An on-line detection device for thin-wall bearing assembly and an assembly method thereof

Abstract

This invention relates to an online inspection device and method for assembling thin-walled bearings. The inspection device employs flexible clamping and a non-contact infrared scanning probe during the inspection process, strictly controlling deformation risks and ensuring accurate measurement data. It includes a software analysis system, achieving closed-loop management from hardware control to data analysis. The assembly method reconstructs the ring morphology based on the inspection data, obtains the ring radial runout data, and performs fitting judgment through virtual assembly. Fit judgment can be completed online without trial assembly, thus avoiding the problem of disassembly and reassembly due to unqualified finished bearings, effectively improving production efficiency and product yield, and ensuring the consistency of finished bearing precision. Furthermore, it proposes the concept of bearing identification and database management. By combining bearing identification and database management, it supports rapid switching and assembly of different thin-walled bearing rings, meeting the needs of small-batch, high-precision manufacturing for high-end equipment.

Description

Technical Field

[0001] This invention relates to the field of bearing assembly technology, and more specifically to an online detection device and its assembly method in the assembly process of thin-walled bearings. Background Technology

[0002] Thin-walled bearings, as critical support components, are widely used in high-end equipment such as aerospace, precision medical equipment, and industrial robot joints. However, during the assembly of thin-walled bearings, due to morphological errors between the inner and outer rings and the large number of rollers, improper assembly relationships between the rings can lead to substandard precision in the finished bearings, reducing the yield rate.

[0003] Currently, industrial enterprises mainly employ two methods for assembling thin-walled bearings: one is a selection method based on dimensional measurement and calculation, such as the patent application CN108274425A, which measures the height difference between the outer and inner rings and uses adjusting shims to fill the preload to achieve assembly; the other relies on a repeated verification process of "trial assembly-inspection-disassembly," as mentioned in the background technology of the patent application CN105408732A, which requires performance testing on a special device before adjustment or replacement of the bearing rings. The former is mostly based on the assumption of a rigid circle, focusing only on macroscopic dimensions or a single cross-section, making it difficult to reflect the true microscopic morphology of the raceway along the axial and circumferential directions, and ignoring the local deformation of thin-walled parts caused by their own weight or clamping, affecting assembly consistency; the latter is inefficient, and frequent disassembly and assembly can easily cause micro-motion damage to the precision raceway surface, impairing the original precision. Therefore, establishing an online detection device and its assembly method for the assembly process of thin-walled bearings is of great value for achieving efficient and accurate intelligent assembly of thin-walled bearings. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide an online testing device and assembly method for thin-walled bearings. Through the online testing device and the provided assembly method, rapid and accurate measurement and fitting judgment of the inner and outer rings of thin-walled bearings can be achieved, providing a basis for the assembly of the rings and solving the problems of low yield and repeated matching required for high-precision thin-walled bearings.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An online inspection device for thin-walled bearing assembly is installed in a thin-walled bearing assembly production line. The online inspection device includes: a positioning and clamping mechanism, an adjustable scanning probe, a data acquisition system, and a software analysis system. The positioning and clamping mechanism includes a rotary table, a cylinder pushing assembly, and a flexible clamp. The cylinder pushing assembly is set on both sides of the production line and is used to smoothly push the bearing ring to be tested to the center of the rotary table. The flexible clamp is installed on the rotary table and is used to fix the bearing ring at the center of rotation. The adjustable scanning probe acquires the original point cloud data of the inner and outer ring contours of the bearing through point cloud acquisition. The data acquisition system is connected to the adjustable scanning probe and is used to receive and transmit raw point cloud data, integrate the roundness error data of the inner and outer rings of the bearing, and transmit it to the software analysis system. The software analysis system runs on a computer and mainly includes an equipment control module, a data processing module, and a database management module. The equipment control module is used to issue motion commands to control the mechanical movements of the online detection device. The data processing module is responsible for receiving raw point cloud data, performing preprocessing, reconstructing the surface morphology of the channel, and calculating radial runout data. The database management module is used to store measurement results, associate ring identification, and execute inner and outer ring selection logic.

