Putty spraying method for curved surface component

Abstract

The present invention discloses a method for spraying putty on a curved surface component, which relates to the field of putty spraying. The method comprises: measuring an object to be sprayed based on a surface structured light measurement technology and a photometric stereo vision measurement technology, generating surface three-dimensional morphology data of the object to be sprayed, and identifying a background area and a defect area of ​​the object to be sprayed; determining a depth to be filled in the defect area based on a background area, a defect area and a defect depth recognition model; determining spraying parameters based on a coating deposition model, the spraying parameters including an optimal spraying thickness, an optimal spraying speed, and an optimal spacing between adjacent spraying paths for a single spraying per unit time; dividing each spray layer in the defect area by layered slicing based on the optimal spraying thickness and the depth to be filled in the defect area, and smoothing point cloud data of the contour area of ​​each spray layer by a Bezier curve; spraying each spray layer in the defect area in sequence based on the spraying parameters; the application improves the putty spraying effect of the curved surface component.

Description

Technical Field

[0001] The invention relates to the technical field of putty spraying, in particular to a putty spraying method for a curved surface component. Background Art

[0002] The main bodies of large components represented by EMUs and ordinary passenger cars are formed by welding. Due to the requirement of flatness, putty is used extensively for leveling. At present, most companies use manual scraping for putty coating construction, which has a long construction process cycle, a low degree of automation in putty construction, a large workload for workers, and easy waste of putty raw materials during manual scraping construction. The putty spraying method based on industrial robots controls the spray gun to spray the defective areas of the components through the robot, which can free the workers from heavy processing operations and smoke-filled on-site environment, and can significantly shorten the production cycle and improve the efficiency of production and processing.

[0003] In order to improve the quality of putty spraying on the surface of large curved components, patent document CN202310426383.8 discloses a complex surface spraying path planning method and system based on multi-chromosome particle swarms. First, the free-form surface is coarsely segmented and finely segmented respectively. Then, a grating path is used to plan the connection method of the characteristic lines in the path of each defect area according to the endpoints of the characteristic lines. Then, according to different types of grating paths and the connection order of each defect area, a multi-chromosome particle swarm algorithm is used to optimize multiple possible full coverage paths to obtain the optimal spraying path, thereby reasonably avoiding holes of different sizes and shapes in complex free-form surfaces to obtain the optimal spraying path and improve the quality of spraying. In addition, patent document CN202310010987.4 discloses an intelligent putty spraying method and system for car bodies. Based on the collected data, a deep convolutional network is used to identify car body welds and grooves, and then spray path planning is performed by point cloud slicing, realizing intelligent defect recognition and putty spraying.

[0004] However, the method disclosed in patent document CN202310426383.8, although it can generate a raster path for the entire spray object and optimize the connection order of each defect area, the chromosome example group algorithm adopted does not handle the optimization problem of discrete defect areas well and is prone to falling into local optimality. However, the switching timing between each discrete defect area is not optimal, and the segmentation of free-form surfaces requires operators to have a high level of professionalism in the spraying process; the method disclosed in patent document CN202310010987.4 uses a deep convolutional network to identify the characteristic areas of the spraying, and then slices the characteristic area data to generate the spray path, but the convolutional network has a large amount of computational complexity, especially based on three-dimensional point cloud data. In addition, whether it is a raster spray path or a slicing method to generate the spray path, it cannot adapt well to the characteristic areas with irregular contour edges. Summary of the Invention

[0005] In order to solve the problems in the prior art of poor switching timing between various defective areas when spraying curved surface components and the inability to adapt well to irregular contour edges, the present application provides a putty spraying method for curved surface components.

[0006] In a first aspect, the present application provides a method for spraying putty on a curved surface component, which adopts the following technical solution: the putty spraying method comprises the following steps:

[0007] Step S100: measuring the object to be sprayed based on the surface structured light measurement technology and the photometric stereo vision measurement technology to generate three-dimensional surface topography data of the object to be sprayed;

[0008] Step S200: identifying the background area and defect area of ​​the object to be sprayed based on the three-dimensional surface topography data;

[0009] Step S300: determining a depth to be filled in the defect area based on the background area, the defect area and a pre-built defect depth recognition model of the object to be sprayed;

[0010] Step S400: Determining spraying parameters based on the defect area and a pre-built coating deposition model; the spraying parameters include an optimal spraying thickness, an optimal spraying speed, and an optimal spacing between adjacent spraying paths per unit time.

[0011] Step S500: Based on the optimal spraying thickness of a single spraying in the unit time and the depth to be filled in the defective area, the spray layer in the defective area is divided by a layered slicing method, and the point cloud data of the contour area of ​​each spray layer is smoothed by a Bezier curve; and each spray layer in the defective area is sprayed in turn based on the spraying parameters.

[0012] By adopting the above technical solution, in view of the characteristics of curved components and the unevenness of defective areas, the surface three-dimensional morphology data of the object to be sprayed is generated by integrating surface structured light measurement technology and photometric stereo vision measurement technology, and the complete surface information of the object to be sprayed is obtained. The background area and defective area of ​​the area to be sprayed can be accurately identified, and the contour and depth of the defective area to be filled can be accurately identified, avoiding the problem of incomplete and inaccurate surface information collection of the object to be sprayed; by adopting a pre-built defect depth recognition model, the depth to be filled of each defective area is automatically identified and processed in the form of code, avoiding the problem of switching between various defective areas during spraying and the problem that the segmentation of free-form surfaces requires the operator to have a high degree of professionalism in the spraying process; the point cloud data of each spray layer contour area is smoothed by a Bezier curve, so that the recognition result of the irregular boundary of the defective area is as close to the actual situation as possible, which can better adapt to the irregular edge of the defective area and improve the putty spraying effect of the curved component.

