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

Abstract

The application discloses a multi-axis linkage machining process control method and system for precision mechanical parts, relates to the related technical field of numerical control machining control, and comprises the following steps: dividing K discrete machined surfaces according to the curvature distribution of the machined surface of a CAD model of a part; dividing into K groups of grid units and mapping and extracting K groups of grid attributes; performing multi-axis linkage machining parameter matching, outputting K groups of grid linkage machining parameters; performing cross-grid machining control compatibility analysis, outputting multi-axis linkage machining trajectories and multi-axis linkage timing control parameters; and performing multi-axis linkage machining process control for the production of precision mechanical parts on the surface of a preformed blank by a five-axis linkage numerical control machining center. The application solves the technical problems of weak adaptive regulation and control capability of machining parameters, lack of dynamic compatibility of machining trajectory planning and poor multi-axis linkage industrial machining control precision and stability in the prior art, and achieves the technical effect of improving the precision and stability of multi-axis linkage industrial machining control.

Description

Technical Field

[0001] This application relates to the technical field of CNC machining control, specifically to a method and system for controlling the multi-axis linkage machining process of precision mechanical parts. Background Technology

[0002] Precision mechanical parts are widely used in aerospace, medical devices, and high-end equipment. 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 operation of multiple motion axes. However, in actual multi-axis linkage machining, the curvature distribution of the machined surface of precision mechanical parts is complex and varied, and the machining characteristics of different areas vary greatly. Traditional machining parameter setting methods use uniform parameters to machine the entire surface, which cannot fully consider the actual needs of each area. This makes it difficult to guarantee machining accuracy and easily leads to problems such as overcutting and undercutting, affecting the quality and performance of the parts. Furthermore, the motion coordination and timing control of each axis are crucial. If the machining parameters and machining trajectory planning are unreasonable, interference and collisions between axes may occur, which may not only damage the machining equipment and tools but also cause machining interruptions and reduce production efficiency.

[0003] Therefore, current technologies suffer from weak adaptive control of machining parameters and a lack of dynamic compatibility in machining trajectory planning, resulting in poor accuracy and stability of multi-axis linkage industrial machining control. Summary of the Invention

[0004] This application provides a method and system for controlling the multi-axis linkage machining process of precision mechanical parts, which solves the technical problems of weak adaptive adjustment capability of machining parameters and lack of dynamic compatibility of machining trajectory planning in the prior art, resulting in poor control accuracy and stability of multi-axis linkage industrial machining, and achieves the technical effect of improving the accuracy and stability of multi-axis linkage industrial machining control.

[0005] This application provides a method for controlling the multi-axis linkage machining process of precision mechanical parts. The method includes: dividing the machining surface curvature distribution of the CAD model of the parts into K discrete machining surfaces; dividing the K discrete machining surfaces into K sets of grid units, and extracting the K sets of grid attributes from the CAD model of the parts; matching multi-axis linkage machining parameters based on the K sets of grid attributes, and outputting the K sets of grid-level linkage machining parameters; performing cross-grid machining control compatibility analysis on the K sets of grid units based on the adjacent distribution relationship of the K sets of grid units on the K discrete machining surfaces, and outputting the multi-axis linkage machining trajectory and multi-axis linkage timing control parameters; and using a 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 preformed blank to control the multi-axis linkage machining process of precision mechanical parts production.

[0006] In a possible implementation, the multi-axis linkage machining process control method for the 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 machined, the cross-grid parameter tuning scale optimization of the K groups of grid-level linkage machining parameters is performed, 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 multi-dimensional features of the trajectory beginning and end, dynamic linkage compatibility analysis is performed on the K linkage machining control trajectories, 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: Based on the adjacent distribution relationship of the first grid cells on the first discrete machining surface, a first grid adjacency matrix is ​​constructed, wherein the first grid adjacency matrix uses the first grid cell as the source node and connects M adjacent grid cells by out-degree, M≤4, where M is a positive integer; in the first grid adjacency matrix, based on grid attribute characteristics, the M normal vector angle differences and M curvature change gradients between the first grid cell and the M adjacent grid cells are calculated; 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 M adjacent grid cells are calculated; the M normal vector angle differences, M curvature change gradients and M parameter jump rates are weighted and fused to output M cross-grid parametric quantization scales; the first adjacent grid cell corresponding to the minimum value among the M cross-grid parametric 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 tabu, until the first processing cross-grid sequence and the first cross-grid processing parameter sequence are output.

[0008] In a possible implementation, the multi-axis linkage machining process control method for the precision mechanical parts further performs the following processing: extracting K sets of trajectory start and end machining parameters from the K cross-grid machining parameter sequence mapping; using the K sets of trajectory start and end coordinates and the K sets of trajectory start and end machining parameters as trajectory start and end multi-dimensional features, performing dynamic linkage compatibility analysis, and smoothly splicing the K linkage machining control trajectories according to the analysis results to output the multi-axis linkage machining trajectory; splicing the K cross-grid machining parameter sequences according to the timing of the multi-axis linkage machining trajectory to output the multi-axis linkage timing control parameters.

[0009] In a possible implementation, the multi-axis linkage machining process control method for the precision mechanical parts further performs the following processing: extracting the circumscribed polyhedron boundary of the CAD model of the parts; using the circumscribed polyhedron boundary to match available blanks to obtain a preformed blank; positioning the surface model to be machined by projecting the CAD model of the parts onto the preformed blank; and discretizing the surface model to be machined based on the 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 the 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 gridding directions, dividing the K discrete surfaces to be machined into K groups of grid units using the K grid sizes; and extracting the K groups of grid attributes corresponding to the K groups of grid units from the CAD model of the parts.

