CN117871658A - Weld joint detection device and method based on magnetic image sensor - Google Patents
Weld joint detection device and method based on magnetic image sensor Download PDFInfo
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Abstract
The invention belongs to the field of electromagnetic nondestructive detection, and particularly relates to a welding seam detection device and method based on a magnetic image sensor. According to the technical scheme, a wide range of weld widths can be covered by a high-precision detection result provided by the weld detection equipment based on the magnetic sensor; it has a stationary detection capability and exhibits excellent anti-shake and anti-interference capabilities. The equipment has wide application prospect in the aspects of weld quality control and defect detection, and can improve the reliability and efficiency of a welding process. The device can be applied to the field of weld joint detection of navigation instruments, is not limited and is interfered by gravity, and can make an important contribution to subsequent repair or quality inspection work even if defects are found.
Description
Technical Field
The invention belongs to the field of electromagnetic nondestructive detection, and particularly relates to a welding seam detection device and method based on a magnetic image sensor.
Background
Nondestructive testing is to inspect and test the structure, properties and states of the inside and the surface of a test piece by using modern technology and equipment without damaging the test piece. The main stream nondestructive testing method comprises eddy current testing, ultrasonic testing, magnetic powder testing, penetration testing and ray testing.
Eddy current inspection is capable of detecting surface defects and near surface defects of a workpiece, however the presence of lift-off effects often plagues the eddy current inspection operation. The surface finish and flatness requirements of the workpiece to be tested are high.
Radiographic inspection determines the location and magnitude of defects by comparing changes in radiographic intensity. Because of low sensitivity of X-rays in an air layer, the method cannot accurately detect the interface of the bonding and debonding state.
When the ultrasonic detection method encounters a defect, the sound wave can be reflected and refracted, and the material performance and structural change can be known by detecting the disturbance degree and state of the ultrasonic wave. Acoustic wave propagation media are required due to their attenuation in air, and the introduction of coupling agents such as oil or water can reduce efficiency.
Magnetic particle testing is only applicable to ferromagnetic materials. The mechanism of magnetic flux leakage detection determines the complexity of the detection device and correspondingly increases the unreliability of the detection system.
Penetration detection is commonly used for detecting surface defects by capillary action principle, and near-surface defects are difficult to detect.
Whereas the basic principle of TMR sensors is the tunnel magnetoresistance effect, and the principle of tunnel magnetoresistance effect generation is the tunneling effect. TMR magnetic sensors use the change in magnetic field to cause a change in magnetoresistance, on the other hand, we can measure the change in external magnetic field by observing the change in resistance of the TMR magnetic sensor. Therefore, we can consider that the TMR sensor is a resistor, but the resistance value of the TMR sensor changes along with the change of the value of the externally applied magnetic field.
Therefore, it is necessary to provide a new welding seam detection device and method for a magnetic image sensor, which solve the above technical problems.
Disclosure of Invention
Based on the technical problems, the specific technical scheme of the invention is as follows:
the invention provides a welding seam detection device based on a magnetic image sensor, which comprises an upper computer and a sensing module communicated with the upper computer, wherein the sensing module comprises a shell, the shell comprises an upper shell box body and a lower shell box body sleeved outside the upper shell box body, an array probe, a data acquisition card and a reset mechanism are arranged in the upper shell box body, the data acquisition card is connected with the array probe in a communication manner, a handle is arranged on the shell, and the handle can be pushed and pulled to enable the upper shell box body to slide in the lower shell box body along a preset track; the reset mechanism provides reset support for switching the device between a detection state and a non-detection state; the lower shell box body is of a three-frame through groove structure, supporting edges extend from the inner side walls of the two grooves to the bottom of the parallel grooves, the reset mechanism comprises a spring group and a buffer bottom plate, and the buffer bottom plate is attached to the bottom of the grooves; one surface of the buffer bottom plate is attached to the inner wall of the tank bottom, the other surface of the buffer bottom plate is connected with the bottom of the upper shell tank body, the data acquisition card is arranged at the bottom of the upper shell tank body, one end, far away from the buffer bottom plate, of the data acquisition card is provided with the array probe, and the array probe faces the direction of a three-frame through tank notch of the lower shell tank body; the spring group comprises two groups of springs, one end of each group of springs is connected with the supporting rib, the other end of each group of springs is connected to the end part, close to the inner side wall, of the buffer bottom plate along the direction of the inner side wall of the groove, and the handle is pressed to enable the array probe to be close to the notch.