[0013] In a specific implementation scheme, step S100 specifically includes:

[0014] Step S110: measuring the object to be sprayed based on the surface structured light measurement technology to obtain initial surface structured light point cloud data; filtering the initial surface structured light point cloud data to filter out high-frequency information in the initial surface structured light point cloud data and retain low-frequency information in the initial surface structured light point cloud data to generate a surface structured light three-dimensional model;

[0015] Step S120: Measuring the object to be sprayed based on photometric stereo vision measurement technology to obtain initial photometric stereo point cloud data, filtering the initial photometric stereo point cloud data to remove low-frequency information in the initial photometric stereo point cloud data and retain high-frequency information in the initial photometric stereo point cloud data, thereby generating a photometric stereo 3D model;

[0016] Step S130: generating surface three-dimensional topography data of the object to be sprayed based on the surface structured light three-dimensional model and the photometric stereo three-dimensional model.

[0017] By adopting the above technical solution, by filtering out high-frequency information in the initial surface structured light point cloud data, the influence of noise can be reduced, the data processing process can be simplified, and the data volume and computational complexity can be reduced; by filtering out low-frequency information in the initial photometric stereo point cloud data, the measurement results can be made more stable and unaffected by lighting conditions; by filtering and screening the initial surface structured light point cloud data and the initial photometric stereo point cloud data, the accuracy and reliability of the surface three-dimensional morphology data of the object to be sprayed are improved.

[0018] In a specific implementation scheme, step S200 specifically includes:

[0019] Step S210: determining the curvature of each point in the three-dimensional surface topography data based on the three-dimensional surface topography data;

[0020] Step S220: Marking the point with the smallest curvature in the three-dimensional surface topography data as a seed point, dividing the seed point into a defect area of ​​the object to be sprayed, and calculating the angle α between each point in the neighborhood of the seed point and the seed point;

[0021] Step S230: Determine the defect area and background area of ​​the object to be sprayed based on the angle α between each point in the neighborhood of the seed point and the seed point and the curvature of each point in the neighborhood of the seed point.

[0022] By adopting the above technical solution, the defect area and background area of ​​the object to be sprayed can be accurately identified through judgment and classification based on the curvature of each point in the three-dimensional surface topography data.

[0023] In a specific implementation scheme, step S230 specifically includes:

[0024] Step S231: Determine the size of the angle α and a preset angle threshold; if the angle α between the point and the seed point is less than the preset angle threshold, then classify the point as a defect area; if the angle α between the point and the seed point is greater than or equal to the preset angle threshold, then further determine the size of the curvature of the point and a preset curvature threshold; if the curvature of the point is less than the preset curvature threshold, then classify the point as a defect area; if the curvature of the point is greater than or equal to the preset curvature threshold, then classify the point as a background area;

[0025] Step S232: traverse all points in the neighborhood of the seed point to obtain the defect area and background area of ​​the object to be sprayed.

[0026] By adopting the above technical solution, the defect area and background area of ​​the object to be sprayed are accurately divided according to the angle α between each point in the neighborhood of the seed point and the seed point and the curvature of each point in the neighborhood of the seed point.

[0027] In a specific embodiment, before determining the curvature of each point in the three-dimensional surface topography data, the method further includes:

[0028] The surface three-dimensional topography data is subjected to outlier removal processing, downsampling processing and resampling processing.

[0029] By adopting the above technical solution, the surface three-dimensional morphology data is preprocessed by removing outliers and downsampling, which reduces the impact of noise on the accuracy of defect area recognition and speeds up the processing of subsequent point cloud data. Resampling the point cloud data can smooth out the local abnormal features in the disordered point cloud.

[0030] In a specific implementation scheme, step S300 specifically includes:

[0031] Step S310: Based on the background area of ​​the object to be sprayed, calculating the normal vector of each point in the background area, and determining the average vector of the background area based on the normal vector of each point in the background area;

[0032] Step S320: Calculate the rotation transformation matrix between the average vector and the Z-axis direction;

[0033] Step S330: transforming each point in the background area and the defect area based on the rotation transformation matrix, and projecting the transformed points in the background area and the defect area along the positive direction of the Z axis to generate two-dimensional point cloud data of the background area and two-dimensional point cloud data of the defect area;

[0034] Step S340: Based on the two-dimensional point cloud data of the background area, a global weighted least squares method is used for fitting to generate an ideal reference surface of the object to be sprayed; based on the distance between the two-dimensional point cloud data of the defect area and the ideal reference surface, the depth to be filled of each point in the defect area is determined.

[0035] By adopting the above technical solution, the three-dimensional point cloud data of the background area and the defect area are converted into a two-dimensional environment to identify the depth of the defect area to be filled, thereby improving the efficiency of the overall data processing.

[0036] In a specific implementation scheme, step S400 specifically includes:

[0037] Step S410: constructing a coating deposition model;

[0038] The coating deposition model is:

[0039]

[0040] Wherein, f(x, y) is the ideal spray thickness of a single spray per unit time corresponding to the point (x, y), k is the coating thickness correction coefficient, u1 and u2 are the coordinates of the center point of the coating deposition model, δ1 and δ2 are 1 / 3 of the effective semi-axis length in the X-axis direction and the Y-axis direction of the coating deposition model, respectively;

[0041] Step S420: constructing a first reference plane and a second reference plane based on the free-form surface of the defect area;

[0042] Step S430: Based on the coating deposition model, calculate the ideal spraying thickness f1(x1, y1) of a single spraying per unit time corresponding to a point on the first reference plane; based on the ideal spraying thickness f1(x1, y1) of a single spraying per unit time and the pre-built reference plane switching model, calculate the ideal spraying thickness f3(x3, y3) of a single spraying per unit time corresponding to a point on the free surface of the defect area;