[0011] In a possible implementation, the multi-axis linkage machining process control method for the precision mechanical parts further performs the following processing: extracting the coordinate sets of K surface boundary vertices of the K discrete surfaces to be machined from the CAD model of the parts; performing principal component analysis on the coordinate sets of the K surface boundary vertices to locate the 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 value bounding boxes; and extracting the K bounding rectangles aligned with the K discrete surfaces to be machined from the K individual 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 multiple sample attributes, multiple sample grid sizes, and multiple sample linkage machining parameters; inputting the first grid attribute and 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's CAD model as the first part's restored coordinates; using the first part's restored coordinates to perform spatial scene-based correction on the first sample linkage machining parameters, and outputting the first grid-level linkage machining parameters.

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

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

[0015] This application proposes a multi-axis linkage machining process control method and system for precision mechanical parts. Based on the curvature distribution of the machining surface in the CAD model of the part, K ​​discrete machining surfaces are defined; these surfaces are divided into K groups of grid units, and K grid attributes are extracted and mapped; multi-axis linkage machining parameters are matched, and K groups of grid-level linkage machining parameters are output; cross-grid machining control compatibility analysis is performed, and multi-axis linkage machining trajectories and multi-axis linkage timing control parameters are output; a five-axis linkage CNC machining center performs multi-axis linkage machining process control on the surface of a pre-formed blank for the production of precision mechanical parts. This solves the technical problems of weak adaptive adjustment capability of machining parameters and lack of dynamic compatibility in machining trajectory planning in existing technologies, leading to poor accuracy and stability in multi-axis linkage industrial machining control, and achieves the technical effect of improving the accuracy and stability of multi-axis linkage industrial machining control. Attached Figure Description

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

[0017] Figure 1 A schematic flowchart of a multi-axis linkage machining process control method for precision mechanical parts provided in this application embodiment.

[0018] Figure 2A schematic diagram of the multi-axis linkage machining process control system for precision mechanical parts provided in the embodiments of this application.

[0019] Figure labeling: 10 for surface division module, 20 for grid attribute extraction module, 30 for processing parameter matching module, 40 for compatibility analysis module, and 50 for timing control parameter loading module. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

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

[0023] This application provides a method for controlling the multi-axis linkage machining process of precision mechanical parts, such as... Figure 1 As shown, the method includes:

[0024] Step S100: Based on the curvature distribution of the machining surfaces in the CAD model of the component, divide the surface into K discrete surfaces to be machined.

[0025] Preferably, the three-dimensional geometric data of the CAD model of the component is acquired, and the curvature of the entire machining surface is extracted using differential geometry calculations or numerical analysis. Curvature reflects the degree of bending of the surface at a certain point (such as the size of the radius of curvature), and is used to measure the geometric characteristics of the surface. This allows for the identification of regions with different curvature characteristics, including low curvature regions where the surface is close to a plane (such as a gentle curved surface), where the tool movement trajectory is relatively simple during machining and the requirements for axis linkage accuracy are low; and high curvature regions where the surface is severely bent (such as concave cavities, bosses, and complex contours), where multi-axis rapid linkage is required during machining, and tool interference or machining errors are prone to occur. Then, a curvature threshold range is set, and the machining surfaces of the CAD model of the part are traversed. The continuous complex surface is divided into regions with curvature values ​​within the same threshold range, and the same discrete machining surface is divided into K independent sub-regions (i.e. discrete machining surfaces). The curvature distribution in each sub-region is relatively uniform or has similar machining characteristics. Here, K is a positive integer, such as K=3. For example, the machining requirements (such as tool posture, feed rate, and depth of cut) of each sub-region tend to be consistent, so as to facilitate parameter matching and trajectory planning. The curvature characteristics of each discrete machining surface determine the machining parameters of the part, such as multi-axis linkage speed, tool axial angle, and cutting force control. Each discrete machining surface can independently plan the machining trajectory to avoid trajectory jitter or interference caused by cross-region curvature abrupt changes.

[0026] 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 surface model to be processed by projecting the component CAD model onto the preformed blank; and step S140, discretizing the surface model to be processed based on curvature distribution characteristics to obtain the K discrete surfaces to be processed.

[0027] Preferably, a minimum polyhedron, such as a cuboid or prism, that just encloses the CAD model of the part is generated using a convex hull algorithm or a bounding box algorithm. This is used to extract the circumscribed polyhedron boundary of the part's CAD model and determine the maximum geometric dimensions (length, width, and height) and spatial orientation of the part. Then, based on the circumscribed polyhedron dimensions, stock blanks with dimensions greater than or equal to the polyhedron and meeting the machining allowance requirements are selected, such as bars, plates, and forgings. The blanks must have machining allowances reserved in all directions, such as axial allowance ≥ 5mm and radial allowance ≥ 3mm. At the same time, blanks with volumes closest to the circumscribed polyhedron are selected to reduce material waste and avoid prolonged processing time and material waste due to excessively large blanks, thereby improving efficiency and reducing production costs. Then, the machining area of ​​the virtual part CAD model is precisely mapped onto the physical blank to determine the machining start position. Specifically, the coordinate system of the part CAD model is aligned with the clamping coordinate system of the blank (such as the machine tool table coordinate system), ensuring that the origin and coordinate axis directions of the two are consistent. Through coordinate transformation algorithm, the machining surface data of the part CAD model is projected into the actual physical space of the blank to generate a three-dimensional positioning model of the surface to be machined on the blank, i.e., positioning the surface to be machined model. Next, the curvature values ​​(such as Gaussian curvature, average curvature) of each point on the surface to be machined model are calculated to generate the curvature distribution. Finally, the curvature threshold is set according to the machining accuracy requirements, and the surface to be machined is divided into K discrete sub-regions according to the curvature similarity. Among them, the curvature change of each sub-region is ≤ curvature threshold, thereby realizing the matching of different machining strategies for different curvature regions. For example, a small diameter tool and low feed rate are used in the high curvature region, and a large diameter tool and high-speed cutting are used in the low curvature region, thereby ensuring the fine matching of machining parameters.