[0006] Furthermore, the adjustable scanning probe is an infrared non-contact scanning probe capable of radial and axial movement. It automatically switches the detection direction and positioning mode according to the type of the ring being measured, and scans and measures multiple equally spaced sections of the inner and outer ring contours. The device control module drives the movement of the adjustable scanning probe and the rotation of the rotary table through a motion controller.

[0007] Furthermore, the flexible clamp uses evenly distributed flexible contacts that gently contact the surface of the bearing race under pneumatic control, fixing the bearing race at the center of rotation.

[0008] Furthermore, the data acquisition system reserves multiple independent data acquisition channels. Each data acquisition channel is independently connected to an adjustable scanning probe and a matching positioning clamping mechanism. Each data acquisition channel can complete the ring scanning and data reception in parallel without interfering with each other.

[0009] An assembly method for thin-walled bearings includes the following steps: (1) Multi-section roundness error measurement: Using an online detection device, the roundness error data of the inner and outer ring raceways of the thin-walled bearing at multiple equally spaced sections are collected, and each ring is marked with an identification mark (ID). (2) Data preprocessing and coordinate transformation: The roundness error data in polar coordinates is combined with the axial section position information to convert it into three-dimensional Cartesian coordinates; (3) Reconstructing the surface morphology of the channel: The contours of the entire inner and outer rings are reconstructed using an adaptive parameterization method with chord length parameterization; (4) Rotation accuracy calculation and screening: Input the reconstructed outer and inner ring surfaces into the software analysis system, use the least squares method to fit and establish the reference axis, simulate rotation and obtain radial runout data, compare it with the corresponding accuracy level range specified in the national standard, screen out unqualified rings, bind the radial runout data of qualified rings with the corresponding ring identification (ID) and store it in the core database; (5) Intelligent assembly decision: Based on the preset target accuracy level, the inner and outer rings are selected from the core database for virtual combination. The radial runout value of each combination is calculated according to the error complementarity principle. The accuracy is judged against the national standard limit value. Unqualified combinations are eliminated. Finally, the combination with the smallest radial runout of the assembly is selected from all qualified combinations as the optimal solution. The assembly scheme is output and the database record is updated.

[0010] Furthermore, the process of reconstructing the surface morphology in step (3) is as follows: (3.1) First, the three-dimensional coordinate data is processed using an adaptive parameterization method: when the amount of data exceeds the preset threshold, an adaptive sampling method based on curvature features is adopted to retain more feature points in areas with greater curvature, while appropriately sparsening in flat areas to reduce the data size. (3.2) Subsequently, the chord length parameterization method is used for curve fitting. The parameter values ​​are assigned to the data points according to the Euclidean distance between adjacent data points, so that the parameter distribution is proportional to the physical length of the curve. (3.3) After obtaining the parameters, the node vector is calculated based on the NURBS curve global interpolation algorithm. The control vertex is calculated by solving the linear equation system based on the NURBS basis function, and a NURBS contour curve that can accurately pass through each data point is generated. (3.4) A two-way curve mesh construction strategy is adopted. In the axial direction, the reconstructed NURBS curves of each section are used as the longitudinal curve family. In the circumferential direction, data points at the same angle position of all sections are extracted to reconstruct the transverse curve family. After the longitudinal curve family and the transverse curve family are uniformly reparameterized, the node vectors in the two directions are calculated and the surface control point mesh is generated. Finally, a bicubic NURBS surface is constructed to realize the reconstruction of the entire surface of the channel.

[0011] Furthermore, the specific process for obtaining radial runout data in step (4) is as follows: The reconstructed bicubic NURBS surface data is imported into the software analysis system. The least squares method is used to fit the reconstructed surface as a whole to establish the best fitting reference axis. Then, the ring is simulated to rotate around the reference axis in a virtual environment. The radial distance change of each point on the groove surface relative to the reference axis is automatically calculated. The difference between the maximum and minimum radial distances on the groove surface is extracted as the radial runout value of the ring.