[0043] The reference plane switching model is:

[0044]

[0045] Wherein, h1 is the vertical distance from the center point of the spray gun to the first reference plane, h2 is the vertical distance from the center point of the spray gun to the second reference plane, θ is the angle between the first tangent line and the second reference plane, and the first tangent line is a tangent line passing through a point on the free-form surface and tangent to the free-form surface;

[0046] Step S440: Determine the optimal spray thickness of a single spraying per unit time and the optimal spacing between adjacent spraying paths based on the pre-constructed objective function minE(v, D) and the value of the ideal spray thickness f3(x3, y3) of a single spraying per unit time corresponding to the point on the free surface of the defect area;

[0047] The objective function minE(v,D) is:

[0048]

[0049] Wherein, q(L, v, D) is the actual spraying thickness function, which represents the actual spraying thickness of a single spray per unit time corresponding to a point on the free surface of the defect area when the spraying speed is v, the spacing between adjacent spraying paths is D, and the perpendicular distance between the current spraying path and the previous spraying path is L; and a and b are constants;

[0050] q d is the ideal spraying thickness f3(x3, y3) of a single spraying per unit time corresponding to the point on the free surface of the defect area;

[0051] Step S450: The spraying speed v is set to be fixed, and the second-order derivative of the objective function minE(v, D) is calculated. When the second-order derivative of the objective function minE(v, D) is zero, the corresponding value of L is the optimal spraying thickness of a single spraying per unit time; when the second-order derivative of the objective function minE(v, D) is zero, the corresponding value of D is the optimal spacing between adjacent spraying paths;

[0052] Step S460: Determine the optimal spraying speed based on the actual spraying thickness function q(L, v, D), the optimal spraying thickness of a single spraying per unit time, and the optimal spacing between adjacent spraying paths.

[0053] By adopting the above technical solution, a dynamic coating deposition model is established. In view of the characteristics of the defect area as an irregular surface, a second reference plane and a first reference plane are established. The optimal spraying thickness of a single spraying per unit time on the free-form surface of the defect area and the optimal spacing between adjacent spraying paths are determined according to the coating deposition model. The optimal spraying speed is then determined according to the actual spraying thickness function q(L, v, D), thereby obtaining accurate spraying parameters for spraying the defect area. This greatly improves the adaptability of the coating deposition model to free-form surface parts and can accurately control the thickness of a single spray of putty.

[0054] In a second aspect, the present application provides a putty spraying device for a curved component, the device applying the putty spraying method described in the first aspect, the device comprising a three-dimensional topography data generation unit, an area recognition unit, a depth calculation unit to be filled, a spraying parameter calculation unit, and a defect area layer division unit;

[0055] A three-dimensional shape data generating unit is used to measure the object to be sprayed based on the surface structured light measurement technology and the photometric stereo vision measurement technology to generate the surface three-dimensional shape data of the object to be sprayed;

[0056] an area recognition unit, which recognizes a background area and a defect area of ​​the object to be sprayed based on the three-dimensional surface topography data;

[0057] A depth calculation unit for filling, which determines the depth of the defect area to be filled based on the background area, the defect area and a pre-built defect depth recognition model of the object to be sprayed;

[0058] A spray parameter calculation unit, which determines spray parameters based on the defect area and a pre-built coating deposition model; the spray parameters include the optimal spray thickness of a single spray per unit time, the optimal spray speed, and the optimal spacing between adjacent spray paths;

[0059] The defect area hierarchical division unit divides the spray layer of the defect area by a layered slicing method based on the optimal spray thickness of a single spraying in the unit time and the depth to be filled in the defect area, and smoothes the point cloud data of the contour area of ​​each spray layer by a Bezier curve; and sprays each spray layer of the defect area in sequence based on the spray parameters.

[0060] In a third aspect, the present application provides a terminal comprising: a processor, a memory and a communication bus; the communication bus is used to realize connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to realize the steps of the putty spraying method as in the first aspect or any feasible implementation scheme of the first aspect.

[0061] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed, executes the steps of the putty spraying method in the first aspect or any one of the possible implementation schemes of the first aspect.

[0062] In summary, the technical solution of this application includes at least the following beneficial technical effects:

[0063] 1. In view of the characteristics of curved surface components and the unevenness of defective areas, the three-dimensional surface topography data of the object to be sprayed is generated by integrating surface structured light measurement technology and photometric stereo vision measurement technology. The complete surface information of the object to be sprayed is obtained, and the background area and defective area to be sprayed can be accurately identified. The outline of the defective area and the depth to be filled are also accurately identified, thus avoiding the problem of incomplete and inaccurate surface information collection of the object to be sprayed.

[0064] 2. Through the pre-built defect depth recognition model, the depth to be filled in each defect area is automatically identified and processed in the form of code, avoiding the problem of switching between defect areas during spraying and the problem that the segmentation of free-form surfaces requires operators to have a high level of professionalism in the spraying process;

[0065] 3. The point cloud data of each spray layer contour area is smoothed by Bezier curve, so that the recognition result of the irregular boundary of the defect area is as close to the actual situation as possible, which can better adapt to the irregular edge of the defect area and improve the putty spraying effect of the curved surface component. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is an overall flow chart of the putty spraying method for curved surface components in an embodiment of the present application;

[0067] Figure 2 This is a process framework diagram of a method for spraying putty on a curved surface component according to an embodiment of the present application;

[0068] Figure 3 This is a framework diagram of the processing process for generating three-dimensional surface topography data in an embodiment of the present application;

[0069] Figure 4 This is an overall framework diagram for processing surface three-dimensional topography data and determining background areas and defect areas in an embodiment of the present application;

[0070] Figure 5 2 is a schematic diagram of a judgment process for dividing a background area and a defect area according to the angle α between point A and the seed point and the curvature of point A in an embodiment of the present application;

