Three-dimensional digital model generation method and system applied to polishing machine

Abstract

The invention relates to the technical field of digital models, in particular to a three-dimensional digital model generation method and system applied to a polishing machine. The method comprises the following steps that three-dimensional geometric data of the surface of a workpiece and kinematics parameter data of a multi-section swing grinding head are obtained; gridding discrete processing is carried out on the surface of the workpiece according to the three-dimensional geometric data, and surface grid node data containing space coordinate information and normal vector information are obtained; mathematical modeling is conducted on the difference swing tracks of the grinding heads according to the kinematics parameter data, grinding head space-time position mapping data are obtained, and the difference swing tracks are specifically uniform distribution of the multiple grinding heads in the time domain and maximum complementary coverage of the multiple grinding heads in the space domain by setting sine function combinations of pi phase differences between the different grinding heads; according to the method, different phase differences are set for different grinding heads, so that the optimal distribution of the movement tracks of the grinding heads in the time domain and the space domain is realized, and the problems of track overlapping and blind area coverage in a multi-grinding-head system are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital models, and in particular to a method and system for generating a three-dimensional digital model used in a polishing machine. Background Art

[0002] A polishing machine is a mechanical device used to improve the surface finish and reduce roughness of a workpiece. Depending on its structure and function, it can be categorized as handheld, desktop, floor-based, automated, and specialized. Automated polishing machines achieve efficient production through CNC and robotics technologies. Its main components include a motor to provide power, a polishing disc that directly contacts the workpiece, a speed changer, a control system for adjusting operating parameters, a fixture to secure the workpiece, a cooling system, and a dust collection device. The polishing machine uses high-speed friction between the polishing disc and the workpiece surface to create a smooth and shiny surface through three stages: rough polishing, fine polishing, and mirror polishing. Key technical parameters include speed range, power, polishing accuracy, workpiece size range, and degree of automation. The machine is suitable for processing a variety of materials, including metal, stainless steel, and glass. The three-dimensional digital model used in polishing machines is a virtual model constructed using computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies to simulate the structure, performance, and operating process of the polishing machine. Through precise modeling, the core components of the polishing machine, including the motor, polishing disc, speed change device, fixture, cooling system and dust collection device, are fully described, and their operating characteristics such as power transmission, speed regulation and workpiece fixation are simulated according to actual working conditions.

[0003] Traditional methods for generating 3D digital models for polishing machines often face the following challenges: Early polishing equipment mostly employed single or simple dual-head designs. These devices faced an unavoidable physical limitation when processing complex workpiece surfaces: the motion trajectory of a single grinding head was geometrically impossible to achieve completely uniform coverage of the workpiece surface. The polishing process involves not only the motion trajectory of the grinding head but also the complex contact mechanism between the grinding head and the workpiece surface. The contact state of each grinding head with the workpiece surface changes dynamically at different times and locations. Factors such as pressure distribution, relative velocity, and material removal mechanisms during this contact process affect the final polishing effect. Traditional analytical methods lacked an effective means to uniformly model and calculate these complex physical processes. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide a three-dimensional digital model generation method and system for a polishing machine to solve at least one of the above technical problems.

[0005] To achieve the above object, a method for generating a three-dimensional digital model of a polishing machine includes the following steps:

[0006] Step S1: Acquire three-dimensional geometric data of the workpiece surface and kinematic parameter data of the multi-segment swing grinding head;

[0007] Step S2: performing mesh discretization processing on the workpiece surface according to the three-dimensional geometric data to obtain surface mesh node data containing spatial coordinate information and normal vector information;

[0008] Step S3: mathematically modeling the differential swing trajectory of each grinding head based on the kinematic parameter data to obtain the grinding head spatiotemporal position mapping data, wherein the differential swing trajectory is specifically achieved by setting a sine function combination with a π phase difference between different grinding heads to achieve uniform distribution of multiple grinding heads in the time domain and maximum complementary coverage in the spatial domain;

[0009] Step S4: Dynamic coverage calculation is performed on the contact relationship between the effective action area of ​​the grinding head and the workpiece surface based on the surface grid node data and the spatiotemporal position mapping data of the grinding head, thereby obtaining three-dimensional polishing coverage data including the cumulative polishing time and the coordinated polishing intensity distribution. The dynamic coverage calculation is specifically performed to accurately predict the polishing amount for each grid node on the workpiece surface based on the grinding head action radius and the polishing intensity distribution.

[0010] Step S5: Evaluate and optimize the polishing quality uniformity based on the three-dimensional polishing coverage data to obtain three-dimensional digital polishing model data.

[0011] The present invention further provides a three-dimensional digital model generation system for a polishing machine, which is used to execute the above-mentioned three-dimensional digital model generation method for a polishing machine. The three-dimensional digital model generation system for a polishing machine includes:

[0012] A three-dimensional geometric data acquisition module is used to obtain the three-dimensional geometric data of the workpiece surface and the kinematic parameter data of the multi-segment swing grinding head;

[0013] The surface mesh processing module is used to perform mesh discretization processing on the workpiece surface according to the three-dimensional geometric data to obtain surface mesh node data containing spatial coordinate information and normal vector information;

[0014] The grinding head trajectory modeling module is used to mathematically model the differential swing trajectory of each grinding head based on the kinematic parameter data to obtain the grinding head spatiotemporal position mapping data. The differential swing trajectory is specifically achieved by setting a sine function combination with a π phase difference between different grinding heads to achieve uniform distribution of multiple grinding heads in the time domain and maximum complementary coverage in the spatial domain.

[0015] A dynamic coverage calculation module is used to dynamically calculate the contact relationship between the effective working area of ​​the grinding head and the workpiece surface based on the surface grid node data and the spatiotemporal position mapping data of the grinding head, and obtain three-dimensional polishing coverage data including the cumulative polishing time and the coordinated polishing intensity distribution. The dynamic coverage calculation process specifically predicts the precise polishing amount for each grid node on the workpiece surface based on the grinding head effective radius and polishing intensity distribution;

[0016] The uniformity evaluation and optimization module is used to evaluate and optimize the uniformity of polishing quality based on the three-dimensional polishing coverage data to obtain three-dimensional digital polishing model data.

[0017] The present invention significantly improves the uniformity and overall consistency of the surface quality of polished workpieces through a systematic and refined three-dimensional digital polishing model generation process. First, statistical analysis of polishing intensity based on three-dimensional polishing coverage data can accurately capture the global characteristics of the workpiece surface polishing process, such as mean, variance and standard deviation, and provide a scientific basis for the subsequent quantification of uniformity deviation. This data-driven statistical feature analysis makes the traditional polishing quality assessment that relies on experience judgment more objective and refined, thereby avoiding quality fluctuations caused by human subjective factors. By establishing a deviation distribution histogram, the system can clearly reflect the deviation of polishing intensity in each area, realize accurate assessment of the uniformity deviation of the workpiece surface, and lay a solid foundation for identifying and locating areas with substandard quality. In the process of identifying quality abnormal areas, a reasonable deviation threshold is set and grid nodes exceeding the threshold are screened out, effectively distinguishing local defects or blind spots in the polishing process, and ensuring the accurate positioning of abnormal areas. This fine-grained quality anomaly detection not only improves the sensitivity of detection, but also provides a clear direction for accurately adjusting the grinding head parameters, avoiding the waste of resources caused by blind adjustment. Furthermore, by calculating geometric characteristic parameters such as the center of gravity coordinates, area size, and boundary shape of the abnormal region, the system can provide a detailed characterization of the spatial distribution characteristics of the abnormal region, enhancing its understanding of the spatial characteristics of the defect. This geometric characteristic data not only facilitates matching analysis of the grinding head's range of action but also promotes the precise correlation between the abnormal region and the grinding head's motion trajectory, improving the targetedness and effectiveness of subsequent parameter optimization. Based on the calculation of the overlapping area ratio between the abnormal region's geometric characteristics and the grinding head's range of action, the system establishes a grinding head responsibility score table, quantifying the grinding head's contribution to different abnormal regions. This scoring mechanism enables the precise screening of critical grinding heads, avoids the waste of resources caused by indiscriminate adjustments, and significantly improves the efficiency and effectiveness of the polishing process. The setting of the responsibility threshold ensures that the identification of key grinding heads is neither too broad nor too strict, balancing comprehensiveness and accuracy of the adjustment. For the selected grinding heads for optimization, a systematic adjustment strategy is formulated based on the correspondence between defect type and parameter adjustment direction, ensuring scientific and reasonable parameter modification. By carefully adjusting the grinding head's swing amplitude, frequency, and phase difference, it is possible to effectively optimize for different defect types, improving the overall uniformity and coverage integrity of the polishing process while avoiding process fluctuations and reduced polishing quality caused by over-adjustment. After the grinding head parameter configuration is updated, the system generates a new grinding head motion trajectory and three-dimensional polishing coverage effect through simulation, establishing a closed-loop feedback mechanism that enables continuous and dynamically adjustable optimization of the polishing process. The coverage data after each parameter adjustment not only quantifies the optimization effect, but also provides data support for the next round of optimization, ensuring the scientific nature and precision of the adjustment process.This iterative process achieves the gradual convergence of the polishing effect through multiple rounds of parameter updates and simulation calculations, and ultimately forms a high-quality, uniform three-dimensional digital polishing model. This technical approach based on the combination of simulation and adjustment of digital models breaks through the limitations of traditional empirical polishing methods, making the polishing process more intelligent and automated, reducing manual intervention and improving production efficiency. The convergence judgment mechanism effectively avoids invalid cycles and excessive adjustments in the optimization process by quantitatively evaluating the changes in the statistical characteristics of the polishing intensity in continuous iterations, thereby ensuring the stability and accuracy of the model. This judgment process ensures that the final output three-dimensional digital polishing model not only reflects the ideal polishing effect, but also has a high degree of repeatability and reliability. Overall, this method achieves a precise match between the polishing machine grinding head movement and the workpiece surface quality through multi-level data processing and parameter optimization, significantly improving the surface quality and processing consistency of the polished workpiece, and reducing process risks and resource waste. This technical solution is suitable for various complex curved surfaces and high-precision polishing scenarios, especially in the fields of electronic components, optical elements, and precision mechanical parts. It can effectively improve the polishing quality and meet the stringent requirements of high-end manufacturing on the surface quality of workpieces. At the same time, it provides solid digital technical support for intelligent manufacturing and polishing processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0019] Figure 1 A schematic flow chart of the steps of a method for generating a three-dimensional digital model of a polishing machine according to the present invention;

[0020] Figure 2 for Figure 1 Detailed step flow diagram of step S3;

[0021] Figure 3 for Figure 1 Detailed step flow chart of step S4 in FIG. DETAILED DESCRIPTION

[0022] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0024] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0025] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for generating a three-dimensional digital model of a polishing machine, the method comprising the following steps:

[0026] Step S1: Acquire three-dimensional geometric data of the workpiece surface and kinematic parameter data of the multi-segment swing grinding head;

[0027] The embodiment of the present invention first obtains the three-dimensional geometric data of the workpiece surface and the kinematic parameter data of the multi-segment oscillating grinding head. The three-dimensional geometric data can be used to obtain point cloud data of the workpiece surface through a three-dimensional laser scanner. The point cloud data has high density and sub-millimeter accuracy. The original point cloud is converted into STL format data in the form of triangular facets through post-processing software, which is further used for subsequent modeling; the kinematic parameters of the multi-segment oscillating grinding head are obtained by reading the grinding head drive setting parameters in the polishing machine control system. The parameters include the oscillation frequency, amplitude, center position, initial phase of the oscillation path, periodic function type and other data of each grinding head. In this embodiment, four independent oscillating grinding heads are used, and the oscillation frequency is set to 5 Hz, the amplitude is 20 mm, and the initial phases are 0 degrees, 90 degrees, 180 degrees and 270 degrees respectively, so as to achieve uniform distribution on the time axis, providing a basis for subsequent spatial coverage optimization.

