A gear three-dimensional structure construction method, system, device and medium

Abstract

This application discloses a method, system, device, and medium for constructing a three-dimensional structure of a gear. The method acquires at least two sets of regional point cloud data of the gear under test and the corresponding gear rotation angles. The regional point cloud data is obtained by scanning with a laser scanner based on a multi-axis rotary clamping mechanism that adjusts the gear rotation angle; any two sets of regional point cloud data correspond to different gear rotation angles. The rotation center axis of the gear under test is calculated based on all regional point cloud data. Each pair of adjacent regional point cloud data is registered based on the rotation center axis and the corresponding gear rotation angles to obtain registered regional point cloud data. The pose map structure of the gear under test is constructed based on the registered regional point cloud data, resulting in the three-dimensional structure of the gear. This method enables comprehensive scanning of helical tooth surfaces with complex curvatures and achieves non-contact, efficient, and high-precision detection of gears.

Description

Technical Field

[0001] This application relates to the field of gear processing technology, and in particular to a method, system, equipment and medium for constructing a three-dimensional gear structure. Background Technology

[0002] Spiral bevel gears are important components in industrial development, bearing high performance requirements and heavy loads. Quality inspection of spiral bevel gears is a crucial process for verifying the quality of gear manufacturing.

[0003] Traditional spiral bevel gear quality inspection mainly uses contact coordinate measuring machines. However, this contact-based measurement method is not only cumbersome to operate, but can only inspect a few local points at a time, resulting in incomplete inspection data and an inability to quickly obtain global information about the spiral bevel gear tooth surface. In addition, traditional spiral bevel gear quality inspection requires multiple inspections to complete the overall inspection, which has problems such as large testing errors, low data reliability, and poor inspection efficiency. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0005] The main objective of this disclosure is to propose a method, system, device, and storage medium for constructing a three-dimensional gear structure, which can quickly and accurately acquire global three-dimensional point cloud information of the gear and achieve efficient and high-precision data detection.

[0006] A first aspect of this application provides a method for constructing a three-dimensional gear structure for a central controller, the method comprising:

[0007] At least two sets of regional point cloud data of the gear to be tested and the gear rotation angle corresponding to the regional point cloud data are obtained. The regional point cloud data is obtained by scanning with a laser scanner based on the gear rotation angle adjusted by a multi-axis rotation clamping mechanism. The gear rotation angles corresponding to any two sets of regional point cloud data are different.

[0008] The rotation center axis of the gear under test is calculated based on all the point cloud data of the region.

[0009] Based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, each pair of adjacent regional point cloud data is registered to obtain the registered regional point cloud data.

[0010] Based on the registered regional point cloud data, the pose graph structure of the gear under test is constructed to obtain the three-dimensional structure of the gear under test.

[0011] The step of calculating the rotation center axis of the gear under test based on all the point cloud data of the region includes:

[0012] The gear cone surface region in each of the aforementioned point cloud data is segmented to obtain the first point cloud data;

[0013] Perform plane fitting on the first point cloud data to obtain the plane normal vector of the first point cloud data;

[0014] The normal line of the gear under test is calculated based on the plane normal vector;

[0015] The rotation center axis of the spiral bevel gear under test is calculated based on the normal straight line of the gear under test.

[0016] This application provides a method for constructing a three-dimensional gear structure. It involves acquiring at least two sets of regional point cloud data of the gear under test and the corresponding gear rotation angles. The regional point cloud data is obtained by scanning with a laser scanner based on a multi-axis rotary clamping mechanism that adjusts the gear rotation angle. Any two sets of regional point cloud data correspond to different gear rotation angles. The rotation center axis of the gear under test is calculated based on all regional point cloud data. Each pair of adjacent regional point cloud data is registered based on the rotation center axis and the corresponding gear rotation angles to obtain registered regional point cloud data. A pose map structure of the gear under test is constructed based on the registered regional point cloud data, resulting in a three-dimensional structure of the gear. This method enables comprehensive scanning of helical tooth surfaces with complex curvatures and achieves non-contact, efficient, and high-precision detection of gears.

[0017] In some embodiments of this application, the step of segmenting the gear cone region in all the said regional point cloud data to obtain the first point cloud data includes:

[0018] All the point cloud data of the aforementioned regions are preprocessed to obtain first processed point cloud data, wherein the preprocessing includes at least one of filtering and downsampling;

[0019] The point cloud data of the gear conical surface region is extracted from the first processed point cloud data to obtain the second processed point cloud data;

[0020] The second processed point cloud data is filtered according to a preset threshold to obtain the first point cloud data.

[0021] In some embodiments of this application, the step of registering each pair of adjacent sets of regional point cloud data based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data to obtain registered regional point cloud data includes:

[0022] Based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, all the regional point cloud data are initially registered to obtain the corresponding coarsely registered regional point cloud data.

[0023] A second registration is performed on each pair of adjacent coarsely registered region point cloud data to obtain the corresponding registered region point cloud data.

[0024] In some embodiments of this application, before constructing the pose graph structure of the gear under test based on the registered region point cloud data, the method further includes:

[0025] Calculate the transformation matrix between each pair of adjacent regional point cloud data based on the coarsely registered regional point cloud data and the registered regional point cloud data.

[0026] The information matrix of the gear under test is calculated based on the regional point cloud data, the registered regional point cloud data, and the transformation matrix.

[0027] In some embodiments of this application, constructing the pose graph structure of the gear under test based on the registered regional point cloud data includes:

[0028] The pose graph node of the gear under test is constructed based on all the registered regional point cloud data and the gear rotation angle corresponding to the registered regional point cloud data.

[0029] Based on the pose graph nodes and the information matrix, the pose graph structure of the gear under test is constructed according to the transformation matrix.

[0030] In some embodiments of this application, the step of constructing the pose map structure of the gear under test based on the registered regional point cloud data to obtain the three-dimensional structure of the gear under test includes:

[0031] The pose graph structure of the gear under test is optimized using a preset graph optimization algorithm to obtain the three-dimensional structure of the gear under test.

