Low-coherence imaging quality evaluation method and device and electronic equipment

Abstract

The invention discloses a low-coherence imaging quality evaluation method and device and electronic equipment, and relates to the technical field of laser welding monitoring. The method comprises the steps that point cloud data related to a key hole in the laser welding process is obtained, and a peripheral point cloud projection drawing of the key hole is obtained based on the point cloud data; executing a keyhole area fitting process for multiple times to determine a keyhole area based on fitting parameters obtained in each fitting process; wherein in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection drawing, grids are divided according to a preset scale, and fitting parameters of the keyhole area are determined based on grid parameters of each grid; the preset scales in different fitting processes are different; and determining an imaging quality evaluation result based on the point cloud data in the keyhole region and the region area of the keyhole region. Therefore, the accuracy of the key depth information collected in an optical low-coherence imaging mode is evaluated, and preconditions are provided for key depth online monitoring in the laser welding process.

Description

Technical Field

[0001] The present application relates to the field of laser welding monitoring technology, and in particular to a low-coherence imaging quality evaluation method, device and electronic equipment. Background Art

[0002] Laser welding is a high-precision welding method that uses a high-energy density laser beam as a heat source. During the laser welding process, the metal vaporizes in the molten pool to form a long and thin keyhole. A continuous and stable keyhole is an important factor in achieving full melting of the solder and reducing the formation of welding defects, and the key parameters of the keyhole are its depth and width. When the depth and width are too small, the molten pool forming rate is slow and the component forming efficiency is low; when the depth and width are too large, the high-temperature liquid metal area of ​​the molten pool is too large, and the grain growth environment temperature is high, which reduces the forming size and shape accuracy of the parts.

[0003] It can be seen that it is very important to monitor the keyhole depth online during laser welding. At present, the main way to monitor the keyhole depth is to use a monitoring system based on optical low-coherence imaging to achieve monitoring. However, due to the possibility of unknown errors or errors in the calibration process, the accuracy of collecting keyhole depth information through optical low-coherence imaging is low, which in turn causes the failure of keyhole depth monitoring. Therefore, it is very necessary to evaluate the accuracy of collecting keyhole depth information through optical low-coherence imaging. Summary of the invention

[0004] The main purpose of this application is to provide a low-coherence imaging quality evaluation method, device and electronic equipment to evaluate the accuracy of key depth information collected by optical low-coherence imaging, thereby providing a prerequisite for online monitoring of key depth during laser welding.

[0005] To achieve the above objectives, the present application provides a low coherence imaging quality evaluation method, comprising: Acquire point cloud data related to the keyhole during laser welding, and obtain a peripheral point cloud projection map of the keyhole based on the point cloud data; The keyhole region fitting process is performed multiple times to determine the keyhole region based on the fitting parameters obtained in each fitting process; wherein in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and a grid is divided according to a preset scale, and the fitting parameters of the keyhole region are determined based on the grid parameters of each grid; the preset scales in different fitting processes are different; An imaging quality evaluation result is determined based on the point cloud data in the keyhole region and the region area of ​​the keyhole region.

[0006] Optionally, obtaining the peripheral point cloud projection map of the keyhole based on the point cloud data includes: segmenting the point cloud data using a preset segmentation formula to obtain a peripheral point cloud data set and a keyhole point cloud data set; projecting the peripheral point cloud data set to a preset welding surface to obtain the peripheral point cloud projection map.

[0007] Optionally, the preset segmentation formula is:

[0008] In the formula, is the point cloud data of the jth point before segmentation, is the peripheral point cloud data set, is the keyhole point cloud data set, is the vertical distance between the jth point and the center of the laser transmitter head, is the vertical distance from the center point of the laser transmitter to the preset welding surface, is the preset distance threshold.

[0009] Optionally, the grid parameters include the number of point clouds in the grid, the grid area and the distance between the grid and the central grid, and the central grid is the grid where the origin of the coordinate system is located; the determining the fitting parameters of the keyhole area based on the grid parameters of each of the grids includes: for any grid, using a preset indicator formula and based on the number of point clouds in the grid, the grid area and the distance between the grid and the central grid, determining a grid evaluation parameter; using a preset screening algorithm and based on the grid evaluation parameters of each of the grids, determining multiple target grids; bringing the point cloud data of the center point of each of the target grids into the Hough transform algorithm to obtain the fitting parameters of the keyhole area.

[0010] Optionally, the method of using a preset screening algorithm and based on a grid evaluation parameter of each of the grids to determine multiple target grids includes: sorting the grid evaluation parameters in descending order; using the preset screening algorithm to select a grid evaluation parameter within a preset percentage interval from the smaller side of the grid evaluation parameter, and using the grid corresponding to the selected grid evaluation parameter as the target grid.

[0011] Optionally, the preset indicator formula is:

[0012] In the formula, is the grid evaluation parameter of the ith grid, is the number of point clouds of the i-th grid, is the grid area of ​​the ith grid, is the distance between the center point of the i-th grid and the center point of the central grid.

[0013] Optionally, the point cloud data in the keyhole area includes the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, and the coordinate data of each point; and determining the imaging quality evaluation result based on the point cloud data in the keyhole area and the area of ​​the keyhole area includes: using a preset quality evaluation formula and based on the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, the coordinate data of each point, the coordinate data of the center point of the keyhole area, and the area of ​​the keyhole area, to determine the imaging quality evaluation result.

