Multi-axis linkage machining process control method and system for precision mechanical parts

Abstract

The invention discloses a multi-axis linkage machining process control method and system for precision mechanical parts, and relates to the related technical field of numerical control machining control, and the method comprises the steps: dividing K discrete to-be-machined surfaces according to the machining surface curvature distribution of a part CAD model; segmenting into K groups of grid units and mapping and extracting K groups of grid attributes; performing multi-axis linkage machining parameter matching, and outputting K groups of grid-level linkage machining parameters; performing cross-grid processing control compatibility analysis, and outputting a multi-axis linkage processing track and a multi-axis linkage time sequence control parameter; and the five-axis linkage numerical control machining center controls the multi-axis linkage machining process of precise mechanical part production on the surface of the preformed blank. The technical problems that in the prior art, the self-adaptive regulation and control capacity of machining parameters is weak, and machining track planning lacks dynamic compatibility, so that the control precision and stability of multi-axis linkage industrial machining are poor are solved, and the technical effect of improving the control precision and stability of multi-axis linkage industrial machining is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to CNC machining control, and specifically to a multi-axis linkage machining process control method and system for precision mechanical parts. Background Art

[0002] Precision mechanical parts are widely used in aerospace, medical devices, high-end equipment and other fields. They often have complex curved surface structures and strict dimensional accuracy requirements. Multi-axis linkage machining achieves precise machining of complex curved surfaces through the coordinated cooperation of multiple motion axes. However, in the actual multi-axis linkage machining process, the curvature distribution of the machining surface of precision mechanical parts is complex and changeable, and the machining characteristics of different areas vary greatly. The traditional machining parameter setting method uses unified parameters to process the entire machining surface, which cannot fully consider the actual needs of each area, resulting in difficulty in ensuring machining accuracy, and prone to problems such as overcutting and undercutting, affecting the quality and performance of parts; and the motion coordination and timing control of each axis are crucial. If the machining parameters and machining trajectory planning are unreasonable, it may cause interference and collision between the axes, which will not only damage the machining equipment and tools, but also cause machining interruptions and reduce production efficiency.

[0003] Therefore, in the current related technologies, there are technical problems such as weak adaptive control capabilities of processing parameters and lack of dynamic compatibility in processing trajectory planning, which lead to poor control accuracy and stability of multi-axis linkage industrial processing. Summary of the Invention

[0004] This application solves the technical problems in the prior art of weak adaptive control capabilities of processing parameters and lack of dynamic compatibility in processing trajectory planning, which lead to poor control accuracy and stability of multi-axis linkage industrial processing, by providing a multi-axis linkage processing process control method and system for precision mechanical parts. It achieves the technical effect of improving the accuracy and stability of multi-axis linkage industrial processing control.

[0005] The present application provides a multi-axis linkage machining process control method for precision mechanical parts, the method comprising: dividing K discrete surfaces to be machined according to the curvature distribution of the machining surface of the component CAD model; after dividing the K discrete surfaces to be machined into K groups of grid units, extracting K groups of grid attributes from the component CAD model mapping; matching multi-axis linkage machining parameters according to the K groups of grid attributes, and outputting K groups of grid-level linkage machining parameters; performing cross-grid machining control compatibility analysis on the K groups of grid-level linkage machining parameters according to the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be machined, and outputting a multi-axis linkage machining trajectory and multi-axis linkage timing control parameters; a five-axis linkage CNC machining center loads the multi-axis linkage timing control parameters in real time along the multi-axis linkage machining trajectory on the surface of the preform blank to control the multi-axis linkage machining process of the production of precision mechanical parts.

[0006] In a possible implementation, the multi-axis linkage machining process control method for precision mechanical parts further performs the following processing: based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, the K groups of grid-level linkage machining parameters are cross-grid parameter adjustment scale optimization, and K cross-grid machining parameter sequences are output; the grid unit center coordinates are called and smoothly connected along the K cross-grid machining parameter sequences, and K linkage machining control trajectories are output; based on the multivariate features of the trajectory head and tail, the K linkage machining control trajectories are dynamically linked and compatibility analyzed, and the multi-axis linkage machining trajectory and multi-axis linkage timing control parameters are output.

[0007] In a possible implementation, the multi-axis linkage machining process control method for precision mechanical parts further performs the following processing: constructing a first grid adjacency matrix based on the adjacent distribution relationship of the first grid unit on the first discrete surface to be processed, wherein the first grid adjacency matrix uses the first grid unit as the source node and connects M adjacent grid units with out-degree, M≤4, and M is a positive integer; in the first grid adjacency matrix, based on the grid attribute characteristics, calculating the M normal vector angle differences and M curvature change gradients between the first grid unit and the M adjacent grid units; in the first In the grid adjacency matrix, based on the processing parameter characteristics, the M parameter jump rates of the first grid cell and the M adjacent grid cells are calculated; the M normal vector angle differences, M curvature change gradients and M parameter jump rates are weightedly fused to output M cross-grid parameter adjustment quantization scales; the first adjacent grid cell corresponding to the minimum value in the M cross-grid parameter adjustment quantization scales is extracted, and a recursive optimization operation is performed with the first adjacent grid cell as the update source node and the first grid cell as the update taboo until a first processing cross-grid sequence and a first cross-grid processing parameter sequence are output.

[0008] In a possible implementation, the multi-axis linkage machining process control method for precision mechanical parts further performs the following processing: extracting the K trajectory head and tail machining parameter groups of the K linkage machining control trajectories from the K cross-grid machining parameter sequence mappings; using the K trajectory head and tail coordinate groups and the K trajectory head and tail machining parameter groups of the K linkage machining control trajectories as trajectory head and tail multivariate features, performing dynamic linkage compatibility analysis, and smoothly splicing the K linkage machining control trajectories according to the analysis results, and outputting the multi-axis linkage machining trajectory; splicing the K cross-grid machining parameter sequences according to the multi-axis linkage machining trajectory timing, and outputting the multi-axis linkage timing control parameters.

[0009] In a possible implementation, the multi-axis linkage machining process control method for precision mechanical parts further performs the following processing: extracting the circumscribed polyhedron boundary of the part CAD model; using the circumscribed polyhedron boundary to match available blanks to obtain a preformed blank; positioning the model of the surface to be machined by projecting the part CAD model onto the preformed blank; discretizing the model of the surface to be machined based on curvature distribution characteristics to obtain the K discrete surfaces to be machined.