[0028] Step S2: performing mesh discretization processing on the workpiece surface according to the three-dimensional geometric data to obtain surface mesh node data containing spatial coordinate information and normal vector information;

[0029] After obtaining the three-dimensional geometric data, the embodiment of the present invention performs mesh discretization processing on the workpiece surface. Specifically, the triangular face data in STL format is imported into the self-developed mesh division module for uniform subdivision to generate a regularly distributed triangular mesh. The mesh density can be dynamically adjusted according to the complexity of the workpiece and the target polishing accuracy. In this embodiment, the mesh node spacing is set to 1 mm to ensure that small geometric fluctuations can be captured during the polishing simulation; for each mesh node, its spatial coordinate value in three-dimensional space is calculated, and its unit normal vector is determined according to the normal direction of the triangular face in which it is located. The coordinates and normal vectors of all nodes together constitute the surface mesh node data, providing an accurate geometric basis for subsequent polishing contact judgment and strength calculation.

[0030] Step S3: mathematically modeling the differential swing trajectory of each grinding head based on the kinematic parameter data to obtain the grinding head spatiotemporal position mapping data, wherein the differential swing trajectory is specifically achieved by setting a sine function combination with a π phase difference between different grinding heads to achieve uniform distribution of multiple grinding heads in the time domain and maximum complementary coverage in the spatial domain;

[0031] The embodiment of the present invention uses the kinematic parameter data of the grinding head extracted in step S1 to mathematically model the multi-segment differential swing grinding head trajectory to obtain the precise spatial position of each grinding head at any time point; in this embodiment, the center trajectory of each grinding head is modeled as a periodic swing model based on a sine function, specifically expressed as a uniaxial sinusoidal oscillation in a direction perpendicular to the normal of the workpiece surface, and the differential motion is achieved by setting different phase differences between the grinding heads. The phase differences of the four grinding heads are 0, π / 2, π, and 3π / 2, respectively, so that their swinging behaviors are evenly distributed on the time axis; each time step is set to 10 milliseconds, and the spatial coordinates of each grinding head in the entire processing cycle are calculated through a discrete time series to generate the grinding head spatiotemporal position mapping data, and combined with the relationship between the initial installation position of the grinding head and the workpiece posture, the center position of the grinding head at each moment is uniformly represented in the global coordinate system, providing a basis for subsequent contact judgment and coverage modeling.

[0032] Step S4: Dynamic coverage calculation is performed on the contact relationship between the effective action area of ​​the grinding head and the workpiece surface based on the surface grid node data and the spatiotemporal position mapping data of the grinding head, thereby obtaining three-dimensional polishing coverage data including the cumulative polishing time and the coordinated polishing intensity distribution. The dynamic coverage calculation is specifically performed to accurately predict the polishing amount for each grid node on the workpiece surface based on the grinding head action radius and the polishing intensity distribution.

[0033] The embodiment of the present invention performs dynamic coverage calculation and processing based on the aforementioned surface grid node data and the grinding head spatiotemporal position mapping data, establishes an effective radius model for each grinding head, and constructs a spatial attenuation model of the polishing intensity; in this embodiment, the effective effective radius of a single grinding head is set to 25 mm, a radius sphere is constructed with the center of the grinding head as the center of the sphere, and the Euclidean distance formula is used to determine whether the surface grid node is within the sphere, thereby identifying all nodes acted upon by the grinding head at a certain point in time; in order to simulate the phenomenon that the actual polishing intensity weakens at the edge position, a Gaussian distribution function is introduced as a polishing intensity attenuation model, that is, the polishing intensity is the largest at the center of the grinding head, and decreases exponentially with increasing distance. In this embodiment, the standard deviation of the Gaussian function is set to 10 mm; by superimposing all moments in the time dimension, the cumulative polishing time of each grid node and the total polishing intensity received by the node at all moments are obtained, thereby forming three-dimensional polishing coverage data containing time accumulation characteristics and spatial intensity distribution.

[0034] Step S5: Evaluate and optimize the polishing quality uniformity based on the three-dimensional polishing coverage data to obtain three-dimensional digital polishing model data.

[0035] After obtaining three-dimensional polishing coverage data including the cumulative polishing time and coordinated polishing intensity distribution of each grid node, the embodiment of the present invention evaluates and optimizes the quality uniformity of the coverage data; specifically, the mean variance analysis method is used to evaluate the dispersion of the polishing intensity in the grid nodes of the entire workpiece surface, and the standard deviation is used as a quantitative indicator of polishing uniformity. The smaller the standard deviation, the more uniform the polishing quality. If it is found that the coverage time or intensity of a certain area is significantly low, the system can feedback and adjust the grinding head swing parameters, such as appropriately changing the phase difference or increasing the amplitude to improve the coverage rate of the area. In this embodiment, the minimum polishing time required for the area with intensity lower than 80% of the average value is recalculated, and a new polishing strategy is generated by optimizing the control parameters. Finally, the polishing response results of all grid nodes are integrated into three-dimensional digital polishing model data. The model data can not only be used for pre-polishing strategy generation, but also as a control standard for post-polishing quality verification, and used to guide the next step of automatic calibration or intelligent compensation operation.

[0036] It is particularly important that step S1 includes the following steps:

[0037] Step S11: Scanning the surface of the workpiece using a three-dimensional scanning device to obtain original three-dimensional geometric data containing geometric profile information of the workpiece;

[0038] In an embodiment of the present invention, a surface scan of a workpiece is performed using a three-dimensional scanning device, and its geometric profile information is collected to generate original three-dimensional geometric data. Specifically, a laser line structured light scanner is used in this embodiment, which has an accuracy of 0.05 mm and can efficiently obtain the spatial morphology of complex curved workpieces (such as optical lens blanks, aircraft engine blades, etc.). During the scanning process, the workpiece is mounted on an adjustable rotating platform, and full surface coverage is achieved through multi-angle rotation. The resulting scanning result is high-density point cloud data, in which each point in the data records the spatial three-dimensional coordinate value, forming an original three-dimensional point cloud set representing the true geometric structure of the workpiece. This data serves as the basis for geometric modeling and provides input for subsequent coordinate system standardization processing.

[0039] Step S12: Standardize the coordinate system of the workpiece surface according to the original three-dimensional geometric data, establish a unified spatial coordinate reference system, and convert the workpiece geometric data into the coordinate system to obtain the three-dimensional geometric data of the workpiece surface.

[0040] The embodiment of the present invention standardizes the coordinate system of the workpiece surface. Specifically, the point cloud data is imported into the point cloud processing software (such as Geomagic or self-developed modeling system), and the principal direction of the point cloud is first identified by the principal component analysis (PCA) method to determine the principal axis direction of the workpiece, and accordingly establish a new spatial coordinate reference system. The reference system usually takes the geometric center of gravity of the workpiece as the origin, the maximum length direction as the Z axis, the secondary length direction as the X axis, and the remaining directions as the Y axis, thereby achieving coordinate unification; then the original point cloud is converted to the standard reference system through translation and rotation operations to eliminate the geometric deviation caused by inconsistent scanning postures. In this embodiment, for a complex curved surface part, a local space coordinate system with the center of the workpiece as the origin and the long axis direction as the main direction is established, and its three-dimensional geometric data is converted to this coordinate system to ensure compatibility with the machine tool coordinate system used in the polishing path calculation.

[0041] Step S13: Acquire the mechanical structural parameters of the multi-stage oscillating grinding head system, including the physical dimensions, installation position coordinates, material properties, and effective radius of each grinding head, and obtain physical property data of the grinding head;

[0042] The embodiment of the present invention obtains the mechanical structure parameters of the multi-stage swing grinding head system to form the physical property data of the grinding head. The specific operation is to consult the mechanical design drawings and equipment parameter table provided by the equipment manufacturer, extract the actual physical dimensions of each grinding head (such as the diameter and length of the grinding head), the fixed installation position coordinates (the spatial position relative to the machine tool reference coordinate system), the material properties of the grinding head (such as polyurethane, elastic rubber, etc., which affect the polishing contact behavior) and the effective action radius of the grinding head, that is, the spatial range in which the grinding head produces a significant polishing effect on the workpiece per unit time. Taking the actual four-grinding head linkage polishing system as an example, each grinding head has a diameter of 50 mm, is made of medium-hardness polyurethane, has an action radius of 25 mm, and is respectively installed on an independent linkage arm at the end of the XYZ three-axis. The installation position is offset outward by different distances based on the center point of the machine tool. These parameters together constitute the physical property data of the grinding head used for modeling and trajectory determination.

[0043] Step S14: acquiring motion control parameters of the multi-segment oscillating grinding heads, including the oscillation frequency range, oscillation amplitude range, oscillation center position, and oscillation plane direction of each grinding head, to obtain motion characteristic data of the grinding heads;

[0044] The embodiment of the present invention collects the motion control parameters of each grinding head to form the grinding head motion characteristic data. Specifically, the control system parameter setting file of the CNC polishing machine is read, which includes the swing frequency range of each grinding head (that is, the range of variation of the swing period, generally 3 to 10 Hz), the swing amplitude range (the maximum deviation from the center, generally 10 to 30 mm), the position point of the swing center in space (combined with the grinding head installation position to determine the starting point of its running trajectory) and the swing plane direction (the main direction vector of the grinding head swinging in space, usually represented by a vector to represent its normal direction). In this embodiment, in order to achieve a nonlinear uniform coverage strategy, the frequencies of the four grinding heads are set to 5, 6, 7, and 8 Hz, respectively, the swing amplitude is 20 mm, and the swing planes are XY, YZ, XZ, and the plane is tilted 30 degrees. These parameters are used to generate a multi-grinding head differential running trajectory model to improve the complementarity of spatial distribution.