[0028] Step S200: After dividing the K discrete surfaces to be processed into K groups of grid units, extract the K groups of grid attributes from the CAD model of the component.

[0029] 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 bounding rectangles and K curvature parameters of the K discrete surfaces to be processed, and outputting K grid sizes; step S230, using the K bounding rectangles as the gridding direction, dividing the K discrete surfaces to be processed into K groups of grid units using the K grid sizes; and step S240, extracting the K groups of grid attributes corresponding to the K groups of grid units from the CAD model of the component.

[0030] Preferably, for K discrete surfaces to be processed, the curvature parameters of their feature points (such as center points and boundary points) are extracted using differential geometry (e.g., derivative calculation based on NURBS surfaces). These parameters include the maximum and minimum curvature values ​​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 and minimum curvature values, characterizing the local shape of the surface), and average curvature (reflecting the overall curvature of the surface). Then, based on the K bounding rectangles and K curvature parameters of the K discrete surfaces to be processed, grid density feature matching is performed to automatically calculate the appropriate grid size for each region. This ensures that the grid density matches the processing difficulty, allowing for the use of high-density grids (small sizes) in complex surface regions (high curvature) and low-density grids (large sizes) in simple surface regions (low curvature). Finally, the K grid sizes are calculated and output using the following formula: The reference dimension is a basic step size preset based on the machine tool accuracy and the material being processed, such as 0.1~0.5mm for precision machining; This is the curvature influence factor. As the curvature parameter increases, the influence factor increases, and the grid size decreases, achieving a denser grid in high-curvature areas and a sparser grid in low-curvature areas. The direction correction coefficient in the major axis direction (the direction of surface extension) is greater than 1, such as 1.2, to increase the step size and improve efficiency; the direction correction coefficient in the minor axis direction (the direction of curvature change) is less than 0, such as 0.8, to decrease the step size and ensure accuracy. The grid density is dynamically adjusted according to the surface complexity to achieve a balance between computational efficiency and machining accuracy.

[0031] Preferably, the circumscribed rectangular coordinate system of each discrete surface to be machined is aligned with the machine tool coordinate system to ensure that the grid direction is consistent with the actual machining direction. Based on the calculated grid size, regular or irregular grid units (the smallest independently controllable machining unit), such as rectangular grids or triangular grids, are generated on each discrete surface to be machined, thus obtaining K sets of grid units. Finally, K sets of grid attributes corresponding to the K sets of grid units are extracted from the part CAD model, that is, the spatial coordinates of the grid units are mapped to the design data, and the attribute values ​​at the corresponding positions are extracted. These may include radius of curvature, normal vector, material hardness, surface roughness, and material thickness, as shown in Table 1.

[0032] Table 1. Correspondence between raster attributes and extraction methods

[0033]

[0034] Among them, the radius of curvature and normal vector directly determine the tool path and orientation, while material hardness and surface roughness guide the selection of cutting parameters. For example, high-hardness materials require a reduction in cutting speed, and high-roughness materials require an additional finishing process.

[0035] Furthermore, step S220 also includes step S221, extracting the coordinate sets of the K surface boundary vertices of the K discrete surfaces to be processed from the CAD model of the component; step S222, performing principal component analysis on the coordinate sets of the K surface boundary vertices to locate the K principal axis directions; step S223, performing bounding box optimization along the K principal axis directions with volume as the optimization vector to construct the K individual value bounding boxes; and step S224, extracting the K bounding rectangles aligned with the K discrete surfaces to be processed from the K individual value bounding boxes.

[0036] Preferably, the boundary curves of K discrete surfaces to be processed are identified from the CAD model of the parts. 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 to obtain the coordinate sets of K surface boundary vertices. Then, principal component analysis is performed on the coordinate sets of K surface boundary vertices. That is, by calculating the covariance matrix of the coordinate sets of surface boundary vertices, the direction with the largest data variance is found. For example, the first principal component corresponds to the direction in which the coordinate sets of surface boundary vertices extend the longest, and the second principal component is perpendicular to the first principal component. The two constitute the principal axis direction of the surface. Then, along the K principal axes, bounding box optimization is performed with volume as the optimization vector. Specifically, the edges of the bounding box are aligned with the principal axes determined by PCA to ensure consistency with the natural direction of the surface. The size of the bounding box is then adjusted along the principal axes to just contain all boundary vertices. An iterative algorithm (such as a greedy algorithm) is used to find the parameter configuration that minimizes the volume of the bounding box, thereby generating a 3D bounding box that minimizes the discrete surfaces to be processed, i.e., constructing the bounding box with K positive values ​​for output. Finally, the 3D bounding box is projected onto a 2D plane along the processing direction (such as the Z-axis) to obtain a rectangular projection area. The edges of this rectangle are aligned with the principal axes and completely contain the projection of the discrete surfaces to be processed, generating a 2D boundary suitable for processing planning. The length and width of the circumscribed rectangle directly determine the minimum size requirement of the blank, and the direction of the rectangle guides tool path planning, such as performing line cutting along the long side.