[0071] Figure 6 This is a schematic diagram of calculating the ideal spraying thickness of a single spraying per unit time corresponding to a point on the free surface of the defect area in an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0073] Example 1:

[0074] Reference Figure 1 and Figure 2 This embodiment discloses a method for spraying putty on a curved surface component, comprising the following steps:

[0075] Step S100: measuring the object to be sprayed based on the surface structured light measurement technology and the photometric stereo vision measurement technology to generate three-dimensional surface topography data of the object to be sprayed;

[0076] Step S200: identifying the background area and defect area of ​​the object to be sprayed based on the three-dimensional surface topography data;

[0077] Step S300: determining a depth to be filled in the defect area based on the background area, the defect area and a pre-built defect depth recognition model of the object to be sprayed;

[0078] Step S400: Determining spraying parameters based on the defect area and a pre-built coating deposition model; the spraying parameters include an optimal spraying thickness, an optimal spraying speed, and an optimal spacing between adjacent spraying paths per unit time.

[0079] Step S500: Based on the optimal spraying thickness of a single spraying in the unit time and the depth to be filled in the defective area, the spray layer in the defective area is divided by a layered slicing method, and the point cloud data of the contour area of ​​each spray layer is smoothed by a Bezier curve; and each spray layer in the defective area is sprayed in turn based on the spraying parameters.

[0080] Since most large curved components have the characteristics of high metal reflectivity and low texture, direct measurement using surface structured light is prone to distortion or loss of measurement data. Therefore, in view of the characteristics of curved components and the unevenness of defect areas, the surface three-dimensional topography data of the object to be sprayed is generated by integrating surface structured light measurement technology and photometric stereo vision measurement technology, and the complete surface information of the object to be sprayed is obtained. The background area and defect area of ​​the area to be sprayed can be accurately identified, and the contour and depth of the defect area to be filled can be accurately identified, avoiding the problem of incomplete and inaccurate surface information collection of the object to be sprayed. By adopting a pre-built defect depth recognition model, the depth to be filled of each defect area is automatically identified and processed in the form of code, avoiding the problem of switching between various defect areas during spraying and the problem that the segmentation of free-form surfaces requires the operator to have a high level of professionalism in the spraying process. The point cloud data of each spray layer contour area is smoothed by Bezier curve, so that the recognition result of the irregular boundary of the defect area is as close to the actual situation as possible, which can better adapt to the irregular edge of the defect area and improve the putty spraying effect of curved components.

[0081] Further, refer to Figure 3 Step S100 specifically includes:

[0082] Step S110: measuring the object to be sprayed based on the surface structured light measurement technology to obtain initial surface structured light point cloud data; filtering the initial surface structured light point cloud data to filter out high-frequency information in the initial surface structured light point cloud data and retain low-frequency information in the initial surface structured light point cloud data to generate a surface structured light three-dimensional model;

[0083] Step S120: Measuring the object to be sprayed based on photometric stereo vision measurement technology to obtain initial photometric stereo point cloud data, filtering the initial photometric stereo point cloud data to remove low-frequency information in the initial photometric stereo point cloud data and retain high-frequency information in the initial photometric stereo point cloud data, thereby generating a photometric stereo 3D model;

[0084] Step S130: Generating three-dimensional surface topography data of the object to be sprayed based on the surface structured light 3D model and the photometric stereo 3D model. Specifically, valid point cloud data is extracted from each of the surface structured light 3D model and the photometric stereo 3D model, and the three-dimensional surface topography data of the object to be sprayed is generated through data complementation, thereby achieving a fusion of the surface structured light 3D model and the photometric stereo 3D model.

[0085] Specifically, when filtering the initial surface structured light point cloud data, two-dimensional wavelet multi-scale decomposition is used to separate the low-frequency information and high-frequency information in the initial surface structured light point cloud data; similarly, when filtering the initial photometric stereo point cloud data, two-dimensional wavelet multi-scale decomposition is used to separate the low-frequency information and high-frequency information in the initial photometric stereo point cloud data.

[0086] When generating 3D surface topography data based on a surface structured light 3D model and a photometric stereo 3D model, the sixth-layer approximation image, obtained by reconstructing the initial surface structured light point cloud data through 2D wavelet multiscale decomposition, is added to the fifth-layer detail image, obtained through 2D wavelet multiscale decomposition and reconstruction of the initial photometric stereo point cloud data. This fused feature information is then combined with the intrinsic reference information of the surface structured light to perform 3D reconstruction, resulting in the 3D surface topography data of the object to be sprayed. This intrinsic reference information can be used to convert depth information in the pixel coordinate system into 3D coordinate points in the camera coordinate system.

[0087] Among them, the sixth-layer approximation image represents six-fold two-dimensional wavelet multi-scale decomposition of the initial surface structured light point cloud data, filtering out the high-frequency information in the point cloud data obtained by each decomposition, and reconstructing the low-frequency information in the point cloud data obtained after the six decompositions and filtering to obtain the sixth-layer approximation image; the fifth-layer detail image represents five-fold two-dimensional wavelet multi-scale decomposition of the initial photometric stereo point cloud data, filtering out the low-frequency information in the point cloud data obtained by each decomposition, and finally reconstructing the high-frequency information in the point cloud data obtained after the five decompositions and filtering to obtain the fifth-layer detail image.

[0088] Specifically, when generating a photometric stereo 3D model, by sequentially performing steps S110 to S130 on the initial surface structured light point cloud data and initial photometric stereo point cloud data acquired at each posture of the object to be sprayed, accurate 3D surface topography data for each posture of the object to be sprayed can be obtained. The 3D surface topography data for each posture includes multiple pieces of point cloud data. The multiple pieces of point cloud data are then stitched together based on the calibrated position of the spray robot to obtain a photometric stereo 3D model of the object to be sprayed. The posture represents the position and orientation of the spray robot's end-use tool relative to the reference coordinate system.

