j]=Data[i,j]-Data[i+3,j]
(4-1)
Wherein Rebf [ i, j ] is an array for storing the background field data (corresponding channels (1 and 4, 2 and 5, 3 and 6) have the same data change trend, and the data subtraction can effectively extract the change and inhibit noise, and can be regarded as background field data removal), i is the channel number, and j is a data sequence number. Pointing into a line in the drawing interface yields three curves Rebf, rebf2, rebf3 with the background field removed as shown in fig. 4. The positions of the 6 sensors are arranged in this way because the second weak magnetic sensor 2 is selected as the core, and the conditions are 2-1,2-2,2-3, respectively, when the defect judgment is made. 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 is a large error in the signal difference, however, the arrangement may also use the first weak magnetic sensor 1 as a core, and the judgment condition should be 1-2,1-1,1-3.
Fig. 5 shows two judgment curves generated by performing differential operation and gradient curves Decide, decide, decide of sensor signals located directly above a weld seam according to the redundancy detection principle by adopting a data redundancy mode and improving the judgment accuracy by different channel data. For conditions 1,2, the data Rebf [ i, j ] with the background field removed is processed as follows, i being 0 and 1:
Decide[i,j]=Rebf[i,j]-Rebf[i+1,j]
(4-2)
For condition 3, the data Rebf [2, j ] with the background field removed is processed as follows:
Decide[2,j]=Rebf[2,j+1]-Rebf[2,j]
(4-3)
Wherein Decide [ i, j ] is an array for storing judging defect data, i is the channel number, and j is a data serial number.
It is necessary to integrate two or more judging conditions and calculate the probability value of the defect according to a formula to judge whether the defect exists at the position. When a certain judging condition shows no defect here or the calculated result is that the probability of the defect existence here is smaller, judging that the defect exists here.
The conditions for judging whether the weld defects are generally three, namely (1) whether the difference between detection signals of the front sensor and the rear sensor which are positioned right above the weld is abnormal, (2) whether the comparison signal of the weld and the substrate material is abnormal, and (3) whether the gradient of the detection signal of the first sensor which is positioned right above the weld is abnormal.
The probability calculation method for judging the defect is (1) that if two conditions are judged, the two conditions must be all established to judge the defect, and (2) that if three conditions are judged, if one of the conditions 2 and 3 is judged to be the defect on the premise that the condition 1 is judged to be the defect, the defect can be judged to be the defect.
The defect probability calculation formula is:
Probability=A1*0.6+A2*0.2+A3*0.2
(4-4)
Wherein the value of A 1、A2、A3 is 0 or 1, which represents whether an abnormal signal exists. When Procapability is greater than or equal to 0.8, it can be judged that there is a defect. The reliability of the detection system is greatly improved through comprehensive calculation of various conditions.
2. Condition judgment
(1) Detecting a signal
The method for judging the magnetic signal abnormality is a3 sigma method. Wherein the method of judging signal abnormality is the same for condition 1 and condition 2.
Firstly, extracting magnetic field gradient value of "judging curve", and storing the obtained magnetic field gradient value into array Decide-f [ i, j ]. The formula is as follows:
Decide-f[i,j]=Decide[i,j+1]-Decide[i,j]
(4-5)
wherein Decide-f [ i, j ] is a weld magnetic induction intensity gradient data array, i is the channel number, and j is a data serial number.
After obtaining the magnetic field gradient value curve, further obtaining the threshold range of each judgment curve through formulas 4-6 and 4-7Wherein, The average value of the magnetic field gradient values is calculated, and σ is the standard deviation of the magnetic field gradient values. Concrete embodimentsThe method for calculating sigma is as follows:
Where n is the total number of data points of the judgment curve, and Δb (i) is the magnetic field gradient value of the ith data point of the judgment curve. When K is 3, calculating a magnetic field gradient value to obtain a threshold line (namely the upper limit and the lower limit of a threshold range) with the confidence probability of 99.73 percent of the interval, comparing the magnetic field gradient value corresponding to the judgment curve with the threshold line, and judging the position of a sampling point corresponding to the magnetic field gradient value exceeding the threshold range as an abnormal region.
The invention arranges the weak magnetic sensor into L shape, and generates a detection curve containing various detection information through numerical operation among a plurality of groups of sensor detection data. And obtaining various defect judgment information through abnormal recognition of the detection curve, and improving the accuracy of defect judgment through a comprehensive judgment method of the various judgment information.
The invention discloses a welding seam detection method based on a weak magnetic technology, which mainly comprises an upper computer, a weak magnetic signal acquisition system, a probe and a tool, wherein corresponding structural units are shown in fig. 6.
The weak magnetic detection instrument adopts a fluxgate sensor as a magnetic field measurement probe, and the resolution of the sensor can reach 1nT, so that the magnetic induction intensity signal on the surface of a test piece can be detected with high precision.