[0037] Step S300: Match multi-axis linkage machining parameters according to the K sets of grid attributes, and output K sets of grid-level linkage machining parameters.

[0038] 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; step S320, inputting the first grid attribute and 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 scene-based correction on the first sample linkage processing parameters, and outputting the first grid-level linkage processing parameters.

[0039] Step S320 further includes the fact that the data composition of the first grid attribute includes a first radius of curvature, a first normal vector, a first material hardness, a first material thickness, and a first surface roughness.

[0040] Preferably, machining parameter data from actual production, simulation experiments, and process manuals are collected to form machining parameter samples that include geometric features (such as curvature and material thickness), process parameters (such as cutting speed and feed rate), and machining effects (such as surface roughness and machining efficiency). A linked machining parameter library is then constructed based on these samples. This library stores multiple sample attributes, multiple sample grid sizes, and multiple sample linked machining parameters. Next, multi-axis linked machining parameter matching is performed based on K sets of grid attributes. This involves using grid attributes (radius of curvature, normal vector, material hardness, etc.) as input conditions to search and match within the linked machining parameter library, outputting K sets of grid-level linked machining parameters. For example, for grids with small radii of curvature (e.g., ≤3mm), use small step feeds (e.g., 0.1mm / step); for grids with large radii of curvature (e.g., ≥10mm), use large step feeds (e.g., 0.5mm / step) to improve efficiency. For grids with high material hardness (e.g., HB300), reduce the feed rate (e.g., ≤0.1mm / r) and increase the spindle speed (e.g., ≥12000r / min) to suppress cutting vibration; for grids with low hardness (e.g., HB100), increase the feed rate (e.g., 0.3mm / r). For precision grids with surface roughness ≤0.8μm, enable smooth cutting modes (e.g., helical interpolation, cycloidal machining) and match small cutting depths (e.g., ≤0.05mm).

[0041] Preferably, the first grid attributes of the first grid unit, such as the first radius of curvature 3mm, the first normal vector (0, 0, 1), the first material hardness HB200, the first material thickness 2mm, the first surface roughness Ra0.8μm, and the first grid size (0.5mm×0.5mm), are input into the linkage machining parameter library to search for similar samples. Feature matching is performed in the linkage machining parameter library. For example, based on cosine similarity, the sample with the closest attributes and size in historical machining is determined, and the corresponding linkage machining parameters of the first sample are output, such as the spindle speed 12000r / min, the feed rate 500mm / min, and the tool tilt angle 5°.

[0042] Preferably, the center coordinates of the first grid unit are retrieved from the CAD model of the component and converted into machine tool coordinates (considering clamping deviation and workpiece deformation compensation) to obtain the spatial position reference during actual machining, thus obtaining the restored coordinates of the first component. Finally, the restored coordinates of the first component are used to perform spatial scene-based correction on the linkage machining parameters of the first sample. For example, if it is near a thin wall (thickness 2mm), the feed rate is reduced (e.g., from 500→300mm / min) to avoid deformation; if the angle of the normal vector change is >10°, the tool tilt angle is adjusted (e.g., from 5°→3°) to ensure cutting consistency. Finally, the machining parameters that are accurately adapted to the current grid space scene are output, namely the first grid-level linkage machining parameters.

[0043] Step S400: Based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, perform cross-grid processing control compatibility analysis on the K groups of grid-level linkage processing parameters, and output multi-axis linkage processing trajectory and multi-axis linkage timing control parameters.

[0044] Step S400 further includes step S410, which optimizes the K sets of grid-level linkage machining parameters across grids based on the adjacent distribution relationship of the K sets of grid units on the K discrete surfaces to be processed, and outputs K cross-grid machining parameter sequences; step S420, which calls and smoothly connects the grid unit center coordinates along the K cross-grid machining parameter sequences, and outputs K linkage machining control trajectories; step S430, which performs dynamic linkage compatibility analysis on the K linkage machining control trajectories based on the multi-dimensional features of the trajectory beginning and end, and outputs the multi-axis linkage machining trajectory and multi-axis linkage timing control parameters.

[0045] Preferably, based on the adjacent distribution relationship of K groups of grid units on K discrete surfaces to be processed (e.g., grid A is adjacent to grids B and C), cross-grid parameter tuning is performed on the K groups of grid-level linkage machining parameters. Specifically, for the parameter differences between adjacent grids (e.g., the feed rate of grid A is 500 mm / min and the feed rate of grid B is 300 mm / min), the parameter change scale of the transition interval is calculated by dynamic programming (e.g., the feed rate linearly decreases from 500 to 300 in the middle region from grid A to grid B), and then K cross-grid machining parameter sequences are output. Each sequence contains the parameter transition rules between the grid and adjacent grids, such as the parameter change rate and transition distance, to avoid machining defects caused by abrupt parameter changes in adjacent areas, such as vibration caused by sudden changes in cutting force and tool marks on the surface, thereby ensuring the uniformity of the machined surface quality.

[0046] Preferably, the grid cell center coordinates are called and smoothly connected along the K cross-grid machining parameter sequences. Specifically, the grid center coordinates are extracted according to the machining order of the K cross-grid machining parameter sequences, such as sorting by row spacing or circumferential cutting path. Then, the discrete coordinate points are fitted into a continuous and smooth motion trajectory by interpolation algorithm. At the same time, the feed rate, tool posture, etc. in the parameter sequence are associated. For example, a certain point on the trajectory corresponds to a feed rate of 400 mm / min and a tool tilt angle of 5°. Then, K linkage machining control trajectories are output, and each trajectory corresponds to a continuous motion path of a surface or area to be machined.