[0014] Optionally, the preset quality evaluation formula is:

[0015] In the formula, is the imaging quality evaluation result, is the area of ​​the keyhole region, is the number of point clouds in the keyhole area, is the signal strength of the zth point in the ith grid in the keyhole area, is the total signal strength in the keyhole region, is the coordinate data of the zth point in the ith grid in the keyhole area, is the coordinate data of the center point of the keyhole area.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a low-coherence imaging quality evaluation device, including: an acquisition module, used to acquire point cloud data related to the keyhole during the laser welding process, and obtain a peripheral point cloud projection map of the keyhole based on the point cloud data; a fitting module, used to execute the keyhole area fitting process multiple times to determine the keyhole area based on the fitting parameters obtained in each fitting process; wherein, in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and a grid is divided according to a preset scale, and the fitting parameters of the keyhole area are determined based on the grid parameters of each grid; the preset scale in different fitting processes is different; an evaluation module, used to determine the imaging quality evaluation result based on the point cloud data in the keyhole area and the area area of ​​the keyhole area.

[0017] The present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the low-coherence imaging quality evaluation method as described in any one of the above is implemented.

[0018] The low-coherence imaging quality evaluation method of the present application obtains point cloud data related to the keyhole during the laser welding process, and obtains a peripheral point cloud projection map of the keyhole based on the point cloud data; then the keyhole area fitting process is performed multiple times at different preset scales. In each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and a grid is divided according to a preset scale, and the fitting parameters of the keyhole area are determined based on the grid parameters of each grid, and then the keyhole area is fitted based on the fitting parameters obtained in each fitting process; finally, the imaging quality evaluation result is determined based on the point cloud data in the keyhole area and the regional area of ​​the keyhole area, thereby realizing the evaluation of the accuracy of collecting key depth information by optical low-coherence imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a schematic diagram of a laser welded keyhole of a specific example of the present application; Figure 2 It is one of the flow charts of the low coherence imaging quality evaluation method of the embodiment of the present application; Figure 3 This is the second flow chart of the low coherence imaging quality evaluation method according to an embodiment of the present application; Figure 4 is a keyhole-related point cloud distribution map of a specific example of the present application; Figure 5 It is a peripheral point cloud projection diagram of a specific example of this application; Figure 6 This is the third flow chart of the low coherence imaging quality evaluation method according to an embodiment of the present application; Figure 7 is a schematic diagram of grid division of a specific example of the present application; Figure 8 is a schematic structural diagram of a low coherence imaging quality evaluation device according to an embodiment of the present application; Fig. 9 An example of a physical structure diagram of an electronic device is provided; In the figure: 800, low coherence imaging quality evaluation device; 810, acquisition module; 820, fitting module; 830, evaluation module; 910, processor; 920, communication interface; 930, memory; 940, communication bus.

[0020] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] Laser welding uses a high-energy-density laser beam to bombard the workpiece, causing the weld joint to reach a molten state, forming a molten pool, and then achieving weld connection by controlling parameters. Laser welding has the characteristics of low heat input, high speed, large weld depth-to-width ratio, and small heat-affected deformation zone. It is widely used in automobile manufacturing, electronic devices, aerospace and other fields.

[0023] Figure 1 FIG. 1 is a schematic diagram of a laser welding keyhole of a specific example of the present application. Figure 1 As shown in the figure, during the laser welding process, the metal will vaporize and form an elongated keyhole in the liquid molten pool. A continuous and stable keyhole is an important factor in achieving full melting of the solder and reducing the formation of welding defects. The key parameters of the keyhole are the depth (vertical distance from the keyhole surface to the deepest point of the keyhole) and the width. When the width and depth are too small, the molten pool forming rate is slow and the component forming efficiency is low; when the width and depth are too large, the high-temperature liquid metal area of ​​the molten pool is too large, and the grain growth environment temperature is high, which reduces the forming size and shape accuracy of the parts.

[0024] Therefore, it is very important to monitor the key depth (keyhole depth) during laser welding online. The current key depth monitoring system mainly uses a monitoring system based on optical low-coherence imaging. However, the premise of using optical low-coherence imaging technology to accurately monitor the key depth is that the monitoring system based on optical low-coherence imaging can accurately and effectively collect keyhole information during welding. However, during the information collection process, the collection results may be unreliable due to incorrect welding process parameter settings, sensor installation errors, low reflectivity of the welding object, etc., which in turn causes the failure of key depth monitoring.

[0025] To this end, the embodiments of the present application provide a method, device and electronic device for evaluating the quality of low-coherence imaging. The point cloud data of the keyhole periphery during welding is converted into a two-dimensional peripheral point cloud projection map, and then the keyhole area is fitted based on the peripheral point cloud projection map to determine the keyhole area in the optical low-coherence imaging. Finally, based on the point cloud data of the keyhole area in the optical low-coherence imaging and the area of ​​the keyhole area, the quality of the low-coherence imaging is evaluated to judge the quality of the optical low-coherence imaging, so as to serve as a basis for troubleshooting negative factors such as improper welding process parameter settings, large sensor installation errors, sensor incompatibility or low reflectivity of the welding object, and improve the credibility of online monitoring of the keyhole depth in laser welding.

[0026] Figure 2 is one of the flow charts of the low coherence imaging quality evaluation method of the embodiment of the present application, and the low coherence imaging quality evaluation method can be executed by a processor in an optical low coherence imaging monitoring device. Figure 2 As shown, the low coherence imaging quality evaluation method may include the following steps: Step 210: Acquire point cloud data related to the keyhole during the laser welding process, and obtain a peripheral point cloud projection map of the keyhole based on the point cloud data.

