Tooth profile precision detection method and device of internal tooth tube and electronic equipment

Abstract

The invention discloses a tooth profile precision detection method and device for an internal tooth tube and electronic equipment, and relates to the related technical field of precision measurement, and the method comprises the steps: carrying out the multi-angle scanning of the inner wall of the internal tooth tube, obtaining the original tooth profile point cloud data, carrying out the motion error analysis based on the original tooth profile point cloud data, and generating a motion compensation parameter; correcting the original tooth profile point cloud data based on the motion compensation parameters to generate a corrected tooth profile data set, performing three-dimensional space registration according to the corrected tooth profile data set, and drawing a tooth profile distribution map; and according to the tooth profile distribution map, carrying out region division on the inner tooth tube, traversing a plurality of tooth profile regions to carry out tooth profile precision detection, and generating a tooth profile precision detection report. The technical problem that tooth surface error evaluation is inaccurate due to motion errors in traditional tooth profile detection of the internal tooth tube is solved, and the technical effects that non-contact full tooth surface detection is achieved through multi-sensor cooperative scanning and dynamic motion compensation, and the accuracy and reliability of tooth profile precision detection of the internal tooth tube are improved are achieved.

Description

Technical Field

[0001] This application relates to the field of precision measurement technology, specifically to a method, device, and electronic equipment for detecting the tooth profile accuracy of an internal toothed tube. Background Technology

[0002] Internal gear tubes are widely used in precision machining, especially in the manufacture of automated equipment, gear transmission systems, and various high-precision industrial components. The tooth profile accuracy of internal gear tubes is crucial to their functionality and reliability. Traditional inspection methods generally suffer from technical bottlenecks: contact measurements are prone to data deviations due to probe wear and are difficult to access complex areas such as the bottom of the tooth groove; single-view optical scanning suffers from missing tooth surface data due to limited viewing angle, failing to fully characterize the three-dimensional morphology. More seriously, motion errors such as machine tool spindle dynamic runout and feed rate fluctuations directly affect the measurement results, causing distortion of tooth profile parameters and resulting in the accumulation of errors in the evaluation of key parameters such as tooth tip roundness and tooth direction deviation. Existing technologies lack effective real-time compensation mechanisms. Therefore, there is an urgent need for a full-process inspection solution that integrates multi-angle collaborative scanning, motion error closed-loop correction, and three-dimensional tooth surface gradient analysis to achieve accurate feedback and quality control optimization in the internal gear tube manufacturing process. Summary of the Invention

[0003] This application provides a method, device, and electronic equipment for detecting the tooth profile accuracy of internal toothed tubes, which solves the technical problem of inaccurate tooth surface error assessment caused by motion errors in traditional internal toothed tube tooth profile detection. It achieves the technical effect of non-contact full tooth surface detection through multi-sensor collaborative scanning and dynamic motion compensation, thereby improving the accuracy and reliability of internal toothed tube tooth profile accuracy detection.

[0004] This application provides a method for detecting the tooth profile accuracy of an internal toothed tube. The method includes: scanning the inner wall of the internal toothed tube from multiple angles to obtain original tooth profile point cloud data; performing motion error analysis based on the original tooth profile point cloud data to generate motion compensation parameters; correcting the original tooth profile point cloud data based on the motion compensation parameters to generate a corrected tooth profile dataset; performing three-dimensional spatial registration according to the corrected tooth profile dataset to draw a tooth profile distribution map; dividing the internal toothed tube into regions according to the tooth profile distribution map; traversing multiple tooth profile regions to perform tooth profile accuracy detection; and generating a tooth profile accuracy detection report.

[0005] This application also provides a device for detecting the tooth profile accuracy of an internal toothed tube, comprising: Motion error analysis unit: performs multi-angle scanning of the inner wall of the internal toothed tube to obtain original tooth shape point cloud data, performs motion error analysis based on the original tooth shape point cloud data, and generates motion compensation parameters; 3D spatial registration unit: corrects the original tooth shape point cloud data based on the motion compensation parameters, generates a corrected tooth shape dataset, performs 3D spatial registration according to the corrected tooth shape dataset, and draws a tooth shape distribution map; Tooth shape accuracy detection unit: divides the internal toothed tube into regions according to the tooth shape distribution map, traverses multiple tooth shape regions to perform tooth shape accuracy detection, and generates a tooth shape accuracy detection report.

[0006] This application also provides an electronic device, including: A memory is used to store executable instructions; a processor is used to execute the executable instructions stored in the memory to implement a method for detecting the tooth profile accuracy of an internal toothed tube.

[0007] This application proposes a method, apparatus, and electronic device for detecting the tooth profile accuracy of internal toothed tubes. The method involves multi-angle scanning of the inner wall of the internal toothed tube to acquire original tooth profile point cloud data. Motion error analysis is performed based on the original tooth profile point cloud data to generate motion compensation parameters. The original tooth profile point cloud data is then corrected based on the motion compensation parameters to generate a corrected tooth profile dataset. Three-dimensional spatial registration is performed according to the corrected tooth profile dataset to create a tooth profile distribution map. The internal toothed tube is then divided into regions based on the tooth profile distribution map, and tooth profile accuracy is detected across multiple tooth profile regions, generating a tooth profile accuracy detection report. This method solves the technical problem of inaccurate tooth surface error assessment caused by motion errors in traditional internal toothed tube tooth profile detection. It achieves non-contact full tooth surface detection through multi-sensor collaborative scanning and dynamic motion compensation, thereby improving the accuracy and reliability of internal tooth profile accuracy detection. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0009] Figure 1 A schematic flowchart of a method for detecting the tooth profile accuracy of an internal toothed tube provided in an embodiment of this application; Figure 2 A schematic diagram of a tooth profile accuracy detection device for an internal toothed tube provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0010] Explanation of reference numerals in the attached figures: motion error analysis unit 11, three-dimensional spatial registration unit 12, tooth profile accuracy detection unit 13, memory 21, processor 22, input device 23, output device 24. Detailed Implementation

[0011] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application.

[0012] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or apparatuses. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0014] This application provides a method for detecting the tooth profile accuracy of an internal toothed tube, such as... Figure 1 As shown, the method includes: The inner wall of the internal toothed tube is scanned from multiple angles to obtain the original tooth shape point cloud data. Based on the original tooth shape point cloud data, motion error analysis is performed to generate motion compensation parameters.