[0045] Step S15: performing parameter validity verification processing based on the grinding head physical property data and the grinding head motion characteristic data, checking the rationality range of parameter values ​​and identifying parameter conflicts or missing conditions, and obtaining the grinding head basic parameter data;

[0046] The embodiment of the present invention carries out parameter validity verification processing to generate basic parameter data of the grinding head. The specific method includes writing a parameter verification program, performing range logic judgment and internal consistency check on the input data, such as judging whether the swing amplitude of each grinding head exceeds its effective radius, whether there are multiple grinding head installation positions that overlap, whether the swing direction is valid, whether the parameters are missing, etc. If an abnormality is detected, the user is prompted to correct or complete the information; in this embodiment, it is found that the set swing amplitude of a grinding head is 35 mm, which exceeds the safe range of its physical effective radius of 30 mm. The system automatically issues an alarm and marks it as invalid, requiring it to be reset; ultimately, only the parameter set that passes the verification is retained as the basic parameter data of the grinding head, providing a reliable basis for the subsequent generation of identification indexes and kinematic models.

[0047] Step S16: performing unique identifier allocation processing on each grinding head according to the basic parameter data of the grinding head, and establishing an index mapping relationship between the grinding head identifier and the parameter information to obtain kinematic parameter data with identification index.

[0048] The embodiment of the present invention assigns a unique identifier to each grinding head and establishes an index mapping relationship. The specific method is to generate unique codes for the grinding heads in sequence according to their installation order or logical numbering (such as MH01, MH02, MH03, MH04), and construct an index table to establish a one-to-one mapping between each grinding head identifier and its corresponding physical properties and motion feature data; the index structure supports fast retrieval and association calculation, and is particularly suitable for synchronous analysis of multiple grinding head trajectories in high-dimensional simulation scenarios. In this embodiment, a parameter mapping data structure in JSON format is established, with the identifier of each grinding head as the primary key, corresponding to all basic parameter information such as its swing frequency, amplitude, material, direction, etc.; the structure is used as standard input data in the polishing path modeling module and the dynamic coverage calculation module to achieve unified management and calling of the grinding head behavior in the subsequent three-dimensional digital model generation process.

[0049] Preferably, step S2 includes the following steps:

[0050] Step S21: performing triangular meshing processing on the surface area according to the three-dimensional geometric data to obtain triangular mesh data, wherein the mesh density is set to be no less than 100 mesh nodes per square centimeter;

[0051] This embodiment of the present invention performs triangulated meshing of the surface area of ​​a workpiece, converted to a unified reference coordinate system, to construct a standard digital model suitable for subsequent polishing path simulation and surface interaction calculations. Specifically, a meshing module based on the Delaunay triangulation algorithm is used to reconstruct the surface of the point cloud data into a continuous triangular mesh surface. In this embodiment, the algorithm selected maintains mesh smoothness while adapting to complex surfaces with large curvatures. First, a dense point cloud patch is generated through sampling and reconstruction. Then, mesh control is performed to a density of at least 100 mesh nodes per square centimeter, dividing each square centimeter into approximately 50 to 60 triangles to ensure sufficient model detail to reflect the surface topography. For example, for a complex optical component with a surface area of ​​150 square centimeters, approximately 15,000 nodes are generated, forming nearly 30,000 triangular patches. This meshing density meets the surface accuracy requirements of subsequent calculations while achieving a balance between computational performance and data volume.

[0052] Step S22: performing coordinate assignment and normal vector calculation processing on each mesh node according to the triangular mesh data to obtain node geometric attribute data of node coordinate information and normal vector information;

[0053] The embodiment of the present invention extracts geometric properties of each mesh node in the triangular mesh, including spatial coordinate assignment and normal vector calculation. In the specific implementation, first, based on the mesh division result, the XYZ coordinate information of each node in the three-dimensional coordinate system is extracted as the spatial positioning value of the node; then, for each adjacent triangular facet where the node is located, the normal vector of the facet is calculated by vector cross product according to its vertex coordinates, and then all the facet normal vectors belonging to a node are weighted averaged (weighted by the facet area) to obtain the unit normal vector of the node. The normal vector is a key parameter in polishing path planning and determines the contact angle and direction control strategy of the grinding head. In this embodiment, for high curvature areas (such as the edge of an aviation blade), the accuracy of the normal vector direction is improved by increasing the weight of the surrounding facets, thereby ensuring that the grinding head posture adjustment in subsequent simulations is more accurate. The node geometric attribute data finally generated includes the three-dimensional coordinate value and unit normal vector value of each node, forming a high-precision surface point description set.

[0054] Step S23: performing unique identifier assignment processing on the grid nodes according to the node geometric attribute data to obtain surface grid node data with index identifiers.

[0055] The embodiment of the present invention performs a unique identifier assignment process on each grid node to obtain surface grid node data with index identifiers. The specific operation method is to assign a globally unique index number to each node in sequence according to the storage order or spatial layout order of the nodes in the grid data structure, for example, encoding in the form of "GN00001", "GN00002", etc.; at the same time, a mapping table between the node identifier and its coordinate information and normal vector information is established so that the subsequent path calculation module can quickly access and reference each node attribute. In this embodiment, for the aforementioned approximately 15,000 grid nodes, the system automatically generates the corresponding node index and stores it in the database in the form of a JSON structure. Each node record contains: an index ID, a three-dimensional coordinate array, and a unit normal vector array. This structure is frequently called in the processes of grinding head path generation, coverage analysis, polishing residual prediction, etc., and is a key data organization form that supports the accuracy and efficiency of the three-dimensional digital model algorithm. Through this indexing mechanism, the subsequent steps can efficiently match the spatial relationship between the grinding head model and the specific surface area, and construct a polishing behavior prediction model based on geometric constraints.

[0056] Preferably, step S3 includes the following steps:

[0057] Step S31: extracting the swing amplitude, swing frequency, swing center position and swing plane angle of each grinding head from the kinematic parameter data, and establishing a structured grinding head parameter table for each grinding head;

[0058] The present invention extracts the core motion parameters of each grinding head from established kinematic parameter data with identifiers and indices, including swing amplitude, swing frequency, swing center position, and swing plane angle. The swing amplitude refers to the maximum displacement of the grinding head from its center position within a cycle, the swing frequency is the number of swing cycles completed per unit time, the swing center position defines the reference point of the grinding head in the coordinate system, and the swing plane angle describes the angle between the plane of the swing motion and the XY plane of the reference coordinate system. After extracting these parameters, a grinding head parameter table is constructed using a structured data organization method. Each row of the table corresponds to a grinding head, and each column stores attributes such as the grinding head number, physical location, swing characteristics, and material type. For example, for a composite polishing machine equipped with six swinging grinding heads, the generated grinding head parameter table for the third grinding head might contain the following field values: number H003, position (250, 0, 120), swing frequency 3Hz, swing amplitude 12mm, center coordinates (250, 0, 120), and swing plane angle 30 degrees. This table is the basic input data for subsequent polishing path generation and synchronization control.

[0059] Step S32: Determine the phase difference value of each grinding head using the total number of grinding heads in the grinding head parameter table, and assign a different phase starting point to each grinding head by equally dividing the complete cycle to generate a phase difference configuration list;

[0060] The embodiment of the present invention designs different phase difference values ​​for each grinding head based on the total number of grinding heads, and generates a phase difference configuration list. Phase difference refers to the time start-up delay between multiple periodic motion parts, which is used to avoid mechanical interference or system resonance problems caused by multiple grinding heads reaching extreme positions at the same time. In this embodiment, a complete cycle equal division strategy is adopted to distribute the phase starting point, that is, the 360-degree phase angle is evenly distributed to all grinding heads. For example, in the case of 6 grinding heads, the phase difference between adjacent grinding heads is set to 60 degrees, and the specific distribution is 0 degrees for the first grinding head, 60 degrees for the second, 120 degrees for the third, and so on. The phase difference configuration list finally generated is a structured table containing the grinding head numbers and the corresponding phase offset angles, which is convenient for introducing personalized initial states in subsequent path description calculations.

[0061] Step S33: generating a grinding head trajectory description set including a personalized phase difference swing trajectory description for each grinding head according to the grinding head parameter table and the phase difference configuration list;

[0062] Based on the grinding head parameter table and phase difference configuration list, this embodiment of the present invention generates a personalized phase difference swing trajectory description set for each grinding head in step S33. Specifically, the swing behavior of each grinding head is abstracted as a two-dimensional reciprocating motion trajectory on its swing plane. A time-displacement function is constructed based on its swing amplitude and frequency. A unique phase difference initial angle is introduced to ensure staggered operation. In implementation, a sinusoidal motion model driven by timing parameters is used to generate a displacement sequence, and the swing plane direction vector and center coordinate position are superimposed to perform a three-dimensional transformation. For example, for grinding head numbered H003, which swings on a 30-degree inclined plane with an amplitude of 12 mm, a frequency of 3 Hz, and an initial phase of 120 degrees, simulation calculations can be used to obtain its trajectory vector in three-dimensional space at any point in time. This trajectory vector is then integrated into a trajectory description set containing fields such as time step, position vector, and motion direction. This set is used for subsequent spatiotemporal path expansion and polishing point coverage calculations.

[0063] Step S34: performing time sampling on the entire polishing cycle at preset time intervals based on the grinding head trajectory description set, calculating the three-dimensional spatial coordinate positions of all grinding heads at each time point, thereby forming a grinding head position coordinate sequence table arranged in chronological order;

[0064] In the embodiment of the present invention, based on the generated set of grinding head trajectory descriptions, in step S34, time sampling is performed on the entire polishing cycle at fixed time intervals, and the three-dimensional spatial coordinate position of each grinding head at that time point is calculated hour by hour to form a grinding head position coordinate sequence table. The time sampling interval can be determined based on the minimum response time of the polishing motion control system, with a typical value of 10 milliseconds, that is, 100 samples per second. At each time point, the spatial displacement value corresponding to the time in the trajectory description of each grinding head is read, and the three-dimensional coordinate calculation is performed based on its center point coordinates, swing direction angle, and displacement amplitude, ultimately obtaining a path data consisting of a timestamp and a grinding head coordinate value. For example, in a 2-second polishing cycle, 200 time nodes will be generated, each node records the position vectors of 6 grinding heads, and the entire sequence table contains a total of 1,200 sets of coordinate points. The data organization structure uses a time primary key index, and each entry contains a timestamp and a three-dimensional position corresponding to multiple grinding head numbers.

[0065] Step S35: using the grinding head position coordinate sequence table to establish a quick query correspondence between the timestamp and the grinding head position information, thereby obtaining the grinding head spatiotemporal position mapping data.