Further, the upper computer is electrically connected with the sensing module through a signal wire; the upper shell box body and the lower shell box body are made of nonferromagnetic materials; the array probe is formed by arranging and combining a plurality of TMR probes in an array mode.
Further, when the handle is pressed to enable the array probe to be close to the notch, the array probe is detected to the notch at the bottom of the lower shell box body and is parallel to the notch.
A weld joint detection method based on a magnetic image sensor is characterized in that a multi-probe combined array probe is used for synchronous signal acquisition, and a permission time period signal is integrated and then uploaded to a terminal.
The method comprises the following steps:
s1, selecting the number of sensor channels according to the size of the detection piece;
s2, selecting an area to be detected, pressing the device, and performing scanning detection;
s3, performing defect judgment on the to-be-detected area and performing algorithm calculation;
s4, selecting recording or non-recording according to the judging result;
s5, moving to the next to-be-detected area, and repeating the steps S2-S4 until the process is finished.
Further, the number of sensor channels is selected in S1, specifically, the waveform, the frequency and the amplitude of the excitation signal are set on the upper computer interface, and the initialization calibration is completed.
Further, the calculation of the S3 algorithm is specifically as follows: carrying out one-time derivation processing on the magnetic induction intensity values of the points on each original curve to obtain corresponding magnetic field gradient values, and then applying a formulaEach sensor channel corresponds to a threshold range (μ -3σ, μ+3σ).
Further, the step S4 is specifically based on determining whether to record, in which the magnetic field gradient value corresponding to each sampling point is compared with the threshold line, that is, the upper and lower limits of the threshold range, and the position of the sampling point corresponding to the magnetic field gradient value exceeding the threshold range is determined as the defect position, and is recorded.
Further, S3, carrying out algorithm calculation on defect judgment of the to-be-detected area, namely carrying out least square fitting on the magnetic induction intensity values of the acquisition points on the original curve, and determining abnormal points by using the minimum comprehensive error of the residual values; wherein, after the residual value is subjected to a 3σ method, the position where the value larger than 3σ or smaller than-3σ appears is a defect position.
Further, when the magnetic field gradient value and the residual error value are both defects, the measuring point is judged to be the defect position.
According to the technical scheme, a wide range of weld widths can be covered by a high-precision detection result provided by the weld detection equipment based on the TMR magnetic sensor; it has a stationary detection capability and exhibits excellent anti-shake and anti-interference capabilities. The equipment has wide application prospect in the aspects of weld quality control and defect detection, and can improve the reliability and efficiency of a welding process.
Drawings
FIG. 1 is a schematic structural diagram of embodiment 1;
FIG. 2 is a schematic diagram of a sensor module structure;
FIG. 3 is a schematic diagram of a weld detection path;
FIG. 4 is an original graph of a single sensor scanning weld for pre-crack defects;
FIG. 5 is a graph of weld pre-crack defects detected by the magnetic image sensor of example 2;
FIG. 6 is a differential graph of five-pass scanning defect stitching of example 2;
FIG. 7 is a residual method original graph of example 3;
FIG. 8 is a plot of the residual method originals and fits of example 3;
FIG. 9 is a residual value diagram of example 3;
FIG. 10 is a measurement diagram of example 3;
fig. 11 is a schematic diagram for judging defects in example 3.