[0015] Specifically, firstly, a high-precision laser displacement sensor or other scanning equipment is used to scan the inner wall of the internal toothed tube from multiple angles and positions, acquiring detailed geometric data of the tube's surface and generating raw tooth profile point cloud data. This raw point cloud data consists of multiple precise spatial coordinate points, comprehensively reflecting the actual tooth profile of the internal toothed tube. Subsequently, motion error analysis is performed based on this raw point cloud data. During actual processing, the internal toothed tube may be affected by factors such as uneven equipment movement, machine tool vibration, and thermal expansion, leading to minor deviations in the tooth profile. To identify these errors, a motion error analysis method is used. By analyzing the motion trajectory feature matrix constructed based on the point cloud data, the offsets in axial feed and rotation angles are understood, thereby generating motion compensation parameters. These parameters are used in subsequent correction steps to help correct tooth profile deviations caused by motion errors, thus improving the accuracy and reliability of the final inspection.

[0016] Furthermore, the inner wall of the internal toothed tube is scanned from multiple angles to obtain the original tooth profile point cloud data. The methods include: A first laser displacement sensor group is arranged axially along the inner wall of the internal toothed tube, and a second laser displacement sensor group is arranged radially along the inner wall of the internal toothed tube. The first and second laser displacement sensor groups are orthogonally arranged to determine a complementary scanning area. Synchronization control parameters for the spindle speed of the internal toothed tube and the sensor sampling frequency are set, and the internal toothed tube is driven to rotate at a constant speed according to the synchronization control parameters to perform a full tooth surface coverage scan of the complementary scanning area, thereby generating the original tooth profile point cloud data.

[0017] Preferably, along the axial direction of the inner wall of the internal toothed tube, the first laser displacement sensor group is arranged in a ring at 30° intervals, covering a 120° circumferential area of ​​the inner wall. The first laser displacement sensor group will scan the inner wall from the axial direction of the internal toothed tube, ensuring that the height and distance of each point in this area are accurately measured. This arrangement ensures that multiple laser displacement sensors can work together in the axial direction to obtain detailed information about the tooth profile of the inner wall. In addition, along the radial direction of the inner wall of the internal toothed tube, the second laser displacement sensor group is obliquely aligned with the tooth root area at a 45° angle. This arrangement ensures the comprehensiveness of the scanning area, especially the precise scanning of the tooth root of the internal toothed tube. The second laser displacement sensor group will obtain the tooth profile information of the inner wall from the radial angle to capture more details, especially the shape and changes of the tooth root. The first and second laser displacement sensor groups are arranged orthogonally, meaning they are spatially perpendicular and complement each other's scanning areas. This configuration allows the two sensor groups to cover different parts of the internal toothed tube, avoiding blind spots and achieving a comprehensive scan of the entire inner wall of the tube, thus providing higher density and more accurate point cloud data. To ensure data accuracy and consistency, synchronization control parameters are set between the internal toothed tube spindle speed and the sensor sampling frequency. The spindle speed is the rotational speed of the internal toothed tube spindle, usually expressed in revolutions per minute (RPM), and the sensor sampling frequency is the number of times the laser displacement sensor collects data per second, usually expressed in Hertz (Hz). The synchronization control parameters require calculating a periodic relationship: the time required for one spindle rotation (60 divided by the spindle speed), and then calculating the number of samples per rotation cycle based on the sensor sampling frequency (multiplying the time required for one spindle rotation by the sensor sampling frequency), ensuring that each sampling point is evenly distributed during rotation. By defining the synchronization control parameters, the internal toothed tube can be ensured to rotate at a uniform speed during the scanning process, resulting in a uniform distribution of point cloud data at each location and good temporal correlation. This allows the laser displacement sensor to sample each region multiple times within each rotation cycle, improving data accuracy and reliability. Finally, based on the timestamps of the point cloud data at each location, the data collected by each sensor are aligned at multiple time points to generate raw tooth profile point cloud data containing the detailed geometry of the internal toothed tube's inner wall. This raw tooth profile point cloud data not only includes the three-dimensional coordinates of each point on the inner wall of the internal toothed tube but also the normal vector data, providing a foundation for subsequent motion error analysis, correction, and accuracy detection.

[0018] Furthermore, motion error analysis is performed based on the original tooth profile point cloud data to generate motion compensation parameters. The method includes: Based on the original tooth profile point cloud data, the motion position of the internal tooth tube is identified, and a motion trajectory feature matrix is ​​constructed. Frequency domain separation processing is performed on the motion trajectory feature matrix to generate feature separation parameters. Axial feed velocity data is extracted based on the axial direction of the internal tooth tube's inner wall, and rotation angle data is extracted based on the radial direction of the internal tooth tube's inner wall. Motion distortion analysis is performed based on the axial feed velocity data and the rotation angle data to generate motion distortion parameters. Missing features are reconstructed according to the motion distortion parameters and the feature separation parameters to generate tooth profile reconstruction feature point cloud parameters, which are then added to the motion compensation parameters.

