Metal welding online detection method based on LIBS technology

Abstract

The present invention relates to LIBS technology, and specifically provides an online detection method for metal welding based on LIBS technology, comprising the following steps: (S1) an identification unit identifies the position of a weld using image recognition technology; (S2) a driving unit drives a light emitting module to move until the emitted light of the light emitting module is focused on the weld; the detection light at the weld is received by a light receiving module, and the relative light intensity corresponding to various elements is obtained; (S3) the relative light intensity of the element to be measured is corrected; (S4) the relative content of each element is obtained using the relative light intensity; (S5) the content of the element to be measured is obtained according to the relative content. The present invention has the advantages of being online and having accurate results, and is applied to the welding of metal workpieces such as steel pipes.

Description

Technical Field

[0001] The invention relates to LIBS technology, and in particular to a metal welding online detection method based on LIBS technology. Background Art

[0002] LIBS technology is an emission spectroscopy analysis method that uses laser as a light source to excite samples and obtain the spectrum of elements in the sample for sample analysis. This method requires the laser energy to be focused on the sample surface after beam expansion and focusing in order to obtain a high-energy spectrum.

[0003] Currently, LIBS technology is used in the analysis of fixed samples. If the existing LIBS technology is applied to mobile samples, the following technical problems will be encountered:

[0004] 1. Since the light source is fixed, it is impossible to ensure that the light emitted by the light source is incident on the weld.

[0005] 2. It cannot be guaranteed that the outgoing light is focused on the weld.

[0006] 3. It is impossible to provide light intensity correction function in real time, that is, the detection accuracy cannot be guaranteed.

[0007] Due to the above technical problems, LIBS technology cannot be applied in steel pipe welding. Summary of the invention

[0008] In order to solve the deficiencies in the above-mentioned prior art solutions, the present invention provides a metal welding online detection method based on LIBS technology.

[0009] The objective of the present invention is achieved through the following technical solutions:

[0010] A metal welding online detection method based on LIBS technology,

[0011] The detection method comprises the following steps:

[0012] (S1) The recognition unit recognizes the position of the weld using image recognition technology;

[0013] (S2) the driving unit drives the light emitting module to move until the emitted light of the light emitting module is focused on the welding seam;

[0014] The detection light at the weld is received by the light receiving module to obtain the relative light intensity corresponding to various elements;

[0015] (S3) Correcting the relative light intensity of the element to be measured;

[0016] (S4) Obtaining the relative content of each element using relative light intensity;

[0017] (S5) Obtaining the content of the element to be measured according to the relative content.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. Realized real-time and online detection;

[0020] Based on the weld seam recognition result of the first recognition unit, the driving unit drives the light source of the LIBS detection unit to move, and combined with the work of the second recognition unit, the emitted light of the light source is accurately focused on the weld seam, thereby realizing real-time, online detection;

[0021] 2. Good detection accuracy;

[0022] By utilizing the fast and accurate characteristics of LIBS technology, fast and accurate detection of metal and non-metal in metal welds is achieved;

[0023] By building a model and comparing the results, the optimal correction mode corresponding to the material model is obtained. When testing the sample, the correction mode corresponding to the sample is found, thus ensuring the accuracy of the test results;

[0024] The accuracy of the correction model is further improved by using multiple iterations of the coefficients;

[0025] By using the standard samples arranged on the support seat, real-time calibration is achieved, further improving the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The disclosure of the present invention will become easier to understand with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only used to illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention. In the drawings:

[0027] Figure 1 It is a schematic diagram of the process of the metal welding online detection method according to the present invention;

[0028] Figure 2 is a structural diagram of a detection implementation device according to the present invention;

[0029] Figure 3 It is the background image when there is no welded steel pipe;

[0030] Figure 4 is the collected image of the steel pipe;

[0031] Figure 5 is the captured background image;

[0032] Figure 6 is the identification area in the captured image;

[0033] Figure 7 is the grayscale image of the captured background image;

[0034] Figure 8 is the grayscale image of the recognition area of ​​the acquired image;

[0035] Fig. 9 is the recognized image after background subtraction;

[0036] Fig.10 is the recognition image after binarization;

[0037] Fig.11 is the recognition image after eliminating noise points;

[0038] Fig.12 is the steel pipe outline image;

[0039] Fig.13 It is the steel pipe weld image;

[0040] Fig.14 It is a schematic diagram of the positional relationship between the offset point and the fitting curve after using the present invention;

[0041] Fig.15 It is a schematic diagram of the position relationship between the offset point and the fitting curve in the prior art.