[0089] After obtaining the initial surface structured light point cloud data measured using surface structured light measurement technology, the low-frequency information in the initial surface structured light point cloud data primarily contains the overall shape and general surface geometry of the object to be sprayed, corresponding to the gentler variations on the object's surface, such as the overall shape and concave-convex features of a curved surface. The high-frequency information in the initial surface structured light point cloud data primarily contains the subtle details and texture information on the object's surface, corresponding to the steeper variations on the object's surface, such as subtle concave-convex features, textures, and edges. In other words, the low-frequency information in the initial surface structured light point cloud data provides the overall shape and general structure of the object to be sprayed, while the high-frequency information in the initial surface structured light point cloud data provides the details and texture of the object's surface. However, since the high-frequency information in the initial surface structured light point cloud data is often more susceptible to noise, filtering out the high-frequency information can reduce the impact of noise, improve the accuracy and reliability of the measurement results of the object to be sprayed, and therefore improve the accuracy and reliability of the three-dimensional surface topography data of the object to be sprayed. Furthermore, it can simplify the data processing process, reducing data volume and computational complexity.

[0090] After the initial photometric stereo point cloud data is obtained by measuring the photometric stereo vision measurement technology, the low-frequency information in the initial photometric stereo point cloud data corresponds to the overall brightness change of the surface of the object to be sprayed. If it is not filtered out, the measurement results may be affected by the lighting conditions, resulting in inconsistent measurement results under different lighting conditions. Therefore, by filtering out the low-frequency information in the initial photometric stereo point cloud data, the measurement results can be made more stable and not affected by the lighting conditions, further improving the accuracy and reliability of the measurement results of the object to be sprayed, that is, further improving the accuracy and reliability of the surface three-dimensional morphology data of the object to be sprayed.

[0091] Further, refer to Figure 4 Step S200 specifically includes:

[0092] Step S210: determining the curvature of each point in the three-dimensional surface topography data based on the three-dimensional surface topography data; wherein the curvature of each point in the three-dimensional surface topography data may be determined by using a singular value decomposition method;

[0093] In step S210, a covariance matrix consisting of a neighborhood point set of each point in the three-dimensional surface topography data may be obtained based on the three-dimensional surface topography data, and a normal and a curvature of each point in the three-dimensional surface topography data may be determined based on the covariance matrix.

[0094] Step S220: Marking the point with the smallest curvature in the three-dimensional surface topography data as a seed point, dividing the seed point into a defect area of ​​the object to be sprayed, and calculating the angle α between each point in the neighborhood of the seed point and the seed point;

[0095] Step S230: Determine the defect area and background area of ​​the object to be sprayed based on the angle α between each point in the neighborhood of the seed point and the seed point and the curvature of each point in the neighborhood of the seed point.

[0096] Specifically, since the point with the smallest curvature is often located in the plane area, using the point with the smallest curvature as the seed point can make the points around the seed point more likely to belong to the same plane, and can better preserve the plane structure with small curvature changes; in addition, the curvature change at the point with the smallest curvature is small, which can also reduce the influence of noise and outliers; therefore, by selecting the point with the smallest curvature as the seed point, the accuracy and stability of point cloud data processing can be improved, and the recognition accuracy of the background area of ​​the defect area can be improved.

[0097] When obtaining points in the neighborhood of the seed point, the neighborhood can be represented by the number of points within a certain distance range or a space sphere with a certain radius. Those skilled in the art can set the range of the seed point neighborhood according to the area of ​​the object to be sprayed.

[0098] Therefore, through steps S210 to S230, the defect area and background area of ​​the object to be sprayed are accurately identified through judgment and classification based on the curvature of each point in the three-dimensional surface topography data.

[0099] Further, refer to Figure 5 , step S230 specifically includes:

[0100] Step S231: Determine the size of the angle α and a preset angle threshold; if the angle α between the point and the seed point is less than the preset angle threshold, then classify the point as a defect area; if the angle α between the point and the seed point is greater than or equal to the preset angle threshold, then further determine the size of the curvature of the point and a preset curvature threshold; if the curvature of the point is less than the preset curvature threshold, then classify the point as a defect area; if the curvature of the point is greater than or equal to the preset curvature threshold, then classify the point as a background area;

[0101] Step S232: traverse all points in the neighborhood of the seed point to obtain the defect area and background area of ​​the object to be sprayed.

[0102] Furthermore, after obtaining the defect area and the background area of ​​the object to be sprayed, the method further includes: performing principal component analysis on the defect area of ​​the object to be sprayed to determine the minimum effective boundary of the defect area.

[0103] Therefore, the defect area and background area of ​​the object to be sprayed are accurately divided according to the angle α between each point in the neighborhood of the seed point and the seed point and the curvature of each point in the neighborhood of the seed point.

[0104] Further, refer to Figure 4In step S210, before determining the curvature of each point in the three-dimensional surface topography data, the method further includes:

[0105] The surface three-dimensional topography data is subjected to outlier removal processing, downsampling processing and resampling processing.

[0106] By performing pre-processing operations such as outlier removal and downsampling on the surface three-dimensional morphology data, the impact of noise on the accuracy of defect area recognition is reduced, and the processing speed of subsequent point cloud data is accelerated. Since there are still some abnormal local feature data in the point cloud data after outlier removal and downsampling, such as some abnormalities in the normal vector of the target point calculated according to the field, it is also necessary to use the moving least squares method to resample the point cloud data to smooth the local abnormal features in the disordered point cloud.