[0019] Preferably, the motion of the internal toothed tube is identified based on the original tooth profile point cloud data. This process begins by selecting key points from the original tooth profile point cloud data. These key points are typically located in characteristic regions of the internal tooth profile (such as the tooth root and tooth surface), exhibiting significant trajectory changes during motion. The purpose of selecting these key points is to simplify calculations and avoid the complexity of processing the entire point cloud data. The selected key points are marked in the original tooth profile point cloud data and sorted according to the sampling time sequence to prepare for subsequent time-series analysis. Subsequently, the coordinates of the key points at different time points are compared, and the displacement of each key point between two samplings is calculated using Euclidean distance. For rotational motion, the rotational speed of the internal toothed tube spindle and the sensor's position relative to the internal toothed tube are recorded. The rotational angle of the internal toothed tube at each time point is calculated by multiplying the spindle speed by the time interval. Then, based on the identified motion position and the calculated displacement and rotation angle, a motion trajectory feature matrix is ​​constructed. Each row in the matrix represents the position of a key point at a specific time, and each column represents the displacement, rotation angle, etc., of the key point at that time. For each motion trajectory data column in the motion trajectory feature matrix, frequency domain analysis methods such as Fourier transform are used to convert it from the time domain to the frequency domain. After obtaining the frequency domain representation, high-frequency and low-frequency components can be identified. High-frequency components usually correspond to high-frequency noise or errors that occur during the movement of the internal gear tube, such as irregular movements caused by equipment vibration or mechanical loosening, which may indicate unstable factors in the machining process. Low-frequency components correspond to the stable motion mode of the internal gear tube, such as uniform axial feed and rotational motion, which may reflect stability factors such as spindle speed and machining feed. Based on frequency domain separation, the amplitude and phase information of each frequency component are analyzed to generate feature separation parameters. These parameters represent the performance of the motion trajectory in different frequency bands, which helps to distinguish different types of motion errors or interferences. Subsequently, feed rate and rotation angle data were extracted from the axial and radial directions of the inner wall of the internal gear tube. The axial feed rate data reflects the feed speed of the internal gear tube in the axial direction, while the rotation angle data reflects the rotation angle of the internal gear tube. These data are key parameters for analyzing motion errors and distortions. The extracted axial feed rate and rotation angle data were then compared with the ideal axial feed rate and ideal rotation angle in the ideal motion trajectory. The ideal motion trajectory was pre-constructed according to the design requirements of the internal gear tube.By comparing the ideal axial feed rate and ideal rotation angle in the ideal motion trajectory with the collected axial feed rate and rotation angle data point by point, the difference between the two is calculated to identify the distortion in the motion process. Then, Fourier transform is performed on these error data to analyze the errors of different frequency components, identify low-frequency (such as systematic error), high-frequency (such as noise) and mid-frequency (such as periodic error) components, and extract parameters such as distortion amplitude (maximum value of error or root mean square error), distortion frequency (main frequency component in the frequency domain), and periodic error parameters (such as amplitude and frequency of periodic error) to form motion distortion parameters for subsequent error compensation and correction. Then, based on motion distortion parameters and feature separation parameters, missing regions in the point cloud data caused by errors are identified. These regions typically manifest as localized sparse point clouds or missing data. Specifically, the distortion amplitude and frequency in the motion distortion parameters help locate regions with larger errors (by comparing with corresponding thresholds), while the feature separation parameters further clarify the impact of errors at different frequencies, understanding the types of errors, such as periodic errors and random errors, thereby determining missing and error regions. For missing regions, new point cloud data is generated based on the surrounding existing point cloud data using interpolation. For periodic error regions, periodic compensation is performed based on the distortion frequency and amplitude to ensure that the reconstructed point cloud is consistent with the periodic motion errors in the actual processing. For random errors, fitting methods such as least squares are used to adjust the tooth shape to ensure smooth connection with the surrounding point cloud. After reconstruction, the resulting new point cloud data is defined as the tooth shape reconstruction feature point cloud. This tooth shape reconstruction feature point cloud includes all supplementary data points and can correct inaccurate data caused by motion errors, making the final tooth shape data more accurate. Finally, the tooth profile reconstruction feature point cloud parameters are added to the motion compensation parameters, providing higher precision motion compensation data for subsequent tooth profile correction and detection, thereby effectively correcting the tooth profile distortion caused by motion errors during the machining process of the internal tooth tube.

[0020] The original tooth shape point cloud data is corrected based on the motion compensation parameters to generate a corrected tooth shape dataset. Three-dimensional spatial registration is performed according to the corrected tooth shape dataset to draw a tooth shape distribution map.

[0021] Specifically, after completing motion distortion analysis and generating motion compensation parameters, these parameters are applied to the correction process of the original tooth profile point cloud data. During this process, dynamic spatial transformation is used to precisely correct the original tooth profile point cloud data, eliminating deviations caused by equipment motion errors during processing. The correction steps include adjusting the positions of various points in the point cloud data to make them closer to the ideal tooth profile trajectory, thereby generating a more accurate corrected tooth profile dataset. Subsequently, the corrected tooth profile dataset is registered in three-dimensional space with the benchmark three-dimensional tooth profile dataset to ensure consistency between different tooth profile datasets, providing a guarantee for accurate tooth profile accuracy analysis. After completing the three-dimensional spatial registration, a tooth profile distribution map is drawn based on the registration results. The tooth profile distribution map is a tool that intuitively displays the accuracy of internal tooth profiles. It transforms tooth profile data into a graphical form, showing the deviation distribution of various regions on the tooth surface. The map typically includes information such as tooth surface curvature and tooth surface deviation values, which can help analyze the overall quality of the tooth profile and whether there are any non-compliant error areas, providing accurate data support for the final tooth profile accuracy inspection and quality analysis.

[0022] Furthermore, the original tooth profile point cloud data is corrected based on the motion compensation parameters to generate a corrected tooth profile dataset. Three-dimensional spatial registration is then performed according to the corrected tooth profile dataset to draw a tooth profile distribution map. The method includes: Based on the tooth profile reconstruction feature point cloud parameters, the original tooth profile point cloud data is dynamically spatially transformed to obtain a corrected tooth profile dataset; the historical tooth profile dataset of the internal tooth tube is retrieved, and calculations are performed based on the historical tooth profile dataset to determine the reference three-dimensional tooth profile dataset; the corrected tooth profile dataset is registered with the reference three-dimensional tooth profile dataset to obtain tooth surface curvature distribution parameters; based on the tooth surface curvature distribution parameters, multiple key matching points are determined, and tooth surface deviation is calculated according to the multiple key matching points to obtain tooth surface deviation values; the tooth profile distribution map is constructed based on the tooth surface deviation values.