[0012] Furthermore, in step (5), the specific process of selecting the inner and outer circles for virtual combination is as follows: (5.1) Based on the qualified inner ring, generate a virtual assembly combination of “inner ring ID + outer ring ID” with all outer rings one by one. For each combination, calculate the radial runout value of the bearing assembly based on the principle of complementary error between inner and outer rings. (5.2) After each set of virtual assembly radial runout values ​​is calculated, the radial runout value of the assembly is compared with the national standard radial runout allowable range of bearing assemblies of the same precision grade. If the radial runout value of the assembly exceeds the allowable range, the assembly is deemed unqualified and removed; if the assembly value is ≤ the allowable range, the assembly is deemed a qualified assembly. (5.3) From all qualified assembly combinations, select the combination with the smallest radial runout of the bearing assembly as the optimal assembly scheme for the inner ring.

[0013] Furthermore, in step (5), if there are multiple inner circles and multiple outer circles in a batch assembly scenario, each qualified inner circle is sequentially processed according to the process of steps (5.1)-(5.3) to screen for virtual assembly and optimal assembly scheme of the outer circle. If a single outer circle is preferred by multiple inner circles, the final allocation is completed in combination with the production line work order priority.

[0014] Furthermore, in step (5), updating the database records includes: synchronously updating the assembly results and the radial runout value of the bearing assembly to the core database, and binding them with the corresponding inner ring ID and outer ring ID.

[0015] Beneficial effects: 1. The online detection device of the present invention adopts flexible clamping and non-contact infrared scanning probe in the detection process, strictly controls the risk of deformation, ensures the accuracy of measurement data, and guarantees the authenticity and integrity of the original morphology data.

[0016] 2. The assembly method proposed in this invention introduces the concept of bearing identification and database management. By combining bearing identification and database management, it supports the rapid switching and assembly of different thin-walled bearing rings, meeting the needs of small-batch, high-precision manufacturing of high-end equipment.

[0017] 3. The assembly method of the present invention combines detection data and reconstructs the ring morphology through data processing to obtain the ring radial runout data. The assembly method is used to judge the fit through virtual assembly. The fit judgment can be completed through online detection without trial assembly, thereby avoiding the problem of disassembly and reassembly caused by unqualified finished bearings, effectively improving production efficiency and product yield, and ensuring the consistency of finished bearing precision. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the online detection device of the present invention; Figure 2 This is a structural block diagram of the online detection device of the present invention; Figure 3 This is a schematic diagram of the software analysis system. Figure 4 This is a schematic diagram of the measurement cross section in an embodiment of the present invention; Figure 5This is a flowchart of the assembly method of the present invention.

[0019] Reference numerals: 1. Rotary worktable; 2. Adjustable scanning probe; 3. Cylinder push assembly; 4. X-axis adjustment device; 5. Z-axis adjustment device; 6. Flexible fixture. Detailed Implementation

[0020] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1-3 As shown, an online inspection device for thin-walled bearing assembly is installed in a thin-walled bearing assembly production line, including: a positioning and clamping mechanism, an adjustable scanning probe 2, a data acquisition system, and a software analysis system.

[0022] The positioning and clamping mechanism includes: a rotary table 1, a cylinder pushing assembly 3, and a flexible clamp 6; the cylinder pushing assembly 3 is set on both sides of the production line and is used to smoothly push the bearing ring to be tested to the center of the rotary table 1. The flexible clamp 6 is installed at the end of the cylinder pushing assembly 3. The flexible clamp 6 adopts evenly distributed flexible contacts, which gently contact the surface of the bearing ring under pneumatic control, adaptively adjust the contact force, and fix the bearing ring to the center of the rotary table 1.

[0023] The use of a rotary table 1 in conjunction with a flexible fixture 6 can effectively solve the problem of poor rigidity and easy deformation caused by clamping force in thin-walled bearings. While ensuring stable clamping, the flexible fixture 6 minimizes the interference of auxiliary support force on the bearing rings, thus ensuring the authenticity and reliability of subsequent data acquisition.