[0032] To achieve the above objectives, a second aspect of the present invention provides a gear three-dimensional structure construction system, the system comprising:

[0033] The acquisition module is used to acquire at least two sets of regional point cloud data of the gear to be tested and the gear rotation angle corresponding to the regional point cloud data. The regional point cloud data is obtained by scanning with a laser scanner based on adjusting the gear rotation angle by a multi-axis rotation clamping mechanism. The gear rotation angles corresponding to any two sets of regional point cloud data are different.

[0034] The calculation module is used to calculate the rotation center axis of the gear under test based on all the point cloud data of the region;

[0035] The registration module is used to register each pair of adjacent regional point cloud data based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, so as to obtain the registered regional point cloud data.

[0036] The construction module is used to construct the pose graph structure of the gear under test based on the registered regional point cloud data, so as to obtain the three-dimensional structure of the gear under test.

[0037] The step of calculating the rotation center axis of the gear under test based on all the point cloud data of the region includes:

[0038] The gear cone surface region in each of the aforementioned point cloud data is segmented to obtain the first point cloud data;

[0039] Perform plane fitting on the first point cloud data to obtain the plane normal vector of the first point cloud data;

[0040] The normal line of the gear under test is calculated based on the plane normal vector;

[0041] The rotation center axis of the spiral bevel gear under test is calculated based on the normal straight line of the gear under test.

[0042] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to perform the above-described method for constructing a three-dimensional gear structure.

[0043] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for constructing a three-dimensional gear structure.

[0044] It is understood that the beneficial effects of the second to fourth aspects compared with the related technologies are the same as the beneficial effects of the first aspect compared with the related technologies. Please refer to the relevant description in the first aspect above, which will not be repeated here. Attached Figure Description

[0045] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0046] Figure 1 This is a flowchart illustrating a method for constructing a three-dimensional gear structure according to an embodiment of this application;

[0047] Figure 2 This is a schematic diagram of the multi-axis rotary clamping mechanism provided in the embodiments of this application;

[0048] Figure 3This is a schematic diagram of the point cloud data of the spiral bevel gear provided in the embodiments of this application;

[0049] Figure 4 This is a schematic diagram of the gear point cloud preprocessing result provided in the embodiments of this application;

[0050] Figure 5 This is the gear point cloud preprocessing interactive interface provided in the embodiments of this application;

[0051] Figure 6 These are the data points required for calculating the rotation center, as provided in the embodiments of this application.

[0052] Figure 7 This is a schematic diagram of coordinate normalization transformation provided in the embodiments of this application;

[0053] Figure 8 This is the coarse registration region point cloud data provided in the embodiments of this application;

[0054] Figure 9 This is a schematic diagram of optimized fine registration provided in an embodiment of this application;

[0055] Figure 10 This is a schematic diagram of the three-dimensional construction result of the gear tooth surface provided in the embodiment of this application;

[0056] Figure 11 This is a schematic diagram of a gear three-dimensional structure construction system provided in an embodiment of this application;

[0057] Figure 12 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0058] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0059] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0060] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0061] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0062] With the rapid development of the aviation industry, the spiral bevel gear, a key transmission component of the aero-engine as its core power system, is subject to increasingly higher performance requirements and operating loads. Therefore, the quality inspection of spiral bevel gears is a crucial process for verifying the quality of gear manufacturing.

[0063] Traditional quality inspection of spiral bevel gears uses a contact-type coordinate measuring machine. This contact-based measurement method is cumbersome and can only inspect a few local points at a time, failing to obtain global information about the gear tooth surface and thus unable to meet the requirements of efficient quality inspection of spiral bevel gears for aero-engines.

[0064] Furthermore, current traditional contact-based measurement of the quality of aerospace spiral bevel gears mainly relies on coordinate measuring machines (CMMs), which suffers from cumbersome operation, low efficiency, and the ability to only detect local information on the tooth surface, failing to meet the current quality inspection requirements for aerospace spiral bevel gears. In addition, traditional spiral bevel gear quality inspection requires multiple tests to complete the overall inspection, resulting in significant testing errors, low data reliability, and poor inspection efficiency.

[0065] Based on this, embodiments of this application provide a method, system, electronic device, and medium for constructing a three-dimensional gear structure, which aims to quickly and accurately acquire global three-dimensional point cloud information of the gear and achieve efficient and high-precision data detection.

[0066] The gear three-dimensional structure construction method, system, electronic device and medium provided in the embodiments of this application are specifically described through the following embodiments. First, the gear three-dimensional structure construction method in the embodiments of this application is described.

[0067] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0068] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0069] The gear three-dimensional structure construction method provided in this application relates to the field of spiral bevel gear processing technology. The gear three-dimensional structure construction method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the gear three-dimensional structure construction method, but is not limited to the above forms.

[0070] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0071] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0072] Therefore, referring to Figure 1This application provides a method for constructing a three-dimensional gear structure. This method is applied to a central controller, which can be a server, an electronic device, or a mobile terminal, etc. There are no specific limitations here. The method includes the following steps S110 to S140.

[0073] Step S110: Obtain at least two sets of regional point cloud data of the gear to be tested and the gear rotation angle corresponding to the regional point cloud data. The regional point cloud data is obtained by scanning with a laser scanner based on the gear rotation angle adjusted by the multi-axis rotation clamping mechanism. The gear rotation angles corresponding to any two sets of regional point cloud data are different.

[0074] In this step, the rotation angle of the gear under test is adjusted by a multi-axis rotary clamping mechanism, and the regional point cloud data of the gear under test is obtained by scanning the gear under test with a laser scanner. The gear rotation angle corresponding to each set of regional point cloud data is obtained, so as to obtain global information on the shape and size of the gear under test, improve the stability and reliability of aviation spiral bevel gear detection, and can be applied to the three-dimensional reconstruction of various models of aviation spiral bevel gears within the measurement range.