The probe tool comprises a sensor mounting groove 10, an adapter device 11, a wire harness box 12 and an aviation socket 4 as shown in fig. 7. Inside the sensor mounting groove 10, as shown in fig. 8, 6 weak magnetic sensors are arranged in the mounting groove. The first weak magnetic sensor 1, the second weak magnetic sensor 2 and the third weak magnetic sensor 3 form an L-shaped arrangement form, the fourth weak magnetic sensor 4, the fifth weak magnetic sensor 5 and the sixth weak magnetic sensor 6 form another L-shaped arrangement form, the switching device 11 is used for connecting the sensor mounting groove 10 and the wire harness box 12, the wire harness box 12 plays a role in structurally supporting the whole probe tool, the stability of the tool is guaranteed, the replacement of the sensor can be conveniently realized through the dismounting of the wire harness box 12, the aviation plug is fixed, the aviation plug is prevented from shaking, and the tool material selected in the design is nonmagnetic non-metallic material in order to avoid interference to sensor signal acquisition caused by overlarge magnetic conductivity of the tool material.
The schematic diagram of the detection path of the probe is shown in fig. 9, the direction indicated by the arrow represents the movement direction of the probe, the welding seam workpiece 7 is horizontally placed on the experiment table during detection scanning, so that the defects are scanned at a constant speed from right to left from the leftmost end of the test piece during detection, 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 of the sensor are positioned right above the welding seam during detection, and the third weak magnetic sensor 3 and the sixth weak magnetic sensor 6 are positioned above the substrate.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
1. Description of experimental principles:
Taking a certain welding line detection as an example, taking the surface of the workpiece 7 to be detected as a flat surface, selecting 6 weak magnetic sensors to be arranged in a sensor mounting groove 10 according to the relative arrangement of fig. 8, and scanning the welding line 8 at a constant speed. The distance between the probe and the welding line 8 is as small as possible, and the relative pose of the probe and the welding line is required to be ensured to be fixed.
After the welding line is scanned by using the probe tool, the system stores continuous detection data of 6 sensor channels, and generates an original curve with the acquisition point number as an X axis and the magnetic induction intensity as a Y axis. Removing background field from original curve by using formula 4-1, and obtaining three defect judging conditions by using formulas 4-2 and 4-3 and combining defect probability calculation formula 4-4.
The three methods for judging the defects are the same, namely, as the change of the weak magnetic signal at the defect is weak, the defects are difficult to judge only by the compared signals, the gradient value extraction is carried out on the curve after the difference to obtain the gradient value curve reflecting the abrupt change of the weak magnetic signal strength, and a proper threshold line is set. And comparing the spatial magnetic field gradient value of each sampling point with a threshold line, and judging the sampling point region corresponding to the spatial magnetic field gradient value exceeding the threshold range as the defect. The final judgment is that the first judgment condition judges the defect, and at least one of the second judgment condition and the third judgment condition judges the defect, otherwise, the final judgment is regarded as misjudgment. After judging the defect position, carrying out two-dimensional imaging representation on the detection result on the defect position through a spline interpolation algorithm.
2. Description of examples:
The detection workpiece 7 is a flat welding line, the material of the workpiece is austenitic stainless steel, and three defects are manufactured on the welding line. The surface of the workpiece to be measured is regarded as a flat surface, and the welding line 8 is scanned at a constant speed according to the schematic diagram of the probe detection moving path in fig. 9. According to the experimental principle, the detected magnetic signals are preprocessed, judging conditions are obtained to judge defects, and the results are displayed in a two-dimensional imaging mode.
The original curve of the scanned weld is as follows:
fig. 10, 11 and 12 show the original curve, background field removal curve and defect judgment curve obtained by scanning the weld template by the opposite probe. And removing a background field from the original curve according to an experimental principle, obtaining two judging defect curves by removing a background field condition curve according to a redundancy detection principle, and finishing preprocessing operation.
According to fig. 15, it is finally determined that three defective areas exist. The judgment condition 1 shows that among four areas of the sampling points 55-65, 13245, 200-210, and 26375, the first three areas satisfy the judgment condition 2 or the condition 3, confirming that there is a defect, whereas in the fourth area, neither the condition 2 nor the condition 3 indicates a defect, and thus this area is judged to be defect-free.
The foregoing invention is only a few specific embodiments of the invention, but the embodiments of the invention are not limited thereto, and any changes that can be thought by those skilled in the art should fall within the protection scope of the invention.