[0010] In a possible implementation, the multi-axis linkage machining process control method for precision mechanical parts further performs the following processing: extracting K curvature parameters of the K discrete surfaces to be machined; performing grid density feature matching based on the K circumscribed rectangles and K curvature parameters of the K discrete surfaces to be machined, and outputting K grid sizes; using the K circumscribed rectangles as rasterization directions, and using the K grid sizes to divide the K discrete surfaces to be machined into K groups of grid units; and extracting the K groups of grid attributes corresponding to the K groups of grid units from the part CAD model.

[0011] In a possible implementation, the multi-axis linkage machining process control method of the precision mechanical parts further performs the following processing: extracting K surface boundary vertex coordinate sets of the K discrete surfaces to be machined from the part CAD model; performing principal component analysis on the K surface boundary vertex coordinate sets to locate K principal axis directions; performing bounding box optimization along the K principal axis directions with volume as the optimization vector to construct and output K individual positive value bounding boxes; extracting the K circumscribed rectangles aligned with the K discrete surfaces to be machined from the K individual positive value bounding boxes.

[0012] In a possible implementation, the multi-axis linkage machining process control method for precision mechanical parts further performs the following processing: constructing a linkage machining parameter library based on multi-source machining parameter samples, wherein the linkage machining parameter library stores a plurality of sample attributes, a plurality of sample grid sizes, and a plurality of sample linkage machining parameters in an associated manner; inputting the first grid attribute and the first grid size of the first grid unit into the linkage machining parameter library to search for similar samples, and outputting the first sample linkage machining parameters; calling the center coordinates of the first grid unit from the part CAD model as the first part restoration coordinates; and performing spatial scenario-based correction on the first sample linkage machining parameters using the first part restoration coordinates, and outputting the first grid-level linkage machining parameters.

[0013] In a possible implementation, the multi-axis linkage machining process control method for precision mechanical parts further performs the following processing: the data structure of the first grid attribute includes a first curvature radius, a first normal vector, a first material hardness, a first material thickness and a first surface roughness.

[0014] The present application also provides a multi-axis linkage processing control system for precision mechanical parts, the system comprising: a processing surface division module, for dividing K discrete processing surfaces according to the processing surface curvature distribution of the component CAD model; a grid attribute extraction module, for dividing the K discrete processing surfaces into K groups of grid units, and then extracting K groups of grid attributes from the component CAD model mapping; a processing parameter matching module, for matching multi-axis linkage processing parameters according to the K groups of grid attributes, and outputting K groups of grid-level linkage processing parameters; a compatibility analysis module, for performing cross-grid processing control compatibility analysis on the K groups of grid-level linkage processing parameters according to the adjacent distribution relationship of the K groups of grid units on the K discrete processing surfaces, and outputting multi-axis linkage processing trajectories and multi-axis linkage timing control parameters; a timing control parameter loading module, for a five-axis linkage CNC machining center to load the multi-axis linkage timing control parameters in real time along the multi-axis linkage processing trajectory on the surface of the preformed blank to control the multi-axis linkage processing process of precision mechanical parts production.

[0015] The multi-axis linkage machining process control method and system proposed in this application for precision mechanical parts is to divide K discrete surfaces to be machined according to the curvature distribution of the machining surface of the part CAD model; divide them into K groups of grid units and map and extract K groups of grid attributes; perform multi-axis linkage machining parameter matching and output K groups of grid-level linkage machining parameters; perform cross-grid machining control compatibility analysis and output multi-axis linkage machining trajectory and multi-axis linkage timing control parameters; and use a five-axis linkage CNC machining center to control the multi-axis linkage machining process for the production of precision mechanical parts on the surface of preformed blanks. This solves the technical problems in the prior art of weak adaptive control capabilities of machining parameters and lack of dynamic compatibility in machining trajectory planning, which lead to poor control accuracy and stability of multi-axis linkage industrial machining, and achieves the technical effect of improving the control accuracy and stability of multi-axis linkage industrial machining. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flow chart of a multi-axis linkage machining process control method for precision mechanical parts provided in an embodiment of the present application.

[0018] Figure 2Schematic diagram of the structure of a multi-axis linkage machining process control system for precision mechanical parts provided in an embodiment of the present application.

[0019] Description of reference numerals: to-be-processed surface division module 10 , grid attribute extraction module 20 , processing parameter matching module 30 , compatibility analysis module 40 , timing control parameter loading module 50 . DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The present application provides a multi-axis linkage machining process control method for precision mechanical parts, such as Figure 1 As shown, the method includes: Step S100 : dividing K discrete surfaces to be machined according to the curvature distribution of the machining surface of the component CAD model.

[0024] Preferably, the three-dimensional geometric data of the component CAD model is obtained, and the curvature of the entire processing surface is extracted using differential geometry calculations or numerical analysis, where the curvature reflects the degree of bending of the surface at a certain point (such as the size of the curvature radius), which is used to measure the geometric characteristics of the surface, and then identify areas with different curvature characteristics, including low-curvature areas, where the surface is close to a plane (such as a flat surface), the tool motion trajectory during processing is relatively simple, and the requirements for axis linkage accuracy are low; high-curvature areas where the surface is severely curved (such as cavities, bosses, and complex contours), and multi-axis rapid linkage is required during processing, which is prone to tool interference or processing errors. Then, the curvature threshold range is set, the machining surface of the component CAD model is traversed, the continuous complex surface is segmented, and the area with curvature value within the same threshold range is divided into the same discrete surface to be machined, and K independent sub-areas (i.e., discrete surfaces to be machined) are obtained. The curvature distribution in each sub-area is relatively uniform or has similar machining characteristics, where K is a positive integer, such as K=3. For example, the machining requirements of each sub-area (such as tool posture, feed speed, and cutting depth) tend to be consistent, which facilitates parameter matching and trajectory planning. The curvature characteristics of each discrete surface to be machined determine the machining parameters of the component, such as multi-axis linkage speed, tool axial angle, and cutting force control. Each discrete surface to be machined can independently plan the machining trajectory to avoid trajectory jitter or interference caused by sudden changes in curvature across regions.

[0025] Furthermore, step S100 also includes step S110, extracting the circumscribed polyhedron boundary of the component CAD model; step S120, using the circumscribed polyhedron boundary to match available blanks to obtain a preformed blank; step S130, positioning the model of the surface to be processed by projecting the component CAD model onto the preformed blank; step S140, discretizing the model of the surface to be processed based on the curvature distribution characteristics to obtain the K discrete surfaces to be processed.