[0024] The adjustable scanning probe 2 is an infrared non-contact scanning probe. The probe can move radially and axially through the X-axis adjustment device 4 and the Z-axis adjustment device 5. It can automatically switch the detection direction and positioning mode according to the type of bearing ring being measured, so as to scan and measure multiple equally spaced sections of the inner and outer ring contours, and acquire the bearing inner and outer ring contour data through point cloud acquisition.

[0025] The online inspection device can automatically switch the inspection direction and positioning mode of the adjustable scanning probe 2 according to the model (inner ring or outer ring) and size specifications of the ring being tested. By adopting the equal-spacing cross-section scanning method, it can achieve the optimal balance between measurement efficiency, data processing convenience and reconstruction accuracy while ensuring the integrity of the channel morphology inspection and the regularity of the data. At the same time, infrared non-contact measurement avoids the risk of scratching the precision channel surface that may be caused by traditional contact probes.

[0026] The data acquisition system is connected to the adjustable scanning probe 2 and is used to receive and transmit the raw point cloud data collected by the adjustable scanning probe. After the raw point cloud data is initially integrated, the contour of each sampling point is compared with the ideal circle contour, the contour deviation and corresponding angle information of each sampling point are extracted, and finally the roundness error data in polar coordinates is generated and transmitted to the software analysis system for subsequent processing.

[0027] This data acquisition system can be expanded to multiple data acquisition channels, each channel is independently connected to an adjustable scanning probe 2 and a matching positioning and clamping mechanism; the production line adopts a streamlined design, which diverts the inner and outer rings to be inspected to different inspection stations on the production line according to their type. Each station corresponds to a data acquisition channel. Through the streamlined design, each channel can complete the ring scanning and data reception in parallel without interference. By acquiring multiple sets of roundness error data of the inner and outer rings simultaneously through multiple channels, the inspection and assembly efficiency of the production line is effectively improved.

[0028] The software analysis system runs on a computer and mainly includes an equipment control module, a data processing module, and a database management module. The equipment control module is used to issue motion commands and control mechanical actions through the controller, including the movement of the adjustable scanning probe, the rotation of the rotary table, and the movement of the cylinder-driven components; the data processing module is responsible for receiving raw point cloud data, performing data preprocessing, reconstructing the surface morphology of the channel, and calculating radial runout data; the database management module is used to store measurement results, associate ring identification identifiers (IDs), and execute subsequent selection logic.

[0029] The integrated software analysis system realizes closed-loop management from hardware control to data analysis. It can not only intuitively display the three-dimensional morphology and error distribution of the channel, but also digitize the physical parameters of each ring and store them in the database, providing a solid data foundation for subsequent ring assembly and realizing the digitalization and intelligentization of the production process.

[0030] Furthermore, this embodiment also provides a method for assembling thin-walled bearings, such as... Figure 4-5 As shown, it includes the following steps (1)-(5). (1) Measurement of roundness error data of bearing inner and outer rings at multiple cross sections: In order to obtain complete morphological data of the inner and outer ring channels, the above-mentioned online detection device was used to measure multiple cross sections of the inner and outer ring channels of the thin-walled bearing, and the roundness error data of the bearing inner and outer ring channels at different cross sections were obtained, such as Figure 4 As shown, the roundness error data can be represented in polar coordinates as X k (k=1, 2, ..., i), further, each roundness error data X k Each component consists of two parts: Δr and θ. Therefore, the roundness error data for k cross-sections can be specifically expressed as follows: ; Where, △r i θ represents the deviation between the contour of the i-th sampling point and the ideal circle; i This represents the angle of the i-th sampling point.

[0031] Furthermore, each ring is identified, such as: inner ring #001, inner ring #002..., outer ring #001, outer ring #002..., to facilitate subsequent data input.

[0032] (2) Data preprocessing and coordinate transformation: The roundness error data X in polar coordinates on the measured inner and outer ring sections are transformed. k Convert to three-dimensional Cartesian coordinates using the polar-Cartesian coordinate transformation formula.

[0033] The formula for the transformation is: ; Where, θ i z is the angle of the k-th cross section at the i-th sampling point; i is the z-axis position of the k-th cross section; i is the number of equally divided sampling points in the 360-degree circumferential direction; k is the number of axial cross sections.