Claims (8)
1. A weld defect detection method, comprising:
Arranging six weak magnetic sensors in an L shape in a pairwise stacking way, wherein a second weak magnetic sensor (2) and a fifth weak magnetic sensor (5) which are stacked into a first group are positioned right above a welding line, a first weak magnetic sensor (1) and a fourth weak magnetic sensor (4) which are stacked into a second group are positioned right behind the first group, and a third weak magnetic sensor (3) and a sixth weak magnetic sensor (6) which are stacked into a third group are arranged side by side and aligned on one side of the first group, so that a plurality of magnetic induction intensity signal values of the welding line and a substrate material are obtained;
preprocessing the magnetic induction intensity signal values, namely performing difference value calculation on two magnetic induction intensity signal values in the same group to obtain magnetic induction intensity data after removing a background field;
setting at least two conditions for judging defects according to the characteristics of the welding seam and a weak magnetic detection principle;
and substituting the magnetic induction intensity data after removing the background field into conditions for judging defects to respectively identify the defect characteristics by utilizing a redundancy detection principle, and calculating the correct probability of defect judgment according to a probability statistics principle.
2. The method for detecting a weld defect according to claim 1, wherein the conditions for judging the defect include condition 1, condition 2 and condition 3,
The condition 1 comprises that whether 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 is abnormal or not is judged;
the condition 2 comprises judging whether 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 is abnormal;
the condition 3 includes judging whether or not there is abnormality in the magnetic induction intensity signal value of the second weak magnetic sensor (2) after the background field is removed.
3. The method for detecting the weld defects according to claim 2, wherein the preprocessing comprises the steps of connecting magnetic induction intensity signal values detected by the same weak magnetic sensor in a line to obtain six corresponding real-time curves, and performing differential calculation on the real-time curves corresponding to the two weak magnetic sensors stacked in one group to obtain three curves for removing the background field, wherein the curve for removing the background field corresponding to the weak magnetic sensor stacked in the first group is Rebf, the curve for removing the background field corresponding to the weak magnetic sensor stacked in the second group is Rebf, and the curve for removing the background field corresponding to the weak magnetic sensor stacked in the third group is Rebf3.
4. The weld defect detection method according to claim 3, wherein the three curves from which the background field is removed are subjected to differential operation according to a redundancy detection principle to generate two judgment curves, wherein a difference between Rebf1 and Rebf2 yields a judgment curve Decide, a difference between Rebf2 and Rebf3 yields a judgment curve Decide2, a gradient curve of the second weak magnetic sensor (2) is denoted as Decide3, and point values in the judgment curve Decide, the judgment curve Decide and the gradient curve Decide are arrays of judgment defect data.
5. The method of detecting a weld defect according to claim 4, wherein it is necessary to combine two or more conditions for judging a defect and calculate the probability of the defect according to a formula to judge whether the defect exists there, and when one of the conditions for judging a defect shows that there is no defect or the calculated result is that the probability of the defect exists there is small, the defect is judged to be defective, and the method of judging a defect is a, if the two conditions are both satisfied, the defect can be judged to be present, and b, if the three conditions are present, if one of the conditions 2 and 3 is present, the defect can be judged to be present.
6. The weld defect detection method of claim 5, wherein the probability calculation formula of the defect is:
Probability=A1*0.6+A2*0.2+A3*0.2
Wherein, the value of A 1 corresponds to condition 1, A 2 corresponds to condition 2, A 3 corresponds to condition 3, A 1、A2、A3 is 0 or 1,0 represents no abnormality, 1 represents abnormality, and when the Probability is more than or equal to 0.8, the defect can be judged to exist here.
7. The weld defect detection method of claim 6, wherein the method of abnormality determination between magnetic induction signal values is a3σ method, wherein the method of determining signal abnormality for condition 1 and condition 2 is the same, the 3σ method comprising:
Firstly, extracting magnetic field gradient values of two judging curves, wherein the magnetic field gradient values are one-time derivative values of each corresponding point value on the two judging curves, and storing the obtained magnetic field gradient values into an array Decide-f [ i, j ] with the following formula:
Decide-f[i,j]＝Decide[i,j+1]-Decide[i,j]
Wherein Decide-f [ i, j ] is a weld magnetic induction intensity gradient data array, i is the number of channels, j is a data serial number, wherein the number of channels corresponding to the first weak magnetic sensor (1) is channel 0, the data serial number corresponding to the first weak magnetic sensor (1) is 1, and so on;
After obtaining the magnetic field gradient value curves, further obtaining the threshold range of each judgment curve Wherein, The average value of the magnetic field gradient values is calculated, and σ is the standard deviation of the magnetic field gradient values.
8. The weld defect detection method of claim 7, wherein theThe method for calculating sigma is as follows:
Wherein n is the total number of data points of the judging curve, deltaB (i) is the magnetic field gradient value of the ith data point of the judging curve, when K is 3, the magnetic field gradient value is calculated to obtain a threshold line with the confidence probability of 99.73 percent, the magnetic field gradient value corresponding to the judging curve is compared with the threshold line, and the position of the sampling point corresponding to the magnetic field gradient value exceeding the threshold range is judged to be an abnormal region.
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