[0042] In the accompanying drawings, 1-steel pipe, 11-light transmitting module, 12-light receiving module, 13-analysis module, 21-first identification unit, 22-second identification unit, 31-driving unit, 41-gas supply unit. DETAILED DESCRIPTION

[0043] Figure 1-Figure 15 The following description describes optional specific embodiments of the present invention to teach those skilled in the art how to implement and reproduce the present invention. In order to teach the technical solution of the present invention, some conventional aspects have been simplified or omitted. Those skilled in the art will understand that variations or replacements derived from these specific embodiments will be within the scope of the present invention. Those skilled in the art will understand that the following features can be combined in various ways to form multiple variations of the present invention. Thus, the present invention is not limited to the following optional specific embodiments, but is only limited by the claims and their equivalents.

[0044] Example 1.

[0045] An on-line detection method for metal welding based on LIBS technology according to an embodiment of the present invention is as follows: Figure 1 As shown, the following steps are included:

[0046] (S1) The recognition unit recognizes the position of the weld using image recognition technology.

[0047] ( S2 ) The driving unit 31 drives the light emitting module 11 to move until the emitted light of the light emitting module 11 is focused on the welding seam.

[0048] The detection light at the weld is received by the light receiving module to obtain the relative light intensity corresponding to various elements.

[0049] (S3) Correct the relative light intensity of the element to be measured.

[0050] (S4) Obtain the relative content of each element using relative light intensity.

[0051] (S5) Obtaining the content of the element to be measured according to the relative content.

[0052] In order to accurately identify the weld position, further, the identification method is as follows:

[0053] A background image without a welding workpiece and a captured image of the welding workpiece are obtained respectively, and the identification areas in the background image and the captured image are converted into grayscale images.

[0054] The background is subtracted by subtracting the gray value of the image to obtain the recognition image.

[0055] The workpiece contour is extracted from the recognition image, and the upper boundary line is obtained from the contour.

[0056] Get a weld in the upper boundary line.

[0057] In order to accurately obtain the upper boundary line, further, the upper boundary line is obtained as follows:

[0058] The workpiece contour image is divided into multiple columns, and the mutation point of each column is found from top to bottom. The upper boundary line is the line connecting the mutation points of all columns.

[0059] In order to improve the accuracy of the correction, the light intensity is further corrected in the following manner:

[0060] According to the material type at the weld, a correction mode corresponding to the interfering elements in the material is selected in a database, wherein the interfering elements in the material type and the correction modes correspond one to one in the database.

[0061] In order to more specifically calibrate the light intensity of the sample to be tested, further, the corresponding relationship in the database is obtained in the following manner:

[0062] Standard samples of various material types are tested, and the coefficients corresponding to the additive interference correction mode and the multiplicative interference correction mode of the interfering elements are obtained using the obtained light intensity values ​​and nominal content values ​​of the measured elements and interfering elements.

[0063] For the interfering elements of the standard sample of any material type, compare the detection results of the interfering elements in the material type using the additive interference correction mode, the multiplicative interference correction mode and the mixed correction mode, select the correction mode with the best detection result corresponding to the interfering elements of the material type, and save it; the additive interference correction mode is, int i =int i 0 +Σ(f ij ·C j ), the multiplicative interference correction mode is, int i =int i 0 ·(1+Σ(f ij ·A j )), int i 0 、int i are the light intensity before and after correction of the i-th element to be measured, C j is the content of the jth interfering element, f ij is the interference coefficient of the jth interfering element on the ith element to be measured, A j It is the product of the content of interfering elements and the light intensity.