[0023] Preferably, based on the tooth profile reconstruction feature point cloud parameters, the original tooth profile point cloud data is dynamically transformed using a composite spatial transformation coordinate system. The position and orientation of the original point cloud data are adjusted in real time to make it more consistent with the actual processing state. This process usually involves transformation operations such as translation, rotation, and scaling to ensure that the geometric shape of the point cloud data in space is corrected, thereby generating a new, corrected tooth profile dataset. Subsequently, based on the design parameters of the internal gear tube and the geometric principles of gears, the number of teeth in the internal gear tube is calculated. The number of teeth in the internal gear tube is usually related to the gear module, inner diameter, and tooth pitch. According to the gear design requirements, the tooth height (addition height and dedendum) of each tooth profile is calculated. Tooth height is one of the key parameters of the tooth profile and usually needs to be determined based on the standard module. Tooth width is usually related to the load-bearing capacity and design load of the tooth profile, and the actual application requirements of the internal gear tube need to be considered during calculation. The inner diameter of the internal gear tube is a very important parameter in its design and manufacturing process. It determines the start and end positions of the tooth profile and can be calculated using the number of teeth and module. The pressure angle is a basic parameter of gear meshing. It affects the geometry of the tooth profile. A suitable pressure angle is calculated using standard gear geometry formulas. Usually, a standard value (20° or 25°) is selected during design based on requirements. Next, based on the calculated parameters such as the number of teeth, tooth height, tooth width, and pressure angle, an ideal three-dimensional tooth profile model of the internal tooth tube is constructed in a three-dimensional coordinate system. During construction, CAD software or programming tools (such as numpy and matplotlib in Python) can be used to convert the tooth profile parameters into the ideal three-dimensional tooth profile model. After determining the ideal three-dimensional tooth profile model, historical tooth profile datasets of the internal tooth tube are called. These datasets typically include design data of the internal tooth tube, tooth profile data from past production processes, or other reference standard data. By comparing the actual tooth profile data in the historical tooth profile dataset with the ideal tooth profile in the ideal three-dimensional tooth profile model, the deviation between them is calculated. If there is a significant difference between the historical data and the theoretical calculation results (greater than the deviation tolerance threshold), the design parameters such as the number of teeth, tooth height, and tooth width need to be adjusted to further optimize the ideal three-dimensional tooth profile model, ensuring that the ideal tooth profile more accurately reflects the actual production conditions. After optimization, a benchmark three-dimensional tooth profile dataset is generated based on the optimized ideal three-dimensional tooth profile model. This benchmark three-dimensional tooth profile dataset serves as an ideal reference tooth profile, providing a standard profile of the internal tooth tube tooth profile for comparative analysis with actual machined tooth profile data. Then, the corrected tooth profile dataset is registered in three-dimensional space with the reference three-dimensional tooth profile dataset. The goal of the registration process is to align the corrected actual tooth profile data with the ideal reference data so that the tooth profile features of the two datasets are accurately matched in space. This process is achieved by the least squares method or the iterative nearest point algorithm, which are used to calculate and minimize the geometric difference between the two datasets.After registration, the accuracy of the results is evaluated by calculating the average distance or root mean square error (RMSE) between the registered point cloud data. If the registration error exceeds the predetermined tolerance range, the registration process may need to be adjusted or other registration algorithms may need to be selected. After registration, the reference 3D tooth profile dataset and the corrected tooth profile dataset are aligned as much as possible. At this point, based on the point cloud data in the aligned corrected tooth profile dataset, the normal vector of the point is estimated by fitting the neighborhood surface of the data point (such as fitting a quadratic surface using the least squares method). The curvature tensor is constructed using the coefficients of the second basic form. Then, by solving the eigenvalues ​​of the curvature tensor, the maximum and minimum values ​​of the principal curvatures are obtained, i.e., the maximum curvature and the minimum curvature. The principal curvatures are usually taken as the larger and smaller values ​​of the maximum and minimum curvatures, representing the maximum and minimum bending degree of the tooth surface at that point. By summarizing and storing all the calculated principal curvatures, the tooth surface curvature distribution parameters are obtained. Furthermore, based on the obtained tooth surface curvature distribution parameters, data filtering is performed to determine the tooth surface curvature of multiple key matching points. These matching points are typically locations on the tooth surface with important geometric features, such as the tooth root and tooth tip. By calculating the difference between the tooth surface curvature of these key points and the corresponding reference tooth surface curvature in the reference 3D tooth profile dataset, the deviation of each point between the reference tooth profile and the actual tooth profile can be obtained. These deviation values ​​are key indicators for measuring tooth profile accuracy and can indicate the type of deviation present during machining (such as excessive or insufficient tooth surface deviation). Finally, based on the calculated tooth surface deviation values, a tooth profile distribution map is constructed. The tooth profile distribution map is a visualization tool used to show the deviation of various regions on the tooth surface. In the map, deviation values ​​are usually presented in a color-coded manner, with the intensity of the color representing different degrees of deviation. The map can not only show areas with large deviations on the tooth surface but also help identify tooth profile areas that may have problems, providing a basis for subsequent quality control, correction, and optimization, ensuring that errors in machining are effectively controlled.

[0024] Furthermore, based on the tooth profile reconstruction feature point cloud parameters, a dynamic spatial transformation is performed on the original tooth profile point cloud data to obtain a corrected tooth profile dataset. The method includes: Based on the tooth profile reconstruction feature point cloud parameters, the operation is analyzed to obtain translation compensation vector and rotation compensation vector; a composite spatial transformation coordinate system is constructed, and the original tooth profile point cloud data is mapped to the composite spatial transformation coordinate system to generate tooth profile spatial transformation data; the tooth profile spatial transformation data is corrected point by point according to the translation compensation vector and the rotation compensation vector to generate the corrected tooth profile dataset.

[0025] Optionally, the analysis is performed based on the tooth profile reconstruction feature point cloud parameters. This analysis examines the geometric characteristics of the tooth profile reconstruction feature point cloud data to identify geometric deviations caused by machining errors or motion distortions. Specifically, the centroid (i.e., the centroid of the point set) of the tooth profile reconstruction feature point cloud parameters is calculated using the geometric center method or the least squares method. The centroid of the original tooth profile point cloud data is then calculated using the same method. By calculating the difference between these two centers, a translation compensation vector is obtained. This translation compensation vector is used to adjust the position of each point in the point cloud data in space. Furthermore, principal component analysis (PCA) or other geometric methods are used to determine the principal directions of the two centroids. PCA can calculate the principal directions of the data points by analyzing the covariance matrix of the data. The rotation axis is then determined by calculating the cross product of the two principal directions, and the rotation angle is determined by calculating the angle between the two direction vectors. The calculated rotation axis and rotation angle together form a rotation compensation vector, which is used to correct deviations caused by rotation errors. Subsequently, a composite spatial transformation matrix is ​​constructed based on the dynamic runout error parameters of the machine tool spindle, the tooth profile reconstruction feature point cloud parameters, and the translation compensation vector. This composite spatial transformation matrix is ​​a corrected three-dimensional spatial coordinate system, providing a unified framework for subsequent correction operations. Through this composite spatial transformation coordinate system, the original tooth profile point cloud data can be mapped to a new coordinate system, forming tooth profile spatial transformation data. The purpose of this step is to unify the position and orientation references of the data, enabling subsequent compensation operations to be performed within the same spatial framework. Afterwards, the tooth profile spatial transformation data is corrected point-by-point using translation compensation vectors and rotation compensation vectors. In this process, for each point in the data, the translation compensation vector is added to the coordinates of that point to complete translation compensation. Then, based on the rotation axis and rotation angle in the rotation compensation vector, a rotation matrix is ​​constructed using the Rodrigues rotation formula. This rotation matrix is ​​multiplied by the coordinates after translation compensation to complete rotation compensation, thereby generating a corrected tooth profile dataset. This corrected tooth profile dataset represents the precise tooth profile structure of the internal tooth tube after spatial compensation, which can more accurately reflect the actual machining state of the internal tooth tube and provide a more reliable data foundation for subsequent tooth profile accuracy inspection.

[0026] Furthermore, a composite spatial transformation coordinate system is constructed, including the following methods: Extract the dynamic runout error parameters of the machine tool spindle, perform three-dimensional rotation analysis based on the dynamic runout error parameters, and construct a rotation component matrix; calculate the axial scaling factor based on the tooth profile reconstruction feature point cloud parameters; multiply the rotation component matrix, the translation compensation vector, and the axial scaling factor in kinematic chain order to construct the composite spatial transformation coordinate system.

