Deep groove ball bearing inner ring groove curvature measuring method based on line structured light

Abstract

The invention discloses a deep groove ball bearing inner ring groove curvature measuring method based on linear structured light, and belongs to the technical field of precise instrument manufacturing and precise testing and metering. An existing measuring method is high in measuring cost and low in precision. Calibrating a laser plane of a linear laser, a camera and an air floating turntable; the inner ring of the deep groove ball bearing to be measured is placed on the air floating rotary table, the air floating rotary table is driven to drive the inner ring of the deep groove ball bearing to be measured to rotate, the calibrated line laser is used for emitting laser to the inner ring of the deep groove ball bearing to be measured, the calibrated camera collects the laser reflected by the laser plane of the calibrated line laser, and the laser serves as a laser stripe image; and extracting coordinates of a laser stripe center point from each frame of laser stripe image by using a Steger method, converting the coordinates into three-dimensional world coordinates, taking the three-dimensional world coordinates as point cloud of a single contour, converting the point cloud into a coordinate system where the calibrated air floating turntable is located, and processing the converted point cloud to obtain the curvature of the inner ring groove of the deep groove ball bearing to be measured. The device is used for measuring the bearing inner ring groove curvature.

Description

Technical Field

[0001] This invention belongs to the field of precision instrument manufacturing and precision testing and measurement technology. Background Technology

[0002] Deep groove ball bearings play a vital role in numerous fields and applications due to their simple structure, high load capacity, and smooth operation. The inner ring groove curvature of a deep groove ball bearing is one of the key parameters affecting bearing performance, directly influencing contact stress, lifespan, friction, vibration, noise, and load capacity. Therefore, accurate measurement of the groove curvature radius coefficient is crucial during the design and manufacturing process.

[0003] The current measurement methods mainly include the following: (1) Contact contour measurement method: By contact contour measurement, the coordinate values ​​of each point on the surface of the bearing to be measured are obtained, and then the coordinate values ​​of the measuring points are fitted into an arc. The radius of the arc is the radius of curvature of the ball bearing. This method can obtain high accuracy, but requires professional measuring equipment and operating skills, and is prone to damaging the workpiece; (2) Ball head scraping method: This is a traditional measurement method. By applying color to the groove, and then scraping it with a ball head, the size of the groove curvature radius is judged according to the removal of color. This method is simple and easy to implement, but the accuracy is low; (3) Groove curvature meter measurement method: The groove curvature meter directly measures the groove radius through the measuring probe and uses a specific method to calculate and analyze the groove parameters. This method is currently the measuring instrument with the highest measurement accuracy, but it has extremely high requirements for the equipment storage environment and bearing measurement environment, and the measurement cost is high;

[0004] In summary, current methods for measuring the inner ring groove curvature of deep groove ball bearings suffer from high measurement costs and low measurement accuracy. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of high measurement cost and low measurement accuracy in current methods for measuring the inner ring curvature of deep groove ball bearings. This invention proposes a method for measuring the inner ring curvature of deep groove ball bearings based on line structured light.

[0006] A method for measuring the inner ring groove curvature of deep groove ball bearings based on line structured light, the method comprising the following:

[0007] Step 1: Calibrate the laser plane of the line laser, the camera, and the air-bearing turntable;

[0008] Step 2: Place the inner ring of the deep groove ball bearing to be tested on the air-bearing turntable and drive the air-bearing turntable to rotate the inner ring of the deep groove ball bearing to be tested. Use the calibrated line laser to emit laser light onto the inner ring of the deep groove ball bearing to be tested. The laser light reflected from the laser plane of the calibrated line laser is collected by the calibrated camera and used as a laser stripe image. Use the Steger method to extract the coordinates of the center point of the laser stripe from each frame of the laser stripe image. After converting the coordinates into three-dimensional world coordinates, it is used as the point cloud of a single contour. Convert the point cloud data to the coordinate system of the calibrated air-bearing turntable and process the converted point cloud data to obtain the groove curvature of the inner ring of the deep groove ball bearing to be tested.