[0027] Step 220: Execute the keyhole region fitting process multiple times to determine the keyhole region based on the fitting parameters obtained in each fitting process; wherein, in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and grids are divided according to a preset scale, and fitting parameters of the keyhole region are determined based on grid parameters of each grid; the preset scales in different fitting processes are different.

[0028] Step 230: Determine an imaging quality evaluation result based on the point cloud data in the keyhole region and the area of ​​the keyhole region.

[0029] It should be noted that the keyhole-related point cloud data may include point cloud data of the keyhole space and the welding plane space. In this embodiment, the positional relationship between the welding plane space and the keyhole space is represented in the form of a point cloud. Figure 1 The keyhole space marked in the figure, the welding plane space can be Figure 1 The space above the middle keyhole and below the laser launch head.

[0030] In this embodiment, the laser welding equipment can be parameterized and started to start welding on the test piece. The laser welding equipment can include a laser transmitter head, a water cooling system, a gas protection system, a high-speed galvanometer, etc. Specifically, manually set process parameters are input first, and the controller in the laser welding equipment controls the laser transmitter head to weld on the test piece according to the set process parameters; wherein the set welding path can be a straight line, and the welding surface of the test piece is a horizontal plane.

[0031] During the welding process, the metal vaporizes to form a keyhole in the liquid molten pool. At this time, the optical low-coherence imaging monitoring device can be controlled to perform a three-dimensional scan of the keyhole surface. During the scanning process, the optical low-coherence imaging monitoring device will record the interference signal after the reference light and the keyhole surface light are mixed. These interference signals can reflect the intensity change of the light reflected from the keyhole surface. Furthermore, the frequency information of the light reflected from the keyhole surface can be extracted by Fourier transforming the collected interference signal, and then the depth information of the keyhole surface can be calculated. Finally, based on the depth information of each measuring point and combined with the scanning position, the three-dimensional coordinate data of the keyhole space and the welding plane space (that is, the peripheral space between the keyhole and the laser transmitter head) can be obtained. These three-dimensional coordinate data are point cloud data.

[0032] In this embodiment, the optical low-coherence imaging monitoring device can continuously collect point cloud data related to the keyhole during the entire laser welding process, and finally obtain multiple sets of point cloud data; when evaluating the quality of the optical low-coherence imaging, one set of point cloud data can be selected from the multiple sets of point cloud data to evaluate the quality of the optical low-coherence imaging. In addition, after obtaining the point cloud data related to the keyhole, the point cloud data can be visualized to generate a two-dimensional or three-dimensional point cloud image, so that the staff can view the distribution of the point cloud data or facilitate the subsequent processing of the point cloud data.

[0033] After obtaining the point cloud data related to the keyhole, the point cloud data can be divided into the point cloud data of the keyhole space and the point cloud data of the keyhole periphery (i.e., the welding plane space). Furthermore, the point cloud data of the keyhole periphery can be projected onto the welding plane to obtain a peripheral point cloud projection map.

[0034] In step 220, after obtaining the peripheral point cloud projection map of the keyhole, the shape curve of the keyhole area can be fitted based on the peripheral point cloud projection map to obtain a mathematical equation that can be used to characterize the shape of the keyhole area, and then the keyhole area can be determined based on the fitted mathematical equation.

[0035] Specifically, a Cartesian coordinate system can be constructed on the peripheral point cloud projection map, and the origin of the coordinate system can be the projection point of the center point of the laser transmitter head. Meshing is performed on the projection map of the peripheral point cloud with the established coordinate system, and finally the fitting parameters of the keyhole area are determined based on the mesh parameters of each mesh.

[0036] It should be noted that the preset scale refers to the side length of a single grid. The preset scale can be set by the staff according to actual needs. The value range of the preset scale can be: 10% to 20% of the maximum width of the keyhole edge along the welding direction; for example, if the maximum width of the keyhole edge is 10mm, the preset scale can be set to 1×1mm. In addition, the fitting parameters of the keyhole area refer to: fitting the outer contour of the keyhole area, and obtaining a fitting equation for characterizing the outer contour of the keyhole area. The parameters of the fitting equation are the fitting parameters described in this application.

[0037] In the embodiment of the present application, after a keyhole region fitting process, a preset scale can be reset. , and according to the new preset scale Re-mesh the peripheral point cloud projection map. After re-meshing at different preset scales, determine the fitting parameters of a keyhole area again based on the grid parameters of each grid. Thus, the above steps can be repeated continuously, and multiple sets of fitting parameters can be obtained by changing the preset scale multiple times to divide the grid and performing multiple keyhole area fitting processes. Finally, a set of optimal fitting parameters can be selected from these fitting parameters as the fitting equation parameters that characterize the shape of the keyhole area.

[0038] In step 230, after the fitting parameters of the keyhole region are obtained, a mathematical equation composed of the fitting parameters can be used to characterize the shape of the keyhole region. In this embodiment, after the keyhole region in the peripheral point cloud projection image is found by fitting the keyhole region, the optical low-coherence imaging quality evaluation result can be obtained based on the point cloud data falling in the keyhole region and the area of ​​the keyhole region.