Reference numerals: the device comprises a host computer 1, a signal wire 2, a sensing module 3, a handle 4, an upper shell box 5, a lower shell box 6, a spring 7, an array probe 8 and a data acquisition card 9.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the drawings, are merely for convenience in describing the present invention and simplifying 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 thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," a, b, c, d, e, f, etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Example 1:
referring to fig. 1, fig. 2 and fig. 3, the embodiment discloses a welding seam detection device based on a magnetic image sensor, which comprises an upper computer 1 and a sensing module 3 communicated with the upper computer 1, wherein the sensing module 3 comprises a shell, the shell comprises an upper shell box 5 and a lower shell box 6 sleeved outside the upper shell box 5, an array probe 8, a data acquisition card 9 and a reset mechanism are arranged in the upper shell box 5, the data acquisition card 9 is in communication connection with the array probe 8, a handle is arranged on the shell, and the upper shell box 5 can slide in the lower shell box 6 along a preset track by pushing and pulling the handle; the reset mechanism provides reset support for switching the device between a detection state and a non-detection state.
The lower shell box 6 is of a three-frame through groove structure, supporting edges extend from the inner side walls of the two grooves to the bottom of the groove in parallel, and the reset mechanism comprises a spring group and a buffer bottom plate which is attached to the bottom of the groove; one surface of the buffer bottom plate is attached to the inner wall of the tank bottom, the other surface of the buffer bottom plate is connected with the bottom of the upper shell box body 5, the data acquisition card 9 is arranged in the upper shell box body, one end, far away from the buffer bottom plate, of the data acquisition card 9 is provided with an array probe 8, and the array probe 8 faces to the direction of a three-frame through tank notch of the lower shell box body; the spring group comprises two groups of springs 7, one end of each group of springs is connected with the supporting edge, the other end of each group of springs is connected to the end part, close to the inner side wall, of the buffer bottom plate along the direction of the inner side wall of the groove, and the array probe is close to the notch by pressing the handle.
Three schemes are available for reference, one of which is a tension spring, which is located between the buffer bottom plate and the lower housing box, or between the upper outer wall of the upper housing box and the lower inner wall of the lower housing box. Pressing the handle, the spring will be stretched, bringing the array probe 8 close to the notch of the lower housing case.
Secondly, the width of the buffer bottom plate is wider than the bottom of the upper shell groove by using a compression spring, and referring to fig. 1, springs 7 are arranged on two sides, and the springs 7 are compressed and released to reset after the handle is pressed due to supporting edges below the springs 7.
The side wall of the upper shell box body can be provided with a longitudinal through groove according to the position of the spring, and the space of the spring can be buried in the upper shell box body or the lower shell box body.
The upper computer 1 is electrically connected with the sensing module 3 through a signal wire 2.
The upper shell box body and the lower shell box body are made of nonferromagnetic materials.
The array probe is formed by arranging and combining a plurality of TMR probes in an array manner.
When the handle is pressed to enable the array probe to be close to the notch, the array probe extends to the notch at the bottom of the lower shell box body and is parallel to the notch.
As a preferred example, the parameters are as follows, TMR6318 magnetic image sensor size: the device housing dimensions were 191.5mm long by 16mm wide by 17mm high: the device handle dimensions are 240mm long by 40mm wide by 80mm high: 100mm long by 10mm wide by 60mm high. And a circuit module such as a data acquisition card is arranged in the device.
Considering the existence of weld flash in a weld zone, the surface condition is irregular, and two detection errors are easily generated in the scanning process of the surface of a test piece by the sensor: detection errors caused by unstable shaking of the probe; interference signals caused by uneven ambient magnetic field in the moving process of the probe. In order to solve the problem, a TMR6318 magnetic image sensor with the detection width reaching 180mm is adopted as a detection probe, so that a means of completing detection by standing the probe above a welding line is realized, and detection errors generated by movement of the sensor are greatly reduced.