[0075] In one embodiment, the spatial orientation of the gear is adjusted by a multi-axis rotary clamping mechanism, enabling the line laser scanner to acquire regional point cloud data from different observation directions. Each set of regional point cloud data corresponds to a specific gear rotation angle (e.g., 0°, 45°, 90°, etc.). The gear rotation angle is also acquired in real time by a high-precision encoder built into the clamping mechanism, providing initial pose constraints for subsequent point cloud registration. This achieves full tooth surface coverage through multi-view point cloud + angle constraints, avoiding errors caused by missing tooth tip / root data in traditional methods. Furthermore, robust registration requirements can be met by using differences in scanning angles.

[0076] like Figure 2 As shown, the multi-axis rotary clamping mechanism includes a three-jaw chuck, a rotary slide, and a tilting slide. The three-jaw chuck in the multi-axis rotary clamping mechanism fixes the spiral bevel gear during scanning. By adjusting the tilting slide and the rotary slide, a suitable scanning angle is obtained, allowing the spiral bevel gear to rotate at equal angles around its central axis. This acquires 3D point cloud data of the spiral bevel gear tooth surface from different angles, achieving omnidirectional scanning of the gear tooth surface and obtaining auxiliary information on the gear rotation angle. The gear rotation angle is recorded in real-time by a high-precision encoder built into the multi-axis rotary clamping mechanism (e.g., triggering a scan every 5° rotation), providing initial pose constraints for subsequent point cloud registration.

[0077] Specifically, a high-precision linear motion platform driven by a linear motor includes a motion plate for driving the helical bevel gear under test to move along the Y direction and generating pulse signals to cooperate with linear laser sampling point cloud, thereby obtaining the three-dimensional point cloud of the tooth surface of the bevel gear under test.

[0078] Furthermore, a 2D line laser sensing head acquires high-density three-dimensional point cloud data of the helical bevel gear tooth surface under test. The line laser controller receives the data collected by the sensing head and transmits data and instructions to the server. The server receives the point cloud data acquired by the line laser and sends relevant instructions, as well as deploying and implementing the three-dimensional point cloud preprocessing and registration algorithm for the helical bevel gear tooth surface.

[0079] Specifically, the 2D line laser is fixed above a high-precision linear motion platform via a bracket. Furthermore, the bracket allows adjustment of the line laser's height, position, and angle. The line laser sensor head is connected to the line laser controller via a signal line, and the controller is connected to a server via a network cable. The server and line laser controller exchange data at high speed using a transmission protocol. The line laser controller receives encoded signals from the encoder of the high-precision linear motion platform via signal lines and the encoder's AB phase. A multi-axis rotary clamping mechanism is fixed to the motion platform and moves along the Y-axis with the motion plate during scanning of the spiral bevel gear.

[0080] In some embodiments, based on a line laser scanning platform and a multi-axis rotary clamping mechanism, three-dimensional point cloud data of the spiral bevel gear tooth surface and its gear rotation angle auxiliary information are scanned and acquired from different perspectives to obtain global point cloud information of the spiral bevel gear tooth surface, such as... Figure 3 The image shows a schematic diagram of the collected point cloud data of the spiral bevel gear.

[0081] The gear rotation angle information is provided by a multi-axis rotary clamping mechanism. When scanning the spiral bevel gear to be tested, the spiral bevel gear to be tested is fixed by the three-jaw chuck of the clamp. Then, the rotating slide is used to rotate the spiral bevel gear to be tested at the required angle. After one rotation, the tooth surface point cloud data under different perspectives are acquired. Through the combination of multi-axis active rotation scanning and angle encoding feedback, high-precision detection of the entire tooth surface is achieved, which effectively solves the core problems of insufficient data coverage and low registration efficiency of existing technologies. It has significant patent innovation and industrial applicability.

[0082] Furthermore, by leveraging the advantages of non-contact measurement using a line laser scanning platform and high-precision data acquisition via a multi-axis rotary clamping mechanism, efficient and high-precision acquisition of the surface shape and size data of the helical bevel gear under test can be achieved. Specifically, the line laser scanner can acquire high-precision, high-density point clouds of the tooth surface. In addition, based on the multi-axis rotary clamping mechanism, omnidirectional scanning of helical tooth surfaces with complex curvatures can be achieved, and angular information can be acquired to assist in the efficient 3D reconstruction of the point cloud of the helical bevel gear under test.

[0083] Step S120: Calculate the rotation center axis of the gear under test based on the point cloud data of all regions.

[0084] In this step, the point cloud data of all regions of the gear under test is segmented to extract point cloud data for calculating the rotation center of the gear, resulting in the first point cloud data. Then, plane fitting is performed on the first point cloud data to obtain the plane normal vector of the gear under test. The normal line of the gear under test is calculated based on the plane normal vector, thus obtaining the rotation center axis of the gear under test. The coordinates of the rotation center axis of the gear under test are then calculated using the conical geometric characteristics (intersection of normal vector projection) of the point cloud data. A benchmark is directly constructed using the point cloud geometric parameters of the spiral bevel gear under test, which can adapt to complex industrial environments, maintaining high accuracy even under interference from oil stains and wear, thereby improving testing efficiency and repeatability.

[0085] In some embodiments, before calculating the rotation center axis of the spiral bevel gear under test based on the three-dimensional point cloud data in step S120, the following steps are included:

[0086] Preprocessing is performed on all regional point cloud data to obtain first processed point cloud data. Preprocessing includes at least one of filtering and downsampling.

[0087] The point cloud data of the gear cone surface region is extracted from the first processed point cloud data to obtain the second processed point cloud data;

[0088] The first point cloud data is obtained by filtering the second point cloud data according to the preset threshold.

[0089] like Figure 4 The preprocessing operation for the three-dimensional point cloud data of the gear under test includes filtering the three-dimensional point cloud data of the gear under test obtained by scanning at different scanning angles (to remove irrelevant point clouds, such as the base plate) and downsampling (to accelerate the calculation).