[0039] Specifically, this embodiment mainly uses the three parameters of point cloud density, signal strength of point cloud and central concentration in the keyhole area as evaluation indicators of optical low coherence imaging quality; therefore, the point cloud data in the keyhole area may include the three parameters of point cloud density, signal strength of point cloud and central concentration of point cloud. Finally, the score of optical low coherence imaging quality can be calculated by the preset quality evaluation formula constructed for the three aspects of point cloud density, signal strength of point cloud and central concentration, and the score can be used as the imaging quality evaluation result. If the point cloud density in the keyhole area is low, or the signal strength of the point cloud is weak, or the central concentration of the point cloud is low, it can be considered that the welding process parameters are improperly set, the sensor installation error is large, the sensor is not compatible or the reflectivity of the welding object is low; at this time, the key depth information collected by the optical low coherence imaging monitoring device is of low accuracy, so the staff can adjust the optical low coherence imaging monitoring device and re-collect the key depth information.

[0040] Figure 3 This is the second flow chart of the low coherence imaging quality evaluation method of the embodiment of the present application, such as Figure 3 As shown, in some embodiments, step 210 obtains a peripheral point cloud projection map of the keyhole based on the point cloud data, which may include the following steps: Step 310: Segment the point cloud data using a preset segmentation formula to obtain a peripheral point cloud data set and a keyhole point cloud data set.

[0041] Step 320: Project the peripheral point cloud data set onto a preset welding surface to obtain a peripheral point cloud projection map.

[0042] In the description of the aforementioned embodiment, the point cloud data related to the keyhole during the laser welding process obtained by the processor in the optical low-coherence imaging monitoring device mainly includes the point cloud data of the keyhole space and the point cloud data of the space surrounding the keyhole (i.e., the point cloud data of the welding plane space). Figure 4 This is a keyhole-related point cloud distribution diagram of a specific example of this application. Figure 1 and Figure 4 It can be seen that the point cloud data collected by the optical low-coherence imaging monitoring equipment is mainly distributed in the keyhole area and the area between the keyhole and the laser emitter head, and the point clouds of these two areas can be clearly distinguished in space. The embodiment of the present application mainly realizes the segmentation of the point cloud data through the coordinates of each point in the point cloud data to obtain the peripheral point cloud data set and the keyhole point cloud data set.

[0043] Continue to refer Figure 4Specifically, a three-dimensional coordinate system can be established with the center point of the laser emitter as the origin O. The positive direction of the X-axis of the three-dimensional coordinate system is the welding direction of the current position, the positive direction of the Y-axis of the three-dimensional coordinate system is the direction toward the keyhole, and the positive direction of the Z-axis of the three-dimensional coordinate system can be any direction perpendicular to the plane formed by the X-axis and the Y-axis. The processor of the optical low-coherence imaging monitoring device can obtain the required point cloud data based on the three-dimensional coordinate system. The point cloud data includes the three-dimensional coordinates of all points in the point cloud. It should be noted here that the welding positions corresponding to different groups of point cloud data may be different. All welding positions can be connected from front to back to form the aforementioned welding path. The current position is the welding position corresponding to the currently selected set of point cloud data.

[0044] Further, from Figure 4 It can be seen from the figure that the peripheral point cloud ( Figure 4 The horizontal strip point cloud distribution at the top of the figure) and the keyhole point cloud ( Figure 4 The point cloud distribution of the inverted triangle located at the bottom) can be distinguished from the Y value. Based on this, the embodiment of the present application can pre-construct a segmentation formula (i.e., a preset segmentation formula) to segment the point cloud data through the preset segmentation formula.

[0045] In some implementations, the preset segmentation formula may be:

[0046] In the formula, is the point cloud data of the jth point before segmentation, is the peripheral point cloud data set, is the keyhole point cloud data set, is the vertical distance between the jth point and the center of the laser transmitter head, It is the vertical distance from the center point of the laser transmitter to the preset welding surface. is the preset distance threshold. It should be noted that the preset welding surface can be defined as: the distance from the origin O is , and the plane whose normal vector is parallel to the Y axis; in addition, the preset distance threshold can be manually set by the staff according to actual needs, and the preset distance threshold is not limited here.

[0047] Therefore, the Y coordinate in the point cloud can be less than or equal to + The point cloud data corresponding to the point is divided into the peripheral point cloud data set, and the point cloud with Y coordinate greater than + The point cloud data corresponding to the point is segmented into the keyhole point cloud data set, realizing the segmentation of the point cloud data.

[0048] After obtaining the peripheral point cloud data set, the point cloud data in the peripheral point cloud data set can be projected onto the preset welding surface based on the three-dimensional coordinates of each point in the peripheral point cloud data set to obtain a peripheral point cloud projection diagram. Specifically, because the preset welding surface is a plane parallel to the plane formed by the X-axis and the Z-axis, in short, the Y values ​​of the points on the preset welding surface are the same, so the Y values ​​of all points in the peripheral point cloud data set can be removed or set to the Y values ​​corresponding to the preset welding surface, and the three-dimensional coordinates of each point in the peripheral point cloud data set can be converted into two-dimensional coordinates to obtain the desired peripheral point cloud projection diagram.

[0049] Figure 5 This is a peripheral point cloud projection diagram of a specific example of this application. Figure 5 As shown in the figure, the peripheral point cloud in the peripheral point cloud projection diagram is in the shape of an elliptical ring, and the central area of ​​the elliptical ring is the keyhole area. Therefore, the outline of the keyhole area is also an ellipse, so when the keyhole area is subsequently fitted, the mathematical equation parameters obtained by fitting are also elliptical equation parameters.