[0047] Preferably, based on the multi-dimensional characteristics of the beginning and end of the K linked machining control trajectories (such as start coordinates, end coordinates, tool posture, motion direction, speed change rate, etc.) and machine tool kinematic constraints (such as travel limits of each axis, acceleration limits, tool interference range, etc.), a dynamic linkage compatibility analysis is performed on the K linked machining control trajectories. Specifically, it checks whether there are motion conflicts at the connection points of adjacent trajectories (such as the tool needing to cross an inaccessible area to move from the end of trajectory 1 to the start of trajectory 2) or speed / posture changes (such as the rotational speed suddenly dropping from 12000 r / min to 5000 r / min, exceeding 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 or adjusting the feed rate change rate, to ensure that the motion of each axis is coordinated and interference-free during multi-axis linkage. Finally, multi-axis linkage machining trajectories and multi-axis linkage timing control parameters are generated, that is, the coordinated control parameters of each axis at different times, such as speed, position, and posture, thereby avoiding hard collisions or motion loss of control 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.

[0048] Further, step S410 also includes step S411, constructing a first grid adjacency matrix based on the adjacent distribution relationship of the first grid cells on the first discrete surface to be processed, wherein the first grid adjacency matrix uses the first grid cell as the source node and connects M adjacent grid cells by out-degree, M≤4, and M is a positive integer; step S412, in the first grid adjacency matrix, based on grid attribute features, calculating the M normal vector angle differences and M curvature change gradients between the first grid cell and the M adjacent grid cells; step S413, in the first grid adjacency matrix, based on... Processing parameter characteristics: Calculate the M parameter jump rates of the first grid cell and M adjacent grid cells; Step S414: Weight and fuse the M normal vector angle differences, M curvature change gradients, and M parameter jump rates to output M cross-grid parametric quantization scales; Step S415: Extract the first adjacent grid cell corresponding to the minimum value among the M cross-grid parametric quantization scales, and perform a recursive optimization operation with the first adjacent grid cell as the update source node and the first grid cell as the update tabu, until the first processing cross-grid sequence and the first cross-grid processing parameter sequence are output.

[0049] Preferably, based on the adjacent distribution relationship of the first grid unit on the first discrete surface to be processed, with the first grid unit as the center (source node) and the number of connections of its adjacent grid units M≤4 (usually referring to the four neighborhoods of top, bottom, left, and right in a two-dimensional plane), a first grid adjacency matrix is ​​constructed, where M is a positive integer. The first grid adjacency matrix is ​​used to characterize the spatial distribution relationship of grid units on the discrete surface to be processed. Then, based on the grid attribute characteristics, the angle difference between the source node (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 transitions are 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 surface transitioning from flat to steep), the tool path or feed rate needs to be adjusted during processing to avoid overcutting or undercutting.

[0050] Preferably, in the first grid adjacency matrix, based on the machining parameter characteristics, the difference magnitude 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 machining parameters between adjacent grids, which may lead to inconsistent machining surface quality or sudden changes in tool load. Then, the geometric features (normal angle difference, curvature gradient) and machining parameter features (jump rate) are fused by weighted summation to obtain the cross-grid parameter tuning quantization scale for each adjacent node. The weights are set according to machining process experience. For example, when the surface accuracy requirement is high, the normal angle difference and curvature gradient have greater weights; when the machining stability requirement is high, the parameter jump rate has greater weights. The smaller the cross-grid parameter tuning quantization scale, the closer the geometric features and machining parameters of the source node and adjacent nodes are, the smoother the parameter transition during cross-grid machining, and the lower the machining risk.

[0051] Preferably, by comparing the cross-grid parameter tuning 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 cell), and the selected adjacent node (the node corresponding to the minimum value) is set as the new source node. The search continues to find the optimal next node among its adjacent nodes. The processed source node (such as the first grid cell) is added to the tabu list to avoid repeated visits and ensure path uniqueness. When all relevant grid cells have been traversed or the processing area boundary is reached, the recursion stops, and 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) are generated, thereby significantly improving the efficiency and reliability of multi-axis linkage processing.

[0052] Furthermore, step S430 also includes step S431, extracting the K trajectory start and end machining parameter groups of the K linked machining control trajectories from the K cross-grid machining parameter sequence mapping; step S432, using the K trajectory start and end coordinate groups and the K trajectory start and end machining parameter groups of the K linked machining control trajectories as trajectory start and end multi-dimensional features, performing dynamic linkage compatibility analysis, and smoothly splicing the K linked machining control trajectories according to the analysis results to output the multi-axis linked machining trajectory; step S433, splicing the K cross-grid machining parameter sequences according to the timing of the multi-axis linked machining trajectory to output the multi-axis linkage timing control parameters.