[0090] In this embodiment, firstly, all regional point cloud data are preprocessed to obtain first processed point cloud data. Preferably, each group of regional point cloud data is filtered. Further, each group of regional point cloud data is downsampled to obtain the preprocessed first processed point cloud data.

[0091] Furthermore, the point cloud data of the gear conical surface region in the first processed point cloud data is extracted. Preferredly, Euclidean clustering is used to extract the conical surface region to obtain the second processed point cloud data. Then, the second processed point cloud data is filtered according to a preset threshold. Preferredly, the conical surface points are filtered by normal vector consistency check (angle threshold < 15°) to obtain the first point cloud data, thereby reducing detection data errors and improving detection accuracy.

[0092] In some implementations, the 3D point cloud data of the gear under test can be filtered using methods such as statistical filtering, radius filtering, pass-through filtering, and voxel mesh downsampling. Statistical filtering is a method for identifying and removing outliers based on the neighborhood characteristics of points. It calculates the mean and standard deviation of the distance between each point and its neighbors, and then removes points whose average distance deviation from its neighbors is large. Radius filtering determines whether to retain a point by checking the number of neighbors within a specified radius. If a point does not have enough neighbors in its neighborhood, it is considered an outlier and is removed. Pass-through filtering allows users to selectively retain a portion of the points in the point cloud based on a range of coordinates along a certain axis. For example, a threshold range can be set in the x, y, and z dimensions to retain only points that meet the criteria. Voxel mesh downsampling divides the space into uniformly sized cubes (voxels), and then uses the centroid or center point of all points within each voxel to represent the entire voxel. This method can significantly reduce the amount of point cloud data while maintaining its basic structure. The specific choice depends on the processing requirements and data characteristics, and is not limited here. Typically, multiple filtering techniques are used in combination to achieve the best results. For example, statistical filtering is first used to remove obvious noise points, then voxel grid downsampling is applied to reduce the amount of data, and finally pass-through filtering is used to focus on the region of interest.

[0093] In some embodiments, the calculation of the rotation center axis of the spiral bevel gear under test based on the three-dimensional point cloud data in step S120 includes the following steps S210 to S240:

[0094] Step S210: Segment the gear cone region in the point cloud data of each region to obtain the first point cloud data;

[0095] Step S220: Perform plane fitting on the first point cloud data to obtain the plane normal vector of the first point cloud data;

[0096] Step S230: Calculate the normal line of the gear to be tested based on the plane normal vector;

[0097] Step S240: Calculate the rotation center axis of the spiral bevel gear under test based on the normal straight line of the gear under test.

[0098] In this embodiment, segmenting the gear under test requires preprocessing the 3D point cloud data first, such as... Figure 5This is an interactive interface for preprocessing the point cloud of the gear under test. Based on the actual scanned point cloud (region point cloud data) of the gear, a region growing algorithm is used to segment the first point cloud data, containing only the conical surface feature region. The region growing algorithm is an image segmentation technique primarily used to divide an image into multiple parts or regions. Specifically, it starts with a set of predefined seed points and gradually merges adjacent pixels with similar properties (such as grayscale value, color, texture, etc.) into the same region.

[0099] Specifically, the point cloud data of the area scanned by the gear under test is filtered, and Euclidean clustering is used to extract the conical surface area to achieve region segmentation. Then, the conical surface points of the gear under test are screened by normal vector consistency check to remove noise from the three-dimensional point cloud data.

[0100] In some embodiments, the gear under test is first segmented into regions during the preprocessing stage to extract the point cloud data required for calculating the rotation center, such as... Figure 6 To calculate the data points needed for the rotation center, a plane fitting algorithm is used to fit the extracted point cloud data to obtain the plane's normal vector.

[0101] Furthermore, the normal line perpendicular to the conical surface is calculated using the linear formula. Specifically, the two normal lines of the conical surface pointing to the rotation center axis are projected onto the plane of the end face of the gear under test (perpendicular to the rotation center axis). Then, the intersection point of the two projected lines on the end face of the gear under test is calculated. The rotation center axis is then calculated using the linear formula. The normal line is obtained by making the plane of the gear under test perpendicular to the conical surface of the gear under test.

[0102] In this embodiment, by using actual point cloud computing, the measurement method can adapt to any deformation state of the gear under test, making it more widely applicable. Moreover, by replacing the traditional manual / calibration block-dependent mode with geometric feature-driven measurement, it has made significant progress in terms of adaptability to complex industrial environments, measurement efficiency, and error chain control, laying a core benchmark for subsequent global registration and contact imprint analysis.

[0103] In some embodiments, the point cloud data of the gear under test is preprocessed. First, data filtering is performed (e.g., statistical outlier removal, mean K=50, standard deviation threshold 1.5). Then, region segmentation is performed, preferably using Euclidean clustering to extract the conical region (clustering distance threshold δ=2mm). Finally, noise removal is performed, specifically by screening conical points through a normal vector consistency check (angle threshold <15°), ultimately obtaining the first segmented point cloud data. The selected conical region must satisfy axial symmetry to ensure that the plane fitting result reflects the geometric symmetry characteristics of the gear under test.

[0104] Furthermore, preferably, the normal vector direction of the gear under test is calculated based on the first point cloud data obtained after region segmentation using a least-squares plane fitting algorithm, thereby obtaining the plane normal vector of the first point cloud data. Least-squares plane fitting is a data fitting technique used to find the best fitting plane for a set of data points. Specifically, it is achieved by minimizing the sum of squares of the vertical distances (errors) from the actual data points to the fitting plane, thus extracting meaningful information from the chaotic data and providing a foundation for subsequent processing.