[0034] By combining the axial section position information, the polar coordinates of each measurement point within each section are converted to Cartesian coordinates to support subsequent 3D reconstruction of curves and surfaces.

[0035] (3) Reconstructing the surface morphology of the channel: Since the multiple cross-sectional measurement data obtained by measurement cannot completely reproduce the morphology of the ring, an adaptive parameterization method is used to reconstruct the entire contour of the inner and outer rings based on the preprocessed Cartesian coordinate data. First, curve fitting is performed by the chord length parameterization method to obtain cubic non-uniform rational B-spline (NURBS) curves. Then, the curves obtained by multiple measurements are constructed into a surface by the bidirectional NURBS curve interpolation algorithm, thereby realizing a high-fidelity reconstruction of the overall surface morphology of the channel.

[0036] The reconstruction of the channel surface morphology specifically includes the following steps (3.1)-(3.4). (3.1) First, the three-dimensional coordinate data is processed using an adaptive parameterization method: when the amount of data exceeds the preset threshold, an adaptive sampling method based on curvature features is adopted to retain more feature points in areas with greater curvature and appropriately sparse them in flat areas. The data size is reduced by retaining feature points in areas with high curvature.

[0037] (3.2) The chord length parameterization method in the adaptive parameterization method is used for curve fitting, and the parameter allocation method is as follows: ; Where u0 is the initial value of the fitting parameters for the first sampling point; u i u represents the fitted parameter value corresponding to the i-th sampling point. i-1 P represents the fitted parameter value for the (i-1)th sampling point; L is the sum of the chord lengths of all adjacent sampling points; i Let P be the three-dimensional Cartesian coordinates of the i-th sampling point. i-1 Let be the three-dimensional Cartesian coordinates of the (i-1)th sampling point.

[0038] This method assigns parameters based on the Euclidean distance between adjacent control points, which can reflect the physical length characteristics of the geometry. It is applicable to most practical application scenarios and has good geometric fidelity and computational efficiency.

[0039] (3.3) After obtaining the parameters, construct the profile curve based on the parameterization results; first, substitute the parameters into the following mathematical expression for the NURBS curve, define the curve shape through the control point coordinates, weights, and B-spline basis functions, and obtain the NURBS profile curve: ; Among them, {d i} represents the coordinates of the control point; {w i} represents the control point weights; N i,k (u) is the B-spline basis function; u is the parameter on the curve.

[0040] After determining the optimal parameterized configuration, the node vector T=[t0,t1,...,t] is calculated based on the global interpolation method of NURBS curves. m+k+1 ], and calculate the control vertex {d} by solving a system of linear equations based on NURBS basis functions. i This generates a NURBS profile curve that accurately passes through each data point.

[0041] (3.4) Based on the geometric characteristics of the channel surface, a two-way curve mesh construction strategy is adopted. In the u direction (axial direction), the NURBS profile curves of each section are used as the longitudinal curve family. In the v direction (circumferential direction), data points at the same angle position of each section are extracted to reconstruct the transverse curve family. After uniformly reparameterizing the longitudinal curve family and the transverse curve family, the node vectors in the u and v directions are calculated and the control point mesh is generated to construct the bicubic NURBS surface and realize the reconstruction of the entire surface of the channel.

[0042] (4) Substitute into the software analysis system to calculate the rotational accuracy: Substitute the reconstructed NURBS surface into the software analysis system to analyze the three-dimensional data of the bearing rings, obtain the radial runout data, compare it with the corresponding accuracy level range specified in the national standard, screen out unqualified rings, bind the radial runout data of qualified rings with the corresponding ring identification, and store it in the core database.