[0027] Optionally, by extracting the motion state data of the machine tool spindle, the dynamic runout error parameters of the spindle can be obtained. The dynamic runout error is usually caused by factors such as uneven rotation of the machine tool spindle, load changes, or mechanical vibration. By monitoring the actual rotation trajectory of the spindle through high-precision sensors or measuring equipment, the runout error of the spindle under different working conditions can be identified. These runout errors reflect the changes in position and angle during the rotation of the spindle, which are usually offsets or swings along the rotation axis. Subsequently, based on the extracted dynamic yaw error parameters, a three-dimensional rotation analysis is performed. The goal of the rotation analysis is to transform the dynamic yaw error into rotational components, which reflect the rotational error of the principal axis in three directions (X, Y, and Z axes). By converting the yaw error parameters into rotation angles and offsets, a rotational component matrix is ​​constructed. This rotational component matrix describes the rotational transformation around the X, Y, and Z axes. Each rotational component matrix corresponds to the rotation of one coordinate axis. Specifically, the rotational component matrix around the X-axis describes the rotation angle around the X-axis, the rotational component matrix around the Y-axis describes the rotation angle around the Y-axis, and the rotational component matrix around the Z-axis describes the rotation angle around the Z-axis. These rotational matrices are used to describe the dynamic yaw correction of the principal axis in each direction. Because the internal tooth tube may be affected by mechanical elasticity or thermal expansion during the machining process, the axial direction of the tooth profile may expand or contract. Therefore, the coordinate points in the tooth profile reconstruction feature point cloud parameters are projected as axial coordinates (usually in the Z-axis direction), and the maximum and minimum values ​​of the axial coordinates are extracted. The difference between the maximum and minimum values ​​is defined as the axial length. By dividing this tooth profile axial length by the theoretical tooth profile axial length, the axial expansion / contraction ratio factor is obtained. This axial expansion / contraction ratio factor reflects the degree of deformation of the tooth profile in the axial direction. Next, the rotation component matrix, translation compensation vector, and axial scaling factor are multiplied in the order of the kinematic chain. That is, the rotation component matrix around the X-axis, the rotation component matrix around the Y-axis, and the rotation component matrix around the Z-axis are multiplied to obtain the composite rotation matrix. Then, the composite rotation matrix is ​​multiplied with the translation compensation vector. The data corresponding to the Z-axis in the multiplication result is multiplied with the axial scaling factor to construct the composite spatial transformation matrix. This composite spatial transformation matrix includes transformations such as translation, rotation, and scaling, and can describe the transformation of point cloud data from the original coordinate system to the new coordinate system. By applying this composite spatial transformation matrix, a new coordinate system can be obtained. This coordinate system is defined as the composite spatial transformation coordinate system, which provides a more accurate reference framework for subsequent tooth profile data analysis, registration, or correction.

[0028] The internal toothed tube is divided into regions based on the tooth shape distribution map. Multiple tooth shape regions are traversed to perform tooth shape accuracy detection, and a tooth shape accuracy detection report is generated.

[0029] Specifically, after completing the 3D spatial registration, the generated tooth profile distribution map provides the deviation distribution of various regions on the tooth surface of the internal tooth tube. Based on this map, the tooth surface of the internal tooth tube is first divided into regions. The region division is based on key features in the tooth profile distribution map, such as abrupt changes in tooth surface curvature. Through these features, the entire tooth surface is divided into multiple sub-regions, each representing a specific tooth profile region, such as the tooth tip region or the working tooth surface region. These regions may have different accuracy requirements and deviation characteristics. After completing the region division, the accuracy of each tooth profile region is tested, and the test results are synchronized to the error tracing analysis module to determine the error level of each tooth profile region. Finally, the error levels of each tooth profile region are summarized to generate a tooth profile accuracy test report. This tooth profile accuracy test report presents the test results in the form of cloud maps, etc., for easy analysis and comparison. Through these steps, it is ensured that the tooth profile accuracy of the internal tooth tube meets the design requirements, while providing specific test results, which helps to improve production quality and machining accuracy.

[0030] Furthermore, the internal toothed tube is divided into regions based on the tooth profile distribution map, and tooth profile accuracy is detected by traversing multiple tooth profile regions to generate a tooth profile accuracy detection report. The method includes: The tooth surface of the internal tooth tube is dynamically divided into multiple tooth shape regions according to the tooth shape distribution map. Gradient accuracy detection is performed on the multiple tooth shape regions to obtain a set of partition error statistical features. The tooth shape process parameters of the internal tooth tube are introduced and fused with the correction tooth shape dataset to construct an error source tracing analysis module. The partition error statistical feature set is synchronized to the error source tracing analysis module for source tracing, and an error level is generated. A three-dimensional error cloud map is constructed according to the error level. The three-dimensional error cloud map is added to the tooth shape accuracy detection report.