[0105] Furthermore, the normals of the fitting planes of each conical surface are projected onto the end face plane of the gear under test (usually the XY plane), generating projection lines. The rotation center axis is then solved by the intersection of two or more projection lines. This enables dynamic analysis of the geometric features of the gear under test, eliminating dependence on external calibration, and directly using the geometric parameters of the point cloud of the gear under test to construct a benchmark. This allows for rapid calculation of the rotation center axis, significantly improving detection efficiency and repeatability.

[0106] Step S130: Based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, register each pair of adjacent regional point cloud data to obtain the registered regional point cloud data.

[0107] In this step, all regional point cloud data are first registered sequentially according to the rotation center axis and the gear rotation angle corresponding to the regional point cloud data to obtain the corresponding coarsely registered regional point cloud data. Then, a second registration is performed on each pair of adjacent coarsely registered regional point cloud data to obtain the corresponding registered regional point cloud data.

[0108] Specifically, firstly, the gear rotation angle corresponding to the regional point cloud data and the calculated rotation center axis of the gear under test are used to perform rotational coarse registration on the three-dimensional point cloud data of the gear under test to achieve global initial alignment of the point cloud of the gear under test. The gear rotation angle is preferably provided by the gear rotation angle information provided by the multi-axis rotation clamping mechanism, such as the high-precision encoder built into the multi-axis rotation clamping mechanism that is recorded in real time.

[0109] Furthermore, based on the coarse registration region point cloud data, a second registration is performed on each two adjacent sets of coarse registration region point cloud data to obtain the corresponding registered region point cloud data, thereby reducing data errors in the detection process.

[0110] In some embodiments, in step S130, each pair of adjacent sets of regional point cloud data is registered based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data to obtain the registered regional point cloud data, including the following steps S310 to S320:

[0111] Step S310: Perform initial registration of all region point cloud data according to the rotation center axis and the gear rotation angle corresponding to the region point cloud data to obtain the corresponding coarse registration region point cloud data.

[0112] Step S320: Perform secondary registration on each pair of adjacent coarsely registered region point cloud data to obtain the corresponding registered region point cloud data.

[0113] In this embodiment, based on the gear rotation angle information provided by the multi-axis rotary clamping mechanism during the acquisition process and the calculated rotation center axis of the gear under test, the three-dimensional point cloud data of the gear under test is coarsely registered by rotation.

[0114] Specifically, a parametric rotation matrix is ​​used to perform an equal-angle rotation transformation of the point cloud of the gear under test around the rotation center axis based on the preprocessed 3D point cloud data and the fixed scanning angle, thus obtaining a coarse registration result, such as... Figure 7 The image shows the point cloud data for the coarse registration area.

[0115] Furthermore, the robust kernel-based iterative closest point (ICP) algorithm is used to perform precise registration of the initially aligned 3D point cloud data based on the robust kernel-based iterative closest point algorithm between each pair of adjacent coarsely registered region point cloud data, resulting in the corresponding registered region point cloud data. The robust kernel-based iterative closest point (ICP) algorithm is an improved version of the traditional ICP algorithm, aiming to improve the stability of point cloud registration in noisy, outlier, and partially overlapping scenarios.

[0116] Specifically, traditional ICP iteration includes matching point pairs, calculating transformations, applying transformations, and determining convergence. The key improvement of robust ICP lies in the error calculation and optimization stages. Robust ICP significantly improves the robustness of traditional ICP without significantly increasing computational complexity by introducing an error weighting mechanism. The choice of kernel function (such as Huber / Tukey) and parameter tuning need to be optimized according to the specific scenario and are not limited here, thereby balancing noise suppression and convergence speed.

[0117] Furthermore, the specific steps of robust ICP include: first, finding the nearest neighbor (corresponding point) in the target point cloud for each point in the source point cloud, and then calculating the residual, that is, calculating the geometric error of each corresponding point pair.

[0118] Furthermore, robust kernel functions can be applied. By applying a kernel function to each residual, weighted optimization is performed, and the optimal rotation matrix and translation vector are solved using weighted least squares. The weights are determined by the derivative of the kernel function (e.g., the Huber kernel assigns low weights to large errors). Iterative updates are then performed by applying transformations and repeating the above steps until convergence (e.g., the change in transformation parameters is less than a threshold or the residuals stabilize), thereby effectively suppressing outliers and noise interference and improving registration accuracy.

[0119] In some embodiments, before constructing the pose graph structure of the gear under test based on the registered region point cloud data in step S140, the following steps S410 to S420 are included:

[0120] Step S410: Calculate the transformation matrix between each pair of adjacent pairs of regional point cloud data based on the coarsely registered regional point cloud data and the registered regional point cloud data.

[0121] Step S420: Calculate the information matrix of the gear to be tested based on the regional point cloud data, the registered regional point cloud data, and the transformation matrix.

[0122] In this embodiment, the transformation matrix is ​​obtained by an iterative nearest point registration system based on robust sums and functions, and then the information matrix is ​​calculated based on the 3D point cloud data, the first point cloud data, and the transformation matrix.

[0123] Specifically, the methods for obtaining the fine registration transformation matrix and information matrix are as follows (the basic algorithm module comes from the Open3d point cloud processing code library): the transformation matrix is ​​obtained by the robust sum-function-based ICP fine registration algorithm module, specifically through the function `registration_icp()`. The information matrix is ​​calculated from the source point cloud, the target point cloud, and their transformation matrices, specifically through the function `get_information_matrix_from_point_clouds()`.

[0124] Specifically, the robust and function-based iterative nearest point fine registration adopts ICP fine registration with Huber loss function, and combines the GTSAM graph optimization library (Georgia Tech Smoothing and Mapping) to optimize the pose graph, thus solving the problem of error accumulation in traditional point cloud stitching.

[0125] Step S140: Construct the pose graph structure of the gear under test based on the registered regional point cloud data to obtain the three-dimensional structure of the gear under test.

[0126] In this step, based on the constructed pose graph structure, the graph nodes and edges of the pose graph structure are optimized by a preset global registration system to obtain the 3D point cloud of the gear to be tested.