[0050] Figure 6 This is the third flow chart of the low coherence imaging quality evaluation method of the embodiment of the present application. Figure 6 As shown, in some embodiments, the grid parameters may include the number of point clouds in the grid, the grid area, and the distance between the grid and the central grid, where the central grid is the grid where the origin of the coordinate system is located; step 220 determines the fitting parameters of the keyhole area based on the grid parameters of each grid, which may include the following steps: Step 610: For any grid, determine the grid evaluation parameters using a preset indicator formula based on the number of point clouds in the grid, the grid area, and the distance between the grid and the center grid.

[0051] Step 620: Determine multiple target grids using a preset screening algorithm and based on grid evaluation parameters of each grid.

[0052] Step 630: The point cloud data of each target grid center point is brought into the Hough transform algorithm to obtain the fitting parameters of the keyhole area.

[0053] In this embodiment, after the peripheral point cloud projection is obtained, the projection point of the center point of the laser transmitter head (i.e., the center point of the peripheral point cloud) can be used as the origin O. proj Build an X proj Y proj Coordinate system (such as Figure 5 shown), based on X proj Y proj The coordinate system can obtain the two-dimensional coordinates of each point in the peripheral point cloud (X proj , Y proj ). After the coordinate system is constructed, you can Grid division is performed on the peripheral point cloud projection map, and the center point of one of the grids is aligned with the origin O proj Overlap, and the grid can be defined as the center grid.

[0054] Figure 7 is a schematic diagram of grid division of a specific example of this application, such as Figure 7 As shown, after the peripheral point cloud projection map is meshed, each point in the peripheral point cloud is distributed in each grid, the number of grid midpoints on the peripheral point cloud is large, and the number of grid midpoints in the middle of the peripheral point cloud and outside the peripheral point cloud is small.

[0055] Based on this, the number of point clouds in each grid can be determined according to the two-dimensional coordinates of each point, the coordinates of the grid center point, and the preset scale; the grid area can be directly calculated using the preset scale. If the preset scale is , then the grid area is The distance between the grid and the center grid can be calculated by the coordinates of the center point of the grid and the coordinates of the center point of the center grid.

[0056] Furthermore, after the grid is divided according to a preset scale, the grid evaluation parameters of the grid can be determined by using a preset indicator formula based on the number of point clouds in the grid, the grid area, and the distance between the grid and the center grid. It should be noted that the grid evaluation parameter is a comprehensive evaluation indicator used to characterize the positional relationship between the grid and the keyhole area. The embodiment of the present application mainly determines the comprehensive evaluation indicator of the grid from two aspects: the point cloud density in the grid and the distance from the center grid. It can be understood that when the point cloud density in the grid is high and the distance from the center grid is far, it indicates that the grid may be at the location of the peripheral point cloud; when the point cloud density in the grid is low and the distance from the center grid is close, it indicates that the grid may be in the keyhole area. Therefore, the relative positional relationship between the grid and the keyhole area can be characterized by the two indicators of the point cloud density in the grid and the distance from the center grid.

[0057] Taking a grid as an example, the number of point clouds in the grid, the grid area, and the distance between the grid and the center grid can be substituted into the preset indicator formula to obtain the grid evaluation parameters of the grid. In the same way, the grid evaluation parameters of all grids are calculated, and then the target grid is selected based on the grid evaluation parameters of all grids.

[0058] In some implementations, the preset indicator formula may be:

[0059] In the formula, is the grid evaluation parameter of the ith grid, is the number of point clouds of the i-th grid, is the grid area of ​​the ith grid, is the distance between the center point of the i-th grid and the center point of the center grid. The smaller it is, the lower the point cloud density in the grid is and the closer it is to the center grid. Larger values ​​indicate a denser grid and are farther from the center grid.

[0060] After obtaining the mesh evaluation parameters corresponding to all meshes, a preset screening algorithm can be used to determine multiple target meshes based on the mesh evaluation parameters of each mesh. It should be noted that the preset screening algorithm can be a percentage filtering method; the screened target meshes are mainly meshes located at the junction of the keyhole area and the peripheral area, that is, the meshes where the keyhole area contour is located. Through the point cloud data in these target meshes, the mathematical equations that characterize the keyhole area contour can be better fitted, and the keyhole area can be determined more accurately.

[0061] In some embodiments, step 620 uses a preset screening algorithm and based on the grid evaluation parameters of each grid to determine multiple target grids, which may include: sorting the grid evaluation parameters in order from large to small; using the preset screening algorithm to select a grid evaluation parameter within a preset percentage interval from the smaller side of the grid evaluation parameter, and using the grid corresponding to the selected grid evaluation parameter as the target grid.

[0062] Specifically, all grid evaluation parameters can be sorted from large to small, and then the grid evaluation parameters of the preset percentage interval can be selected from the smaller end of the grid evaluation parameters using the percentage filtering method. It should be noted that after all grid evaluation parameters are sorted, the position of each grid evaluation parameter in this sequence can be expressed in the form of percentage. In this embodiment, the preset percentage interval can be set to 8% to 12%. The preset percentage interval can be specifically set by the staff according to the specifications of the peripheral point cloud projection map and the distribution of the peripheral point cloud, and no specific restrictions are made here.

[0063] As an example, after all grid evaluation parameters are sorted in descending order, the percentage filtering method can be used to select the grid evaluation parameters that are at the end of this sequence and are in the range of 8% to 12%, and the grids corresponding to these grid evaluation parameters are determined as target grids. The target grids have relatively low place cloud density and are relatively closer to the central grid.

[0064] As another example, after all grid evaluation parameters are sorted in descending order, the percentage filtering method can be used to select the grid evaluation parameters that are ranked at the end of the sequence at 10%, and the grids corresponding to these grid evaluation parameters are determined as target grids.