[0009] Preferably, the center line of the laser stripe is extracted from each frame of the laser stripe image using the Steger method. The specific process is as follows:

[0010] Step 21: Obtain the laser stripes in each frame of the laser stripe image using binarization, dilation, and maximum connected component filtering methods, and filter out useless information in the image;

[0011] Step 22: Perform Gaussian filtering on the laser stripes to obtain the filtered laser stripes. Use the Hessian matrix to obtain the normal direction of the filtered laser stripes. Apply Taylor polynomial expansion to the pixel grayscale of the filtered laser stripes along the normal direction to obtain the grayscale distribution function. Use the maximum grayscale value in the grayscale distribution function as the coordinate of the center point of the laser stripe along the normal direction.

[0012] Preferably, the Hessian matrix expression is:

[0013] ,

[0014] In the formula, These are the characteristic values ​​of the filtered laser stripes. To find the sign of the partial derivative, It is a two-dimensional Gaussian function. These are the grayscale values ​​of each pixel. , and These are the elements of the Hessian matrix constructed for each pixel.

[0015] Preferably, the coordinates of the converted point cloud data are:

[0016]

[0017] In the formula, Indicates the initial data coordinates. This represents the coordinates of the data points after the transformation. This indicates that the data points will be shifted to the position specified in the original text. The coordinate transformation matrix in the coordinate system centered at the center. Indicates and In the opposite direction Indicates the rotation angle along the X-axis. Then rotate the angle along the Z-axis. The coordinate transformation matrix, This indicates the rotation angle along the Z-axis. Then rotate the angle along the Z-axis. The coordinate transformation matrix, This is the sampling angle transformation matrix.

[0018] Preferably, the converted point cloud data is processed to obtain the inner ring groove curvature of the deep groove ball bearing to be measured, specifically as follows:

[0019] The converted point cloud data is processed using a supervoxel network downsampling method to obtain downsampled data. The downsampled data is then fitted into a quadratic surface, and the Gaussian curvature value of each downsampled data point on the quadratic surface is calculated. The downsampled data points corresponding to the Gaussian curvature values ​​that meet the threshold range are selected, and the least squares method is used for fitting to obtain the inner ring groove curvature of the deep groove ball bearing to be tested.

[0020] The beneficial effects of this invention are:

[0021] This invention first calibrates the key components of the measurement system, then performs rotational scanning to acquire point clouds, processes the point clouds, performs feature fitting calculations, and finally obtains the measured value (curvature of the inner ring groove of a deep groove ball bearing).

[0022] This invention addresses the problems of existing bearing groove curvature measurement technologies, such as workpiece damage from contact measurements, low efficiency, high cost, large dynamic errors in non-contact solutions, and low measurement accuracy due to incomplete surface coverage. It provides a method that reduces mechanical vibration interference through nanometer-level rotation control of an air-bearing turntable, achieves full-surface data acquisition through multi-angle projection with line-structured light, and improves measurement accuracy and repeatability through data processing algorithms. This method enables full-range coverage measurement of deep groove ball bearing groove curvature and is suitable for applications such as aerospace bearing production lines and high-precision robot assembly.

[0023] The non-contact measurement method used in this invention has significant advantages in bearing parts measurement due to its high measurement accuracy, non-damage to the workpiece measurement surface, and fast measurement speed. It has broad application prospects, especially for the measurement of high-precision bearing rings and thin-walled bearing rings.

[0024] This invention overcomes the limitation of traditional single-point laser measurement, which can only obtain local curvature. By using a 360° continuous rotation of an air-bearing turntable and line structured light projection, it achieves full-surface measurement of bearing groove curvature radius within the range of 4~10mm without blind spots. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the measurement system.