[0043] The software analysis system includes the permissible radial runout ranges for bearings of various precision grades as specified in the national standard (GB_T307.1-2017). The permissible radial runout ranges for bearings of different precision grades according to the national standard are shown in Table 1 below: Table 1. Allowable Radial Runout Range for Bearing Accuracy Grade Specifically, after substituting the reconstructed NURBS surface into the software analysis system, the least squares method is used to fit the reconstructed NURBS surface as a whole, establishing the best-fit reference axis. Subsequently, the rotation of the bearing ring around this reference axis is simulated in a virtual environment, and the change in radial distance of each point on the raceway surface relative to the reference axis is calculated. The system automatically extracts the radial runout values ​​of the inner and outer rings, that is, the difference between the maximum and minimum radial distances of the inner and outer ring raceway surfaces. After the calculation is completed, it is compared with the corresponding accuracy grade range specified by the national standard. Unqualified bearing rings are screened out, and the radial runout values ​​of qualified bearing rings (radial runout value ≤ corresponding radial runout range) are bound to the bearing ring identification (such as inner ring #001, inner ring #002..., outer ring #001, outer ring #002...) and stored in the core database of the system.

[0044] (5) Intelligent assembly decision: Based on the preset target accuracy level, the system retrieves all inner and outer rings that have been tested and whose radial runout measured values ​​meet the allowable range of the national standard for the same accuracy level from the core database. All inner and outer rings are bound with a unique identity (ID) and radial runout value. The inner and outer rings are selected for virtual combination. The radial runout value of each combination is calculated based on the error complementarity principle. The accuracy is judged against the national standard limit value. Unqualified combinations are eliminated. Finally, the combination with the smallest radial runout of the assembly is selected from all qualified combinations as the optimal solution. The assembly scheme is output and the database record is updated.

[0045] Specifically, the process of selecting the inner and outer circles for virtual combination is as follows: (5.1) Based on the qualified inner ring, generate a virtual assembly combination of “inner ring ID + outer ring ID” with all outer rings one by one, and calculate the radial runout value of the bearing assembly for each combination (calculate the overall deviation based on the principle of complementary inner and outer ring errors). (5.2) After each set of virtual assembly radial runout values ​​is calculated, the value is compared with the national standard radial runout allowable range of bearing assembly under the same precision grade. If the assembly value exceeds the allowable range, the assembly is deemed unqualified and rejected; if the assembly value is ≤ the allowable range, the assembly is deemed qualified.

[0046] (5.3) From all qualified assembly combinations, select the combination with the smallest radial runout of the bearing assembly as the optimal assembly scheme for the inner ring.

[0047] If there are multiple inner rings and multiple outer rings in a batch assembly scenario, the system will execute the above steps (5.1)-(5.3) sequentially for all qualified inner rings. After completing the full virtual assembly and set judgment, the system will match a unique qualified outer ring for each qualified inner ring according to the principle of "minimum radial runout value of assembly". If a single outer ring is preferred by multiple inner rings, the final allocation will be completed in combination with the production line work order priority.

[0048] After making the optimal assembly decision, the optimal assembly scheme (including the unique ID correspondence between the inner and outer rings) is output to the production line. At the same time, the assembly results and the radial runout value of the bearing assembly are updated to the core database and bound to the corresponding inner and outer ring IDs to complete the data archiving of the entire assembly process.

[0049] This method, by combining automated detection with an intelligent decision-making system, significantly reduces the drawbacks of traditional methods that require repeated trial assembly, effectively improves assembly efficiency and finished product qualification rate, and provides a basis for achieving intelligent, stable, and precise assembly production of high-precision bearings.

[0050] To evaluate the effectiveness and accuracy of the above-mentioned testing device and assembly method, this example selects a thin-walled crossed roller bearing of model RB6013 / P2 as the research object, and uses the online testing device and software analysis system of the present invention to perform rotational accuracy analysis.

[0051] The radial runout value measured and calculated using the device of this invention is compared with the allowable radial runout range of the RB6013 / P2 thin-walled crossed roller bearing in the national standard to verify the accuracy of the fitting judgment. The basic parameters of the RB6013 / P2 thin-walled crossed roller bearing used in the experiment are shown in Table 2.

[0052] Table 2 - Basic Parameters of RB6013 / P2 Bearing During the experiment, the thin-walled crossed roller bearing races to be tested were first placed on the positioning and clamping mechanism. Infrared non-contact scanning probes were used to acquire multi-section roundness error data of the inner and outer races. After receiving the raw point cloud data, the software analysis system performed coordinate transformation and data preprocessing.