[0065] Furthermore, the point cloud data of each target grid center point is brought into the Hough transform algorithm to obtain the fitting parameters of the keyhole area. Since the keyhole area is elliptical from a bird's-eye view (such as Figure 7 As shown in the figure, the fitting curve is also an ellipse, and the fitting equation is an ellipse equation. Specifically, the point cloud data of the center point of each target grid can be randomly extracted to inversely solve the parameters of the ellipse equation and obtain a set of fitting parameters of the ellipse equation. The grid is re-divided according to the preset scale, and the above fitting parameter determination steps are repeated, and finally n sets of fitting parameters of the ellipse equation can be obtained. It should be noted that n is the number of iterations, and the value of n can be manually set by the staff according to actual needs. The larger n is, the more accurate the fitting parameters of the keyhole area are finally obtained.

[0066] For the n sets of fitting parameters obtained above, this embodiment can use hard voting to determine the optimal fitting parameters, and the optimal fitting parameters constitute an elliptical equation for characterizing the shape of the keyhole area. The hard voting rule is: if the n sets of fitting parameters are not consistent, the average value can be taken as the optimal fitting parameter; if there are multiple sets of consistent parameters in the n sets of fitting equation parameters, the mode result can be taken as the optimal fitting parameter.

[0067] As an example, assuming that there are 99 groups of fitting parameters, among which the 2nd and 3rd groups of fitting parameters are consistent, the 30th, 31st, and 32nd groups of fitting parameters are consistent, and the 96th, 97th, 98th, and 99th groups of fitting parameters are consistent, then the 96th, 97th, 98th, and 99th groups of fitting parameters are selected as the majority results and used as the final keyhole area fitting parameters.

[0068] After the fitting parameters of the keyhole region are determined, the keyhole region can be determined according to the fitting equation, and further, the optical low-coherence imaging quality evaluation can be performed based on the relevant data of the fitted keyhole region.

[0069] In some embodiments, the point cloud data in the keyhole area may include the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, and the coordinate data of each point. Step 230 determines the imaging quality evaluation result based on the point cloud data in the keyhole area and the area of ​​the keyhole area, which may include: using a preset quality evaluation formula and based on the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, the coordinate data of each point, the coordinate data of the center point of the keyhole area, and the area of ​​the keyhole area, to determine the imaging quality evaluation result.

[0070] The embodiment of the present application mainly evaluates the optical low-coherence imaging quality of the keyhole region from three perspectives: point cloud density, signal strength, and central concentration. The point cloud density is the ratio between the number of point clouds in the keyhole region in the peripheral point cloud projection map and the area of ​​the keyhole region, the point cloud signal strength is the signal strength returned to the optical low-coherence imaging monitoring device, and the central concentration is the degree of discreteness between the point cloud data and the center point of the fitting area.

[0071] It can be understood that the greater the point cloud density in the keyhole area, the stronger the point cloud signal strength, or the higher the concentration of the point cloud center, the higher the quality of the collected point cloud data and the higher the quality of the optical low-coherence imaging.

[0072] Based on this, in some implementations, the following preset quality evaluation formula can be constructed to achieve quantitative evaluation of the optical low-coherence imaging quality:

[0073] In the formula, is the image quality evaluation result. is the area of ​​the keyhole region, is the number of point clouds in the keyhole area, is the signal strength of the zth point in the ith grid in the keyhole area, is the total signal strength in the keyhole area, is the coordinate data of the zth point in the i-th grid in the keyhole area, is the coordinate data of the center point of the keyhole area, It means to calculate the sum of the distances from the point cloud in the fitting area to the center point of the fitting area. It should be noted that as well as All X proj O proj Y proj Coordinate data in the coordinate system.

[0074] In this embodiment, the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, the coordinate data of each point, the coordinate data of the center point of the keyhole area, and the area of ​​the keyhole area can be substituted into the above-mentioned preset quality evaluation formula to obtain the imaging quality evaluation result. , The smaller it is, the lower the quality of low-coherence imaging is. The larger the value, the higher the low-coherence imaging quality.

[0075] Therefore, the quantitative evaluation of the optical low-coherence imaging quality can be achieved, and the imaging quality evaluation results The level of directly reflects the quality of imaging, which improves the accuracy of online monitoring of the keyhole depth of laser welding. Secondly, the grid evaluation parameters of the grid are calculated based on the point cloud density of the grid in the keyhole area and the distance between the grid and the center grid. The target grid with relatively low point cloud density and relatively close distance to the center grid is screened out by the percentage filtering method. The center point of the target grid is screened out and substituted into the Hough change to obtain the fitting equation of the keyhole area, which can greatly improve the computational efficiency of the Hough change and realize the rapid judgment of the keyhole position of laser welding. Finally, the imaging quality evaluation results obtained can be used as a basis for troubleshooting negative factors such as improper setting of welding process parameters, large sensor installation errors, sensor incompatibility or low reflectivity of the welding object, thereby improving the credibility of online monitoring of the keyhole depth of laser welding.

[0076] Based on the above embodiments, the embodiments of the present application also provide a low coherence imaging quality evaluation device. Figure 8 is a schematic diagram of the structure of a low coherence imaging quality evaluation device according to an embodiment of the present application, such as Figure 8 As shown, the low coherence imaging quality evaluation device 800 may include an acquisition module 810 , a fitting module 820 and an evaluation module 830 .