[0026] Preferably, a minimum polyhedron, such as a cuboid or prism, that just wraps the CAD model of the component is generated through a convex hull algorithm or a bounding box algorithm to extract the circumscribed polyhedron boundary of the component CAD model and determine the maximum geometric dimensions (length, width, height) and spatial posture of the part; then, based on the size of the circumscribed polyhedron, inventory blanks, such as bars, plates, and forgings, whose size is greater than or equal to the polyhedron and meets the machining allowance requirements are screened, and machining allowances must be reserved for the blanks in all directions, such as axial allowance ≥5mm and radial allowance ≥3mm. At the same time, blanks with a volume closest to the circumscribed polyhedron are selected to reduce material waste and avoid extended processing time and material waste due to excessively large blanks, so as to improve efficiency and reduce production costs. Then, the processing area of the virtual component CAD model is accurately mapped on the physical blank to determine the starting position of processing. Specifically, the coordinate system of the component CAD model is aligned with the clamping coordinate system of the blank (such as the machine tool worktable coordinate system) to ensure that the origin and coordinate axis direction of the two are consistent. The processing surface data of the component CAD model is projected into the actual physical space of the blank through the coordinate transformation algorithm to generate a three-dimensional positioning model of the surface to be processed on the blank, that is, the positioning surface model to be processed; then the curvature value (such as Gaussian curvature, mean curvature) of each point on the surface model to be processed is calculated to generate a curvature distribution. Finally, the curvature threshold is set according to the processing accuracy requirements, and the surface to be processed is divided into K discrete sub-regions according to the curvature similarity, where the curvature change of each sub-region is ≤ the curvature threshold, thereby achieving different processing strategies for different curvature regions, such as using small diameter tools and low feed speeds in high curvature areas and large diameter tools and high-speed cutting in low curvature areas, thereby ensuring the fine matching of processing parameters.

[0027] Step S200 : After dividing the K discrete surfaces to be processed into K groups of grid units, extracting K groups of grid attributes from the component CAD model mapping.

[0028] Step S200 further includes step S210, extracting K curvature parameters of the K discrete surfaces to be processed; step S220, performing grid density feature matching based on the K circumscribed rectangles and K curvature parameters of the K discrete surfaces to be processed, and outputting K grid sizes; step S230, using the K circumscribed rectangles as rasterization directions, and using the K grid sizes to divide the K discrete surfaces to be processed into K groups of grid units; step S240, extracting the K groups of grid attributes corresponding to the K groups of grid units from the component CAD model.

[0029] Preferably, for K discrete surfaces to be processed, the curvature parameters of their feature points (such as center points and boundary points) are extracted by differential geometry (such as derivative calculation based on NURBS surfaces), including the maximum curvature value and the minimum curvature value of the surface at a certain point (reflecting the degree of curvature of the surface in different directions), Gaussian curvature (the product of the maximum curvature value and the minimum curvature value, characterizing the local shape of the surface) and the average curvature (reflecting the overall curvature of the surface); then, grid density feature matching is performed based on the K circumscribed rectangles and K curvature parameters of the K discrete surfaces to be processed, and the grid size suitable for each area is automatically calculated so that the grid density matches the processing difficulty, so that a high-density grid (small size) is used in a complex surface area (high curvature) and a low-density grid (large size) is used in a simple surface area (low curvature), and then the K grid sizes are calculated and output, and the calculation formula is: , where the reference size is the basic step size preset according to the machine tool accuracy and processing material, such as 0.1~0.5mm for precision machining; is the curvature influencing factor. As the curvature parameter increases, the influencing factor increases and the grid size decreases, achieving a dense grid in high-curvature areas and a sparse grid in low-curvature areas. The directional correction coefficient in the major axis direction (the direction of surface extension) is set to be greater than 1, such as 1.2, increasing the step size to improve efficiency. The directional correction coefficient in the minor axis direction (the direction of curvature change) is set to be less than 0.8, such as 0.8, reducing the step size to ensure accuracy. The grid density is dynamically adjusted based on the surface complexity to achieve a balance between computational efficiency and machining accuracy.

[0030] Preferably, the circumscribed rectangular coordinate system of each discrete surface to be processed is aligned with the machine tool coordinate system to ensure that the grid direction is consistent with the actual processing direction. Based on the calculated grid size, regular or irregular grid cells (independently controllable minimum processing units), such as rectangular grids or triangular grids, are generated on each discrete surface to be processed, thereby obtaining K groups of grid cells. Finally, K groups of grid attributes corresponding to the K groups of grid cells are extracted from the component CAD model. That is, the spatial coordinates of the grid cells are mapped to the design data, and the attribute values of the corresponding positions are extracted, which may include the radius of curvature, normal vector, material hardness, surface roughness, and material thickness, as shown in Table 1: Table 1 Correspondence between raster attributes and extraction methods

[0031] Among them, the curvature radius and normal vector directly determine the tool path and posture, and the material hardness and surface roughness guide the selection of cutting parameters. For example, high-hardness materials require lower cutting speeds, and high roughness requires additional finishing processes.

[0032] Furthermore, step S220 also includes step S221, extracting K surface boundary vertex coordinate sets of the K discrete surfaces to be processed from the component CAD model; step S222, performing principal component analysis on the K surface boundary vertex coordinate sets to locate K principal axis directions; step S223, performing bounding box optimization along the K principal axis directions with volume as the optimization vector, and constructing and outputting K individual positive value bounding boxes; step S224, extracting the K circumscribed rectangles aligned with the K discrete surfaces to be processed from the K individual positive value bounding boxes.