[0031] Preferably, the tooth surface of the internal toothed tube is dynamically divided into regions based on a tooth profile distribution map. The tooth profile distribution map reflects the curvature deviation of various regions of the internal toothed tube. By analyzing the curvature changes, deviation distribution, and actual machining requirements, the entire tooth surface is divided into the tooth tip region, the working tooth surface region, and the tooth root region. The tooth tip region, located at the top of the internal toothed tube, typically experiences less stress and wear, but may exhibit minor curvature deviations. The working tooth surface region is the area in direct contact with mating components; due to higher contact stress, the tooth surface may experience more significant curvature deviations, especially under high load conditions. The tooth root region, located at the bottom of the internal toothed tube, is typically affected by greater mechanical stress and temperature, which may lead to significant changes in tooth curvature, especially during machining. This dynamic division provides a more accurate regional basis for subsequent precision testing. Subsequently, precision testing is performed within each defined tooth profile region. For the tooth tip region, which typically experiences less stress and wear, a high-density scanning mode with a resolution of 0.1 μm is used for precision testing. To ensure uniform data acquisition within this region, a circumferential equal-angle sampling strategy is employed. This means that the interval and angle of the scanning points remain consistent throughout the region, ensuring uniform and accurate measurements across all areas. For the working tooth surface region, which is in contact with mating components and typically experiences greater contact stress, a higher precision testing method is required. Therefore, an adaptive variable-resolution scanning mode is used, dynamically adjusting the density of scanning points based on the distribution of contact stress. The density of scanning points is increased in areas with higher stress, thereby obtaining more detail and more accurate error data. For the tooth root region, which is typically affected by greater mechanical stress and temperature, a combined laser triangulation and contact probe composite testing technique is used. This composite measurement method overcomes the data loss problem in shadowed areas that may occur with laser measurement. Furthermore, for areas that cannot be directly illuminated by the laser beam, the contact probe provides supplementary data, ensuring comprehensive and accurate measurements. Subsequently, temperature-stress coupling compensation calculations are performed on the measurement data of each tooth profile region, especially in the tooth root region and the working tooth surface region. Since the tooth profile error of the internal tooth tube may be affected by the ambient temperature and stress changes during the machining process, these errors need to be corrected through compensation methods. Specifically, temperature sensors deployed on the tooth profile surface are used to acquire the temperature distribution caused by cutting, friction, and heat generated by the machine tool itself during the machining process, and stress sensors are used to monitor the mechanical stress generated on the tooth profile surface.The temperature correction result is obtained by multiplying the temperature change by the material's coefficient of thermal expansion and the original length of the measurement point. This temperature correction result is then divided by the material's elastic modulus, and the quotient is multiplied by the ratio of stress to the cross-sectional area of ​​the measurement point to obtain the stress correction result. This stress correction result is used to calculate the deviation from the baseline 3D tooth profile dataset, and the mean error, standard deviation, maximum error, minimum error, and peak error location for each region are statistically analyzed. These error features collectively form a partitioned error statistical feature set for subsequent error analysis and accuracy assessment. Next, the tooth profile process parameters of the internal tooth tube (such as cutting speed, feed rate, machine tool error, etc.) are fused with the corrected tooth profile dataset and stored as default input parameters in a pre-built error tracing analysis module. This module can be built based on deep neural networks, random forests, etc. Taking deep neural networks as an example, historical tooth profile process parameters, historical tooth profile data, historical partitioned error statistical features, and historical error level labels can be input into the deep neural network and iteratively trained through forward propagation, loss calculation, backpropagation, and parameter optimization. Furthermore, the partition error statistical feature set is synchronized to the error source analysis module. This module combines the partition error statistical feature set with the internally stored tooth profile process parameters and correction tooth profile dataset of the internal tooth tube to identify the error source and map the error level of each partition, indicating the severity of the error in each area. The error level is classified according to the magnitude of the deviation value and its impact on the tooth profile accuracy, and displayed in a color-coded form to construct a three-dimensional error cloud map. The color change in the three-dimensional error cloud map represents the error degree of different areas, and each point in the map corresponds to an error data point, which can intuitively reflect the deviation of each area on the tooth surface. Finally, the generated three-dimensional error cloud map is added to the tooth profile accuracy inspection report. The report will list the inspection results and error level of each tooth profile area in detail. This report not only provides visualized data support for quality control but also provides a basis for future corrections and process improvements.

[0032] Furthermore, the tooth surface of the internal tooth tube is dynamically divided into multiple tooth shape regions according to the tooth shape distribution map. The method includes: The tooth profile distribution map is traversed to extract abrupt changes in tooth surface curvature, and these abrupt changes are used as region boundary features. Based on these region boundary features, region growth analysis is performed to determine extended partition boundary information. Adjacent partitions are overlapped according to the extended partition boundary information to determine the probability of overlapping regions. The topological connection relationship of the internal tooth tube tooth surface is constructed based on the probability of overlapping regions. Based on the topological connection relationship, dynamic region division is performed according to the extended partition boundary information to generate the multiple tooth profile regions.

[0033] Optionally, the tooth profile distribution map is traversed to extract abrupt curvature change points. These curvature change points are locations where the curvature on the tooth surface changes drastically. These locations are usually markers of tooth profile irregularities or error concentration areas. By analyzing the tooth profile distribution map, these abrupt change points are identified and used as region boundary features, representing the boundaries between different tooth profile regions. Curvature change points typically appear at the tooth tip, tooth root, or local defect areas on the tooth surface, thus playing a crucial role in subsequent region segmentation and accuracy inspection. Subsequently, based on the tooth surface curvature change points as initial boundary features, region growing analysis is performed. Region growing analysis is an image segmentation technique that can merge adjacent regions based on certain similarity metrics (such as curvature changes less than a certain threshold or errors within an acceptable range). By starting from the curvature change points and analyzing the curvature change trends of the surrounding areas, the boundaries of each tooth profile region are automatically expanded and determined. This expanded region boundary information is called expanded partition boundary information, which helps to accurately delineate the extent of each tooth profile region. Next, based on the extended partition boundary information, adjacent tooth profile regions are overlapped. In some cases, the division of tooth surface regions may have blurred boundaries or overlapping areas, requiring overlapping division to ensure the integrity of the regions. By calculating the degree of overlap between adjacent regions, i.e., calculating the area of ​​the intersection of adjacent regions, and then comparing the intersection area with the total area of ​​the two regions, the probability of overlap for each overlapping region is determined. This probability represents the likelihood of the boundaries between adjacent regions coinciding, reflecting the stability and consistency of the divided regions. Then, based on the probability of overlapping regions, the topological connection relationship of the internal tooth tube surface is constructed. The topological connection relationship describes the relative position and connection between each tooth profile region, accurately describing the spatial distribution and connection method of each tooth profile region on the overall tooth surface. The topological connection relationship plays an important role in subsequent accuracy inspection, error analysis, and machining process optimization. Through this relationship, the dependencies and influence ranges between each region can be clearly defined. Finally, based on the constructed topological connection relationship, the entire tooth surface is dynamically divided into multiple tooth profile regions according to the extended partition boundary information, such as the tooth tip region, working tooth surface region, and tooth root region. Each region is precisely divided according to the actual machining conditions and inspection requirements. These areas will provide strong support for subsequent accuracy testing, error analysis, and correction.

[0034] In the above text, refer to Figure 1 A method for detecting the tooth profile accuracy of an internal toothed tube according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes a tooth profile accuracy detection device for an internal toothed tube according to an embodiment of the present invention.

[0035] According to an embodiment of the present invention, a tooth profile accuracy detection device for internal toothed tubes is provided to solve the technical problem of inaccurate tooth surface error assessment caused by motion errors in traditional internal tooth profile detection. It achieves non-contact full tooth surface detection through multi-sensor collaborative scanning and dynamic motion compensation, thereby improving the accuracy and reliability of internal tooth profile accuracy detection. The tooth profile accuracy detection device for internal toothed tubes includes: a motion error analysis unit 11, a three-dimensional spatial registration unit 12, and a tooth profile accuracy detection unit 13.