[0107] Furthermore, step S300 specifically includes:

[0108] Step S310: Based on the background area of ​​the object to be sprayed, calculating the normal vector of each point in the background area, and determining the average vector of the background area based on the normal vector of each point in the background area;

[0109] Step S320: Calculate the rotation transformation matrix between the average vector and the Z-axis direction;

[0110] Step S330: transforming each point in the background area and the defect area based on the rotation transformation matrix, and projecting the transformed points in the background area and the defect area along the positive direction of the Z axis to generate two-dimensional point cloud data of the background area and two-dimensional point cloud data of the defect area;

[0111] Step S340: Based on the two-dimensional point cloud data of the background area, a global weighted least squares method is used for fitting to generate an ideal reference surface of the object to be sprayed; based on the distance between the two-dimensional point cloud data of the defect area and the ideal reference surface, the depth to be filled of each point in the defect area is determined.

[0112] Specifically, in the above steps, it is assumed that the average vector is (x a ,y a ,z a ), first project the average vector onto the YOZ plane, and obtain the angle β between the average vector and the Z axis. Then rotate the average vector around the X axis by -β, and the average vector will be parallel to the Z axis. The rotation transformation matrix is: in

[0113] Therefore, by converting the three-dimensional point cloud data of the background area and the defect area into a two-dimensional environment to identify the depth of the defect area to be filled, the efficiency of the overall data processing is improved.

[0114] Furthermore, step S400 specifically includes:

[0115] Step S410: constructing a coating deposition model. When constructing the coating deposition model, the spraying shape of the spray gun can be regarded as an ellipse. Then the coating deposition model is:

[0116]

[0117] Among them, f(x, y) is the ideal spray thickness of a single spray per unit time corresponding to the point (x, y), k is the coating thickness correction coefficient, u1 and u2 are the coordinates of the center point of the coating deposition model, δ1 and δ2 are 1 / 3 of the effective semi-axis length in the X-axis and Y-axis directions of the coating deposition model, respectively; specifically, since the total spray height of the defective area does not necessarily satisfy the standard Gaussian distribution with the change of δ1 and δ2 values ​​during the spraying operation, the introduction of the height correction coefficient k can make the spraying process closer to the actual spraying operation under the premise that the spray shape of the spray gun meets the requirements, and the value of k can be set according to the major semi-axis, minor semi-axis and center coordinates of the coating deposition model.

[0118] Step S420: constructing a first reference plane and a second reference plane based on the free-form surface of the defect area; referring to Figure 6 , the second reference plane is a horizontal line passing through the free surface; the first reference plane is parallel to the second reference plane; step S430: based on the coating deposition model, calculating the ideal spraying thickness f1(x1, y1) of a single spraying per unit time corresponding to the point on the first reference plane; based on the ideal spraying thickness f1(x1, y1) of a single spraying per unit time and the pre-built reference plane switching model, calculating the ideal spraying thickness f3(x3, y3) of a single spraying per unit time corresponding to the point on the free surface of the defect area;

[0119] The reference plane switching model is:

[0120]

[0121] Wherein, h1 is the vertical distance from the center point of the spray gun to the first reference plane, h2 is the vertical distance from the center point of the spray gun to the second reference plane, θ is the angle between the first tangent line and the second reference plane, and the first tangent line is a tangent line passing through a point on the free-form surface and tangent to the free-form surface;

[0122] The reference plane switching model is described in detail below:

[0123] Reference Figure 6 , set the spraying area corresponding to point C1 on the first reference plane to S C1, the spraying area corresponding to point C2 on the second reference plane is S C2 , the spraying area corresponding to the point S on the free surface is S C3 ;

[0124] From the projection relationship, we can see that S C1 、S C2 , the relationship between h1 and h2 is: Since the total amount of paint sprayed by the spray gun per unit time remains unchanged, the relationship between the ideal spray thickness f1(x1, y1) corresponding to point C1 on the first reference plane and the ideal spray thickness f2(x2, y2) corresponding to point C2 on the second reference plane can be obtained as follows:

[0125] Then, the relationship between the ideal spraying thickness f3(x3, y3) corresponding to point S on the free surface and the ideal spraying thickness f1(x1, y1) corresponding to point C1 on the first reference plane is:

[0126] Step S440: Determine the optimal spray thickness of a single spraying per unit time and the optimal spacing between adjacent spraying paths based on the pre-constructed objective function minE(v, D) and the value of the ideal spray thickness f3(x3, y3) of a single spraying per unit time corresponding to the point on the free surface of the defect area;

[0127] The objective function minE(v,D) is:

[0128]

[0129] Wherein, q(L, v, D) is the actual spraying thickness function, which represents the actual spraying thickness of a single spray per unit time corresponding to a point on the free surface of the defect area when the spraying speed is v, the spacing between adjacent spraying paths is D, and the perpendicular distance between the current spraying path and the previous spraying path is L; and a and b are constants;

[0130] q d is the ideal spraying thickness f3(x3, y3) of a single spraying per unit time corresponding to the point on the free surface of the defect area;

[0131] Step S450: The spraying speed v is set to be fixed, and the second-order derivative of the objective function minE(v, D) is calculated. When the second-order derivative of the objective function minE(v, D) is zero, the corresponding value of L is the optimal spraying thickness of a single spraying per unit time; when the second-order derivative of the objective function minE(v, D) is zero, the corresponding value of D is the optimal spacing between adjacent spraying paths;

[0132] Step S460: Determine the optimal spraying speed based on the actual spraying thickness function q(L, v, D), the optimal spraying thickness of a single spraying per unit time, and the optimal spacing between adjacent spraying paths.