[0026] Figure 2 This is a schematic diagram of a method for measuring the inner ring groove curvature of a deep groove ball bearing based on line structured light. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0029] Example:

[0030] A method for measuring the inner ring groove curvature of deep groove ball bearings based on line structured light, the method comprising the following:

[0031] Step 1: Calibrate the laser plane of the line laser 3, the camera, and the air-bearing turntable 2;

[0032] Step 2: Place the inner ring 1 of the deep groove ball bearing to be tested on the air-bearing turntable 2, drive the air-bearing turntable 2 to rotate the inner ring 1 of the deep groove ball bearing to be tested, use the calibrated line laser 3 to emit laser light into the inner ring 1 of the deep groove ball bearing to be tested, and use the calibrated camera to collect the laser light reflected from the laser plane of the calibrated line laser 3 as a laser stripe image. Use the Steger method to extract the coordinates of the center point of the laser stripe from each frame of the laser stripe image, convert the coordinates into three-dimensional world coordinates, and use them as a point cloud of a single contour. Convert the point cloud to the coordinate system of the calibrated air-bearing turntable 2, process the converted point cloud, and obtain the groove curvature of the inner ring of the deep groove ball bearing to be tested.

[0033] Specifically, Figure 1 The air-bearing turntable 2 consists of an air-bearing turntable 2-2 located on the bottom surface and a three-jaw chuck 2-1 located on the air-bearing turntable 2-2. The inner ring 1 of the deep groove ball bearing to be tested is placed on the three-jaw chuck 2-1. The bottom of the line laser 3 is supported by a bracket 4. Figure 2 The rotary table in this embodiment refers to the air-float rotary table.

[0034] After converting these coordinates to 3D world coordinates, they become a point cloud (a discrete set of 3D coordinates) representing a single contour. The conversion process is as follows:

[0035] 1. Calibrate the camera's intrinsic parameters (focal length, principal point coordinate distortion coefficient, etc.) and the system's extrinsic parameters (relative positional relationship parameters between the laser emitter and the camera, etc.).

[0036] 2. Obtain the two-dimensional image coordinates of the center point of the stripe cross section.

[0037] The coordinates (pixel coordinates) of the center points of the stripes in a 2D image are converted into coordinates in 3D space (world coordinates), ultimately forming a point cloud (a discrete set of 3D coordinates) of a single contour. Specifically, the coordinates of the center points of the laser stripes are calculated using "camera intrinsic parameters" and a "pinhole imaging model," converting the pixel coordinates (u, v) of each center point into a 3D ray direction vector in the camera coordinate system (the camera coordinate system has its origin at the camera's optical center, with the Z-axis along the optical axis). Combined with laser plane constraints, the 3D world coordinates are calculated. The "single laser stripe" projected by the laser projector is essentially a fixed laser plane in space (its equation can be determined through system extrinsic parameters). The center point of the stripe lies simultaneously on both the "camera ray" and the "laser plane," therefore, through the geometric relationship of "ray intersecting the plane," the 3D world coordinates (X, Y, Z) of the center point can be calculated.

[0038] The extracted laser stripe information can be used to calculate the point cloud data of a single contour.

[0039] Establishing a systematic measurement model specifically refers to the visual measurement model and depth information measurement model involved in the line structured light measurement process. The purpose is to decode the information carried by the laser stripes into the three-dimensional contour information of the workpiece. The visual measurement model includes the relationships between the image coordinate system and the pixel coordinate system, the image coordinate system and the camera coordinate system, the world coordinate system and the camera coordinate system, and the establishment of a distortion model. The depth information measurement model is specifically as follows: Assuming the laser plane equation is:

[0040]

[0041] The depth s can then be calculated using equation (1):

[0042]

[0043] Once the s value is obtained, the pixel points can be converted to the camera coordinate system using the camera intrinsic parameter matrix:

[0044]

[0045] The visual measurement model mentioned in step (1) is calibrated using the Zhang Zhengyou calibration method. The Zhang Zhengyou calibration method requires a two-dimensional checkerboard with known actual size as a calibration board. Multiple images are taken by the camera, and the intrinsic parameter matrix, extrinsic parameter matrix and distortion coefficient of the camera are calculated.

[0046] By using a two-dimensional checkerboard to calibrate the given laser plane, the laser plane information can be obtained. Using this constraint, the unique spatial point position corresponding to the laser stripe point can be obtained.