[0036] Motion error analysis unit 11: Performs multi-angle scanning of the inner wall of the internal toothed tube to obtain original tooth shape point cloud data, performs motion error analysis based on the original tooth shape point cloud data, and generates motion compensation parameters; Three-dimensional spatial registration unit 12: Corrects the original tooth shape point cloud data based on the motion compensation parameters, generates a corrected tooth shape dataset, performs three-dimensional spatial registration according to the corrected tooth shape dataset, and draws a tooth shape distribution map; Tooth shape accuracy detection unit 13: Divides the internal toothed tube into regions according to the tooth shape distribution map, traverses multiple tooth shape regions to perform tooth shape accuracy detection, and generates a tooth shape accuracy detection report.

[0037] The specific configuration of the motion error analysis unit 11 will be described in detail below. The motion error analysis unit 11 may further include: reading predetermined hot-dip galvanizing control indicators; based on the first judgment result, when the first degree of modification reaches the modification threshold constraint, acquiring first historical hot-dip galvanizing control information, wherein the first historical hot-dip galvanizing control information refers to the hot-dip galvanizing control information of the first historical steel reinforcement in the first historical hot-dip galvanized composite; traversing and matching the predetermined hot-dip galvanizing control indicators in the first historical hot-dip galvanizing control information to obtain first traversal matching information; and using the first traversal matching information as the optimal hot-dip galvanizing control information.

[0038] The specific configuration of the motion error analysis unit 11 will be described in detail below. The motion error analysis unit 11 further includes: identifying the motion position of the internal tooth tube based on the original tooth profile point cloud data, and constructing a motion trajectory feature matrix; performing frequency domain separation processing based on the motion trajectory feature matrix to generate feature separation parameters; extracting axial feed velocity data based on the axial direction of the internal tooth tube's inner wall, and extracting rotation angle data based on the radial direction of the internal tooth tube's inner wall; performing motion distortion analysis based on the axial feed velocity data and the rotation angle data to generate motion distortion parameters; performing missing reconstruction according to the motion distortion parameters and the feature separation parameters to generate tooth profile reconstruction feature point cloud parameters, and adding the tooth profile reconstruction feature point cloud parameters to the motion compensation parameters.

[0039] The specific configuration of the three-dimensional spatial registration unit 12 will be described in detail below. The three-dimensional spatial registration unit 12 may further include: performing dynamic spatial transformation on the original tooth profile point cloud data based on the tooth profile reconstruction feature point cloud parameters to obtain a corrected tooth profile dataset; retrieving the historical tooth profile dataset of the internal tooth tube, performing theoretical calculations based on the historical tooth profile dataset to determine a reference three-dimensional tooth profile dataset; registering the corrected tooth profile dataset with the reference three-dimensional tooth profile dataset to obtain tooth surface curvature distribution parameters; filtering based on the tooth surface curvature distribution parameters to determine multiple key matching points, calculating tooth surface deviations according to the multiple key matching points to obtain tooth surface deviation values, and constructing the tooth profile distribution map based on the tooth surface deviation values.

[0040] The specific configuration of the three-dimensional spatial registration unit 12 will be described in detail below. The three-dimensional spatial registration unit 12 may further include: performing analysis based on the tooth profile reconstruction feature point cloud parameters to obtain translation compensation vectors and rotation compensation vectors; constructing a composite spatial transformation coordinate system, mapping the original tooth profile point cloud data to the composite spatial transformation coordinate system, and generating tooth profile spatial transformation data; and correcting the tooth profile spatial transformation data point by point according to the translation compensation vectors and the rotation compensation vectors to generate the corrected tooth profile dataset.

[0041] The specific configuration of the three-dimensional spatial registration unit 12 will be described in detail below. The three-dimensional spatial registration unit 12 may further include: extracting the dynamic runout error parameters of the machine tool spindle; performing three-dimensional rotation analysis based on the dynamic runout error parameters to construct a rotation component matrix; calculating the axial scaling factor based on the tooth profile reconstruction feature point cloud parameters; and multiplying the rotation component matrix, the translation compensation vector, and the axial scaling factor in kinematic chain order to construct the composite spatial transformation coordinate system.

[0042] The specific configuration of the tooth profile accuracy detection unit 13 will be described in detail below. The tooth profile accuracy detection unit 13 may further include: dynamically dividing the tooth surface of the internal tooth tube into multiple tooth profile regions according to the tooth profile distribution map; traversing the multiple tooth profile regions to perform gradient accuracy detection and obtain a set of partitioned error statistical features; introducing the tooth profile process parameters of the internal tooth tube, fusing the tooth profile process parameters with the corrected tooth profile dataset, and constructing an error source tracing analysis module; synchronizing the partitioned error statistical feature set to the error source tracing analysis module for source tracing, generating error levels, encoding according to the error levels to construct a three-dimensional error cloud map; and adding the three-dimensional error cloud map to the tooth profile accuracy detection report.

[0043] The specific configuration of the tooth profile accuracy detection unit 13 will be described in detail below. The tooth profile accuracy detection unit 13 may further include: traversing the tooth profile distribution map to extract abrupt changes in tooth surface curvature, and using these abrupt changes as region boundary features; performing region growth analysis based on the region boundary features to determine extended partition boundary information; dividing adjacent partitions into overlapping regions according to the extended partition boundary information to determine the probability of overlapping regions; constructing a topological connection relationship for the internal tooth tube surface based on the overlapping region probability; and dynamically dividing regions according to the extended partition boundary information based on the topological connection relationship to generate the multiple tooth profile regions.

[0044] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, a memory 21, a processor 22, an input device 23, and an output device 24. The processor 22 may be one or more; the memory 21 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0045] The memory 21 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage medium can be, but is not limited to, an infrared or semiconductor device, apparatus or device, or any combination thereof, used to store software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the tooth profile accuracy detection method of an internal toothed tube in this embodiment of the invention. The processor 22 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 21, thereby realizing the aforementioned tooth profile accuracy detection method of an internal toothed tube.

[0046] The tooth profile accuracy detection device for an internal toothed tube provided in this embodiment of the invention can execute the tooth profile accuracy detection method for an internal toothed tube provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0047] Although this application makes various references to certain modules in the apparatus according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not intended to limit the scope of protection of this invention.

[0048] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for detecting the tooth profile accuracy of an internal toothed tube, characterized in that, The method includes: Multi-angle scanning is performed on the inner wall of the internal toothed tube to obtain the original tooth shape point cloud data. Motion error analysis is performed based on the original tooth shape point cloud data to generate motion compensation parameters. Based on the motion compensation parameters, the original tooth shape point cloud data is corrected to generate a corrected tooth shape dataset. The corrected tooth shape dataset is then registered in three-dimensional space to draw a tooth shape distribution map. The internal toothed tube is divided into regions based on the tooth shape distribution map. Multiple tooth shape regions are traversed to perform tooth shape accuracy detection, and a tooth shape accuracy detection report is generated.