[0053] Preferably, from the generated K cross-grid machining parameter sequences, the machining parameter sets (such as the starting / ending speed, tool state, cutting depth, etc.) of their start and end positions are extracted to form K trajectory start and end machining parameter sets, reflecting the process state at the beginning and end of each machining trajectory; then, the K trajectory start and end coordinate sets (the start and end coordinates of each linkage machining control trajectory) and the K trajectory start and end machining parameter sets are used as trajectory start and end multi-dimensional features for dynamic linkage compatibility analysis, that is, checking whether there are spatial abrupt changes at the start and end junctions of adjacent trajectories (such as...). To prevent issues such as vibration, overcutting, or undercutting during machining, measures are taken to avoid sudden changes in process parameters (e.g., rapid speed changes or abrupt tool direction changes). This includes calculating the spatial distance and rate of change of direction (e.g., the angle between normal vectors) of the coordinates of the beginning and end of adjacent trajectories to determine whether the trajectories can be smoothly connected, thus assessing spatial compatibility; and calculating the jump amplitude of machining parameters at the beginning and end of adjacent trajectories (e.g., speed difference or spindle speed difference) to determine whether the parameter changes are within the allowable range of the equipment (e.g., the dynamic response capability of the machine tool feed system), thus assessing the compatibility of process parameters and ensuring that parameter changes are within the executable range of the equipment, avoiding shocks.

[0054] Preferably, based on the compatibility analysis results, parameter adjustments or trajectory interpolation are performed on the beginning and end of trajectories with abrupt changes. For example, transition curves (such as Bézier curves or spline curves) are 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 machining parameters (such as speed and feed rate) to avoid sudden parameter changes, and a multi-axis linkage machining trajectory is output, covering all grid units to be machined. Finally, according to the execution order of the machining trajectory (such as the grid traversal order from left to right or from top to bottom), the cross-grid machining parameter sequence of each grid unit is sorted and spliced ​​in the time dimension, and multi-axis linkage timing control parameters are output. These parameters include the motion coordinates and speeds of each axis at different time points, the time nodes of auxiliary process actions such as spindle start and stop and coolant switching, and the timing connection relationship of machining parameters at each stage. This provides the CNC machine tool with precise control instructions executed in time sequence, ensuring the coordination and stability of the multi-axis linkage machining process.

[0055] In step S500, the five-axis linkage CNC machining center loads the multi-axis linkage timing control parameters in real time on the surface of the preformed blank along the multi-axis linkage machining trajectory to control the multi-axis linkage machining process for the production of precision mechanical parts.

[0056] Preferably, a five-axis linkage CNC machining center typically refers to three linear axes and two rotary axes, enabling arbitrary adjustment of the tool's posture in three-dimensional space. For example, the tool's axis direction can be adjusted via the rotary axes to machine structures such as deep cavities, undercuts, and helical surfaces that are inaccessible to traditional three-axis machine tools. The preformed blank is usually a forging, casting, or profile, which is fixed to the machine tool's worktable by a fixture, with its coordinate system aligned with the machine tool's coordinate system to ensure that the machining trajectory is accurately mapped onto the blank surface. Specifically, the multi-axis linkage machining trajectory (including the coordinate sequence of each axis) and multi-axis linkage... Timing control parameters (including speed, spindle status, auxiliary actions, etc.) are loaded and imported into the five-axis CNC machining center. The interpolation module of the CNC unit performs real-time interpolation calculations on discrete coordinate points (such as the center coordinates of grid cells) in the machining trajectory, generating continuous motion commands for each axis. This is achieved by parsing these commands into machine tool-recognizable motion commands (such as the displacement of each axis) and process commands (such as spindle speed, feed rate, and tool number), thereby enabling coordinated motion of the five axes and ensuring that the tool axis direction (controlled by the normal vector) always maintains a reasonable angle with the machining surface. Through high-precision multi-axis linkage motion and precise control of timing parameters, efficient and high-quality machining of precision mechanical parts from blanks to finished products is achieved, while ensuring the accuracy and stability of machining control.

[0057] In the above text, refer to Figure 1 A method for controlling the multi-axis linkage machining process of precision mechanical parts according to embodiments of the present invention is described in detail. Next, reference will be made to... Figure 2 A multi-axis linkage machining process control system for precision mechanical parts is described according to an embodiment of the present invention.

[0058] The multi-axis linkage machining process control system for precision mechanical parts according to embodiments of the present invention addresses the technical problems in the prior art, such as weak adaptive control capability of machining parameters and lack of dynamic compatibility in machining trajectory planning, leading to poor accuracy and stability in multi-axis linkage industrial machining control. It achieves the technical effect of improving the accuracy and stability of multi-axis linkage industrial machining control. Figure 2 As shown, the multi-axis linkage machining process control system for precision mechanical parts includes: a 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.

[0059] The machining surface division module 10 is used to divide the machining surface into K discrete machining surfaces according to the curvature distribution of the machining surface in the CAD model of the part; the grid attribute extraction module 20 is used to extract the K grid attributes from the CAD model of the part after dividing the K discrete machining surfaces into K groups of grid units; the machining parameter matching module 30 is used to perform multi-axis linkage machining parameter matching based on the K groups of grid attributes and output the K groups of grid-level linkage machining parameters; the compatibility analysis module 40 is used to perform cross-grid machining control compatibility analysis on the K groups of grid-level linkage machining parameters based on the adjacent distribution relationship of the K groups of grid units in the K discrete machining surfaces and output the multi-axis linkage machining 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 on the surface of the preformed blank along the multi-axis linkage machining trajectory to control the multi-axis linkage machining process of precision mechanical parts production.

[0060] The specific configuration of the compatibility analysis module 40 will be described in detail below. The compatibility analysis module 40 further includes: based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, performing cross-grid parameter tuning on the K groups of grid-level linkage processing parameters, and outputting K cross-grid processing parameter sequences; calling and smoothly connecting the grid unit center coordinates along the K cross-grid processing parameter sequences, and outputting K linkage processing control trajectories; based on the multi-dimensional features of the trajectory's beginning and end, performing dynamic linkage compatibility analysis on the K linkage processing control trajectories, and outputting the multi-axis linkage processing trajectory and multi-axis linkage timing control parameters.