[0047] Further specifying the method, the coordinates of the center point of the laser stripe are extracted from each frame of the laser stripe image using the Steger method. The specific process is as follows:

[0048] Step 21: Obtain the laser stripes in each frame of the laser stripe image using binarization, dilation, and maximum connected component filtering methods, and filter out useless information in the image;

[0049] Step 22: Perform Gaussian filtering on the laser stripes to obtain the filtered laser stripes. Use the Hessian matrix to obtain the normal direction of the filtered laser stripes. Apply Taylor polynomial expansion to the pixel grayscale of the filtered laser stripes along the normal direction to obtain the grayscale distribution function. Use the maximum grayscale value in the grayscale distribution function as the coordinate of the center point of the laser stripe along the normal direction.

[0050] Further specifying, the Hessian matrix expression is:

[0051] ,

[0052] In the formula, These are the characteristic values ​​of the filtered laser stripes. To find the sign of the partial derivative, It is a two-dimensional Gaussian function. These are the grayscale values ​​of each pixel. , and These are the elements of the Hessian matrix constructed for each pixel.

[0053] Specifically, after completing the calibration, a centerline extraction algorithm is designed for the laser stripe images acquired by the camera. Based on the characteristics of the actual light stripe images, binarization, dilation, and maximum connected component filtering are used to identify the stripe regions, thus filtering out useless information in the image. Then, based on the Gaussian distribution characteristics of the laser stripe grayscale, Gaussian smoothing is applied to the stripes. Finally, the Steger algorithm is used to extract the center of the light stripes. The Steger algorithm is based on the Hessian matrix to achieve sub-pixel precision positioning of the stripe center. Specifically, the image is treated as a two-dimensional matrix; firstly, Gaussian filtering is applied to the image, and the Gaussian variance needs to be set. w is the width of the light stripe. Then, the eigenvalues ​​and eigenvectors are calculated using the Hessian matrix. The largest eigenvalue and its corresponding eigenvector correspond to the direction of the normal of the center of the light stripe. Then, the gray-scale distribution of the light stripe is Taylor expanded in the direction of the normal. The Taylor expansion is shown in equation (4).

[0054] (4)

[0055] In equation (4), n x n y Let N be the unit vector with normal vector, and N = (tn) x , tn y ), r x r y The expression for is shown in equation (5).

[0056] (5)

[0057] Next, obtain the minimum point on the corresponding normal line; this point is the sub-pixel point at the center of the fringe cross-section. That is, according to... The expression for the calculated parameter t is shown in equation (6).

[0058] (6)

[0059] The location of the center point of the fringe cross section is

[0060] Based on the calibration results and the extracted laser stripe information, the point cloud data of a single contour can be calculated.

[0061] Further specifying, the coordinates of the transformed point cloud data are:

[0062]

[0063] In the formula, Indicates the initial data coordinates. This represents the coordinates of the data points after the transformation. This indicates that the data points will be shifted to the position specified in the original text. The coordinate transformation matrix in the coordinate system centered at the center. Indicates the rotation angle along the X-axis. Then rotate the angle along the Z-axis. The coordinate transformation matrix, This indicates the rotation angle along the Z-axis. Then rotate the angle along the Z-axis. The coordinate transformation matrix, This is the sampling angle transformation matrix.

[0064] Further refine the process by processing the converted point cloud data to obtain the inner ring groove curvature of the deep groove ball bearing under test, specifically:

[0065] The transformed point cloud data is processed using a supervoxel network downsampling method to obtain downsampled point clouds. The downsampled point clouds are then fitted into quadratic surfaces, and the Gaussian curvature value of each downsampled point cloud on the quadratic surface is calculated. The downsampled point clouds corresponding to Gaussian curvature values ​​that meet the threshold range are selected, and least squares fitting is performed to obtain the inner ring groove curvature of the deep groove ball bearing to be tested.