[0066] The embodiment of the present invention establishes the spatiotemporal position mapping data of the grinding head based on the grinding head position coordinate sequence table, and constructs a fast query relationship between the timestamp and the corresponding grinding head position information. This embodiment uses a hash mapping mechanism or a dictionary-based data structure to establish an index, with the key value being the sampling timestamp and the value being the set of spatial coordinates of all grinding heads at that moment, to achieve millisecond-level fast access. The specific data structure can be nested in JSON or binary table format, supporting the rapid acquisition of grinding head status information at any time point in modules such as path planning simulation, collision detection, and grinding head posture control. For example, the value corresponding to a timestamp "1.230s" is: "H001: (x1, y1, z1), H002: (x2, y2, z2)..." This mapping data is used in the dynamic simulation software module to generate a real-time distribution map of the grinding head, determine spatial overlapping areas, and execute position feedback control logic. Through this step, the time resolution process from the physical parameters of the grinding head to the high-precision spatial path is completed, providing accurate input for polishing effect simulation and path optimization.

[0067] Preferably, step S4 includes the following steps:

[0068] Step S41: Calculating the spatial distance between the grinding head and the grid nodes based on the grinding head spatiotemporal position mapping data and the surface grid node data, and obtaining the grinding head node distance matrix data by calculating the three-dimensional Euclidean distance between each grinding head center position and each grid node on the workpiece surface at each time point;

[0069] The embodiment of the present invention needs to spatially pair this data with the three-dimensional grid node data on the workpiece surface to calculate the three-dimensional Euclidean distance between all the grinding head centers and all the grid nodes on the workpiece surface at each time point. Specifically, the grid node data is a set of workpiece surface points obtained by three-dimensional modeling technology (such as laser scanning or CAD model division), and each node has a unique spatial coordinate and index number. In actual implementation, a spatial vector calculation method is used to traverse each sampling time point, and the three-dimensional coordinates of each grinding head at that moment are calculated with all the grid nodes to obtain the corresponding Euclidean distance value, which is the straight-line distance between the center point of the grinding head and the grid node. For example, for a system containing 30,000 grid nodes and 6 grinding heads with a sampling period of 2 seconds and a total of 200 time points, it is necessary to calculate 6 times 200 times 30,000, a total of 36 million distance operations. To improve efficiency, this embodiment adopts a spatial blocking algorithm and a KD tree acceleration structure to spatially divide the grid nodes in advance, and only calculates the distance of nodes that may enter the effective radius of the grinding head, and finally generates the grinding head node distance matrix data. The dimension of the matrix is ​​the number of time points × the number of grinding heads × the number of grid nodes, and each element records a specific three-dimensional distance value.

[0070] Step S42: performing effective action area determination processing based on the grinding head node distance matrix data and the preset grinding head action radius threshold, screening out the grid nodes within the effective action range of each grinding head by distance comparison, and obtaining the grinding head action area coverage relationship data;

[0071] The embodiment of the present invention determines the effective radius of each distance value, and screens out the effective nodes actually covered by each grinding head at each moment, thereby establishing the grinding head effective area coverage relationship data. The "effective radius threshold" here is a spatial influence range preset according to the structure and material properties of the specific grinding head. For example, for a flexible grinding head with a diameter of 30mm, its effective effective radius is set to 15mm, which means that only when the distance between a grid node and the center point of the grinding head is less than 15mm, the node is considered to be affected by the grinding head. During implementation, the program compares each distance value in the aforementioned distance matrix with the effective radius of the corresponding grinding head. If the distance is less than the radius, the grid node is marked as "covered", and the node number, the covered time point, and the effective grinding head number are recorded. The data structure finally generated is a set of "grinding head-time point-node number" triples, which constitute the grinding head effective area coverage relationship data, which is used to subsequently establish the spatial-temporal distribution of grinding intensity.

[0072] Step S43: calculating the instantaneous polishing intensity value of each grid node based on the polishing intensity distribution model of the Gaussian attenuation function based on the grinding head active area coverage relationship data to obtain the node instantaneous polishing intensity data;

[0073] The embodiment of the present invention calculates the instantaneous polishing intensity of each covered grid node and constructs a polishing intensity distribution function in a physical sense using a Gaussian attenuation model. The Gaussian attenuation function takes the center of the grinding head as the maximum intensity point, and the intensity decays exponentially with increasing distance. This model can more realistically reflect the characteristics of the grinding head edge force being smaller and the center force being larger during the actual polishing process. In a specific implementation, first, the corresponding intensity coefficient is obtained from the Gaussian function based on the grinding head number and the distance between the node and the grinding head. Then, the actual polishing intensity value received by the node at that time point is calculated in combination with parameters such as the current rotation speed, load, and material coefficient of the grinding head. For example, if a node is 10 mm away from the center of the grinding head and the effective radius of the grinding head is 15 mm, the Gaussian weight may be 0.5. Combined with its instantaneous load parameters, the final intensity is 0.5 multiplied by the basic grinding force. The instantaneous polishing intensity of all nodes is recorded as a ternary data set of node number, time point, and polishing intensity, which serves as the basis for the next cumulative analysis.

[0074] Step S44: performing a time series-based cumulative calculation process on the instantaneous polishing intensity data of the nodes to obtain node cumulative polishing amount data, wherein the cumulative calculation process is specifically performed by performing a time integral operation on the polishing intensity received by each grid node during the entire polishing cycle;

[0075] According to the instantaneous polishing intensity data of the aforementioned nodes, the embodiment of the present invention performs a time integration on each grid node during the entire polishing cycle to obtain the node cumulative polishing amount data. The cumulative calculation adopts a time series integration method, that is, the polishing intensity value of a node at each sampling time point is multiplied by the sampling interval and then accumulated item by item, which represents the total energy density or total polishing amount received by the node during the polishing cycle. For example, if the sampling interval is 0.01 seconds, and the intensity of a node at 20 moments is 0.3, 0.5, 0.6..., then the cumulative polishing amount of the node is the weighted sum of these values. A sparse matrix storage mechanism is introduced in the calculation process to only record the nodes that are covered at any moment to avoid storage and calculation redundancy. The final result is a cumulative polishing amount data set corresponding to each grid node. The data structure is a binary pair of node number and cumulative value, which is used to evaluate the processing depth and uniformity of different areas on the workpiece surface.

[0076] Step S45: using the node cumulative polishing amount data to perform superposition calculation processing on the synergistic effect of multiple grinding heads to obtain node synergistic polishing intensity distribution data;

[0077] The embodiment of the present invention uses the node cumulative polishing volume data to perform multi-grinding head synergy analysis and generate collaborative polishing intensity distribution data. Since multiple grinding heads may have overlapping effects on the same node in different time periods, it is necessary to superimpose the cumulative contributions of each grinding head to analyze its collaborative processing intensity in the spatial overlapping area. This embodiment adopts a node-centered aggregation method to sum up the cumulative polishing volume of a node under all grinding heads, and analyzes its coverage frequency (that is, the number of times the node is covered by multiple grinding heads simultaneously or at different times) to quantify the synergistic effect. For example, if a node is acted upon by two grinding heads respectively, and its polishing volume is 2.1 and 1.7, then the total collaborative amount is 3.8, and its collaborative overlap is recorded as 2. The final data structure is the node number, collaborative polishing intensity value, and overlap information, which constitute a complete collaborative processing feature data set, reflecting the complex interactive effects under the multi-grinding head system.

[0078] Step S46: According to the node cumulative polishing amount data and the node collaborative polishing intensity distribution data, the cumulative polishing time information and the collaborative polishing intensity distribution information are matched according to the grid node index identifier to obtain three-dimensional polishing coverage data.

[0079] The embodiment of the present invention integrates the node's cumulative polishing time information (i.e., total action time) and the collaborative polishing intensity value based on the node's cumulative polishing amount data and the node's collaborative polishing intensity distribution data, and establishes a corresponding relationship based on the node's index number in the surface grid model, thereby generating three-dimensional polishing coverage data. This data records information such as the cumulative processing time, total energy received, and synergistic effect intensity during the entire polishing cycle for each grid node, and is attached to a three-dimensional geometric model for visualization and evaluation. For example, a node numbered N1023 has a cumulative polishing time of 3.4 seconds and a collaborative intensity of 4.2 units. Through surface mapping technology, the node position can be displayed in the CAD view in the form of color depth or contour lines. The final three-dimensional polishing coverage data can be used as an input parameter for CNC path optimization, residual error prediction, and quality evaluation. It has a high engineering application value and marks the completion of the three-dimensional digital modeling process of the entire polishing process.

[0080] Preferably, step S42 includes the following steps:

[0081] Step S421: performing distance value comparison processing one by one according to the grinding head node distance matrix data and the preset grinding head action radius threshold to obtain distance determination result data;

[0082] After obtaining the grinding head node distance matrix data calculated in step S41, the embodiment of the present invention compares the three-dimensional Euclidean distance value between each grinding head center point and each grid node on the workpiece surface at each moment with the pre-set grinding head effective radius threshold one by one. The effective radius threshold can be set based on the actual physical size of the grinding head and the experience of the effective polishing area, for example, it is set to 10 mm. During the comparison process, a matrix traversal method is used to judge the distance value of each pair of grinding head centers and grid nodes. If the value is less than or equal to the radius threshold, it is determined that the grid node is within the effective action range of the grinding head, and the system records "1" as a valid action mark, otherwise it is recorded as "0". Finally, a Boolean distance judgment result data matrix is ​​formed. The matrix maintains the same structure as the original distance matrix, but only marks the effective action range relationship for subsequent screening.

[0083] Step S422: Screening and extracting grinding head node pairs that meet the action range conditions based on the distance determination result data, and obtaining a valid grinding head node pairing list by retaining grinding head node combinations whose distance values ​​are less than or equal to the action radius threshold;

[0084] The embodiment of the present invention uses the distance judgment result data matrix generated in step S421 to extract all the grinding head and node pairing relationships marked as "1", retain the valid combinations whose distance values ​​are less than or equal to the grinding head's effective radius, and record them in a list to form a valid grinding head node pairing list. Each record in the list contains a grinding head identifier, a node index identifier, and a corresponding spatial distance value. To ensure processing efficiency, the system uses a hash table structure to quickly index the valid node set corresponding to each grinding head, and at the same time arranges the node indexes in ascending order to facilitate subsequent statistical processing. In a specific implementation, if a grinding head acts on the grid node numbered n_1, n_5, or n_12 at a certain moment, the list will contain record items such as (H1, n_1, 8.6 mm), (H1, n_5, 9.2 mm), etc.