If one TMR probe is used alone for detection, the detection area covered by the sensor is limited, and the probe needs to be moved to complete the detection of the whole welding line. Then the sensor gesture is inevitably changed due to the influence of the weld flash in the process of probe movement, and then interference signals are acquired. In the data processing module, the data acquired by a single probe is difficult to compare and analyze, and the filtering analysis in the data processing is difficult.
Sensor tooling is designed, see fig. 2. In order to reduce the influence of an environmental magnetic field on a detected material, a non-ferromagnetic material is adopted to manufacture the tool frame. The TMR6318 magnetic image sensor is arranged on a box body of a non-ferromagnetic material tool, and the bottom of the box body is connected with a shell through a spring, so that the sensor is arranged in the shell when the device is in a free state. When the sensor is not detected, the sensor is arranged in the shell, an operator holds the handle by hand and presses down when detecting, the lower edge of the sensor reaches the lower edge of the shell, and the switch is started to finish the detection of one position.
After the power is turned on, the waveform, the frequency and the amplitude of the excitation signal are set on the interface of the upper computer, and after the initialization calibration is completed, the device is placed above the detection area. When the probe is placed, the upper computer interface receives the detection data. By observing the detection data, whether the detection area has defects can be intuitively judged. For a weld with a shorter length, the device is placed above the weld once to complete detection. For longer welds, the device may be placed in sections to gradually complete the detection of the weld, see FIG. 3. The array probe is a TMR array probe.
And fitting detection data of a plurality of sensors to the same curve by means of an autonomously developed software algorithm, and setting proper upper and lower thresholds according to a test piece to judge the position and the shape of the defect.
The application relates to TMR6318 magnetic image sensor detection based on a magneto-resistance modulation technology. The system mainly comprises a notebook computer as an upper computer for running software, a TMR6318 magnetic image sensor as a signal acquisition device, a nonferromagnetic aluminum alloy material tool, an internal spring design and a data acquisition card.
Example 2:
the embodiment is to explain a welding seam detection method based on a magnetic image sensor on the basis of embodiment 1, and referring to fig. 4 and 5, fig. 6 uses a multi-probe combined array probe to acquire synchronous signals, and the signals of a permitted time period are integrated and then uploaded to a terminal.
The method comprises the following steps:
s1, selecting the number of sensor channels according to the size of the detection piece;
s2, selecting a region to be detected, pressing the device, and performing scanning detection;
s3, performing defect judgment on the to-be-detected area and performing algorithm calculation;
s4, selecting recording or non-recording according to the judging result;
s5, moving to the next to-be-detected area, and repeating the steps S2-S4 until the process is finished.
And S1, selecting the number of sensor channels, namely setting the waveform, the frequency and the amplitude of the excitation signal on an upper computer interface to finish initialization calibration.
The S3 algorithm is specifically calculated as follows: carrying out one-time derivation processing on the magnetic induction intensity values of the points on each original curve to obtain corresponding magnetic field gradient values, and then applying a formulaEach sensor channel corresponds to a threshold range (μ -3σ, μ+3σ).
n is the total number of samples of any one sensor channel, mu is the average value of the magnetic field gradient of the channel, sigma is the standard deviation of the magnetic field gradient of the channel, and delta B (i) is the magnetic field gradient value of the ith sampling point.
And S4, specifically judging whether to record or not according to the judgment, wherein the magnetic field gradient value corresponding to each sampling point is compared with a threshold line, namely the upper limit and the lower limit of a threshold range, and the position of the sampling point corresponding to the magnetic field gradient value exceeding the threshold range is judged to be the defect position and is recorded.
The welding seam detection process is shown in fig. 3, if a person needs to detect the area A, the sensor can be placed in the area A for detection, and the detection tool can be placed in the area B for detection. I.e. a segmented placement device, the detection is done step by step.