[0064] The hybrid correction mode first utilizes the additive interference correction mode and then utilizes the multiplicative interference correction mode.

[0065] In order to improve the accuracy of the correction mode, after obtaining the coefficient once, the correction mode is used to obtain the light intensity, and the coefficient is obtained again. After multiple iterations, the coefficients obtained multiple times are averaged and used as the coefficients in the correction mode.

[0066] In order to accurately obtain the interference coefficient, a nonlinear correction model is further established using the correction mode, such as the content of the element to be measured C ˊ i =Σ(a k int i k ), a k is a coefficient, k is a non-negative integer; the interference coefficient of each interfering element on the element to be measured is fitted by using the light intensity value of the element to be measured and the nominal content value of each element in the standard sample.

[0067] In order to obtain the percentage content of the element to be tested, in step (S5), the calculation method is:

[0068] C % = 100·C / (100+ΣC j ), C % , C are the percentage content and relative content of the element to be measured, ΣC j It is the sum of the relative contents of other elements.

[0069] In order to implement the above detection method, further, Figure 2 As shown, the present invention adopts the following device to implement the detection, and the device includes:

[0070] The LIBS detection unit includes a light emitting module 11, a light receiving module 12 and an analysis module 13. The light emitting module 11 includes a light source and a converging lens. The light receiving module 12 includes a light converging device and a spectrometer. When working, the outgoing light emitted by the light source passes through the converging lens and is focused on the welding workpiece; part of the welding workpiece is evaporated and gasified by light ablation to form a transient plasma and emit light radiation. The light radiation is converged by the light converging device and then incident on the spectrometer. The electrical signal output by the detector of the spectrometer is sent to the analysis module 13, and the analysis module 13 uses the LIBS technology to analyze the electrical signal.

[0071] The first recognition unit 21 is used to recognize the position of the metal weld.

[0072] The driving unit 31 is used to drive the light source to move so that the emitted light of the light source is focused on the welding seam.

[0073] In order to ensure that the emitted light is accurately focused on the weld, the device further includes:

[0074] The second recognition unit 22 is used to recognize whether the emitted light is focused on the welding seam.

[0075] In order to accurately identify, further, the second identification unit 22 adopts a visual identification unit, or a distance measuring unit, which is arranged on the driving unit 31 and is used for the distance between the light source and the weld, so that the distance is equal to the focal length of the converging lens.

[0076] In order to provide a real-time correction function, the device further includes.

[0077] The supporting base is used to support a plurality of standard samples, and the driving unit 31 is used to drive the light source to move so that the emitted light of the light source is focused on the standard samples, thereby calibrating the LIBS detection unit in real time.

[0078] Example 2.

[0079] According to Example 1 of the present invention, an application example of the metal welding online detection method based on LIBS technology in the welding of a steel pipe 1 is provided.

[0080] In this application example, Figure 2As shown, in the implementation device of the detection method, the LIBS detection unit includes a light emitting module 11, a light receiving module 12 and an analysis module 13. The light emitting module 11 includes a laser and a converging lens, and is arranged on a driving unit 31 (a three-dimensional mechanical arm is used in this embodiment to provide vertical up and down movement and horizontal rotation). The light receiving module 12 includes a light converging device and a spectrometer, and a linear array detector is used in the spectrometer.

[0081] The first recognition unit 21 is a visual recognition unit for recognizing the position of the weld between the steel pipes.

[0082] The second recognition unit 22 adopts a distance measuring unit, specifically a laser distance meter, which is arranged on the three-dimensional mechanical arm and is used to obtain the distance between the light source (or the converging lens) and the weld.

[0083] The gas supply unit 41 includes an argon gas tank and a pipeline, and the argon gas discharged from the pipeline purges the excitation part of the weld.

[0084] Steel standard samples of various grades are placed on the support seat, and the three-dimensional mechanical arm drives the light source so that the output light of the laser converges on the standard sample.