[0133] Therefore, through steps S410 to S460, a dynamic coating deposition model is established, and a second reference plane and a first reference plane are established based on the characteristics of the defect area as an irregular surface. The optimal spraying thickness of a single spraying per unit time on the free surface of the defect area and the optimal spacing between adjacent spraying paths are determined according to the coating deposition model, and the optimal spraying speed is determined according to the actual spraying thickness function q(L, v, D), so as to obtain accurate spraying parameters for spraying the defect area, which greatly improves the adaptability of the coating deposition model to free-form surface parts and can accurately control the single spraying thickness of putty.

[0134] Furthermore, step S500 specifically includes:

[0135] Step S510: based on the optimal spraying thickness of a single spraying in the unit time and the depth to be filled in the defective area, the sprayed layer in the defective area is divided into layers and sliced ​​to obtain the number n of layers of the sprayed layer;

[0136] Step S520: smoothing the point cloud data of the contour area of ​​each spray layer using a Bezier curve;

[0137] Step S530: Based on the optimal spacing between adjacent spray paths in the spray parameters, spraying is started from the first smoothed spray layer, and each smoothed spray layer is sprayed from the inside to the outside in sequence to form a spiral offset spray path until the spraying of the nth layer is completed.

[0138] Therefore, by combining the classic spiral path with the contour offset to form an improved spiral offset composite path, the smooth path can be ensured while avoiding multiple starts and stops of the spray gun. This not only reduces the idle stroke and improves work efficiency, but also better meets the actual situation of putty spraying operations.

[0139] Embodiment 2: This embodiment discloses a putty spraying device for a curved surface component, the device applying the putty spraying method described in embodiment 1, the device comprising a three-dimensional shape data generating unit, an area recognition unit, a depth calculation unit to be filled, a spraying parameter calculation unit, and a defect area layer division unit;

[0140] A three-dimensional shape data generating unit is used to measure the object to be sprayed based on the surface structured light measurement technology and the photometric stereo vision measurement technology to generate the surface three-dimensional shape data of the object to be sprayed;

[0141] an area recognition unit, which recognizes a background area and a defect area of ​​the object to be sprayed based on the three-dimensional surface topography data;

[0142] A depth calculation unit for filling, which determines the depth of the defect area to be filled based on the background area, the defect area and a pre-built defect depth recognition model of the object to be sprayed;

[0143] A spray parameter calculation unit, which determines spray parameters based on the defect area and a pre-built coating deposition model; the spray parameters include the optimal spray thickness of a single spray per unit time, the optimal spray speed, and the optimal spacing between adjacent spray paths;

[0144] The defect area hierarchical division unit divides the spray layer of the defect area by a layered slicing method based on the optimal spray thickness of a single spraying in the unit time and the depth to be filled in the defect area, and smoothes the point cloud data of the contour area of ​​each spray layer by a Bezier curve; and sprays each spray layer of the defect area in sequence based on the spray parameters.

[0145] Example 3:

[0146] This embodiment discloses a terminal, including a processor, a memory and a communication bus; the communication bus is used to realize connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to implement the steps of the putty spraying method as described in Example 1.

[0147] Example 4:

[0148] This embodiment discloses a computer-readable storage medium, which stores instructions. When the instructions are executed, the steps of the putty spraying method described in the first embodiment are performed.

[0149] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for spraying putty on a curved surface component, characterized in that: The following steps are involved: Step S100: measuring the object to be sprayed based on the surface structured light measurement technology and the photometric stereo vision measurement technology to generate three-dimensional surface topography data of the object to be sprayed; Step S200: identifying the background area and defect area of ​​the object to be sprayed based on the three-dimensional surface topography data; Step S300: determining a depth to be filled in the defect area based on the background area, the defect area and a pre-built defect depth recognition model of the object to be sprayed; Step S400: determining spraying parameters based on the defect area and a pre-built coating deposition model; The spraying parameters include the optimal spraying thickness of a single spraying per unit time, the optimal spraying speed, and the optimal spacing between adjacent spraying paths; Step S500: Based on the optimal spraying thickness of a single spraying in the unit time and the depth to be filled in the defective area, the sprayed layer in the defective area is divided into layers and sliced, and the point cloud data of each sprayed layer contour area is smoothed using a Bezier curve; spraying each spray layer in the defect area in sequence based on the spraying parameters; Step S400 specifically includes: Step S410: constructing a coating deposition model; The coating deposition model is: Wherein, f(x, y) is the ideal spray thickness of a single spray per unit time corresponding to the point (x, y), k is the coating thickness correction coefficient, u1 and u2 are the coordinates of the center point of the coating deposition model, δ1 and δ2 are 1 / 3 of the effective semi-axis length in the X-axis direction and the Y-axis direction of the coating deposition model, respectively; Step S420: constructing a first reference plane and a second reference plane based on the free-form surface of the defect area; Step S430: Based on the coating deposition model, calculate the ideal spraying thickness f1(x1, y1) of a single spraying per unit time corresponding to a point on the first reference plane; based on the ideal spraying thickness f1(x1, y1) of a single spraying per unit time and the pre-built reference plane switching model, calculate the ideal spraying thickness f3(x3, y3) of a single spraying per unit time corresponding to a point on the free surface of the defect area; The reference plane switching model is: Wherein, h1 is the vertical distance from the center point of the spray gun to the first reference plane, h2 is the vertical distance from the center point of the spray gun to the second reference plane, θ is the angle between the first tangent line and the second reference plane, and the first tangent line is a tangent line passing through a point on the free-form surface and tangent to the free-form surface; Step S440: Determine the optimal spray thickness of a single spraying per unit time and the optimal spacing between adjacent spraying paths based on the pre-constructed objective function minE(v, D) and the value of the ideal spray thickness f3(x3, y3) of a single spraying per unit time corresponding to the point on the free surface of the defect area; The objective function minE(v,D) is: Wherein, q(L, v, D) is the actual spraying thickness function, which represents the actual spraying thickness of a single spray per unit time corresponding to a point on the free surface of the defect area when the spraying speed is v, the spacing between adjacent spraying paths is D, and the perpendicular distance between the current spraying path and the previous spraying path is L; and a and b are constants; q d is the ideal spraying thickness f3(x3, y3) of a single spraying per unit time corresponding to the point on the free surface of the defect area; Step S450: The spraying speed v is set to be fixed, and the second-order derivative of the objective function minE(v, D) is calculated; when the second-order derivative of the objective function minE(v, D) is zero, the corresponding value of L is the optimal spraying thickness of a single spraying per unit time; when the second-order derivative of the objective function minE(v, D) is zero, the corresponding value of D is the optimal spacing between adjacent spraying paths; Step S460: Determine the optimal spraying speed based on the actual spraying thickness function q(L, v, D), the optimal spraying thickness of a single spraying per unit time, and the optimal spacing between adjacent spraying paths.