A plurality of TMR sensors are integrated in the TMR array probe, dynamic curves of the plurality of sensors are obtained in real time by means of software, N curves can be spliced and generated into the same graph window after data processing, and therefore the effect achieved by moving a single sensor for a long distance can be obtained through one-time detection, the detection range is enlarged, and the detection efficiency is improved.
Taking the butt welding seam of a certain low-carbon steel plate as an example, placing a detection device on the welding seam, and completing the detection in a segmented way. Although the surface of the workpiece to be detected is uneven, the sensor is tightly attached to the welding seam due to the fact that the detection device does not move on the surface of the welding seam, and interference signals introduced during detection are few.
Before detection, the normal connection of the line is confirmed, and the software initialization is normal. In the process of collecting signals in real time, each magnetic induction intensity signal corresponds to a sampling point on a detection workpiece, and a software interface can present a magnetic resistance signal intensity curve in real time and store corresponding data.
After the detection of the multi-sensor tool is completed, the system stores corresponding detection data, an original curve with the acquisition point number as an X axis and the magnetic resistance data as a Y axis is generated by depending on upper computer software, and the original curve is used for judging defects. And carrying out differential processing on the basis of the original data to generate a differential curve reflecting the abrupt change of the weak magnetic signal strength, setting a proper threshold line, and judging that the defect exists in a sampling point area corresponding to the spatial magnetic field gradient value beyond the threshold range.
Referring to FIG. 4, a graph of weld surface detection is scanned using single sensor movement. The number of the acquisition points is 100 points, the detection distance is 100mm, the probe moving distance in the whole detection process is 100mm, and the detection time is approximately 5-6 seconds. Referring to fig. 5, the present detection apparatus is used to detect the same region, and the detection time is approximately 200 milliseconds. Comparing fig. 4 with fig. 5, both methods detect the same weld. Since the magnetic image sensor has higher detection resolution, the defect position positioning accuracy is higher.
According to the principle of magnetic resistance modulation, if a defect exists on a test piece, a corresponding magnetic resistance signal abnormal phenomenon exists on a detection curve of the sensor. As shown in fig. 7, the defect position can be clearly displayed, but if the computer is allowed to automatically judge the defect, the corresponding algorithm calculation is required.
When there is an anomaly in the curve, the differential value will vary greatly. The differential curve is based on the original curves, and magnetic induction intensity values of points on each original curve are subjected to one-time derivation processing to obtain corresponding magnetic field gradient values. Further, the corresponding threshold range (mu-3 sigma, mu+3 sigma) of each sensor channel is calculated through an algorithm, wherein mu is the average value of the obtained magnetic field gradient values, and sigma is the standard deviation of the obtained magnetic field gradient values. The specific μ and σ calculation method is as follows:
where n is the total number of samples of any one sensor channel, μ is the average value of the magnetic field gradients of that channel, σ is the standard deviation of the magnetic field gradients of that channel, Δb (i) is the magnetic field gradient value of the ith sample point.
According to the formula and principle, after data processing, a differential curve of the spliced detection curve is obtained, and the differential curve is formed by connecting magnetic field gradient values corresponding to each sampling point. Comparing the magnetic field gradient value corresponding to each sampling point with a threshold line (namely the upper limit and the lower limit of a threshold range), and judging the position of the sampling point corresponding to the magnetic field gradient value exceeding the threshold range as the defect position.
As can be seen from fig. 6, the magnetic field gradient values exceeding the upper and lower threshold lines are mainly concentrated in the range of 44-56 acquisition points, and the acquisition points corresponding to the defect positions indicated by the spliced curves in fig. 4 are 50, and the two acquisition points are matched with each other, so that defects exist in the region. The crack defect of the butt weld of the low-carbon steel flat plate is a crack with the width of 0.3mm, and the detection result is reasonable because the influence of the defect on the magnetic field exists in a larger range far exceeding the size of the defect in electromagnetic detection. The same applies to the analysis of the detection result of the magnetic image sensor.