[0033] Preferably, K discrete boundary curves of the to-be-machined surfaces are identified from the component CAD model, the boundary curves are sampled (e.g., one point is taken every 0.1 mm), and the three-dimensional coordinates of all vertices on the boundary are extracted, thereby obtaining K surface boundary vertex coordinate sets; principal component analysis is then performed on the K surface boundary vertex coordinate sets, that is, by calculating the covariance matrix of the surface boundary vertex coordinate set, the direction with the largest data variance is found, for example, the first principal component corresponds to the direction in which the surface boundary vertex coordinate set extends the longest, and the second principal component is perpendicular to the first principal component, and the two constitute the principal axis direction of the surface. Then, along the K main axis directions, the bounding box optimization is performed with the volume as the optimization vector. Specifically, the edges of the bounding box are aligned to the main axis direction determined by PCA to ensure that it is consistent with the natural direction of the surface. The size of the bounding box is then adjusted in the main axis direction so that it just contains all boundary vertices. The parameter configuration that minimizes the volume of the bounding box is found through an iterative algorithm (such as a greedy algorithm), and then a three-dimensional bounding box that minimizes the inclusion of the discrete surface to be processed is generated, that is, K individual positive value bounding boxes are constructed and output; finally, the three-dimensional bounding box is projected onto the two-dimensional plane along the processing direction (such as the Z axis) to obtain a rectangular projection area. The edges of the rectangle are aligned with the main axis direction and completely contain the projection of the discrete surface to be processed, generating a two-dimensional boundary suitable for processing planning. Among them, the length and width of the circumscribed rectangle directly determine the minimum size requirement of the blank, and the direction of the rectangle guides the tool path planning, such as line cutting along the long side direction.

[0034] Step S300 , performing multi-axis linkage processing parameter matching based on the K groups of grid attributes, and outputting K groups of grid-level linkage processing parameters.

[0035] Step S300 further includes step S310, constructing a linkage processing parameter library based on multi-source processing parameter samples, wherein the linkage processing parameter library stores multiple sample attributes, multiple sample grid sizes and multiple sample linkage processing parameters in an associated manner; step S320, inputting the first grid attribute and the first grid size of the first grid unit into the linkage processing parameter library to search for similar samples, and outputting the first sample linkage processing parameters; step S330, calling the center coordinates of the first grid unit from the component CAD model as the first component restoration coordinates; step S340, using the first component restoration coordinates to perform spatial scenario correction on the first sample linkage processing parameters, and outputting the first grid-level linkage processing parameters.

[0036] Step S320 further includes: the data structure of the first grid attribute includes a first curvature radius, a first normal vector, a first material hardness, a first material thickness and a first surface roughness.

[0037] Preferably, processing parameter data from actual production, simulation experiments, and process manuals are collected to form processing parameter samples including geometric features (such as curvature, material thickness), process parameters (such as cutting speed, feed rate), and processing effects (such as surface roughness, processing efficiency), and a linkage processing parameter library is constructed based on the processing parameter samples. The linkage processing parameter library stores multiple sample attributes, multiple sample grid sizes, and multiple sample linkage processing parameters; then, multi-axis linkage processing parameter matching is performed based on K groups of grid attributes, that is, grid attributes (curvature radius, normal vector, material hardness, etc.) are used as input conditions, and search and match are performed in the linkage processing parameter library to output K groups of grid-level linkage processing parameters. Parameters, for example, for grids with a small curvature radius (such as ≤3mm), use a small step distance (such as 0.1mm / step); for grids with a large curvature radius (such as ≥10mm), use a large step distance (such as 0.5mm / step) to improve efficiency; for grids with high material hardness (such as HB300), reduce the feed rate (such as ≤0.1mm / r) and increase the spindle speed (such as ≥12000r / min) to suppress cutting vibration; for grids with low hardness (such as HB100), increase the feed rate (such as 0.3mm / r); for precision grids with a surface roughness of ≤0.8μm, enable smooth cutting mode (such as spiral interpolation, cycloid machining) and match a small cutting depth (such as ≤0.05mm).

[0038] Preferably, the first grid attributes of the first grid unit, such as the first curvature radius of 3mm, the first normal vector (0, 0, 1), the first material hardness HB200, the first material thickness of 2mm, the first surface roughness Ra0.8μm, and the first grid size (0.5mm×0.5mm) are input into the linkage processing parameter library to search for similar samples, and feature matching is performed in the linkage processing parameter library. For example, based on the cosine similarity, the sample with the closest attributes + size in the historical processing is determined, and the corresponding first sample linkage processing parameters are output, such as the spindle speed of 12000r / min, the feed speed of 500mm / min, and the tool inclination angle of 5°.

[0039] Preferably, the center coordinates of the first grid unit are called from the component CAD model and converted into the coordinates of the machine tool coordinate system (taking into account the clamping deviation and workpiece deformation compensation) to obtain the spatial position reference during actual processing and the restored coordinates of the first component; finally, the restored coordinates of the first component are used to perform spatial scenario correction on the first sample linkage processing parameters. For example, if a thin wall (thickness 2mm) is approaching, the feed speed is reduced (such as from 500→300mm / min) to avoid deformation; if the normal vector sudden change angle is >10°, the tool inclination angle is adjusted (such as from 5°→3°) to ensure cutting consistency; finally, the processing parameters that accurately adapt to the current grid space scene are output, that is, the first grid-level linkage processing parameters.

[0040] Step S400 , performing cross-grid processing control compatibility analysis on the K groups of grid-level linkage processing parameters based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, and outputting multi-axis linkage processing trajectories and multi-axis linkage timing control parameters.

[0041] Step S400 further includes step S410, performing cross-grid parameter adjustment scale optimization on the K groups of grid-level linkage processing parameters based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, and outputting K cross-grid processing parameter sequences; step S420, calling and smoothly connecting the center coordinates of the grid units along the K cross-grid processing parameter sequences, and outputting K linkage processing control trajectories; step S430, performing dynamic linkage compatibility analysis on the K linkage processing control trajectories based on the multivariate features of the trajectories at the beginning and end, and outputting the multi-axis linkage processing trajectory and multi-axis linkage timing control parameters.

[0042] Preferably, according to the adjacent distribution relationship of K groups of grid units on K discrete surfaces to be processed (such as grid A is adjacent to grids B and C), the K groups of grid-level linkage processing parameters are optimized across grids. Specifically, for the parameter differences between adjacent grids (such as grid A has a feed speed of 500 mm / min, and grid B has a feed speed of 300 mm / min), the parameter change scale of the transition interval is calculated by dynamic programming (such as the middle area from grid A to grid B, the feed speed linearly decreases from 500 to 300), and then K cross-grid processing parameter sequences are output. Each sequence contains the parameter transition rules between the grid and the adjacent grids, such as the parameter change rate, transition distance, etc., to avoid processing defects caused by parameter mutations in adjacent areas, such as vibration caused by sudden changes in cutting force and surface tool marks, thereby ensuring the uniformity of the processed surface quality.