[0085] Step S423: performing grouping processing on the active node set of each grinding head based on the grinding head identifier according to the valid grinding head node pairing list, thereby obtaining grinding head group node set data;

[0086] Based on the above-mentioned valid grinding head node pairing list, the embodiment of the present invention performs grouping processing according to the grinding head identifier (such as H1, H2, H3, etc.) as the primary key, extracts all the active node sets corresponding to each grinding head, and forms grinding head group node set data. Each set of data contains a grinding head identifier and the set of grid nodes covered by the grinding head at all times during the entire polishing process. For example, for H1, its active node set may include nodes numbered n_1, n_5, n_12, n_30, etc.; for H2, it may be n_5, n_6, n_15, n_18, etc. This process uses a dictionary data structure, with the grinding head identifier as the key and the corresponding node set as the value, to support subsequent fast traversal and cross analysis.

[0087] Step S424: based on the grinding head group node set data, traverse the active node set of each grinding head and count the active grinding heads covered by each node to obtain the node covered grinding head list data;

[0088] The embodiment of the present invention traverses the set of action nodes of all grinding heads obtained in step S423, and counts how many different grinding heads cover each grid node at the same time. During the operation, the system initializes a dictionary structure with the grid node index as the key, and scans the node sets of all grinding heads one by one. When a node appears in the set of multiple grinding heads, the system appends the identifier of the current grinding head to the corresponding dictionary item. For example, if node n_5 is covered by H1 and H2 at the same time, the record item of the node is [n_5:[H1,H2]], indicating that it is acted upon by two grinding heads. Finally, a list of grinding heads with covered nodes is formed, which is used to analyze the collaborative coverage of the grinding heads and the subsequent collaborative polishing model construction.

[0089] Step S425: constructing a bidirectional index mapping table from grinding head to node and from node to grinding head through the grinding head grouping node set data and the node covered grinding head list data, and obtaining the grinding head action area coverage relationship data.

[0090] In an embodiment of the present invention, the grinding head grouping node set data obtained in step S423 and the node covered grinding head list data in step S424 are jointly tabled to construct mapping index tables of "grinding head→node" and "node→grinding head" respectively. Among them, the "grinding head→node" table can be used to quickly query in which node ranges a certain grinding head performs polishing, and the "node→grinding head" table can be used to analyze whether a certain grid node is in the collaborative action area of ​​multiple grinding heads. In the implementation process, a double hash dictionary structure is used for storage and query operations, and the index compression algorithm is combined to improve data processing efficiency. In specific applications, the bidirectional mapping table provides basic support for subsequent instantaneous polishing intensity calculation and collaborative strength superposition, and also supports efficient rendering and annotation of three-dimensional coverage areas in visual models. Ultimately, the grinding head action area coverage relationship data output by the system contains all valid action relationships and multiple cross-action information between grinding heads and nodes, and is the key structural basic data for generating three-dimensional digital polishing models.

[0091] Preferably, step S43 includes the following steps:

[0092] Step S431: extracting the distance value information between each grinding head and the grid nodes within its action range from the grinding head action area coverage relationship data to obtain a valid distance value data set;

[0093] The embodiment of the present invention extracts the spatial distance value between each grinding head and the grid node it acts on based on the grinding head action area coverage relationship data, forming a valid distance value data set. In this data set, each record consists of three parts: a grinding head identifier, a corresponding grid node index, and the Euclidean space distance from the grinding head to the node. In the actual processing process, the system traverses the "grinding head→node" mapping table, and while extracting the pairing information between the grinding head and the node, it takes out the corresponding three-dimensional distance value from the original distance matrix data and adds it to the set. For example, if the grinding head H1 acts on the node n_12, and the distance is 8.4 mm, it is recorded as (H1, n_12, 8.4). This data set provides a physical basis for subsequent distance normalization and strength calculation, records the spatial coupling relationship between the polishing head and the target action area, and is the key input data for establishing the strength attenuation model.

[0094] Step S432: performing normalized distance calculation processing using the valid distance value data set and a preset Gaussian attenuation parameter to obtain normalized distance ratio data;

[0095] The embodiment of the present invention uses the effective distance value data set obtained in step S431 and the Gaussian attenuation parameter preset by the system to perform normalized distance calculation. The Gaussian attenuation parameter mainly refers to the standard deviation σ in the Gaussian function, which is used to control the sensitivity of distance attenuation. Its value can be determined according to the actual working range of the grinding head, such as being set to 5 mm. The normalized distance is calculated as follows: divide the distance value from each grinding head to the node by σ to obtain the normalized distance ratio of the node. For example, if the distance between a node and the grinding head is 8.4 mm and σ is 5 mm, then its normalized distance ratio is 1.68. The system stores all normalized distance ratios in a unified normalized distance ratio data list, maintains the corresponding relationship with the original distance data set, and provides for the next step of exponential calculation of the Gaussian function.

[0096] Step S433: multiplying the square of the normalized distance ratio by the negative Gaussian attenuation coefficient according to the normalized distance ratio data and taking the natural exponent to obtain Gaussian attenuation factor data;

[0097] The embodiment of the present invention performs Gaussian attenuation calculation on the normalized distance ratio data obtained in step S432, that is, multiplying the square of each ratio by the negative Gaussian attenuation coefficient, and then taking the exponential value of the natural logarithm base to obtain the Gaussian attenuation factor data corresponding to each node. The Gaussian attenuation factor here reflects the degree of influence of distance on polishing intensity. The greater the distance, the smaller the factor. Continuing with the above example, if the normalized ratio is 1.68, its square is 2.82, which is -2.82 after multiplying by the negative sign, and is approximately 0.059 after taking the natural exponent. This attenuation factor indicates that the contribution of the node to the final polishing intensity is weaker due to its distance from the grinding head. The system uses an array structure to record all Gaussian attenuation factors, and forms a triple with the node index and grinding head identifier to form the basic data of node force attenuation, which is used to solve the next step of strength value.

[0098] Step S434: performing intensity calculation based on the Gaussian attenuation factor data and the preset maximum polishing intensity parameter of the grinding head to obtain a basic polishing intensity value of the grid node;

[0099] The embodiment of the present invention calculates the basic polishing intensity value of each node based on the above-mentioned Gaussian attenuation factor data and the preset maximum polishing intensity parameter of the grinding head. The maximum polishing intensity parameter of the grinding head is the maximum unit action intensity set according to the physical structure of the grinding head and the equipment debugging, and the unit can be Newton per square millimeter (N / mm 2 ), for example, set to 1.2N / mm 2The system multiplies the Gaussian attenuation factor corresponding to each node by the maximum strength value of the grinding head to obtain the basic polishing strength value of the node at that location due to the grinding head H. For example, in the above example, the attenuation factor is 0.059 and the maximum strength is 1.2, resulting in a final basic strength of approximately 0.0708N / mm2. All basic strength values ​​are recorded together with their corresponding node index and grinding head identification to generate a node basic polishing strength data set, providing numerical input for the next step of combining surface normal vector correction.

[0100] Step S435: performing correction and result solving processing based on the grinding head action direction and the grid node surface normal vector according to the grid node basic polishing intensity value, thereby obtaining the node instantaneous polishing intensity data.

[0101] The embodiment of the present invention performs direction correction based on the basic polishing strength by combining the action direction of the grinding head and the surface normal vector of the node. First, the system obtains the surface normal vector of each node through three-dimensional geometric modeling or simulation data, and obtains the unit action direction vector of the current motion trajectory of each grinding head. For each grinding head-node pair, the system calculates the cosine value of the angle between the action direction vector of the grinding head and the node normal vector, and uses this value as the direction correction factor. If the directions of the two are exactly the same (that is, the angle is 0 degrees), the cosine value is 1, and the basic strength is not affected; if the directions differ by 90 degrees, the cosine value is 0, and there is no effective strength effect in this direction. The system multiplies the basic strength value by the direction correction factor to obtain the final instantaneous polishing strength value of the node. For example, if the basic strength is 0.0708N / mm 2 , the cosine of the angle is 0.87, then the instantaneous strength is about 0.0616N / mm 2 The resulting instantaneous polishing intensity data covers the entire three-dimensional mesh area of ​​the workpiece, reflecting the combined influence of spatial distance and directional coupling on the intensity of the action, and providing high-fidelity input data for modeling local surface changes in the three-dimensional digital model.

[0102] Preferably, step S435 includes the following steps:

[0103] Based on the basic polishing intensity value of the grid node, the angle between the grinding head and the normal vector of the node surface is corrected by calculating the cosine value of the grinding head action direction and the normal vector of the node surface and using it as a correction coefficient to obtain the polishing intensity value after angle correction.

[0104] In the step of calculating and processing the angle correction between the grinding head and the normal vector of the node surface based on the basic polishing strength value of the grid node, the system has already obtained the basic polishing strength value of each node, as well as the grinding head's action direction vector and the node surface normal vector data in the previous step. The key to this step is to quantitatively correct the strength effectiveness caused by directional differences based on physical action mechanisms. To achieve this correction, the system first processes the data of each "grinding head-node" pair, extracting the current action direction vector of the corresponding grinding head (usually derived from the grinding head's posture information) and the unit normal vector of the node surface. Then, the cosine value of the angle between these two vectors is calculated. This value reflects the degree of consistency between the two directions and ranges from -1 to 1. In actual engineering, considering that polishing is only effective when the grinding head and the surface are essentially oriented in the same direction, the system typically truncates negative values ​​to 0, retaining non-negative cosine values ​​as the "direction correction coefficient." This coefficient is multiplied by the basic strength value to obtain the angle-corrected polishing strength. For example, if the basic polishing strength of a node is 0.085N / mm 2 , the cosine value is 0.92, so the angle-corrected strength is about 0.0782N / mm 2 .

[0105] The speed influence factor is calculated based on the polishing intensity value and the grinding head movement speed information at the current time point to obtain the grinding head node speed modulation intensity data;

[0106] In the step of calculating and processing the speed influence factor based on the polishing intensity value and the grinding head movement speed information at the current time point, the embodiment of the present invention introduces the regulatory influence of the grinding head's running speed on the polishing effect to form a more realistic dynamic action intensity model. This step is based on the common empirical law in the polishing process: under the same pressure, the faster the grinding head moves, the more dispersed the energy acting on a single surface per unit time, resulting in a decrease in the polishing intensity per unit area. To this end, the system collects the movement speed information of each grinding head at the current time point, including the modulus of the tangential speed or trajectory speed vector, in millimeters per second. In the modulation model set by the system, a speed reference coefficient is usually defined, for example, the reference speed is 200mm / s, and the actual speed value is ratioed with the reference value as the speed modulation factor. The system then divides the polishing intensity after angle correction by the speed modulation factor to obtain the grinding head node modulation intensity reflecting the speed influence. For example, if the angle-corrected intensity of a node is 0.0782N / mm 2 , the current speed of the grinding head is 250mm / s, the modulation factor is 1.25, and the speed modulation intensity corresponding to this node is about 0.0626N / mm 2 The system stores this intensity value together with its corresponding node index and grinding head identifier in the grinding head node speed modulation intensity data structure for superposition processing of multiple grinding head effects.