[0085] An online metal welding detection method based on LIBS technology in an embodiment of the present invention is specifically used to detect the percentage content of manganese in a 316 steel pipe 1, such as Figure 1 As shown, the following steps are included:

[0086] (S0) Establish the corresponding relationship between the interference elements and the correction mode in the material type. Specifically:

[0087] The nonlinear correction model was established by using the (additive interference and multiplicative interference) correction modes respectively: the content of the element to be measured C ˊ i =Σ(a k int i k ), a k is a coefficient, k is a non-negative integer. In this embodiment, k=0, 1, 2.

[0088] In additive interference correction mode, int i =int i 0 +Σ(f ij ·C j ). In multiplicative interference correction mode, int i =int i 0 ·(1+Σ(f ij ·A j )). int i 0 、int iare the light intensity before correction (i.e. original light intensity) and the light intensity after correction of the i-th element to be measured, C j is the content of the jth interfering element, f ij is the interference coefficient of the jth interfering element to the ith element to be measured. In this embodiment, for the manganese 293 channel, nickel and chromium are selected as interfering elements.

[0089] Detect standard samples of various material types to obtain the relative light intensity (ratio of the light intensity of the interfering element to that of iron) and relative content (ratio of the content of the interfering element to that of iron) of the element to be tested and the interfering element in the standard samples.

[0090] .

[0091] Corresponding to each interference element, the correction models corresponding to the additive interference correction mode and the multiplicative interference correction mode are used respectively to fit the interference coefficients corresponding to the additive interference correction mode and the multiplicative interference correction mode.

[0092] For example, the additive interference coefficient and multiplicative interference coefficient of nickel, and the additive interference coefficient and multiplicative interference coefficient of chromium.

[0093] For each interfering element, the corrected light intensity is obtained by using the additive interference correction mode, the multiplicative interference correction mode and the mixed correction mode (first using the additive interference correction mode to obtain the corrected light intensity, and then substituting it as the original light intensity into the multiplicative interference correction mode to obtain the final corrected light intensity), and then the relative content of the measured element is obtained by using the mapping relationship between relative content and relative light intensity.

[0094] Select the calibration mode corresponding to the relative content closest to the nominal value of the standard sample as the calibration mode corresponding to the interfering elements in this material type.

[0095] For example, after comparison, the multiplicative interference correction mode corresponding to the interfering element nickel has the best effect, so the interfering element nickel corresponds to the multiplicative interference correction mode; the additive interference correction mode corresponding to the interfering element chromium has the best effect, so the interfering element chromium corresponds to the additive interference correction mode.

[0096] After the interference coefficient is obtained, the correction light intensity is obtained using the correction mode, and then substituted into the correction model to fit the interference coefficient again. For example, 200 sets of interference coefficients are obtained by iterating 199 times, and the average is used as the interference coefficient and saved.

[0097] The data to be used for the first regression.

[0098] .

[0099] The data to be used for the second regression.

[0100] .

[0101] The data to be used for the 200th regression.

[0102] .

[0103] The interference coefficient obtained from each regression fit.

[0104] .

[0105] Establish the correspondence between the interfering elements in the material type and the correction mode and correction coefficient, and save it.

[0106] (S1) The first recognition unit 21 uses image recognition technology to recognize the position of the weld, specifically in the following manner:

[0107] like Figure 3 As shown, the background image without welded steel pipe 1 is obtained; Figure 4 As shown, a captured image of the welded steel pipe 1 is obtained.

[0108] Cut out the identification area (including the weld area) in the background image and the acquired image respectively, such as Figure 5 and Figure 6 shown.

[0109] The background image and the identified area in the acquired image are converted into grayscale images respectively, such as Figure 7 and Figure 8 shown.

[0110] The gray value of the image is used to subtract the background to obtain the recognition image. The same parts are black, and the different parts are white. Fig. 9 shown.

[0111] Binarize the recognition image, such as Fig.10 shown.