[0043] Preferably, the grid unit center coordinates are called and smoothly connected along K cross-grid processing parameter sequences. Specifically, the grid center coordinates are extracted according to the processing order of the K cross-grid processing parameter sequences, such as sorting by line spacing or circular cutting path, and then the discrete coordinate points are fitted into a continuous and smooth motion trajectory through an interpolation algorithm. At the same time, the feed speed, tool posture, etc. in the parameter sequence are associated, such as a point on the trajectory corresponds to a feed speed of 400 mm / min and a tool inclination angle of 5°, and then K linkage processing control trajectories are output, each trajectory corresponding to a continuous motion path of a surface or area to be processed.

[0044] Preferably, a dynamic linkage compatibility analysis is performed on the K linkage control trajectories based on their head and tail multivariate features (such as starting point coordinates, end point coordinates, tool posture, motion direction, speed mutation rate, etc.) and the machine tool kinematic constraints (such as the travel limit of each axis, acceleration limit, tool interference range, etc.). Specifically, the connection points of adjacent trajectories are checked for motion conflicts (such as the tool needs to cross an inaccessible area from the end point of trajectory 1 to the starting point of trajectory 2) or speed / posture mutations (such as the speed suddenly dropping from 12,000 r / min to 5,000 r / min, which exceeds the response capability of the machine tool servo system). Then, through machine tool motion simulation, the trajectory sequence or connection parameters are adjusted, such as adding transition arcs and adjusting the feed rate change rate, to ensure that the motion of each axis is coordinated and interference-free during multi-axis linkage. Finally, the multi-axis linkage machining trajectory and multi-axis linkage timing control parameters are generated, that is, the coordinated control parameters such as the speed, position, and posture of each axis at different times, thereby avoiding hard collisions or motion loss of the machine tool during machining. At the same time, the machining timing is optimized to improve overall efficiency, such as reducing idle travel time and balancing the load of each axis.

[0045] Furthermore, step S410 further includes step S411, constructing a first grid adjacency matrix according to the adjacent distribution relationship of the first grid unit on the first discrete surface to be processed, wherein the first grid adjacency matrix takes the first grid unit as the source node and connects M adjacent grid units with out-degree, M≤4, and M is a positive integer; step S412, in the first grid adjacency matrix, based on the grid attribute characteristics, calculating M normal vector angle differences and M curvature change gradients between the first grid unit and the M adjacent grid units; step S413, in the first grid adjacency matrix, based on Processing parameter features, calculating the M parameter jump rates of the first grid unit and the M adjacent grid units; step S414, weighted fusion of the M normal vector angle differences, M curvature change gradients and M parameter jump rates, and outputting M cross-grid parameter adjustment quantization scales; step S415, extracting the first adjacent grid unit corresponding to the minimum value in the M cross-grid parameter adjustment quantization scales, and performing a recursive optimization operation with the first adjacent grid unit as the update source node and the first grid unit as the update taboo until a first processing cross-grid sequence and a first cross-grid processing parameter sequence are output.

[0046] Preferably, according to the adjacent distribution relationship of the first grid unit on the first discrete surface to be processed, the first grid unit is the center (source node), and the number of connections of its adjacent grid units is M≤4 (usually refers to the four neighborhoods of the upper, lower, left and right in a two-dimensional plane), and a first grid adjacency matrix is constructed, wherein M is a positive integer, and the first grid adjacency matrix is used to characterize the spatial distribution relationship of the grid units on the discrete surface to be processed; then, based on the grid attribute characteristics, the angle difference between the source node (the first grid unit) and the normal vector of each adjacent grid unit (the normal direction of the local surface corresponding to each grid unit) is calculated in the first grid adjacency matrix. The larger the angle difference, the more drastic the direction change of the surface where the adjacent grids are located (such as surface wrinkles or sharp corners), and more refined parameter transition is required during processing; the curvature change gradient is the absolute value of the difference between the curvature radius of the source node and the curvature radius of the adjacent node. In areas with large curvature change gradients (such as the transition of the surface from gentle to steep), the tool path or feed speed needs to be adjusted during processing to avoid overcutting or undercutting.

[0047] Preferably, in the first grid adjacency matrix, based on the processing parameter characteristics, the difference between the source node parameters and the adjacent node parameters is calculated. That is, the relative change rate between the first grid cell and M adjacent grid cells is calculated to obtain M parameter jump rates. The larger the parameter jump rate, the more significant the difference in processing parameters between adjacent grids, which may lead to inconsistent surface quality or sudden changes in tool load. The geometric characteristics (normal angle difference, curvature gradient) and the processing parameter characteristics (jump rate) are then fused through a weighted summation to obtain a cross-grid parameter adjustment quantization scale for each adjacent node. The weighting is set based on processing process experience. For example, when surface accuracy requirements are high, the normal angle difference and curvature gradient are weighted more, while when processing stability requirements are high, the parameter jump rate is weighted more. The smaller the cross-grid parameter adjustment quantization scale, the closer the geometric characteristics and processing parameters of the source node and adjacent nodes are, the smoother the parameter transition during cross-grid processing, and the lower the processing risk.

[0048] Preferably, by comparing the cross-grid parameter adjustment quantization scales of all adjacent nodes, the adjacent node corresponding to the minimum value is selected as the next processing node (i.e., the first adjacent grid unit), and the selected adjacent node (the node corresponding to the minimum value) is set as the new source node, and the optimal next node among its adjacent nodes is continued to be searched, and the processed source node (such as the first grid unit) is added to the taboo table to avoid repeated visits and ensure the uniqueness of the path. When all relevant grid units are traversed or the boundary of the processing area is reached, the recursion is stopped to generate a complete first processing cross-grid sequence (i.e., processing order) and the corresponding first cross-grid processing parameter sequence (processing parameters of each grid), thereby significantly improving the efficiency and reliability of multi-axis linkage processing.

[0049] Furthermore, step S430 also includes step S431, extracting the K trajectory head and tail processing parameter groups of the K linkage processing control trajectories from the K cross-grid processing parameter sequence mapping; step S432, using the K trajectory head and tail coordinate groups and the K trajectory head and tail processing parameter groups of the K linkage processing control trajectories as trajectory head and tail multivariate features, performing dynamic linkage compatibility analysis, and smoothly splicing the K linkage processing control trajectories according to the analysis results, and outputting the multi-axis linkage processing trajectory; step S433, splicing the K cross-grid processing parameter sequences according to the multi-axis linkage processing trajectory timing, and outputting the multi-axis linkage timing control parameters.