[0127] In some embodiments, the pose graph structure of the gear under test is constructed in step S140 based on the registered region point cloud data, including the following steps S510 to S520:

[0128] Step S510: Construct the pose graph node of the gear under test based on all registered area point cloud data and the gear rotation angle corresponding to the registered area point cloud data.

[0129] Step S520: Based on the pose graph nodes and information matrix, construct the pose graph structure of the gear under test according to the transformation matrix.

[0130] In this embodiment, the transformation matrix refers to the relative position and rotation relationship between adjacent point clouds, typically including a rotation matrix and a translation vector. The information matrix reflects the reliability or uncertainty of this transformation; for example, in ICP registration, the information matrix is ​​determined by the overlap or matching error of the point clouds.

[0131] Furthermore, a pose graph is constructed based on the transformation matrix and the information matrix. Specifically, each node can represent the pose (position and orientation) of a point cloud, and the edges can represent the transformation relationship and its uncertainty between adjacent nodes. Then, the transformation matrix and the information matrix are used as edges to connect the corresponding nodes to obtain the pose graph structure of the gear under test. Thus, through global optimization of the pose graph, the overall accuracy and robustness of registration are significantly improved.

[0132] In some embodiments, in step S140, the pose map structure of the gear under test is constructed based on the registered region point cloud data to obtain the three-dimensional structure of the gear under test, including the following step S610:

[0133] Step S610: Optimize the pose diagram structure of the gear under test using a preset image optimization algorithm to obtain the three-dimensional structure of the gear under test.

[0134] In this embodiment, based on the constructed pose graph structure, the graph nodes and edges of the pose graph structure are optimized by a preset global registration system to obtain the three-dimensional structure of the gear to be tested.

[0135] like Figure 9 The diagram shows the optimized fine registration. Specifically, the steps of global optimized registration based on graph optimization include: constructing the pose graph structure of the point cloud of the spiral bevel gear under test through the graph structure module, and adding graph nodes through the pose_graph.nodes.append() function.

[0136] Furthermore, edges are added to the graph nodes using the pose_graph.edges.append() function. The transformation matrices and information matrices of all the point clouds of the spiral bevel gears to be registered are used to construct the pose graph, resulting in the overall graph structure of the point cloud of the spiral bevel gears to be registered. Then, based on the constructed graph structure of the point cloud of the spiral bevel gears to be registered, the overall optimization of the graph nodes and edges is achieved through the global registration module, specifically through the global_optimization() function, to complete the global fine registration of the 3D point cloud of the tooth surface of the spiral bevel gears to be registered.

[0137] Furthermore, the globally optimized point cloud of the spiral bevel gear under test is fused into a whole, ultimately realizing the construction of the three-dimensional point cloud structure of the tooth surface of the spiral bevel gear under test, such as... Figure 10 The image shows a schematic diagram of the three-dimensional structure construction result of the tooth surface of the spiral bevel gear to be tested.

[0138] In one embodiment, firstly, the spiral bevel gear to be tested is fixed on a rotary slide ( Figure 1 The trigger line laser scanner (wavelength 650nm, accuracy ±2μm) moves along the Y-axis, synchronously acquiring point cloud data. A scan is triggered every 5° interval, and then the encoder AB phase signals are received via the laser controller. Figure 2 ), generate point cloud time series and perform noise filtering.

[0139] Furthermore, a pass-through filter is applied to the single-view point cloud. This pass-through filter is mainly used to remove the base plate and other irrelevant point clouds. The specific filtering range may need to be determined based on the actual situation. The conical region is extracted, and then the RANSAC algorithm is used to fit the normal plane of the conical surface. The normal is then projected onto the end face plane of the spiral bevel gear under test. Figure 6 The intersection points are calculated to determine the rotation center axis. RANSAC (Random Sample Consensus) is a robust model fitting algorithm that estimates the optimal model parameters from data containing noise and outliers through random sampling and iterative voting.

[0140] Furthermore, a normalized coordinate transformation is performed on all single-view point clouds (region point cloud data) based on the rotation center axis (direction vector). The formula for the normalized coordinate transformation is as follows:

[0141] First, based on the center of rotation Perform a translation transformation on the point cloud data, the source point cloud being... After translation, it becomes :

[0142] ;

[0143] Then, a rotation transformation is performed on the point cloud data after translation, assuming the normal vector of the rotation center axis is... , Axial unit vector .

[0144] Furthermore, according to the Rodriguez rotation matrix formula, solving for a rotation matrix requires calculating two parameters: one is the axis of rotation of the rotation matrix. The other is the rotation angle. Therefore, the calculation about the axis of rotation is performed. Normalized, it is denoted as The specific formula is as follows:

[0145] ;

[0146] ;

[0147] Further, calculate the rotation angle. :

[0148] ;

[0149] Then, the rotation matrix is ​​calculated using the Rodriguez formula:

[0150] ;

[0151] in, It is the identity matrix. for The cross product matrix.

[0152] Furthermore, after calculating the rotation matrix, the point cloud data after translation transformation is rotated and normalized. The normalized point cloud is denoted as... :

[0153] ;

[0154] in, Represents the elements at different positions in the matrix, and is a scalar.

[0155] If a pose matrix is ​​used to transform the coordinates of point cloud data, then the rotation matrix needs to be... Convert to homogeneous matrix The transformation relationship is as follows:

[0156] ;

[0157] ;

[0158] in, For rotation matrices based on the central axis of rotation, The transformation matrix is... For single-view point clouds, This is a single-view point cloud after coordinate transformation.

[0159] Specifically, a normalized coordinate transformation is performed on the point cloud data of the gear under test based on the rotation center axis, such as... Figure 6 As shown, this makes the center of the point cloud of the gear under test return to zero at the world coordinate center.

[0160] Furthermore, based on the encoder angle θ and the coordinates of the rotation center axis, the point cloud is coarsely registered by rotating it around the center axis by an angle θ. Figure 8 ), perform ICP fine registration with Huber kernel on adjacent viewpoint point clouds (loss threshold k=0.1) to generate transformation matrix and information matrix.