[0077] Among them, the acquisition module 810 is used to acquire point cloud data related to the keyhole during the laser welding process, and obtain a peripheral point cloud projection map of the keyhole based on the point cloud data; the fitting module 820 is used to perform the keyhole area fitting process multiple times to determine the keyhole area based on the fitting parameters obtained in each fitting process; wherein, in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and the grid is divided according to a preset scale, and the fitting parameters of the keyhole area are determined based on the grid parameters of each grid; the preset scales in different fitting processes are different; the evaluation module 830 is used to determine the imaging quality evaluation result based on the point cloud data in the keyhole area and the regional area of ​​the keyhole area.

[0078] Thus, the acquisition module 810 acquires the point cloud data related to the keyhole during the laser welding process, and obtains the peripheral point cloud projection map of the keyhole based on the point cloud data; the fitting module 820 then performs the keyhole area fitting process multiple times at different preset scales, and in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and the grid is divided according to the preset scale, and the fitting parameters of the keyhole area are determined based on the grid parameters of each grid, and then the keyhole area is fitted based on the fitting parameters obtained in each fitting process; finally, the evaluation module 830 determines the imaging quality evaluation result based on the point cloud data in the keyhole area and the area of ​​the keyhole area, thereby realizing the evaluation of the accuracy of collecting key depth information by optical low-coherence imaging.

[0079] In some embodiments, the acquisition module 810 is specifically used to: segment the point cloud data using a preset segmentation formula to obtain a peripheral point cloud data set and a keyhole point cloud data set; project the peripheral point cloud data set to a preset welding surface to obtain a peripheral point cloud projection map.

[0080] In some implementations, the preset segmentation formula is:

[0081] In the formula, is the point cloud data of the jth point before segmentation, is the peripheral point cloud data set, is the keyhole point cloud data set, is the vertical distance between the jth point and the center of the laser transmitter head, It is the vertical distance from the center point of the laser transmitter to the preset welding surface. is the preset distance threshold.

[0082] In some embodiments, the grid parameters include the number of point clouds in the grid, the grid area, and the distance between the grid and the central grid, where the central grid is the grid where the origin of the coordinate system is located; the fitting module 820 is specifically used to: for any grid, determine the grid evaluation parameters using a preset indicator formula and based on the number of point clouds in the grid, the grid area, and the distance between the grid and the central grid; determine multiple target grids using a preset screening algorithm and based on the grid evaluation parameters of each grid; bring the point cloud data of the center point of each target grid into the Hough transform algorithm to obtain the fitting parameters of the keyhole area.

[0083] In some embodiments, the fitting module 820 is further specifically used to: sort the grid evaluation parameters in descending order; select the grid evaluation parameters within a preset percentage interval from the smaller side of the grid evaluation parameters using a preset screening algorithm, and use the grid corresponding to the selected grid evaluation parameter as the target grid.

[0084] In some implementations, the preset indicator formula is:

[0085] In the formula, is the grid evaluation parameter of the ith grid, is the number of point clouds of the i-th grid, is the grid area of ​​the ith grid, is the distance between the center point of the i-th grid and the center point of the center grid.

[0086] In some embodiments, the point cloud data in the keyhole area includes the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, and the coordinate data of each point; the evaluation module 830 is specifically used to: use a preset quality evaluation formula and based on the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, the coordinate data of each point, the coordinate data of the center point of the keyhole area, and the area area of ​​the keyhole area, determine the imaging quality evaluation result.

[0087] In some implementations, the preset quality evaluation formula is:

[0088] In the formula, is the image quality evaluation result. is the area of ​​the keyhole region, is the number of point clouds in the keyhole area, is the signal strength of the zth point in the ith grid in the keyhole area, is the total signal strength in the keyhole area, is the coordinate data of the zth point in the i-th grid in the keyhole area, It is the coordinate data of the center point of the keyhole area.

[0089] It should be noted that for details not disclosed in the low coherence imaging quality evaluation device of this embodiment, please refer to the details disclosed in the embodiment of the low coherence imaging quality evaluation method in the embodiment of this specification, and will not be repeated here.

[0090] Based on the above embodiments, Fig. 9 An example of a physical structure diagram of an electronic device is shown in FIG. Fig. 9 As shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930 and a communication bus 940, wherein the processor 910, the communication interface 920 and the memory 930 communicate with each other through the communication bus 940. The processor 910 may call the logic instructions in the memory 930 to execute the low coherence imaging quality evaluation method, which includes: acquiring point cloud data related to the keyhole during the laser welding process, and obtaining a peripheral point cloud projection map of the keyhole based on the point cloud data; performing the keyhole region fitting process multiple times to determine the keyhole region based on the fitting parameters obtained in each fitting process; wherein in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and a grid is divided according to a preset scale, and the fitting parameters of the keyhole region are determined based on the grid parameters of each grid; the preset scales in different fitting processes are different; and the imaging quality evaluation result is determined based on the point cloud data in the keyhole region and the area of ​​the keyhole region.

[0091] In addition, the logic instructions in the above-mentioned memory 930 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0092] On the basis of the above embodiments, on the other hand, the present invention further provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the low-coherence imaging quality evaluation method provided by the above methods, the method including: acquiring point cloud data related to the keyhole during laser welding, and obtaining a peripheral point cloud projection map of the keyhole based on the point cloud data; performing a keyhole area fitting process multiple times to determine the keyhole area based on the fitting parameters obtained in each fitting process; wherein, in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and grids are divided according to a preset scale, and the fitting parameters of the keyhole area are determined based on the grid parameters of each grid; the preset scales in different fitting processes are different; and the imaging quality evaluation result is determined based on the point cloud data in the keyhole area and the area area of ​​the keyhole area.