[0112] The morphological corrosion and dilation operation is used to eliminate noise points, such as Fig.11 shown.

[0113] Extract the outline of steel pipe 1 and obtain the diameter of steel pipe 1, such as Fig.12 shown.

[0114] The steel pipe 1 outline is extracted from the recognition image, and the upper boundary line is obtained from the outline: the steel pipe 1 outline image is divided into multiple columns, and the mutation point of each column is found from top to bottom. The upper boundary line is the line connecting the mutation points of all columns.

[0115] Get the weld seam on the upper boundary line: find all the maximum points on the upper boundary line, 20 points on the left and right, find the peak height or peak area, the peak height of more than two pixels is considered to be the seam center, and the area of ​​15 pixels on the left and right of the peak center is considered to be the weld seam. Fig.13As shown, the red dot area is the weld location.

[0116] (S2) The driving unit 31 drives the light emitting module 11 to move until the emitted light of the light emitting module 11 is focused on the welding seam. The specific method is:

[0117] The first recognition unit 21 recognizes the position of the weld using image recognition technology;

[0118] The driving unit 31 drives the light emitting module 11 to move according to the position. Meanwhile, the laser rangefinder obtains the distance between the light source and the weld. When the distance between the converging lens and the weld is equal to the focal length of the converging lens, the driving unit 31 stops moving.

[0119] The outgoing light from the laser is focused on the weld by a converging lens, and the light at the weld is received by the light receiving module 12 , split by a spectrometer, and converted into an electrical signal by a linear array detector, and the electrical signal is sent to the analysis module 13 .

[0120] During the excitation process, the laser rangefinder outputs the distance in real time. When the distance between the converging lens and the weld deviates from the focal length of the converging lens, the driving unit 31 drives the light source to move so that the distance between the converging lens and the weld is equal to the focal length. The argon gas provided by the gas supply unit 41 purges the excitation part of the weld.

[0121] The detection light at the weld is received by the light receiving module 12, and the (original) relative light intensity corresponding to each element is obtained.

[0122] The light intensity data are: the relative light intensity of manganese is 0.236124120043884, the relative light intensity of chromium is 3.02214616341552, and the relative light intensity of nickel is 24.2685493177653.

[0123] (S3) According to the corresponding relationship in the database, the correction mode and interference coefficient corresponding to the material type of the current welded steel pipe 1 are selected to correct the original relative light intensity of the element to be measured. The additive interference correction mode is first used to obtain the corrected light intensity of manganese after excluding the influence of the (additive) interfering elements, and then substituted into the multiplicative interference correction mode to obtain the corrected light intensity of manganese after excluding the (multiplicative) interfering elements.

[0124] In this embodiment, in the material model corresponding to the steel pipe 1, the interfering element nickel corresponds to the multiplicative interference correction mode, the interfering element chromium corresponds to the additive interference correction mode, and the corrected light intensity of Mn is: 0.253568212352195.

[0125] (S4) According to the relative light intensity of the element to be measured and other elements, the relative content of each element is obtained by using the mapping relationship between relative content and relative light intensity. The relative content of manganese is 1.10827089728167.

[0126] (S5) The content of the element to be measured is obtained according to the relative content and the following formula, and the percentage content of manganese is 1.51348291632582%.

[0127] C % = 100·C / (100+ΣC j ), C % , C are the percentage content and relative content of the element to be measured, ΣC j It is the sum of the relative contents of other elements in the sample.

[0128] After a period of use, the driving unit 31 drives the light source to move, and cooperates with the laser rangefinder to make the distance between the converging lens and the standard sample equal to the focal length. The outgoing light emitted by the laser is focused on the standard sample through the converging lens, and the excitation light on the standard sample is received by the light receiving module 12 and sent to the analysis module 13. The correction coefficient is obtained according to the measured value output by the analysis module 13 and the nominal value of the standard sample, and is used for the detection of the weld. The derivation of the correction coefficient is a prior art in the field of analytical instruments.