[0107] According to the grinding head node speed modulation intensity data, the multiple grinding head effects on the same grid node at the current time point are superimposed and summed to obtain the node instantaneous polishing intensity data.

[0108] In the embodiment of the present invention, in the step of superimposing and summing the effects of multiple grinding heads on the same grid node at the current time point based on the speed modulation intensity data of the grinding head node, the system searches for all record items of each grid node being affected by multiple grinding heads at the current time point, and sums up all the speed modulation intensities from different grinding heads to obtain the instantaneous total polishing intensity data of the node. This process uses an index merging strategy with the node as the primary key: the system traverses all modulation intensity record items, classifies and aggregates them according to the node index, collects the modulation intensity values ​​contributed by all grinding heads under each node item, and calculates the sum. For example, if the node n_123 is affected by the grinding heads H1, H3, and H5 at the current moment, the speed modulation intensities contributed by the three are 0.0626, 0.0491, and 0.0575 N / mm, respectively. 2 , then the instantaneous polishing strength of node n_123 is about 0.1692N / mm 2 After completing this processing, the system stores the node index and its corresponding instantaneous polishing intensity in the final node intensity distribution model. This model is the core component of the entire 3D digital model generation, reflecting the comprehensive polishing intensity distribution of different areas on the entire polishing surface at any point in time, providing high-precision data support for further analysis of material removal, surface change modeling, and process optimization.

[0109] Preferably, step S45 includes the following steps:

[0110] Step S451: performing neighborhood relationship establishment processing on each grid node on the workpiece surface according to the node cumulative polishing amount data, identifying the direct adjacent nodes of each node and establishing a topological connection relationship between the nodes to obtain node neighborhood topology data;

[0111] The embodiment of the present invention further constructs the spatial topological relationship between nodes based on the cumulative polishing amount of all nodes on the workpiece surface during the polishing process (i.e., the integral result of the polishing intensity per unit time in the temporal dimension). This operation is based on the three-dimensional digital model after meshing, and uses structured or unstructured grids (such as quadrilateral grids or triangular grids) to define the node set. The system traverses all grid cells and, for each node, identifies other nodes that share the same grid cell with it in the geometric space. These nodes constitute the "direct adjacent nodes" of the node, and the spatial connection between them is the "neighborhood topological relationship". To facilitate subsequent processing, the system uses the data structure of the adjacency list (adjacency dictionary) to establish a list of neighboring node indexes for each node, and organizes this adjacency structure together with the node cumulative polishing amount data into "node neighborhood topological data". For example, for node n_57, its adjacent nodes may be n_41, n_42, n_56, n_58, n_73, n_74, etc. The system stores these adjacency relationships as a structured array as the basis for subsequent gradient calculation.

[0112] Step S452: Calculate the local polishing intensity gradient of each node based on the node neighborhood topology data and the node cumulative polishing amount data, and obtain node polishing gradient data by calculating the polishing amount difference between the current node and its neighboring nodes and dividing it by the inter-node distance;

[0113] The embodiment of the present invention relies on the established node neighborhood topological relationship, traverses each node and pairs it with all its neighboring nodes one by one, calculates the ratio of the polishing amount difference to the spatial distance, and thus depicts the "local polishing gradient" of the node in the current topological neighborhood. Specifically, for any node n_i, the system extracts its cumulative polishing amount value and reads the polishing amount and position coordinates of its adjacent node n_j in turn. The system calculates the Euclidean distance between n_i and n_j, and divides the polishing amount difference between the two by the distance as the gradient value in that direction. Subsequently, the system averages or weighted averages the gradients in all neighborhood directions to obtain the comprehensive polishing gradient of n_i. This processing can reflect the rate of change of the polishing distribution in the microscopic area of ​​the workpiece surface, which is conducive to identifying local over-polishing or uneven polishing areas. For example, if the average gradient of node n_57 in its neighborhood is 0.0045mm / N·mm, it means that the polishing intensity of the node changes slowly with the spatial distribution, which is conducive to the establishment of a collaborative polishing mechanism.

[0114] Step S453: performing quantitative calculation processing on the synergistic enhancement effect between multiple grinding heads based on the gradient flatness according to the node polishing gradient data to obtain node synergistic enhancement coefficient data;

[0115] The embodiment of the present invention is based on the calculated node local polishing gradient data to analyze the synergistic effect of improving surface flatness under the simultaneous action of multiple grinding heads. The key to this step is to extract the gradient smoothness as a quantitative indicator of synergistic potential - the smaller the gradient, the smaller the difference in the intensity of the action of multiple grinding heads near the node, and the easier it is to form an effective synergistic coverage effect. The system defines the gradient smoothness factor as the inverse of the gradient value (a lower limit threshold can be set to avoid zero value), and then converts it into a synergistic enhancement coefficient between 0 and 1 according to the normalization standard. The larger the coefficient, the more likely the node is to form an enhancement effect due to the action of multiple grinding heads. For example, on node n_57, if its local gradient is 0.0045mm / N·mm, the system can calculate the corresponding synergistic enhancement coefficient to be 0.89. The system pairs the enhancement coefficient of each node with its index to form "node synergistic enhancement coefficient data" as a regulation factor for subsequent synergistic modulation.

[0116] Step S454: performing synergistic effect modulation calculation processing using the node synergistic enhancement coefficient data and the node cumulative polishing amount data, amplifying and modulating the original polishing intensity by multiplying the cumulative polishing amount by the synergistic enhancement coefficient to obtain node synergistic modulation intensity data;

[0117] The embodiment of the present invention multiplies the cumulative polishing amount of each node by its corresponding synergistic enhancement coefficient to achieve dynamic amplification modulation of the polishing amount, reflecting the strength of the synergistic effect. This operation simulates the situation when multiple grinding heads cover the same area at the same time and their action directions are consistent and the intensity is similar. Due to the energy superposition effect, the material removal efficiency of the area is higher than that under the action of a single grinding head. The system reads the corresponding synergistic enhancement coefficient and the original cumulative polishing amount data one by one according to the node index, and performs multiplication processing to calculate the "synergistic modulation polishing intensity". For example, if the original cumulative polishing amount of node n_57 is 0.038mm and its synergistic enhancement coefficient is 0.89, the polishing intensity after synergistic modulation is 0.03382mm. This operation not only provides an intensity model that is more in line with actual process conditions, but also lays the foundation for subsequent global polishing distribution optimization.

[0118] Step S455: Perform spatial distribution statistical processing on the overall synergistic effect of the workpiece surface through the node collaborative modulation intensity data, and rearrange and normalize the collaborative modulation intensity of each node according to the spatial position to obtain the node collaborative polishing intensity distribution data.

[0119] The embodiment of the present invention uses the node coordinates as an index to map the collaborative modulation intensity to the three-dimensional coordinate system of the workpiece surface according to its spatial position, forming a distribution visualization data model. This operation uses spatial grid reconstruction or point cloud remapping technology to mark the intensity value of each node at its corresponding spatial position, forming a high-resolution collaborative polishing intensity map. At the same time, in order to eliminate the intensity imbalance caused by the difference in polishing coverage times in different areas, the system normalizes all collaborative modulation intensities, for example, linearly mapping all intensity values ​​to the range of 0-1 to form a relative intensity distribution map. The normalization operation also helps to use image processing algorithms or deep learning models for further flatness prediction and process optimization control. For example, if the system finds that the node collaborative intensity in a circular area is concentrated in the range of 0.8-1.0, while the other corner area is only around 0.3, it means that the collaborative effect in this area is weak under the current grinding head configuration, and the trajectory needs to be optimized or the action time needs to be increased. The final "node collaborative polishing intensity distribution data" will serve as an important physical field in the generation of three-dimensional digital models, providing a data basis for subsequent dynamic demonstration, process simulation and precise control strategy formulation.

[0120] Preferably, step S5 includes the following steps:

[0121] Step S51: performing statistical analysis on the polishing intensity distribution of the workpiece surface based on the three-dimensional polishing coverage data, calculating the mean, variance, and standard deviation of the global polishing intensity, and obtaining polishing intensity statistical characteristic data;

[0122] The embodiment of the present invention reads the node collaborative polishing intensity distribution data, which contains the three-dimensional coordinates corresponding to each surface grid node and the polishing intensity value after collaborative modulation, forming a set of numerical point sets with spatial distribution attributes. On this basis, the system performs statistical processing to extract statistical characteristic indicators of global polishing intensity, including mean, variance and standard deviation. In the specific implementation, the system traverses the intensity values ​​of all nodes once, accumulates all the intensity values ​​and divides them by the total number of nodes to obtain the mean, then calculates the square of the difference between the intensity value of each node and the mean, sums them and divides them by the total number to obtain the variance, and finally squares the variance to obtain the standard deviation. These statistical results are encapsulated as "polishing intensity statistical characteristic data" to describe the overall uniformity level of the entire workpiece surface under the current polishing coverage. For example, in a certain prototype test, the global average polishing intensity was 0.042mm and the standard deviation was 0.0067mm, indicating that there may be certain intensity fluctuations in the local area, thereby providing a quantitative basis for subsequent uniformity evaluation.

[0123] Step S52: Quantitatively evaluate the uniformity deviation of each area on the workpiece surface using the polishing intensity statistical characteristic data, calculate the relative deviation between the polishing intensity of each grid node and the global mean, and establish a deviation distribution histogram to obtain uniformity deviation evaluation data;

[0124] The embodiment of the present invention uses the global polishing intensity mean obtained in step S51 as the reference value to perform point-by-point deviation calculations on the polishing intensity of all nodes on the workpiece surface. Specifically, for any node, its relative deviation is defined as the percentage difference obtained by subtracting the mean from the node intensity value and dividing it by the mean. This process reflects the degree of deviation of each node in the overall polishing level. The system then bins and counts the deviation values ​​of all nodes, for example, dividing them into intervals of 5%, counting the number of nodes in each interval and plotting them into a deviation distribution histogram, which is used to visualize the degree of discreteness of the polishing uniformity of the entire surface. For example, on a certain test piece, the system detected that 85% of the node deviations were within ±10%, and the remaining node deviations were larger, which may correspond to abnormal areas. Ultimately, the processing results are organized as "uniformity deviation evaluation data", which is not only used to evaluate the consistency performance of the overall surface of the workpiece, but also provides a direct data source for abnormal area identification.