Example 3:
the method for judging the weld defects by residual errors is further described on the basis of the embodiment 1 and the embodiment 2, and S3, performing algorithm calculation on the defects to be detected, namely, performing least square fitting on the magnetic induction intensity values of the acquisition points on the original curve, and determining the abnormal points by using the minimum comprehensive error of the residual values; wherein, after the residual value is subjected to a 3σ method, the position where the value larger than 3σ or smaller than-3σ appears is a defect position.
And when the magnetic field gradient value and the residual error value are both defects, judging that the measuring point is a defect position.
Referring to fig. 7 and 8, wherein the dotted line is a fitted curve and the solid line is an original curve. For judging the detected defects, besides adopting the magnetic induction intensity values of the points on each original curve to conduct one-time derivation processing to obtain a corresponding magnetic field gradient value method for judging, the method also adopts least square fitting to the magnetic induction intensity values of the points on the original curve, and specifically adopts the data fitting method which specifically comprises the following steps: the method of straight line fitting, quadratic curve fitting and cubic curve fitting is determined according to the minimum comprehensive error of the obtained residual value. And searching for an outlier by adopting a 3 sigma algorithm on the residual error value curve. When the magnetic induction intensity values of the points on each original curve are adopted for one-time derivation processing, a corresponding magnetic field gradient value method is obtained for judging, a residual error value curve 3 sigma algorithm is adopted for searching abnormal points, the defect judgment is determined only when the two methods show that a certain position is a defect, and otherwise, the defect judgment is judged.
Referring to fig. 7, the fitting method adopts a least square method to fit it to a cubic polynomial. Higher order function fitting should be considered for complex workpieces. Fitting equations obtained by the least square method can be directly obtained by using polyfit and polyval functions in matlab. The code is as follows:
function[yi]=fitting(y)
m=length(y);
x=(1:m)';
P= polyfit(x, y, 3);
yi=polyval(P, x);
end
referring to fig. 9, since most of the area of the workpiece surface is defect-free, the actually detected weak magnetic field induction of the workpiece surface is fitted, and an ideal defect-free weak magnetic field induction model of the workpiece surface is constructed. The residual value is characterized as the difference between the original curve and the fitting function, and can be actually regarded as the difference between the actual scanned weak magnetic field induction intensity of the surface of the workpiece and the ideal defect-free weak magnetic field induction intensity model of the surface of the workpiece. The generation of such a difference value is caused by defects in addition to the systematic error, and the residual value caused by the defects is much larger than that caused by the systematic error. This is because the crystal lattice of the material at the defect site changes, and this change causes a change in the electron rotation orbitals around the nucleus, and thus generates a spontaneous weak magnetic field, resulting in a large residual value at the defect site. Therefore, if a value greater than 3σ or less than-3σ appears after the residual value is subjected to the 3σ method, it is considered that a defect appears at this position. As shown in fig. 9, the curve is a measurement line, the straight line is a fitting line, and the total error of the residual values based on the least squares principle is calculated from all the discrete residual values on the entire curve. After the method of straight line fitting, quadratic curve fitting or cubic curve fitting is selected, one of the methods is used for fitting, and then the calculation is performed.
Referring to fig. 10, in which a curve is a measurement line and a sloped dotted line is a fit line, a residual value integrated error minimum based on a least squares principle is calculated from all residual values scattered on the entire curve. After the method of straight line fitting, quadratic curve fitting or cubic curve fitting is selected, one of the methods is used for fitting, and then the calculation is performed.
Referring to fig. 11, the determination of defects is based on the principle in the figure. The judgment of the fitting curve is based on the residual error value of the whole curve; the defect is determined based on the residual value of a small segment curve, wherein the small segment is the part between two parallel solid lines in the graph.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the technical solutions according to the embodiments of the present invention.