[0050] Preferably, the processing parameter groups of the starting position and the ending position (such as the speed at the start / end, the tool state, the cutting depth, etc.) are extracted from the generated K cross-grid processing parameter sequences to form K trajectory head and tail processing parameter groups, reflecting the process status at both ends of each processing trajectory; then the K trajectory head and tail coordinate groups of the K linkage processing control trajectories (the starting point coordinates and the end point coordinates of each linkage processing control trajectory) and the K trajectory head and tail processing parameter groups are used as the trajectory head and tail multivariate features for dynamic linkage compatibility analysis, that is, to check whether there is a spatial mutation at the head and tail connection of adjacent trajectories (such as the coordinates of ... The system can also calculate the spatial distance and direction change rate (such as the normal vector angle) of the starting and ending coordinates of adjacent trajectories to determine whether the trajectories can be smoothly connected to evaluate spatial compatibility; calculate the jump amplitude of the machining parameters at the beginning and end of adjacent trajectories (such as speed difference, spindle speed difference) to determine whether the parameter changes are within the range allowed by the equipment (such as the dynamic response capability of the machine tool feed system) to evaluate the compatibility of process parameters, thereby ensuring that parameter changes are within the range that the equipment can execute and avoiding impact.

[0051] Preferably, based on the compatibility analysis results, parameter adjustments or trajectory interpolation are performed on the beginning and end of trajectories with sudden changes. For example, a transition curve (such as a Bezier curve or a spline curve) is inserted between the beginning and end of adjacent trajectories to make the tool path continuous and smooth; linear interpolation or exponential smoothing is performed on the processing parameters (such as speed and feed rate) to avoid sudden parameter changes, and a multi-axis linkage processing trajectory is output to cover all grid units to be processed. Finally, according to the execution order of the processing trajectory (such as the grid traversal order from left to right and from top to bottom), the cross-grid processing parameter sequence of each grid unit is sorted and spliced in the time dimension, and the multi-axis linkage timing control parameters are output, including the motion coordinates and speed of each axis at different time points, the time nodes of auxiliary process actions such as spindle start and stop, coolant switch, and the time connection relationship of the processing parameters at each stage, thereby providing the CNC machine tool with precise control instructions executed in time sequence to ensure the coordination and stability of the multi-axis linkage processing process.

[0052] In step S500, the five-axis linkage CNC machining center loads the multi-axis linkage timing control parameters in real time along the multi-axis linkage machining trajectory on the surface of the preform blank to control the multi-axis linkage machining process for the production of precision mechanical parts.

[0053] Preferably, a five-axis CNC machining center usually refers to three linear axes and two rotary axes, which can realize arbitrary posture adjustment of the tool in three-dimensional space. For example, the tool axis direction can be adjusted by the rotary axis to process deep cavities, undercuts, spiral surfaces and other structures that traditional three-axis machine tools cannot reach; the preformed blank is usually a forging, casting or profile, which is fixed on the machine tool workbench by a fixture, and its coordinate system is aligned with the machine tool coordinate system to ensure that the processing trajectory is accurately mapped to the blank surface; specifically, the multi-axis linkage processing trajectory (including the coordinate sequence of each axis) and the multi-axis linkage Timing control parameters (including speed, spindle status, auxiliary actions, etc.) are loaded and imported into the five-axis linkage CNC machining center. The interpolation module of the CNC unit performs real-time interpolation calculations on discrete coordinate points in the machining trajectory (such as the center coordinates of the grid unit), generating continuous motion instructions for each axis. These instructions are then parsed into motion instructions (such as the displacement of each axis) and process instructions (such as spindle speed, feed rate, and tool number) that the machine tool can recognize. This enables the five axes to move in coordination, ensuring that the tool axis direction (controlled by the normal vector) always maintains a reasonable angle with the machining surface. Through high-precision motion of multi-axis linkage and precise control of timing parameters, efficient and high-quality machining of precision mechanical parts from blank to finished product is achieved, ensuring the accuracy and stability of machining control.

[0054] In the above, refer to Figure 1 The multi-axis linkage machining process control method of precision mechanical parts according to the embodiment of the present invention is described in detail. Figure 2 A multi-axis linkage machining process control system for precision mechanical parts according to an embodiment of the present invention is described.

[0055] The multi-axis linkage processing control system for precision mechanical parts according to the embodiment of the present invention is used to solve the technical problems existing in the prior art, such as weak adaptive control capability of processing parameters and lack of dynamic compatibility in processing trajectory planning, which lead to poor control accuracy and stability of multi-axis linkage industrial processing, thereby achieving the technical effect of improving the accuracy and stability of multi-axis linkage industrial processing control. Figure 2 As shown, the multi-axis linkage machining process control system for precision mechanical parts includes: a to-be-machined surface division module 10, a grid attribute extraction module 20, a machining parameter matching module 30, a compatibility analysis module 40, and a timing control parameter loading module 50.

[0056] The processing surface division module 10 is used to divide K discrete processing surfaces according to the processing surface curvature distribution of the component CAD model; the grid attribute extraction module 20 is used to extract K groups of grid attributes from the component CAD model mapping after dividing the K discrete processing surfaces into K groups of grid units; the processing parameter matching module 30 is used to match multi-axis linkage processing parameters according to the K groups of grid attributes, and output K groups of grid-level linkage processing parameters; the compatibility analysis module 40 is used to perform cross-grid processing control compatibility analysis on the K groups of grid-level linkage processing parameters according to the adjacent distribution relationship of the K groups of grid units on the K discrete processing surfaces, and output multi-axis linkage processing trajectory and multi-axis linkage timing control parameters; the timing control parameter loading module 50 is used for the five-axis linkage CNC machining center to load the multi-axis linkage timing control parameters in real time along the multi-axis linkage machining trajectory on the surface of the preform blank to control the multi-axis linkage processing process of the production of precision mechanical parts.

[0057] The specific configuration of the compatibility analysis module 40 will be described in detail below. The compatibility analysis module 40 further includes: performing cross-grid parameter adjustment optimization on the K groups of grid-level linkage processing parameters based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, and outputting K cross-grid processing parameter sequences; calling and smoothly connecting the center coordinates of the grid units along the K cross-grid processing parameter sequences, and outputting K linkage processing control trajectories; and performing dynamic linkage compatibility analysis on the K linkage processing control trajectories based on the multivariate features of the trajectories at the beginning and end, and outputting the multi-axis linkage processing trajectories and multi-axis linkage timing control parameters.