[0125] Step S53: identifying and marking the polishing quality substandard areas based on the uniformity deviation evaluation data, and obtaining quality abnormal area marking data by setting a deviation threshold range and screening out a set of grid nodes exceeding the threshold;

[0126] The embodiment of the present invention further sets a deviation threshold range based on step S52, for example, setting the absolute value of the relative deviation greater than 15% as the judgment standard for quality abnormality, and then screening out the set of nodes that exceed the threshold from all nodes. These nodes are judged to be areas with quality problems under the current polishing process. The system further performs spatial clustering processing on these abnormal nodes, classifies spatially adjacent or continuous abnormal nodes into an abnormal area, and generates a unique identification number for each abnormal area. At the same time, the system records the maximum deviation value, center position coordinates and boundary range of each area to construct "quality abnormality area marking data". For example, an area of ​​approximately 32mm is identified at the edge of a workpiece. 2 If a continuous abnormal area with a maximum deviation of +27% is detected, it can be inferred that the area has insufficient grinding head contact or a track coverage blind spot. This marked data not only provides decision-making basis for process engineers but also provides target input for subsequent automatic path replanning systems.

[0127] Step S54: adjusting the grinding head motion parameters and optimizing the polishing coverage effect according to the quality abnormality area marking data, and verifying the convergence of the optimization effect, thereby obtaining three-dimensional digital polishing model data.

[0128] The embodiment of the present invention receives the abnormal area information identified in step S53, starts the polishing path optimization module, and dynamically adjusts the trajectory planning, action time or pressure of the grinding head in the corresponding area. Specifically, the system prioritizes perturbation simulation of the current process parameters, and attempts to extend the residence time of the grinding head in the abnormal area or increase the pressure without changing the global path structure, while avoiding local over-polishing; then the system uses the digital twin simulation model to predict the adjusted collaborative polishing intensity distribution, and compares it with the statistical features in step S51 to determine whether the deviation is significantly reduced. The system confirms whether the optimization goal is achieved (such as the deviation of the node in the abnormal area is reduced to within ±10%) through iterative optimization and difference convergence judgment. Finally, the system reorganizes the collaborative polishing intensity data of all nodes after optimization into "three-dimensional digital polishing model data". This data set contains both the final fitted intensity distribution and the workpiece surface morphology mapping relationship, which can be used for virtual reconstruction and digital verification of the polishing effect. For example, on a high-precision mirror-polished part, after optimizing the dwell time of two abnormal areas for 5 seconds, the overall standard deviation dropped from 0.0067mm to 0.0034mm. The optimization process converged well, forming a model basis that can be directly applied to digital process control.

[0129] It is particularly important that step S54 includes the following steps:

[0130] Step S541: Calculating the geometric characteristic parameters of the center of gravity, area size, and boundary shape of the abnormal area based on the quality abnormal area marking data to obtain the geometric characteristic data of the abnormal area;

[0131] The embodiment of the present invention aggregates all abnormal nodes in each abnormal area through spatial coordinate information based on the quality abnormal area marking data, and calculates the coordinates of the center of gravity of the area, that is, the spatial coordinates of all nodes in the area are weighted averaged according to their area or weight to obtain a point representing the overall position of the area. Then, the system uses the spatial distribution boundary of the aggregated nodes and adopts the boundary detection algorithm of the two-dimensional point set (such as the convex hull algorithm or the α shape algorithm) to extract the boundary contour of the abnormal area, thereby calculating the area size of the area and the geometric feature parameters of the boundary shape, such as the boundary perimeter, shape regularity index (circularity, aspect ratio, etc.). These parameters together constitute the "abnormal area geometric feature data" for accurately depicting the spatial morphological characteristics of the abnormal area. In specific applications, for example, for an area of ​​10mm 2 Up to 50mm 2 The system can accurately describe the geometric shape of the abnormal areas between the grinding heads, thus laying the foundation for the matching analysis of the grinding head coverage.

[0132] Step S542: Calculating the ratio of the overlapping area between the working range of each grinding head and the abnormal area based on the geometric feature data of the abnormal area, and establishing a grinding head responsibility score table;

[0133] The embodiment of the present invention combines the current action range of each grinding head (usually determined by the grinding head swing radius, the motion trajectory envelope and the action pressure area) to calculate the ratio of the overlapping area of ​​each grinding head and the abnormal area. The specific method is to spatially superimpose the two-dimensional projection of the action range of the grinding head and the boundary of the abnormal area, calculate the intersection area of ​​the two using the grid discretization method, and then divide it by the area of ​​the abnormal area to obtain the ratio value. The system uses the overlapping ratio corresponding to each grinding head as its "responsibility" indicator, and summarizes it to form a grinding head responsibility score table, which reflects the relative size of each grinding head's contribution to the abnormal area. For example, if the overlapping area of ​​a certain grinding head in a certain abnormal area accounts for 40% of the area of ​​the area, the grinding head responsibility score is 0.4. This score table provides a quantitative basis for the subsequent screening of key grinding heads, which helps to accurately locate the root cause of the problem.

[0134] Step S543: Screening and identifying key grinding heads using the grinding head responsibility score table, setting a relevance threshold and extracting a set of grinding heads with relevance scores exceeding the threshold, to obtain a list of grinding heads to be optimized;

[0135] In the embodiment of the present invention, a responsibility threshold value (such as 0.2) is set for the grinding head responsibility score table obtained in step S542, and all grinding heads with responsibility scores exceeding the threshold are screened out to form a list of grinding heads to be optimized. The screening process uses a traversal of the responsibility score table to screen the grinding heads that meet the conditions for each abnormal area, and these grinding heads are uniformly classified into the set to be optimized. The purpose of this step is to highlight the grinding heads that have the greatest impact on the abnormal area, avoid invalid adjustments, and improve optimization efficiency. In specific applications, such as in the polishing process of a certain workpiece, 15 grinding heads with a responsibility higher than 0.2 are screened out as key optimization objects to provide targeted targets for subsequent parameter adjustments.

[0136] Step S544: Based on the list of grinding heads to be optimized, a corresponding relationship table between defect types and parameter adjustment directions is established, and the swing amplitude, frequency, or phase difference parameters of the corresponding grinding heads are modified according to a preset adjustment step size to obtain grinding head parameter configuration data;

[0137] The embodiment of the present invention adjusts the key parameters of each grinding head to be optimized based on the list data of the grinding head to be optimized in combination with the pre-established "correspondence table between defect type and parameter adjustment direction". The correspondence table maps different types of defects (such as insufficient polishing intensity, coverage blind area, obvious edge effect, etc.) to specific adjustment directions of the grinding head swing amplitude, swing frequency or phase difference based on the experience of field experts and historical test data. The system modifies the corresponding grinding head parameters one by one according to the preset adjustment step (for example, the swing amplitude is adjusted by 0.5mm each time, the frequency adjustment step is 0.2Hz, or the phase difference is adjusted by 5 degrees) to generate new parameter configuration data. This process ensures that the adjustment is both accurate and smooth, and avoids process instability caused by large parameter jumps. For example, for the defect of insufficient edge polishing, the system will appropriately increase the swing amplitude and frequency of the grinding head to improve the polishing intensity of the area.

[0138] Step S545: Repeat the calculation process from step S543 to step S544 using the grinding head parameter configuration data, regenerate the grinding head trajectory and polishing coverage effect using the new parameters, and obtain the three-dimensional polishing coverage optimization data after parameter adjustment;

[0139] The embodiment of the present invention is based on the grinding head parameter configuration data, and recalculates the motion trajectory of the grinding head and its range of action according to the new parameters to generate a new grinding head trajectory path. Subsequently, the trajectory coverage effect is simulated using a three-dimensional digital model simulation engine to simulate the polishing coverage state of the grinding head on the workpiece surface, and generate the latest three-dimensional polishing coverage optimization data. This data contains the updated node polishing intensity distribution and synergy situation, which serves as a digital representation of the new round of polishing effect. Through this closed-loop iterative method, the system realizes the dynamic matching of the grinding head parameter adjustment and the polishing coverage effect, ensuring that each optimization has actual numerical feedback. For example, in a test path, after adjusting the grinding head frequency and swing parameters, the system simulation shows that the average polishing intensity of the abnormal area has increased by about 12%.

[0140] Step S546: performing convergence determination on the optimization effect based on the updated three-dimensional polishing coverage data to obtain three-dimensional digital polishing model data.

[0141] The embodiment of the present invention is based on updating the three-dimensional polishing coverage data and adopts a convergence judgment algorithm to evaluate the optimization effect. The judgment basis includes the global statistical characteristic changes (mean, standard deviation) of the polishing intensity, whether the deviation value of the key abnormal area is lower than the preset threshold, and the relative improvement of the coverage effect before and after the grinding head parameter adjustment. The system calculates the difference in polishing intensity distribution of two consecutive optimization iterations. When the difference is lower than the predetermined convergence threshold (such as the mean change is lower than 0.001mm and the standard deviation change is less than 0.0005mm), it is determined that the optimization process has reached a stable state and the final three-dimensional digital polishing model data is output. This data contains the spatial distribution information of the polishing coverage after multiple rounds of adjustment, which can reflect the high-quality and uniform polishing effect of the workpiece surface and is suitable for subsequent quality inspection and process control. In actual applications, the system generally reaches convergence after 3 to 5 rounds of iterations, ensuring that the workpiece polishing process is both efficient and precise.

[0142] The present invention further provides a three-dimensional digital model generation system for a polishing machine, which is used to execute the above-mentioned three-dimensional digital model generation method for a polishing machine. The three-dimensional digital model generation system for a polishing machine includes:

[0143] A three-dimensional geometric data acquisition module is used to obtain the three-dimensional geometric data of the workpiece surface and the kinematic parameter data of the multi-segment swing grinding head;

[0144] The surface mesh processing module is used to perform mesh discretization processing on the workpiece surface according to the three-dimensional geometric data to obtain surface mesh node data containing spatial coordinate information and normal vector information;

[0145] The grinding head trajectory modeling module is used to mathematically model the differential swing trajectory of each grinding head based on the kinematic parameter data to obtain the grinding head spatiotemporal position mapping data. The differential swing trajectory is specifically achieved by setting a sine function combination with a π phase difference between different grinding heads to achieve uniform distribution of multiple grinding heads in the time domain and maximum complementary coverage in the spatial domain.

[0146] A dynamic coverage calculation module is used to dynamically calculate the contact relationship between the effective working area of ​​the grinding head and the workpiece surface based on the surface grid node data and the spatiotemporal position mapping data of the grinding head, and obtain three-dimensional polishing coverage data including the cumulative polishing time and the coordinated polishing intensity distribution. The dynamic coverage calculation process specifically predicts the precise polishing amount for each grid node on the workpiece surface based on the grinding head effective radius and polishing intensity distribution;

[0147] The uniformity evaluation and optimization module is used to evaluate and optimize the uniformity of polishing quality based on the three-dimensional polishing coverage data to obtain three-dimensional digital polishing model data.