[0129] After using the calibration mode of the present invention, Fig.14 As shown, in the fitting curve (between relative content and relative light intensity), the offset point is obviously close to the curve.

[0130] As a comparison, Fig.15 As shown, in the prior art, the offset point deviates significantly from the curve.

[0131] Example 3.

[0132] According to the application example of the metal welding online detection method based on LIBS technology in copper pipe welding according to Example 1 of the present invention, the difference from Example 2 is that:

[0133] 1. The second recognition unit 22 uses a visual recognition unit to recognize whether the emitted light is focused on the weld or the standard sample through image recognition technology.

[0134] 2. Configure various standard samples that match the copper tube.

[0135] 3. The three-dimensional robotic arm provides translational movement in the forward, backward, left, right, and up and down directions.

Claims

1. A metal welding online detection method based on LIBS technology, characterized in that: The detection method comprises the following steps: (S1) The recognition unit recognizes the position of the weld using image recognition technology; (S2) The driving unit drives the light emitting module to move until the emitted light of the light emitting module is focused on the weld; the detection light at the weld is received by the light receiving module to obtain the relative light intensity corresponding to various elements; (S3) Correcting the relative light intensity of the element to be measured by: selecting a correction mode corresponding to the interfering element in the material in a database according to the material type at the weld, wherein the correction mode and the interference coefficient are corresponding to the interfering element in the material type in the database; and obtaining the corresponding relationship in the database by: Detect standard samples of various material types, obtain the light intensity values ​​and nominal content values ​​of the measured elements and interfering elements, and obtain the coefficients corresponding to the additive interference correction mode and the multiplicative interference correction mode of the interfering elements; For the same interfering element of the standard sample, compare the detection results of the interfering element processed by the additive interference correction mode, the multiplicative interference correction mode and the mixed correction mode, select the correction mode with the best detection result corresponding to the interfering element in the material type, and save it; The hybrid correction mode is to first use the additive interference correction mode and then use the multiplicative interference correction mode; The additive interference correction mode is, int i =int i 0 +Σ(f ij ·C j ), the multiplicative interference correction mode is, int i =int i 0 ·(1+Σ(f ij ·A j )), int i 0 、int i are the light intensity before and after correction of the i-th element to be measured, C j is the content of the jth interfering element, f ij is the interference coefficient of the jth interfering element on the ith element to be measured, A j It is the product of the content of interfering elements and the light intensity; (S4) Obtaining the relative content of each element using relative light intensity; (S5) Obtaining the content of the element to be measured according to the relative content.

2. The detection method according to claim 1, characterized in that: The identification method is: A background image without a welding workpiece and a captured image of the welding workpiece are obtained respectively, and the identification areas in the background image and the captured image are converted into grayscale images; The background is subtracted by subtracting the gray value of the image to obtain the recognition image; Extract the workpiece contour in the recognition image and obtain the upper boundary line in the contour; Get a weld in the upper boundary line.

3. The detection method according to claim 2, characterized in that: The way to get the upper boundary line is: The workpiece contour image is divided into multiple columns, and the mutation point of each column is found from top to bottom. The upper boundary line is the line connecting the mutation points of all columns.

4. The detection method according to claim 1, characterized in that: After obtaining the coefficient once, the light intensity is obtained using the correction mode, and the coefficient is obtained again. After multiple iterations, the coefficients obtained multiple times are averaged and used as the coefficients in the correction mode.

5. The detection method according to claim 1, characterized in that: The nonlinear correction model was established using the correction mode, and the interference coefficient of each interfering element on the element to be measured under different correction modes was fitted using the light intensity value of the element to be measured and the nominal content value of each element in the standard sample.

6. The detection method according to claim 5, characterized in that: The calibration model is: Element content C ˊ i =Σ(a k int i k ), a k is the coefficient, and k is a non-negative integer.

7. The detection method according to claim 1, characterized in that: In step (S5), the calculation method is: C % = 100·C / (100+ΣC j ), C % , C are the percentage content and relative content of the element to be measured, ΣC j It is the sum of the relative contents of other elements.

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