[0066] Specifically, the position of the rotary table axis in the online structured light coordinate system is calculated to determine the relative pose of the rotary table axis and the online structured light sensor in space. Specifically: a standard sphere is placed on the rotary table, and the online structured light beam is projected onto the standard sphere. The rotary table is rotated by a rotational scanning data acquisition system, and the online structured light sensor measures the beam sequence and extracts data point information. (The standard sphere serves as a calibration tool for the rotary table position; this step collects and calculates the information reflected back to the receiver after the beam is projected onto the sphere to determine the position of the rotary table axis.) Furthermore, the point cloud data from different measurement positions are integrated and stitched together into a coordinate system centered on the actual axis of rotation of the rotary table.

[0067] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for measuring the inner ring groove curvature of deep groove ball bearings based on line structured light, characterized in that, The method includes the following: Step 1: Calibrate the laser plane of the line laser (3), the camera, and the air-bearing turntable (2); Step 2: Place the inner ring (1) of the deep groove ball bearing to be tested on the air-bearing turntable (2), drive the air-bearing turntable (2) to rotate the inner ring (1) of the deep groove ball bearing to be tested, use the calibrated line laser (3) to emit laser to the inner ring (1) of the deep groove ball bearing to be tested, and use the calibrated camera to collect the laser reflected from the laser plane of the calibrated line laser (3) as the laser stripe image. Use the Steger method to extract the coordinates of the center point of the laser stripe from each frame of the laser stripe image, convert the coordinates into three-dimensional world coordinates, and use them as the point cloud of a single contour. Convert the point cloud to the coordinate system of the calibrated air-bearing turntable (2), process the converted point cloud, and obtain the groove curvature of the inner ring of the deep groove ball bearing to be tested.

2. The method for measuring the inner ring groove curvature of a deep groove ball bearing based on line structured light according to claim 1, characterized in that, The coordinates of the center point of the laser stripe are extracted from each frame of the laser stripe image using the Steger method. The specific process is as follows: Step 21: Obtain the laser stripes in each frame of the laser stripe image using binarization, dilation, and maximum connected component filtering methods, and filter out useless information in the image; Step 22: Perform Gaussian filtering on the laser stripes to obtain the filtered laser stripes. Use the Hessian matrix to obtain the normal direction of the filtered laser stripes. Apply Taylor polynomial expansion to the pixel grayscale of the filtered laser stripes along the normal direction to obtain the grayscale distribution function. Use the maximum grayscale value in the grayscale distribution function as the coordinate of the center point of the laser stripe along the normal direction.

3. The method for measuring the inner ring groove curvature of a deep groove ball bearing based on line structured light according to claim 2, characterized in that, The expression for the Hessian matrix is: ， In the formula, These are the characteristic values ​​of the filtered laser stripes. To find the sign of the partial derivative, It is a two-dimensional Gaussian function. These are the grayscale values ​​of each pixel. , and These are the elements of the Hessian matrix constructed for each pixel.

4. The method for measuring the inner ring groove curvature of a deep groove ball bearing based on line structured light according to claim 1, characterized in that, The coordinates of the converted point cloud data are:

5. In the formula, Indicates the initial data coordinates. This represents the coordinates of the data points after the transformation. This indicates that the data points will be shifted to the position specified in the original text. The coordinate transformation matrix in the coordinate system centered at the center. Indicates and In the opposite direction Indicates the rotation angle along the X-axis. Then rotate the angle along the Z-axis. The coordinate transformation matrix, This indicates the rotation angle along the Z-axis. Then rotate the angle along the Z-axis. The coordinate transformation matrix, This is the sampling angle transformation matrix.

6. The method for measuring the inner ring groove curvature of a deep groove ball bearing based on line structured light according to claim 1, characterized in that, The converted point cloud is processed to obtain the inner ring groove curvature of the deep groove ball bearing to be measured, specifically: The transformed point cloud is processed using a supervoxel network downsampling method to obtain a downsampled point cloud. The downsampled point cloud is then fitted into a quadratic surface, and the Gaussian curvature value of each downsampled point cloud on the quadratic surface is calculated. The downsampled point cloud corresponding to the Gaussian curvature value that meets the threshold range is selected and fitted using the least squares method to obtain the inner ring groove curvature of the deep groove ball bearing to be tested.

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