[0148] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0149] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A three-dimensional digital model generation method for a polishing machine, characterized in that: The following steps are involved: Step S1: Acquire three-dimensional geometric data of the workpiece surface and kinematic parameter data of the multi-segment swing grinding head; Step S2: performing mesh discretization processing on the workpiece surface according to the three-dimensional geometric data to obtain surface mesh node data containing spatial coordinate information and normal vector information; Step S3: mathematically modeling the differential swing trajectory of each grinding head based on the kinematic parameter data to obtain the grinding head spatiotemporal position mapping data, wherein the differential swing trajectory is specifically achieved by setting a sine function combination with a π phase difference between different grinding heads to achieve uniform distribution of multiple grinding heads in the time domain and maximum complementary coverage in the spatial domain; Step S4: Dynamic coverage calculation is performed on the contact relationship between the effective action area of ​​the grinding head and the workpiece surface based on the surface grid node data and the spatiotemporal position mapping data of the grinding head, thereby obtaining three-dimensional polishing coverage data including the cumulative polishing time and the coordinated polishing intensity distribution. The dynamic coverage calculation is specifically performed to accurately predict the polishing amount for each grid node on the workpiece surface based on the grinding head action radius and the polishing intensity distribution. Step S5: Evaluate and optimize the polishing quality uniformity based on the three-dimensional polishing coverage data to obtain three-dimensional digital polishing model data.

2. The method for generating a three-dimensional digital model for a polishing machine according to claim 1, wherein: Step S2 includes the following steps: Step S21: performing triangular meshing processing on the surface area according to the three-dimensional geometric data to obtain triangular mesh data, wherein the mesh density is set to be no less than 100 mesh nodes per square centimeter; Step S22: performing coordinate assignment and normal vector calculation processing on each mesh node according to the triangular mesh data to obtain node geometric attribute data of node coordinate information and normal vector information; Step S23: performing unique identifier assignment processing on the grid nodes according to the node geometric attribute data to obtain surface grid node data with index identifiers.

3. The method for generating a three-dimensional digital model for a polishing machine according to claim 2, wherein: Step S3 includes the following steps: Step S31: extracting the swing amplitude, swing frequency, swing center position and swing plane angle of each grinding head from the kinematic parameter data, and establishing a structured grinding head parameter table for each grinding head; Step S32: Determine the phase difference value of each grinding head using the total number of grinding heads in the grinding head parameter table, and assign a different phase starting point to each grinding head by equally dividing the complete cycle to generate a phase difference configuration list; Step S33: generating a grinding head trajectory description set including a personalized phase difference swing trajectory description for each grinding head according to the grinding head parameter table and the phase difference configuration list; Step S34: performing time sampling on the entire polishing cycle at preset time intervals based on the grinding head trajectory description set, calculating the three-dimensional spatial coordinate positions of all grinding heads at each time point, thereby forming a grinding head position coordinate sequence table arranged in chronological order; Step S35: using the grinding head position coordinate sequence table to establish a quick query correspondence between the timestamp and the grinding head position information, thereby obtaining the grinding head spatiotemporal position mapping data.

4. The method for generating a three-dimensional digital model for a polishing machine according to claim 3, wherein: Step S4 includes the following steps: Step S41: Calculating the spatial distance between the grinding head and the grid nodes based on the grinding head spatiotemporal position mapping data and the surface grid node data, and obtaining the grinding head node distance matrix data by calculating the three-dimensional Euclidean distance between each grinding head center position and each grid node on the workpiece surface at each time point; Step S42: performing effective action area determination processing based on the grinding head node distance matrix data and the preset grinding head action radius threshold, screening out the grid nodes within the effective action range of each grinding head by distance comparison, and obtaining the grinding head action area coverage relationship data; Step S43: calculating the instantaneous polishing intensity value of each grid node based on the polishing intensity distribution model of the Gaussian attenuation function based on the grinding head active area coverage relationship data to obtain the node instantaneous polishing intensity data; Step S44: performing a time series-based cumulative calculation process on the instantaneous polishing intensity data of the nodes to obtain node cumulative polishing amount data, wherein the cumulative calculation process is specifically performed by performing a time integral operation on the polishing intensity received by each grid node during the entire polishing cycle; Step S45: using the node cumulative polishing amount data to perform superposition calculation processing on the synergistic effect of multiple grinding heads to obtain node synergistic polishing intensity distribution data; Step S46: According to the node cumulative polishing amount data and the node collaborative polishing intensity distribution data, the cumulative polishing time information and the collaborative polishing intensity distribution information are matched according to the grid node index identifier to obtain three-dimensional polishing coverage data.

5. The method for generating a three-dimensional digital model for a polishing machine according to claim 4, wherein: Step S42 includes the following steps: Step S421: performing distance value comparison processing one by one according to the grinding head node distance matrix data and the preset grinding head action radius threshold to obtain distance determination result data; Step S422: Screening and extracting grinding head node pairs that meet the action range conditions based on the distance determination result data, and obtaining a valid grinding head node pairing list by retaining grinding head node combinations whose distance values ​​are less than or equal to the action radius threshold; Step S423: performing grouping processing on the active node set of each grinding head based on the grinding head identifier according to the valid grinding head node pairing list, thereby obtaining grinding head group node set data; Step S424: based on the grinding head group node set data, traverse the active node set of each grinding head and count the active grinding heads covered by each node to obtain the node covered grinding head list data; Step S425: constructing a bidirectional index mapping table from grinding head to node and from node to grinding head through the grinding head grouping node set data and the node covered grinding head list data, and obtaining the grinding head action area coverage relationship data.

6. The method for generating a three-dimensional digital model for a polishing machine according to claim 5, wherein: Step S43 includes the following steps: Step S431: extracting the distance value information between each grinding head and the grid nodes within its action range from the grinding head action area coverage relationship data to obtain a valid distance value data set; Step S432: performing normalized distance calculation processing using the valid distance value data set and a preset Gaussian attenuation parameter to obtain normalized distance ratio data; Step S433: multiplying the square of the normalized distance ratio by the negative Gaussian attenuation coefficient according to the normalized distance ratio data and taking the natural exponent to obtain Gaussian attenuation factor data; Step S434: performing intensity calculation based on the Gaussian attenuation factor data and the preset maximum polishing intensity parameter of the grinding head to obtain a basic polishing intensity value of the grid node; Step S435: performing correction and result solving processing based on the grinding head action direction and the grid node surface normal vector according to the grid node basic polishing intensity value, thereby obtaining the node instantaneous polishing intensity data.

7. The method for generating a three-dimensional digital model for a polishing machine according to claim 6, wherein: Step S435 includes the following steps: Based on the basic polishing intensity value of the grid node, the angle between the grinding head and the normal vector of the node surface is corrected by calculating the cosine value of the grinding head action direction and the normal vector of the node surface and using it as a correction coefficient to obtain the polishing intensity value after angle correction. The speed influence factor is calculated based on the polishing intensity value and the grinding head movement speed information at the current time point to obtain the grinding head node speed modulation intensity data; According to the grinding head node speed modulation intensity data, the multiple grinding head effects on the same grid node at the current time point are superimposed and summed to obtain the node instantaneous polishing intensity data.

8. The method for generating a three-dimensional digital model for a polishing machine according to claim 7, wherein: Step S45 includes the following steps: Step S451: performing neighborhood relationship establishment processing on each grid node on the workpiece surface according to the node cumulative polishing amount data, identifying the direct adjacent nodes of each node and establishing a topological connection relationship between the nodes to obtain node neighborhood topology data; Step S452: Calculate the local polishing intensity gradient of each node based on the node neighborhood topology data and the node cumulative polishing amount data, and obtain node polishing gradient data by calculating the polishing amount difference between the current node and its neighboring nodes and dividing it by the inter-node distance; Step S453: performing quantitative calculation processing on the synergistic enhancement effect between multiple grinding heads based on the gradient flatness according to the node polishing gradient data to obtain node synergistic enhancement coefficient data; Step S454: performing synergistic effect modulation calculation processing using the node synergistic enhancement coefficient data and the node cumulative polishing amount data, amplifying and modulating the original polishing intensity by multiplying the cumulative polishing amount by the synergistic enhancement coefficient to obtain node synergistic modulation intensity data; Step S455: Perform spatial distribution statistical processing on the overall synergistic effect of the workpiece surface through the node collaborative modulation intensity data, and rearrange and normalize the collaborative modulation intensity of each node according to the spatial position to obtain the node collaborative polishing intensity distribution data.

9. The method for generating a three-dimensional digital model for a polishing machine according to claim 8, wherein: Step S5 includes the following steps: Step S51: performing statistical analysis on the polishing intensity distribution of the workpiece surface based on the three-dimensional polishing coverage data, calculating the mean, variance, and standard deviation of the global polishing intensity, and obtaining polishing intensity statistical characteristic data; Step S52: Quantitatively evaluate the uniformity deviation of each area on the workpiece surface using the polishing intensity statistical characteristic data, calculate the relative deviation between the polishing intensity of each grid node and the global mean, and establish a deviation distribution histogram to obtain uniformity deviation evaluation data; Step S53: identifying and marking the polishing quality substandard areas based on the uniformity deviation evaluation data, and obtaining quality abnormal area marking data by setting a deviation threshold range and screening out a set of grid nodes exceeding the threshold; Step S54: adjusting the grinding head motion parameters and optimizing the polishing coverage effect according to the quality abnormality area marking data, and verifying the convergence of the optimization effect, thereby obtaining three-dimensional digital polishing model data.

10. A three-dimensional digital model generation system for a polishing machine, characterized in that: For executing the three-dimensional digital model generation method for a polishing machine according to claim 1, the three-dimensional digital model generation system for a polishing machine comprises: A three-dimensional geometric data acquisition module is used to obtain the three-dimensional geometric data of the workpiece surface and the kinematic parameter data of the multi-segment swing grinding head; The surface mesh processing module is used to perform mesh discretization processing on the workpiece surface according to the three-dimensional geometric data to obtain surface mesh node data containing spatial coordinate information and normal vector information; The grinding head trajectory modeling module is used to mathematically model the differential swing trajectory of each grinding head based on the kinematic parameter data to obtain the grinding head spatiotemporal position mapping data. The differential swing trajectory is specifically achieved by setting a sine function combination with a π phase difference between different grinding heads to achieve uniform distribution of multiple grinding heads in the time domain and maximum complementary coverage in the spatial domain. A dynamic coverage calculation module is used to dynamically calculate the contact relationship between the effective working area of ​​the grinding head and the workpiece surface based on the surface grid node data and the spatiotemporal position mapping data of the grinding head, and obtain three-dimensional polishing coverage data including the cumulative polishing time and the coordinated polishing intensity distribution. The dynamic coverage calculation process specifically predicts the precise polishing amount for each grid node on the workpiece surface based on the grinding head effective radius and polishing intensity distribution; The uniformity evaluation and optimization module is used to evaluate and optimize the uniformity of polishing quality based on the three-dimensional polishing coverage data to obtain three-dimensional digital polishing model data.

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