[0058] The specific configuration of the compatibility analysis module 40 will be described in detail below. The compatibility analysis module 40 further includes: constructing a first grid adjacency matrix based on the adjacent distribution relationship of the first grid unit on the first discrete surface to be processed, wherein the first grid adjacency matrix uses the first grid unit as the source node and connects M adjacent grid units with out-degree, M≤4, and M is a positive integer; in the first grid adjacency matrix, based on the grid attribute characteristics, calculating the M normal vector angle differences and M curvature change gradients between the first grid unit and the M adjacent grid units; in the first grid adjacency matrix, based on the processing parameters, calculating the M normal vector angle differences and M curvature change gradients between the first grid unit and the M adjacent grid units; in the first grid adjacency matrix, calculating the M normal vector angle differences and M curvature change gradients between the first grid unit and the M adjacent grid units; in the first grid adjacency matrix, calculating the M normal vector angle differences and M curvature change gradients between the first grid unit and the M adjacent grid units based on the processing parameters. The method comprises the following steps: a first grid cell is used to calculate the M parameter jump rates between the first grid cell and the M adjacent grid cells; a weighted fusion of the M normal vector angle differences, the M curvature change gradients and the M parameter jump rates is performed to output M cross-grid parameter adjustment quantization scales; a first adjacent grid cell corresponding to the minimum value in the M cross-grid parameter adjustment quantization scales is extracted, and a recursive optimization operation is performed with the first adjacent grid cell as the update source node and the first grid cell as the update taboo until a first processed cross-grid sequence and a first cross-grid processing parameter sequence are output.

[0059] The specific configuration of the compatibility analysis module 40 will be described in detail below. The compatibility analysis module 40 further includes: extracting K trajectory head and tail processing parameter groups of the K linkage machining control trajectories from the K cross-grid machining parameter sequence mappings; performing dynamic linkage compatibility analysis using the K trajectory head and tail coordinate groups and K trajectory head and tail processing parameter groups of the K linkage machining control trajectories as trajectory head and tail multivariate features; and smoothly splicing the K linkage machining control trajectories based on the analysis results to output the multi-axis linkage machining trajectory; splicing the K cross-grid machining parameter sequences according to the multi-axis linkage machining trajectory timing sequence to output the multi-axis linkage timing control parameters.

[0060] The specific configuration of the surface-to-be-machined segmentation module 10 will be described in detail below. The surface-to-be-machined segmentation module 10 further includes: extracting the circumscribed polyhedron boundary of the component CAD model; using the circumscribed polyhedron boundary to match available blanks to obtain a preform; locating the surface-to-be-machined model by projecting the component CAD model onto the preform; and discretizing the surface-to-be-machined model based on curvature distribution characteristics to obtain the K discrete surfaces to be machined.

[0061] The specific configuration of the grid attribute extraction module 20 will be described in detail below. The grid attribute extraction module 20 further includes: extracting K curvature parameters of the K discrete surfaces to be processed; performing grid density feature matching based on the K bounding rectangles and the K curvature parameters of the K discrete surfaces to be processed, and outputting K grid sizes; using the K bounding rectangles as rasterization directions, and using the K grid sizes to segment the K discrete surfaces to be processed into K groups of grid cells; and extracting the K groups of grid attributes corresponding to the K groups of grid cells from the component CAD model.

[0062] The specific configuration of the grid attribute extraction module 20 will be described in detail below. The grid attribute extraction module 20 further includes: extracting K surface boundary vertex coordinate sets of the K discrete surfaces to be processed from the component CAD model; performing principal component analysis on the K surface boundary vertex coordinate sets to locate K principal axis directions; performing bounding box optimization along the K principal axis directions using volume as the optimization vector to construct and output K individual positive value bounding boxes; and extracting the K circumscribed rectangles aligned with the K discrete surfaces to be processed from the K individual positive value bounding boxes.

[0063] The specific configuration of the processing parameter matching module 30 will be described in detail below. The processing parameter matching module 30 further includes: constructing a linkage processing parameter library based on multi-source processing parameter samples, wherein the linkage processing parameter library stores multiple sample attributes, multiple sample grid sizes, and multiple sample linkage processing parameters in an associated manner; inputting the first grid attribute and the first grid size of the first grid cell into the linkage processing parameter library to search for similar samples, and outputting the first sample linkage processing parameter; calling the center coordinates of the first grid cell from the component CAD model as the first component restoration coordinates; using the first component restoration coordinates to perform spatial scenario-based correction on the first sample linkage processing parameter, and outputting the first grid-level linkage processing parameter.

[0064] The specific configuration of the processing parameter matching module 30 will be described in detail below. The processing parameter matching module 30 further includes: the data structure of the first grid attribute includes a first curvature radius, a first normal vector, a first material hardness, a first material thickness and a first surface roughness.

[0065] The multi-axis linkage machining process control system for precision mechanical parts provided in the embodiment of the present invention can execute the multi-axis linkage machining process control method for precision mechanical parts provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0066] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0067] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A multi-axis linkage machining process control method for precision mechanical parts, characterized in that: The method comprises: According to the curvature distribution of the machining surface of the component CAD model, K discrete machining surfaces are divided; After dividing the K discrete surfaces to be processed into K groups of grid units, extracting the K groups of grid attributes from the component CAD model mapping; Matching multi-axis linkage processing parameters according to the K groups of grid attributes, and outputting K groups of grid-level linkage processing parameters; According to the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, cross-grid processing control compatibility analysis is performed on the K groups of grid-level linkage processing parameters, and multi-axis linkage processing trajectory and multi-axis linkage timing control parameters are output; The five-axis linkage CNC machining center loads the multi-axis linkage timing control parameters in real time along the multi-axis linkage machining trajectory on the surface of the preform blank to control the multi-axis linkage machining process for the production of precision mechanical parts.

2. The multi-axis linkage machining process control method for precision mechanical parts according to claim 1, characterized in that: According to the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, cross-grid processing control compatibility analysis is performed on the K groups of grid-level linkage processing parameters, and a multi-axis linkage processing trajectory and multi-axis linkage timing control parameters are output. The method includes: According to the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, cross-grid parameter adjustment scale optimization is performed on the K groups of grid-level linkage processing parameters, and K cross-grid processing parameter sequences are output; Calling and smoothly connecting the center coordinates of the grid cells along the K cross-grid processing parameter sequences, and outputting K linkage processing control trajectories; Based on the multivariate features of the trajectory head and tail, a dynamic linkage compatibility analysis is performed on the K linkage processing control trajectories, and the multi-axis linkage processing trajectory and multi-axis linkage timing control parameters are output.