[0093] On the basis of the above embodiments, on another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the processor executes the low-coherence imaging quality evaluation method provided by the above methods, the method comprising: acquiring point cloud data related to the keyhole during laser welding, and obtaining a peripheral point cloud projection map of the keyhole based on the point cloud data; performing a keyhole area fitting process multiple times to determine the keyhole area based on the fitting parameters obtained in each fitting process; wherein, in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and grids are divided according to a preset scale, and the fitting parameters of the keyhole area are determined based on the grid parameters of each grid; the preset scales in different fitting processes are different; and the imaging quality evaluation result is determined based on the point cloud data in the keyhole area and the area area of ​​the keyhole area.

[0094] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0095] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

Claims

1. A low coherence imaging quality evaluation method, characterized in that: include: Acquire point cloud data related to the keyhole during laser welding, and obtain a peripheral point cloud projection map of the keyhole based on the point cloud data; The keyhole region fitting process is performed multiple times to determine the keyhole region based on the fitting parameters obtained in each fitting process; wherein in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and a grid is divided according to a preset scale, and the fitting parameters of the keyhole region are determined based on the grid parameters of each grid; the preset scales in different fitting processes are different; An imaging quality evaluation result is determined based on the point cloud data in the keyhole region and the region area of ​​the keyhole region.

2. The low coherence imaging quality evaluation method according to claim 1, characterized in that: The step of obtaining a peripheral point cloud projection image of the keyhole based on the point cloud data includes: Segmenting the point cloud data using a preset segmentation formula to obtain a peripheral point cloud data set and a keyhole point cloud data set; The peripheral point cloud data set is projected onto a preset welding surface to obtain the peripheral point cloud projection map.

3. The low coherence imaging quality evaluation method according to claim 2, characterized in that: The preset segmentation formula is: In the formula, is the point cloud data of the jth point before segmentation, is the peripheral point cloud data set, is the keyhole point cloud data set, is the vertical distance between the jth point and the center of the laser transmitter head, is the vertical distance from the center point of the laser transmitter to the preset welding surface, is the preset distance threshold.

4. The low coherence imaging quality evaluation method according to claim 1, characterized in that: The grid parameters include the number of point clouds in the grid, the grid area, and the distance between the grid and the central grid, where the central grid is the grid where the origin of the coordinate system is located; The determining the fitting parameters of the keyhole region based on the grid parameters of each of the grids comprises: For any grid, determine a grid evaluation parameter using a preset indicator formula based on the number of point clouds in the grid, the area of ​​the grid, and the distance between the grid and the central grid; Determining a plurality of target grids by using a preset screening algorithm and based on grid evaluation parameters of each of the grids; Substitute the point cloud data of the center point of each target grid into the Hough transform algorithm to obtain the fitting parameters of the keyhole area.

5. The low coherence imaging quality evaluation method according to claim 4, characterized in that: The method of using a preset screening algorithm and determining a plurality of target grids based on grid evaluation parameters of each of the grids comprises: Sorting the grid evaluation parameters in descending order; The preset screening algorithm is used to select a grid evaluation parameter within a preset percentage interval from the side with a smaller grid evaluation parameter, and the grid corresponding to the selected grid evaluation parameter is used as the target grid.

6. The low coherence imaging quality evaluation method according to claim 4, characterized in that: The preset indicator formula is: In the formula, is the grid evaluation parameter of the ith grid, is the number of point clouds of the i-th grid, is the grid area of ​​the ith grid, is the distance between the center point of the i-th grid and the center point of the central grid.

7. The low coherence imaging quality evaluation method according to claim 1, characterized in that: The point cloud data in the keyhole area includes the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, and the coordinate data of each point; The determining of the imaging quality evaluation result based on the point cloud data in the keyhole region and the area of ​​the keyhole region includes: The imaging quality evaluation result is determined by using a preset quality evaluation formula and based on the number of point clouds in the keyhole area, the signal strength of each point in the keyhole area, the coordinate data of each point, the coordinate data of the center point of the keyhole area, and the area of ​​the keyhole area.

8. The low coherence imaging quality evaluation method according to claim 7, characterized in that: The preset quality evaluation formula is: In the formula, is the imaging quality evaluation result, is the area of ​​the keyhole region, is the number of point clouds in the keyhole area, is the signal strength of the zth point in the ith grid in the keyhole area, is the total signal strength in the keyhole region, is the coordinate data of the zth point in the ith grid in the keyhole area, is the coordinate data of the center point of the keyhole area.

9. A low coherence imaging quality evaluation device, characterized in that: include: An acquisition module, used for acquiring point cloud data related to the keyhole during laser welding, and obtaining a peripheral point cloud projection map of the keyhole based on the point cloud data; A fitting module, used for performing a keyhole region fitting process for multiple times, so as to determine the keyhole region based on fitting parameters obtained in each fitting process; wherein, in each fitting process, a coordinate system is constructed based on the peripheral point cloud projection map and a grid is divided according to a preset scale, and fitting parameters of the keyhole region are determined based on grid parameters of each grid; the preset scales in different fitting processes are different; An evaluation module is used to determine an imaging quality evaluation result based on the point cloud data in the keyhole region and the area of ​​the keyhole region.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the low coherence imaging quality assessment method according to any one of claims 1 to 8 is implemented.

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