[0053] Subsequently, an adaptive parametric method and NURBS interpolation algorithm were used to reconstruct a high-fidelity three-dimensional morphological surface of the bearing raceway. The reconstructed surface model was then processed directly in the software analysis system of this invention. Based on the built-in algorithm, the system used the least squares method to perform overall fitting of the reconstructed surface, establish the best-fit reference axis, and automatically calculate the radial distance change of each point on the raceway surface relative to the reference axis.

[0054] The software system automatically extracted the calculation results: within a complete rotation measurement cycle, the calculated radial runout of the thin-walled crossed roller bearing race was 1.7967 μm. The system automatically retrieved the national standard allowable range (≤2.5 µm) corresponding to the RB6013 / P2 grade accuracy from the database for comparison. The results showed that the radial runout value of this set of races was less than the national standard requirement, and the system determined that the set of races was qualified.

[0055] Therefore, the online detection and data processing method of the present invention can accurately reflect the geometry and rotational accuracy of thin-walled bearings, effectively replacing the traditional offline trial assembly and repeated disassembly process, and significantly improving assembly efficiency and finished product qualification rate.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An online inspection device for thin-walled bearing assembly, installed in a thin-walled bearing assembly production line, characterized in that, The online detection device includes: a positioning and clamping mechanism, an adjustable scanning probe, a data acquisition system, and a software analysis system; The positioning and clamping mechanism includes a rotary table, a cylinder pushing assembly, and a flexible clamp. The cylinder pushing assembly is set on both sides of the production line and is used to smoothly push the bearing ring to be tested to the center of the rotary table. The flexible clamp is installed on the rotary table and is used to fix the bearing ring at the center of rotation. The adjustable scanning probe acquires the original point cloud data of the inner and outer ring contours of the bearing through point cloud acquisition. The data acquisition system is connected to the adjustable scanning probe and is used to receive and transmit raw point cloud data, integrate the roundness error data of the inner and outer rings of the bearing, and transmit it to the software analysis system. The software analysis system runs on a computer and mainly includes an equipment control module, a data processing module, and a database management module. The equipment control module is used to issue motion commands to control the mechanical movements of the online detection device. The data processing module is responsible for receiving raw point cloud data, performing preprocessing, reconstructing the surface morphology of the channel, and calculating radial runout data. The database management module is used to store measurement results, associate ring identification, and execute inner and outer ring selection logic.

2. The online testing device for thin-walled bearing assembly according to claim 1, characterized in that, The adjustable scanning probe is an infrared non-contact scanning probe capable of radial and axial movement. It automatically switches the detection direction and positioning mode according to the type of ring being measured, and scans and measures multiple equally spaced sections of the inner and outer ring contours. The equipment control module drives the movement of the adjustable scanning probe and the rotation of the rotary table through a motion controller.

3. The online testing device for thin-walled bearing assembly according to claim 1, characterized in that, The flexible clamp uses evenly distributed flexible contacts that gently contact the surface of the bearing race under pneumatic control, fixing the bearing race at the center of rotation.

4. The online testing device for thin-walled bearing assembly according to claim 1, characterized in that, The data acquisition system has multiple independent data acquisition channels. Each data acquisition channel is independently connected to an adjustable scanning probe and a matching positioning clamping mechanism. Each data acquisition channel can complete the ring scanning and data reception in parallel without interfering with each other.

5. A method for assembling thin-walled bearings, characterized in that, Includes the following steps: (1) Multi-section roundness error measurement: Using an online detection device, the roundness error data of the inner and outer ring raceways of the thin-walled bearing at multiple equally spaced sections are collected, and each ring is marked with an identification mark (ID). (2) Data preprocessing and coordinate transformation: The roundness error data in polar coordinates is combined with the axial section position information to convert it into three-dimensional Cartesian coordinates; (3) Reconstructing the surface morphology of the channel: The contours of the entire inner and outer rings are reconstructed using an adaptive parameterization method with chord length parameterization; (4) Rotation accuracy calculation and screening: Input the reconstructed outer and inner ring surfaces into the software analysis system, use the least squares method to fit and establish the reference axis, simulate rotation and obtain radial runout data, compare it with the corresponding accuracy level range specified in the national standard, screen out unqualified rings, bind the radial runout data of qualified rings with the corresponding ring identification (ID) and store it in the core database; (5) Intelligent assembly decision: Based on the preset target accuracy level, the inner and outer rings are selected from the core database for virtual combination. The radial runout value of each combination is calculated according to the error complementarity principle. The accuracy is judged against the national standard limit value. Unqualified combinations are eliminated. Finally, the combination with the smallest radial runout of the assembly is selected from all qualified combinations as the optimal solution. The assembly scheme is output and the database record is updated.