[0061] 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 cells on the first discrete surface to be processed, wherein the first grid adjacency matrix uses the first grid cell as the source node and connects M adjacent grid cells by out-degree, M≤4, where M is a positive integer; in the first grid adjacency matrix, based on grid attribute characteristics, calculating the M normal vector angle differences and M curvature change gradients between the first grid cell and the M adjacent grid cells; in the first grid adjacency matrix, based on processing parameters... The first grid cell and its M neighboring grid cells are used to calculate the jump rates of the M parameters. The M normal vector angle differences, M curvature gradients, and M parameter jump rates are weighted and fused to output M cross-grid parametric quantization scales. The first neighboring grid cell corresponding to the minimum value among the M cross-grid parametric quantization scales is extracted. The first neighboring grid cell is used as the update source node and the first grid cell is used as the update tabu to perform a recursive optimization operation until the first cross-grid processing sequence and the first cross-grid processing parameter sequence are output.

[0062] The specific configuration of the compatibility analysis module 40 will be described in detail below. The compatibility analysis module 40 further includes: extracting K sets of trajectory start and end machining parameters from the K cross-grid machining parameter sequence mapping of the K linked machining control trajectories; using the K sets of trajectory start and end coordinates and the K sets of trajectory start and end machining parameters as trajectory start and end multi-dimensional features, performing dynamic linkage compatibility analysis, and smoothly splicing the K linked machining control trajectories according to the analysis results to output the multi-axis linked machining trajectory; and splicing the K cross-grid machining parameter sequences according to the timing of the multi-axis linked machining trajectory to output the multi-axis linkage timing control parameters.

[0063] The specific configuration of the surface division module 10 will be described in detail below. The surface division module 10 further includes: 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 surface model to be processed by projecting the part CAD model onto the preformed blank; and discretizing the surface model to be processed based on the curvature distribution characteristics to obtain the K discrete surfaces to be processed.

[0064] The specific configuration of the raster attribute extraction module 20 will be described in detail below. The raster attribute extraction module 20 further includes: extracting K curvature parameters from the K discrete surfaces to be processed; performing raster density feature matching based on the K bounding rectangles and K curvature parameters of the K discrete surfaces to be processed, and outputting K raster sizes; using the K bounding rectangles as rasterization directions, dividing the K discrete surfaces to be processed into K groups of raster units using the K raster sizes; and extracting the K groups of raster attributes corresponding to the K groups of raster units from the component CAD model.

[0065] The specific configuration of the raster attribute extraction module 20 will be described in detail below. The raster attribute extraction module 20 further includes: extracting the coordinate sets of K surface boundary vertices of the K discrete surfaces to be processed from the component CAD model; performing principal component analysis on the coordinate sets of the K surface boundary vertices to locate the 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 value bounding boxes; and extracting the K bounding rectangles aligned with the K discrete surfaces to be processed from the K individual value bounding boxes.

[0066] 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 linked processing parameter library based on multi-source processing parameter samples, wherein the linked processing parameter library stores multiple sample attributes, multiple sample grid sizes, and multiple sample linked processing parameters; inputting the first grid attribute and first grid size of the first grid unit into the linked processing parameter library to search for similar samples, and outputting the first sample linked processing parameters; calling the center coordinates of the first grid unit from the component CAD model as the first component restoration coordinates; using the first component restoration coordinates to perform spatial scene-based correction on the first sample linked processing parameters, and outputting the first grid-level linked processing parameters.

[0067] 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 composition of the first grid attribute includes a first radius of curvature, a first normal vector, a first material hardness, a first material thickness, and a first surface roughness.

[0068] The multi-axis linkage machining process control system for precision mechanical parts provided in this embodiment of the invention can execute the multi-axis linkage machining process control method for precision mechanical parts provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0069] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0070] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for controlling the multi-axis linkage machining process of precision mechanical parts, characterized in that, The method includes: Based on the curvature distribution of the machining surfaces in the CAD model of the parts, K discrete machining surfaces are divided. After dividing the K discrete surfaces to be processed into K groups of grid units, the K groups of grid attributes are extracted from the CAD model of the component. Based on the K sets of grid attributes, multi-axis linkage machining parameters are matched, and K sets of grid-level linkage machining parameters are output. Based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, a 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 on the surface of the preformed blank along the multi-axis linkage machining trajectory to control the multi-axis linkage machining process for the production of precision mechanical parts. Based on the adjacent distribution relationship of the K groups of grid cells on the K discrete surfaces to be processed, a 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 method includes: Based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, cross-grid parameter tuning scale optimization is performed on the K groups of grid-level linkage processing parameters, and K cross-grid processing parameter sequences are output. The grid cell center coordinates are called and smoothly connected along the K cross-grid processing parameter sequences to output K linkage processing control trajectories; Based on the multi-dimensional features of the trajectory start and end, dynamic linkage compatibility analysis is performed on the K linkage machining control trajectories, and the multi-axis linkage machining trajectory and multi-axis linkage timing control parameters are output. Based on the adjacent distribution relationship of the K groups of grid cells on the K discrete surfaces to be processed, cross-grid parameter tuning is performed on the K groups of grid-level linkage processing parameters, and K cross-grid processing parameter sequences are output. The method includes: Based on the adjacent distribution relationship of the first grid cell on the first discrete surface to be processed, a first grid adjacency matrix is ​​constructed, wherein the first grid adjacency matrix takes the first grid cell as the source node and connects M adjacent grid cells with an out-degree, where M≤4 and M is a positive integer; In the first grid adjacency matrix, based on grid attribute characteristics, the angle differences between the M normal vectors of the first grid cell and the M curvature change gradients are calculated. In the first grid adjacency matrix, based on the processing parameter characteristics, the M parameter jump rates between the first grid cell and M adjacent grid cells are calculated; The weighted fusion of the M normal vector angle differences, M curvature change gradients, and M parameter jump rates outputs M cross-grid parametric quantization scales; Extract the first neighboring raster cell corresponding to the minimum value among the M cross-raster parametric quantization scales, and perform a recursive optimization operation with the first neighboring raster cell as the update source node and the first raster cell as the update tabu, until the first processing cross-raster sequence and the first cross-raster processing parameter sequence are output.