Claims (9)
1. The welding seam detection device based on the magnetic image sensor comprises an upper computer and a sensing module communicated with the upper computer, and is characterized in that the sensing module comprises a shell, the shell comprises an upper shell box body and a lower shell box body sleeved outside the upper shell box body, an array probe, a data acquisition card and a reset mechanism are arranged in the upper shell box body, the data acquisition card is connected to the array probe in a communication way, a handle is arranged on the shell, and the handle can be pushed and pulled to enable the upper shell box body to slide in the lower shell box body along a preset track; the reset mechanism provides reset support for switching the device between a detection state and a non-detection state; the lower shell box body is of a three-frame through groove structure, supporting edges extend from the inner side walls of the two grooves to the bottom of the parallel grooves, the reset mechanism comprises a spring group and a buffer bottom plate, and the buffer bottom plate is attached to the bottom of the grooves; one surface of the buffer bottom plate is attached to the inner wall of the tank bottom, the other surface of the buffer bottom plate is connected with the bottom of the upper shell tank body, the data acquisition card is arranged at the bottom of the upper shell tank body, one end, far away from the buffer bottom plate, of the data acquisition card is provided with the array probe, and the array probe faces the direction of a three-frame through tank notch of the lower shell tank body; the spring group comprises two groups of springs, one end of each group of springs is connected with the supporting rib, the other end of each group of springs is connected to the end part, close to the inner side wall, of the buffer bottom plate along the direction of the inner side wall of the groove, and the handle is pressed to enable the array probe to be close to the notch.
2. The welding seam detection apparatus of a magnetic image sensor according to claim 1, wherein,
the upper computer is electrically connected with the sensing module through a signal wire; the handle, the upper shell box body and the lower shell box body are all made of nonferromagnetic materials; the array probe is formed by arranging and combining a plurality of TMR probes in an array mode.
3. The apparatus according to claim 1, wherein the array probe is brought to the notch at the bottom of the lower case and parallel to the notch when the handle is pressed to bring the array probe close to the notch.
4. The method for detecting the weld joint detection device of the magnetic image sensor according to claim 1, wherein the array probe is combined by a plurality of probes, the signals are collected synchronously, and the signals of the allowable time periods are integrated and then uploaded to the terminal;
the method comprises the following steps:
s1, selecting the number of sensor channels according to the size of the detection piece;
s2, selecting a region to be detected, pressing the device, performing scanning detection, and drawing point data of each point to form an original curve;
s3, performing defect judgment on the original curve to be detected, and performing algorithm calculation;
s4, selecting recording or non-recording according to the judging result;
s5, moving to the next to-be-detected area, and repeating the steps S2-S4 until the process is finished.
5. The method of claim 4, wherein the selecting the number of sensor channels in S1 is specifically that the waveform, frequency, and amplitude of the excitation signal are set in the host computer interface to complete the initialization calibration.
6. The method for detecting a weld joint detection apparatus of a magnetic image sensor according to claim 4, wherein the S3 algorithm is calculated specifically as: carrying out one-time derivation processing on the magnetic induction intensity values of the points on each original curve to obtain corresponding magnetic field gradient values, and then applying a formulaEach sensor channel corresponds to a threshold range (μ -3σ, μ+3σ).
7. The method according to claim 4, wherein the step S4 is based on the fact that the magnetic field gradient value corresponding to each sampling point is compared with a threshold line, i.e. the upper and lower limits of a threshold range, and the position of the sampling point corresponding to the magnetic field gradient value exceeding the threshold range is determined as the defect position, and the defect position is recorded.
8. The method for detecting a weld joint detection device of a magnetic image sensor according to claim 4, wherein S3 performs an algorithm calculation for determining a defect to be detected, specifically, performs a least square fit for the magnetic induction intensity values of the acquisition points on the original curve, and determines an outlier with a minimum total error of residual values; wherein, after the residual value is subjected to a 3σ method, the position where the value larger than 3σ or smaller than-3σ appears is a defect position.
9. The method according to claim 7 or 8, wherein when the magnetic field gradient value and the residual value are both defective, the measurement point is determined to be the defective position.
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