6. The assembly method for thin-walled bearings according to claim 5, characterized in that, Step (3) The process of reconstructing the surface morphology is as follows: (3.1) First, the three-dimensional coordinate data is processed using an adaptive parameterization method: when the amount of data exceeds the preset threshold, an adaptive sampling method based on curvature features is adopted to retain more feature points in areas with greater curvature, while appropriately sparsening in flat areas to reduce the data size. (3.2) Subsequently, the chord length parameterization method is used for curve fitting. The parameter values ​​are assigned to the data points according to the Euclidean distance between adjacent data points, so that the parameter distribution is proportional to the physical length of the curve. (3.3) After obtaining the parameters, the node vector is calculated based on the NURBS curve global interpolation algorithm. The control vertex is calculated by solving the linear equation system based on the NURBS basis function, and a NURBS contour curve that can accurately pass through each data point is generated. (3.4) A two-way curve mesh construction strategy is adopted. In the axial direction, the reconstructed NURBS curves of each section are used as the longitudinal curve family. In the circumferential direction, data points at the same angle position of all sections are extracted to reconstruct the transverse curve family. After the longitudinal curve family and the transverse curve family are uniformly reparameterized, the node vectors in the two directions are calculated and the surface control point mesh is generated. Finally, a bicubic NURBS surface is constructed to realize the reconstruction of the entire surface of the channel.

7. The assembly method for thin-walled bearings according to claim 5, characterized in that, The specific process for obtaining radial runout data in step (4) is as follows: The reconstructed bicubic NURBS surface data is imported into the software analysis system. The least squares method is used to fit the reconstructed surface as a whole to establish the best fitting reference axis. Then, the ring is simulated to rotate around the reference axis in a virtual environment. The radial distance change of each point on the groove surface relative to the reference axis is automatically calculated. The difference between the maximum and minimum radial distances on the groove surface is extracted as the radial runout value of the ring.

8. The assembly method for thin-walled bearings according to claim 5, characterized in that, In step (5), the specific process of selecting the inner and outer circles for virtual combination is as follows: (5.1) Based on the qualified inner ring, generate a virtual assembly combination of "inner ring ID + outer ring ID" with all outer rings one by one. For each combination, calculate the radial runout value of the bearing assembly based on the principle of complementary error between inner and outer rings. (5.2) After each set of virtual assembly radial runout values ​​is calculated, the radial runout value of the assembly is compared with the national standard radial runout allowable range of bearing assemblies of the same precision grade. If the radial runout value of the assembly exceeds the allowable range, the assembly is deemed unqualified and removed; if the assembly value is ≤ the allowable range, the assembly is deemed a qualified assembly. (5.3) From all qualified assembly combinations, select the combination with the smallest radial runout of the bearing assembly as the optimal assembly scheme for the inner ring.

9. The assembly method for thin-walled bearings according to claim 8, characterized in that, In step (5), if there are multiple inner circles and multiple outer circles in the batch assembly scenario, each qualified inner circle is sequentially processed according to the process of steps (5.1)-(5.3) to screen the virtual assembly and optimal assembly scheme of the outer circle. If a single outer circle is preferred by multiple inner circles, the final allocation is completed in combination with the production line work order priority.

10. The assembly method for thin-walled bearings according to claim 5, characterized in that, In step (5), updating the database records includes: synchronously updating the assembly results and the radial runout value of the bearing assembly to the core database, and binding them with the corresponding inner ring ID and outer ring ID.

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