2. The multi-axis linkage machining process control method for precision mechanical parts as described in claim 1, characterized in that, Based on the multi-dimensional features of the trajectory's beginning and end, a dynamic linkage compatibility analysis is performed on the K linkage machining control trajectories, and the multi-axis linkage machining trajectory and multi-axis linkage timing control parameters are output. The method includes: Extract the first and last machining parameter sets of the K linked machining control trajectories from the K cross-grid machining parameter sequence mapping; The K sets of coordinates at the beginning and end of the K linkage machining control trajectories and the K sets of machining parameters at the beginning and end of the K trajectories are used as multi-dimensional features at the beginning and end of the trajectories. Dynamic linkage compatibility analysis is performed, and the K linkage machining control trajectories are smoothly spliced ​​together according to the analysis results to output the multi-axis linkage machining trajectory. Based on the timing sequence of the multi-axis linkage machining trajectory, the K cross-grid machining parameter sequences are spliced ​​together to output the multi-axis linkage timing control parameters.

3. The multi-axis linkage machining process control method for precision mechanical parts as described in claim 1, characterized in that, The method involves analyzing the curvature distribution of the machining surfaces in the CAD model of a component to divide it into K discrete machining surfaces. Extract the circumscribed polyhedron boundary of the component CAD model; The available blanks are matched using the circumscribed polyhedron boundary to obtain preformed blanks; The model of the component CAD model is positioned by projecting it onto the preform blank; The model of the surface to be processed is discretized based on the curvature distribution characteristics to obtain the K discrete surfaces to be processed.

4. The multi-axis linkage machining process control method for precision mechanical parts as described in claim 1, characterized in that, After dividing the K discrete surfaces to be processed into K groups of grid cells, the method includes extracting the K groups of grid attributes from the CAD model of the component. Extract the K curvature parameters of the K discrete surfaces to be processed; Based on the K circumscribed rectangles and K curvature parameters of the K discrete surfaces to be processed, grid density feature matching is performed, and K grid sizes are output. Using the K bounding rectangles as the rasterization direction, the K discrete surfaces to be processed are divided into K groups of raster units using the K raster sizes; Extract the K groups of grid attributes corresponding to the K groups of grid cells from the CAD model of the component.

5. The multi-axis linkage machining process control method for precision mechanical parts as described in claim 4, characterized in that, The method further includes: Extract the coordinate set of K surface boundary vertices of the K discrete surfaces to be processed from the CAD model of the component; Principal component analysis is performed on the coordinate sets of the K surface boundary vertices to locate the K principal axis directions; Along the K principal axes, bounding box optimization is performed with volume as the optimization vector to construct bounding boxes for outputting K individual positive values; Extract the K bounding rectangles aligned with the K discrete surfaces to be processed from the K individual positive value bounding boxes.

6. The multi-axis linkage machining process control method for precision mechanical parts as described in claim 1, characterized in that, Based on the K sets of grid attributes, multi-axis linkage machining parameters are matched, and K sets of grid-level linkage machining parameters are output. The method includes: Based on multi-source processing parameter samples, a linkage processing parameter library is constructed, wherein the linkage processing parameter library stores multiple sample attributes, multiple sample grid sizes and multiple sample linkage processing parameters in association; Input the first grid attribute and first grid size of the first grid unit into the linkage processing parameter library to search for similar samples, and output the linkage processing parameters of the first sample; The center coordinates of the first grid cell are retrieved from the CAD model of the component and used as the restored coordinates of the first component. The spatial scene-based correction of the linkage processing parameters of the first sample is performed using the restored coordinates of the first component, and the first grid-level linkage processing parameters are output.

7. The multi-axis linkage machining process control method for precision mechanical parts as described in claim 6, characterized in that, The data composition of the first grid attribute includes a first radius of curvature, a first normal vector, a first material hardness, a first material thickness, and a first surface roughness.

8. 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 7, and the system includes: The surface to be machined module is used to divide the surface into K discrete surfaces to be machined based on the curvature distribution of the machining surfaces in the CAD model of the part. The grid attribute extraction module is used to extract the K sets of grid attributes from the CAD model of the component after dividing the K discrete surfaces to be processed into K sets of grid units. The machining parameter matching module is used to match multi-axis linkage machining parameters based on the K sets of grid attributes and output K sets of grid-level linkage machining parameters. The compatibility analysis module is used to perform cross-grid machining control compatibility analysis on the K groups of grid-level linkage machining parameters based on the adjacent distribution relationship of the K groups of grid units on the K discrete surfaces to be processed, and output multi-axis linkage machining trajectory and multi-axis linkage timing control parameters. The timing control parameter loading module is used by the five-axis linkage CNC machining center to load the multi-axis linkage timing control parameters in real time on the surface of the preformed blank along the multi-axis linkage machining trajectory, so as to control the multi-axis linkage machining process of precision mechanical parts production.

Images