2. The method for spraying putty on a curved surface component according to claim 1, wherein: Step S100 specifically includes: Step S110: measuring the object to be sprayed based on the surface structured light measurement technology to obtain initial surface structured light point cloud data; filtering the initial surface structured light point cloud data to filter out high-frequency information in the initial surface structured light point cloud data and retain low-frequency information in the initial surface structured light point cloud data to generate a surface structured light three-dimensional model; Step S120: Measuring the object to be sprayed based on photometric stereo vision measurement technology to obtain initial photometric stereo point cloud data, filtering the initial photometric stereo point cloud data to remove low-frequency information in the initial photometric stereo point cloud data and retain high-frequency information in the initial photometric stereo point cloud data, thereby generating a photometric stereo 3D model; Step S130: generating surface three-dimensional topography data of the object to be sprayed based on the surface structured light three-dimensional model and the photometric stereo three-dimensional model.

3. The method for spraying putty on a curved surface component according to claim 1, wherein: Step S200 specifically includes: Step S210: determining the curvature of each point in the three-dimensional surface topography data based on the three-dimensional surface topography data; Step S220: Marking the point with the smallest curvature in the three-dimensional surface topography data as a seed point, dividing the seed point into a defect area of ​​the object to be sprayed, and calculating the angle α between each point in the neighborhood of the seed point and the seed point; Step S230: Determine the defect area and background area of ​​the object to be sprayed based on the angle α between each point in the neighborhood of the seed point and the seed point and the curvature of each point in the neighborhood of the seed point.

4. The method for spraying putty on a curved surface component according to claim 3, wherein: Step S230 specifically includes: Step S231: Determine the size of the angle α and a preset angle threshold; if the angle α between the point and the seed point is less than the preset angle threshold, then classify the point as a defect area; if the angle α between the point and the seed point is greater than or equal to the preset angle threshold, then further determine the size of the curvature of the point and a preset curvature threshold; if the curvature of the point is less than the preset curvature threshold, then classify the point as a defect area; if the curvature of the point is greater than or equal to the preset curvature threshold, then classify the point as a background area; Step S232: traverse all points in the neighborhood of the seed point to obtain the defect area and background area of ​​the object to be sprayed.

5. The method for spraying putty on a curved surface component according to claim 3, wherein: Before determining the curvature of each point in the three-dimensional surface topography data, the method further includes: The surface three-dimensional topography data is subjected to outlier removal processing, downsampling processing and resampling processing.

6. The method for spraying putty on a curved surface component according to claim 1, wherein: Step S300 specifically includes: Step S310: Based on the background area of ​​the object to be sprayed, calculating the normal vector of each point in the background area, and determining the average vector of the background area based on the normal vector of each point in the background area; Step S320: Calculate the rotation transformation matrix between the average vector and the Z-axis direction; Step S330: transforming each point in the background area and the defect area based on the rotation transformation matrix, and projecting the transformed points in the background area and the defect area along the positive direction of the Z axis to generate two-dimensional point cloud data of the background area and two-dimensional point cloud data of the defect area; Step S340: Based on the two-dimensional point cloud data of the background area, a global weighted least squares method is used for fitting to generate an ideal reference surface of the object to be sprayed; based on the distance between the two-dimensional point cloud data of the defect area and the ideal reference surface, the depth to be filled of each point in the defect area is determined.

7. A putty spraying device for a curved surface component using the putty spraying method for a curved surface component according to any one of claims 1 to 6, characterized in that: include: 3D shape data generation unit, area recognition unit, depth calculation unit to be filled, spraying parameter calculation unit, defect area layer division unit; A three-dimensional shape data generating unit is used to measure the object to be sprayed based on the surface structured light measurement technology and the photometric stereo vision measurement technology to generate the surface three-dimensional shape data of the object to be sprayed; an area recognition unit, which recognizes a background area and a defect area of ​​the object to be sprayed based on the three-dimensional surface topography data; A depth calculation unit for filling, which determines the depth of the defect area to be filled based on the background area, the defect area and a pre-built defect depth recognition model of the object to be sprayed; A spraying parameter calculation unit determines the spraying parameters based on the defect area and a pre-constructed coating deposition model; the spraying parameters include the optimal spraying thickness, optimal spraying speed, and optimal spacing between adjacent spraying paths for a single spraying per unit time; a defect area layering division unit divides the spray layer of the defect area by a layered slicing method based on the optimal spraying thickness of a single spraying per unit time and the depth to be filled in the defect area, and smoothes the point cloud data of the contour area of ​​each spray layer by a Bezier curve; and sprays each spray layer of the defect area in sequence based on the spraying parameters.

8. A terminal, characterized in that: include: A processor, a memory and a communication bus; the communication bus is used to realize connection and communication between the processor and the memory, and the processor is used to execute one or more programs stored in the memory to implement the steps of the curved component putty spraying method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the steps of the method for spraying putty on a curved surface component according to any one of claims 1 to 6 are performed.

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