[0161] When performing coarse registration on point cloud data, the formula for calculating the transformation matrix is ​​the same as the formula for calculating the coordinate normalization part. Since the point cloud data has already undergone coordinate normalization, the calculation of the rotation matrix around the rotation axis is as follows: Axis. Specifically, the unit vector about the axis of rotation is The rotation angle is :

[0162]

[0163] in, for The cross product matrix. The rotation matrix for coarse registration can be converted into the pose matrix for coarse registration. .

[0164] Furthermore, a pose graph is constructed using the GTSAM library. Figure 9 Outputs a globally optimized 3D reconstructed point cloud. Figure 10 This improves overall measurement efficiency and optimizes measurement accuracy, thereby enhancing measurement robustness.

[0165] In this embodiment, at least two sets of regional point cloud data of the gear under test and the corresponding gear rotation angles are acquired. The regional point cloud data is obtained by scanning with a laser scanner based on the gear rotation angle adjusted by a multi-axis rotary clamping mechanism. The gear rotation angles corresponding to any two sets of regional point cloud data are different. The rotation center axis of the gear under test is calculated based on all regional point cloud data. Based on the rotation center axis and the gear rotation angles corresponding to the regional point cloud data, each pair of adjacent regional point cloud data is registered to obtain registered regional point cloud data. Based on the registered regional point cloud data, the pose map structure of the gear under test is constructed to obtain the three-dimensional structure of the gear under test. This method can comprehensively scan helical tooth surfaces with complex curved surfaces and achieve non-contact, efficient, and high-precision detection of gears.

[0166] like Figure 11 As shown in some embodiments of this application, a gear three-dimensional structure construction system is provided. The system includes an acquisition module 1110, a calculation module 1120, a registration module 1130, and a construction module 1140. Specifically:

[0167] The acquisition module 1110 is used to acquire at least two sets of regional point cloud data of the gear to be tested and the gear rotation angle corresponding to the regional point cloud data. The regional point cloud data is obtained by scanning with a laser scanner based on the gear rotation angle adjusted by a multi-axis rotary clamping mechanism. The gear rotation angles corresponding to any two sets of regional point cloud data are different.

[0168] The calculation module 1120 is used to calculate the rotation center axis of the gear under test based on the point cloud data of all regions.

[0169] The registration module 1130 is used to register each pair of adjacent regional point cloud data based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, so as to obtain the registered regional point cloud data.

[0170] Module 1140 is used to optimize the pose diagram structure based on the gear point cloud diagram structure to obtain the three-dimensional point cloud of the spiral bevel gear under test.

[0171] In some implementations, the calculation module 1120 may include: segmenting the gear conical surface region in each region of point cloud data to obtain first point cloud data.

[0172] In some implementations, the calculation module 1120 may include: performing plane fitting on the first point cloud data to obtain the plane normal vector of the first point cloud data.

[0173] In some implementations, the calculation module 1120 may include: calculating the normal line of the gear under test based on the plane normal vector.

[0174] In some implementations, the calculation module 1120 may include: calculating the rotation center axis of the spiral bevel gear under test based on the normal line of the gear under test.

[0175] In some implementations, the calculation module 1120 may include: preprocessing all region point cloud data to obtain first processed point cloud data, wherein the preprocessing includes at least one of filtering and downsampling.

[0176] In some implementations, the calculation module 1120 may include: extracting point cloud data of the gear conical surface region from the first processed point cloud data to obtain second processed point cloud data.

[0177] In some implementations, the calculation module 1120 may include: filtering second processed point cloud data according to a preset threshold to obtain first point cloud data.

[0178] In some implementations, the registration module 1130 may include: performing initial registration of all region point cloud data based on the rotation center axis and the gear rotation angle corresponding to the region point cloud data to obtain the corresponding coarsely registered region point cloud data.

[0179] In some implementations, the registration module 1130 may include: performing secondary registration on each pair of adjacent coarse registration region point cloud data to obtain the corresponding registered region point cloud data.

[0180] In some implementations, the registration module 1130 may include: calculating a transformation matrix between each pair of adjacent sets of regional point cloud data based on the coarsely registered regional point cloud data and the registered regional point cloud data.

[0181] In some implementations, the registration module 1130 may include: calculating the information matrix of the gear to be tested based on the regional point cloud data, the registered regional point cloud data, and the transformation matrix.

[0182] In some implementations, the registration module 1130 may include: constructing a pose graph node of the gear to be tested based on all registered region point cloud data and the gear rotation angle corresponding to the registered region point cloud data.

[0183] In some implementations, the registration module 1130 may include: constructing a pose graph structure of the gear under test based on the pose graph nodes and the information matrix and the transformation matrix.

[0184] In some implementations, the registration module 1130 may include: optimizing the pose graph structure of the gear under test using a preset graph optimization algorithm to obtain the three-dimensional structure of the gear under test.

[0185] It should be noted that the gear three-dimensional structure construction system provided in this embodiment and the gear three-dimensional structure construction method described above are based on the same inventive concept. Therefore, the relevant content of the gear three-dimensional structure construction method described above also applies to the content of the gear three-dimensional structure construction system. Therefore, it will not be repeated here.

[0186] To address the issues of traditional spiral bevel gear quality inspection requiring multiple tests for overall inspection, which suffers from significant testing errors, low data reliability, and poor inspection efficiency, this system acquires at least two sets of regional point cloud data for the gear under test, along with the corresponding gear rotation angles. The regional point cloud data is obtained by scanning with a laser scanner using a multi-axis rotary clamping mechanism to adjust the gear rotation angle; any two sets of regional point cloud data will correspond to different gear rotation angles. The rotation center axis of the gear under test is calculated based on all regional point cloud data. Each adjacent pair of regional point cloud data is registered based on the rotation center axis and the corresponding gear rotation angle, resulting in registered regional point cloud data. Based on the registered regional point cloud data, a pose map structure of the gear under test is constructed, yielding its three-dimensional structure. This allows for comprehensive scanning of spiral tooth surfaces with complex curvature, achieving non-contact, highly efficient, and precise gear inspection.