2. The method for detecting the tooth profile accuracy of an internal toothed tube as described in claim 1, characterized in that, Multi-angle scanning of the inner wall of the internal toothed tube to obtain the original tooth profile point cloud data includes the following methods: A first laser displacement sensor group is arranged axially along the inner wall of the internal toothed tube, and a second laser displacement sensor group is arranged radially along the inner wall of the internal toothed tube. The first laser displacement sensor group and the second laser displacement sensor group are orthogonally arranged to determine complementary scanning areas; The spindle speed of the internal tooth tube and the sampling frequency of the sensor are set to a synchronous control parameter. The internal tooth tube is driven to rotate at a constant speed according to the synchronous control parameter to perform a full tooth surface coverage scan of the complementary scanning area, thereby generating the original tooth profile point cloud data.

3. The method for detecting the tooth profile accuracy of an internal toothed tube as described in claim 1, characterized in that, Motion error analysis is performed based on the original tooth profile point cloud data to generate motion compensation parameters. The method includes: Based on the original tooth shape point cloud data, the motion position of the internal tooth tube is identified, and a motion trajectory feature matrix is ​​constructed. Based on the motion trajectory feature matrix, frequency domain separation processing is performed to generate feature separation parameters; Axial feed rate data is extracted from the inner wall of the internal toothed tube, and rotation angle data is extracted from the inner wall of the internal toothed tube. Motion distortion analysis is performed based on the axial feed rate data and the rotation angle data to generate motion distortion parameters. Based on the motion distortion parameters and the feature separation parameters, missing reconstruction is performed to generate tooth-shaped reconstruction feature point cloud parameters, which are then added to the motion compensation parameters.

4. The method for detecting the tooth profile accuracy of an internal toothed tube as described in claim 3, characterized in that, The original tooth profile point cloud data is corrected based on the motion compensation parameters to generate a corrected tooth profile dataset. Three-dimensional spatial registration is then performed according to the corrected tooth profile dataset to draw a tooth profile distribution map. The method includes: Based on the tooth shape reconstruction feature point cloud parameters, the original tooth shape point cloud data is dynamically spatially transformed to obtain the corrected tooth shape dataset. Retrieve the historical tooth profile dataset of the internal tooth tube, perform theoretical calculations based on the historical tooth profile dataset, and determine the benchmark three-dimensional tooth profile dataset; The corrected tooth profile dataset is registered with the reference three-dimensional tooth profile dataset to obtain the tooth surface curvature distribution parameters; Based on the tooth surface curvature distribution parameters, multiple key matching points are determined. Tooth surface deviation is calculated according to the multiple key matching points to obtain tooth surface deviation values. The tooth profile distribution map is constructed based on the tooth surface deviation values.

5. The method for detecting the tooth profile accuracy of an internal toothed tube as described in claim 4, characterized in that, Based on the tooth shape reconstruction feature point cloud parameters, a dynamic spatial transformation is performed on the original tooth shape point cloud data to obtain a corrected tooth shape dataset. The method includes: Based on the tooth-shaped reconstructed feature point cloud parameters, the translation compensation vector and rotation compensation vector are obtained through analysis. Construct a composite spatial transformation coordinate system, and map the original tooth-shaped point cloud data to the composite spatial transformation coordinate system to generate tooth-shaped spatial transformation data; The tooth profile spatial transformation data is corrected point by point according to the translation compensation vector and the rotation compensation vector to generate the corrected tooth profile dataset.

6. The method for detecting the tooth profile accuracy of an internal toothed tube as described in claim 5, characterized in that, Methods for constructing a composite spatial transformation coordinate system include: Extract the dynamic runout error parameters of the machine tool spindle, perform three-dimensional rotation analysis based on the dynamic runout error parameters, and construct the rotation component matrix; Calculate the axial scaling factor based on the tooth profile reconstruction feature point cloud parameters; The rotation component matrix, the translation compensation vector, and the axial scaling factor are multiplied in kinematic chain order to construct the composite spatial transformation coordinate system.

7. The method for detecting the tooth profile accuracy of an internal toothed tube as described in claim 1, characterized in that, The internal toothed tube is divided into regions based on the tooth profile distribution map. Tooth profile accuracy is then measured across multiple tooth profile regions to generate a tooth profile accuracy measurement report. The method includes: The tooth surface of the internal tooth tube is dynamically divided into multiple tooth shape regions according to the tooth shape distribution map. Gradient accuracy detection is performed by traversing the multiple tooth-shaped regions to obtain a statistical feature set of partitioning errors; The tooth profile process parameters of the internal toothed tube are introduced, and the tooth profile process parameters are fused with the correction tooth profile dataset to construct an error source tracing analysis module; The partition error statistical feature set is synchronized to the error source analysis module for source tracing, error level is generated, and a three-dimensional error cloud map is constructed by encoding according to the error level. Add the three-dimensional error cloud map to the tooth profile accuracy inspection report.

8. The method for detecting the tooth profile accuracy of an internal toothed tube as described in claim 7, characterized in that, The tooth surface of the internal tooth tube is dynamically divided into multiple tooth shape regions according to the tooth shape distribution map. The method includes: The tooth profile distribution map is traversed to extract abrupt changes in tooth surface curvature, and these abrupt changes in tooth surface curvature are used as regional boundary features. Based on the aforementioned regional boundary features, a regional growth analysis is performed to determine the extended partition boundary information; The adjacent partitions are divided into overlapping areas according to the extended partition boundary information, and the probability of overlapping areas is determined. Based on the probability of overlapping regions, the topological connection relationship of the internal tooth surface is constructed. Based on the topological connection relationship, the region is dynamically divided according to the extended partition boundary information to generate the multiple tooth-shaped regions.

9. A device for detecting the tooth profile accuracy of an internal toothed tube, characterized in that, The apparatus is used to implement the tooth profile accuracy detection method for an internal toothed tube according to any one of claims 1 to 8, the apparatus comprising: Motion error analysis unit: Performs multi-angle scanning of the inner wall of the internal tooth tube to obtain original tooth profile point cloud data, performs motion error analysis based on the original tooth profile point cloud data, and generates motion compensation parameters; Three-dimensional spatial registration unit: Based on the motion compensation parameters, the original tooth shape point cloud data is corrected to generate a corrected tooth shape dataset, and three-dimensional spatial registration is performed according to the corrected tooth shape dataset to draw a tooth shape distribution map; Tooth profile accuracy detection unit: Divides the internal tooth tube into regions according to the tooth profile distribution map, traverses multiple tooth profile regions to perform tooth profile accuracy detection, and generates a tooth profile accuracy detection report.

10. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the tooth profile accuracy detection method for an internal toothed tube as described in any one of claims 1-8.

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