3. The multi-axis linkage machining process control method for precision mechanical parts according to claim 2, characterized in that: According to the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, cross-grid parameter adjustment scale optimization is performed on the K groups of grid-level linkage processing parameters, and K cross-grid processing parameter sequences are output. The method includes: Constructing a first grid adjacency matrix based on the adjacent distribution relationship of the first grid unit on the first discrete surface to be processed, wherein the first grid adjacency matrix uses the first grid unit as a source node and has an out-degree connection to M adjacent grid units, where M≤4 and M is a positive integer; In the first grid adjacency matrix, based on grid attribute characteristics, M normal vector angle differences and M curvature change gradients between the first grid cell and M adjacent grid cells are calculated; In the first grid adjacency matrix, based on the processing parameter characteristics, calculating M parameter jump rates between the first grid cell and M adjacent grid cells; Weighted fusion of the M normal vector angle differences, the M curvature change gradients, and the M parameter jump rates to output M cross-grid parameter adjustment quantization scales; A first adjacent grid cell corresponding to a minimum value in the M cross-grid parameter quantization scales is extracted, and a recursive optimization operation is performed with the first adjacent grid cell as an update source node and the first grid cell as an update taboo until a first processed cross-grid sequence and a first cross-grid processing parameter sequence are output.

4. The multi-axis linkage machining process control method for precision mechanical parts according to claim 2, characterized in that: Based on the multivariate features of the trajectory head and tail, dynamic linkage compatibility analysis is performed on the K linkage processing control trajectories, and the multi-axis linkage processing trajectory and multi-axis linkage timing control parameters are output. The method includes: Extracting K trajectory head and tail processing parameter groups of the K linked processing control trajectories from the K cross-grid processing parameter sequence mappings; The K trajectory head and tail coordinate groups and the K trajectory head and tail processing parameter groups of the K linkage processing control trajectories are used as trajectory head and tail multivariate features to perform dynamic linkage compatibility analysis, and the K linkage processing control trajectories are smoothly spliced according to the analysis results to output the multi-axis linkage processing trajectory; The K cross-grid processing parameter sequences are spliced according to the multi-axis linkage processing trajectory timing, and the multi-axis linkage timing control parameters are output.

5. The multi-axis linkage machining process control method for precision mechanical parts according to claim 1, characterized in that: Analyzing the curvature distribution of the machining surface of the component CAD model to divide K discrete surfaces to be machined, the method includes: Extracting the circumscribed polyhedron boundary of the component CAD model; Using the circumscribed polyhedron boundary to match available blanks to obtain a preformed blank; Positioning the surface model to be processed by projecting the component CAD model onto the preform; The model of the surface to be machined is discretized based on the curvature distribution characteristics to obtain the K discrete surfaces to be machined.

6. The multi-axis linkage machining process control method for precision mechanical parts according to claim 1, characterized in that: After dividing the K discrete surfaces to be processed into K groups of grid units, extracting the K groups of grid attributes from the component CAD model mapping, the method includes: Extracting K curvature parameters of the K discrete surfaces to be processed; Performing grid density feature matching based on the K circumscribed rectangles and K curvature parameters of the K discrete surfaces to be processed, and outputting K grid sizes; Using the K circumscribed rectangles as gridding directions, and using the K grid sizes to divide the K discrete surfaces to be processed into K groups of grid units; The K groups of grid attributes corresponding to the K groups of grid cells are extracted from the component CAD model.

7. The multi-axis linkage machining process control method for precision mechanical parts according to claim 6, characterized in that: The method further comprises: Extracting K surface boundary vertex coordinate sets of the K discrete surfaces to be processed from the component CAD model; Performing principal component analysis on the K surface boundary vertex coordinate sets to locate K principal axis directions; Along the K principal axis directions, using the volume as the optimization vector, perform bounding box optimization to construct and output K individual positive value bounding boxes; The K circumscribed rectangles aligned with the K discrete to-be-processed surfaces are extracted from the K individual positive value bounding boxes.

8. The multi-axis linkage machining process control method for precision mechanical parts according to claim 1, characterized in that: Matching multi-axis linkage processing parameters based on the K groups of grid attributes and outputting K groups of grid-level linkage processing parameters, the method includes: Building a linkage processing parameter library based on multi-source processing parameter samples, wherein the linkage processing parameter library stores a plurality of sample attributes, a plurality of sample grid sizes, and a plurality of sample linkage processing parameters in an associated manner; Inputting the first grid attribute and the first grid size of the first grid cell into the linkage processing parameter library to search for similar samples, and outputting the first sample linkage processing parameters; Retrieving the center coordinates of the first grid cell from the component CAD model as the first component restoration coordinates; The first component restoration coordinates are used to perform spatial scenario-based correction on the first sample linkage processing parameters, and first grid-level linkage processing parameters are output.

9. The multi-axis linkage machining process control method for precision mechanical parts according to claim 8, characterized in that: The data structure of the first grid attribute includes a first curvature radius, a first normal vector, a first material hardness, a first material thickness, and a first surface roughness.

10. A multi-axis linkage machining process control system for precision mechanical parts, characterized in that: The system is used to implement the multi-axis linkage machining process control method for precision mechanical parts according to any one of claims 1 to 9, and the system includes: The processing surface division module is used to divide K discrete processing surfaces according to the curvature distribution of the processing surface of the component CAD model; A grid attribute extraction module is used to extract K groups of grid attributes from the component CAD model mapping after dividing the K discrete surfaces to be processed into K groups of grid units; a processing parameter matching module, configured to match multi-axis linkage processing parameters according to the K groups of grid attributes and output K groups of grid-level linkage processing parameters; a compatibility analysis module for performing cross-grid processing control compatibility analysis on the K groups of grid-level linkage processing parameters based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, and outputting a multi-axis linkage processing trajectory and multi-axis linkage timing control parameters; The timing control parameter loading module is used for the five-axis linkage CNC machining center to load the multi-axis linkage timing control parameters on the surface of the preformed blank along the multi-axis linkage machining trajectory in real time to control the multi-axis linkage machining process for the production of precision mechanical parts.

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