[0187] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described gear three-dimensional structure construction method.

[0188] like Figure 12 , Figure 12 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device includes:

[0189] At least one battery;

[0190] At least one memory;

[0191] At least one processor;

[0192] At least one program;

[0193] The program is stored in memory, and the processor executes at least one program to implement the gear three-dimensional structure construction method described above in this disclosure.

[0194] This electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0195] The electronic devices according to embodiments of this application will now be described in detail.

[0196] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure.

[0197] The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1700 and is called and executed by the processor 1600 to implement a gear three-dimensional structure construction method according to an embodiment of this disclosure.

[0198] The input / output interface 1800 is used to implement information input and output.

[0199] The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0200] Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900);

[0201] The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0202] This disclosure also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for constructing a three-dimensional gear structure.

[0203] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0204] The embodiments described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0205] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0206] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0207] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0208] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0209] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0210] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0211] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0212] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0213] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0214] The above is a detailed description of the preferred embodiments of this application. However, the embodiments of this application are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of this application.

[0215] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A method for constructing a three-dimensional gear structure, characterized in that, The method includes: At least two sets of regional point cloud data of the gear to be tested and the gear rotation angle corresponding to the regional point cloud data are obtained. The regional point cloud data is obtained by scanning with a laser scanner based on the gear rotation angle adjusted by a multi-axis rotation clamping mechanism. The gear rotation angles corresponding to any two sets of regional point cloud data are different. The rotation center axis of the gear under test is calculated based on all the point cloud data of the region. Based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, each pair of adjacent regional point cloud data is registered to obtain the registered regional point cloud data. Based on the registered regional point cloud data, the pose graph structure of the gear under test is constructed to obtain the three-dimensional structure of the gear under test. The step of calculating the rotation center axis of the gear under test based on all the point cloud data of the region includes: The gear cone surface region in each of the aforementioned point cloud data is segmented to obtain the first point cloud data; Perform plane fitting on the first point cloud data to obtain the plane normal vector of the first point cloud data; The normal line of the gear under test is calculated based on the plane normal vector; The rotation center axis of the spiral bevel gear under test is calculated based on the normal straight line of the gear under test.

2. The method for constructing a three-dimensional gear structure according to claim 1, characterized in that, The process of segmenting the gear cone region from all the aforementioned point cloud data to obtain the first point cloud data includes: All the point cloud data of the aforementioned regions are preprocessed to obtain first processed point cloud data, wherein the preprocessing includes at least one of filtering and downsampling; The point cloud data of the gear conical surface region is extracted from the first processed point cloud data to obtain the second processed point cloud data; The second processed point cloud data is filtered according to a preset threshold to obtain the first point cloud data.

3. The method for constructing a three-dimensional gear structure according to claim 2, characterized in that, The process of registering each pair of adjacent sets of regional point cloud data based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data to obtain registered regional point cloud data includes: Based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, all the regional point cloud data are initially registered to obtain the corresponding coarsely registered regional point cloud data. A second registration is performed on each pair of adjacent coarsely registered region point cloud data to obtain the corresponding registered region point cloud data.

4. The method for constructing a three-dimensional gear structure according to claim 3, characterized in that, Before constructing the pose graph structure of the gear under test based on the registered regional point cloud data, the method further includes: Calculate the transformation matrix between each pair of adjacent regional point cloud data based on the coarsely registered regional point cloud data and the registered regional point cloud data. The information matrix of the gear under test is calculated based on the regional point cloud data, the registered regional point cloud data, and the transformation matrix.

5. The method for constructing a three-dimensional gear structure according to claim 4, characterized in that, The construction of the pose graph structure of the gear under test based on the registered regional point cloud data includes: The pose graph node of the gear under test is constructed based on all the registered regional point cloud data and the gear rotation angle corresponding to the registered regional point cloud data. Based on the pose graph nodes and the information matrix, the pose graph structure of the gear under test is constructed according to the transformation matrix.

6. The method for constructing a three-dimensional gear structure according to claim 1, characterized in that, The process of constructing the pose map structure of the gear under test based on the registered regional point cloud data to obtain the three-dimensional structure of the gear under test includes: The pose graph structure of the gear under test is optimized using a preset graph optimization algorithm to obtain the three-dimensional structure of the gear under test.

7. A gear three-dimensional structure construction system, characterized in that, The system includes: The acquisition module is used to acquire at least two sets of regional point cloud data of the gear to be tested and the gear rotation angle corresponding to the regional point cloud data. The regional point cloud data is obtained by scanning with a laser scanner based on adjusting the gear rotation angle by a multi-axis rotation clamping mechanism. The gear rotation angles corresponding to any two sets of regional point cloud data are different. The calculation module is used to calculate the rotation center axis of the gear under test based on all the point cloud data of the region; The registration module is used to register each pair of adjacent regional point cloud data based on the rotation center axis and the gear rotation angle corresponding to the regional point cloud data, so as to obtain the registered regional point cloud data. The construction module is used to construct the pose graph structure of the gear under test based on the registered regional point cloud data, so as to obtain the three-dimensional structure of the gear under test. The step of calculating the rotation center axis of the gear under test based on all the point cloud data of the region includes: The gear cone surface region in each of the aforementioned point cloud data is segmented to obtain the first point cloud data; Perform plane fitting on the first point cloud data to obtain the plane normal vector of the first point cloud data; The normal line of the gear under test is calculated based on the plane normal vector; The rotation center axis of the spiral bevel gear under test is calculated based on the normal straight line of the gear under test.

8. An electronic device, characterized in that: It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform a gear three-dimensional structure construction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for causing a computer to perform a method for constructing a three-dimensional gear structure according to any one of claims 1 to 6.

Images