A multi-nozzle cooperative sandblasting path optimization method and device

Abstract

The application relates to the technical field of program control, and discloses a multi-nozzle cooperative sand blasting path optimization method and device.The method comprises the following steps: acquiring surface topography data of a workpiece to be processed; planning a total sand blasting path covering the workpiece to be processed based on the surface topography data; segmenting the total sand blasting path based on the curvature change rate of the surface topography data to obtain a sub-path section of the workpiece to be processed; performing parameter distribution on sand blasting nozzles of the sub-path section based on a pre-established process database to obtain initial working parameters of the sand blasting nozzles; correcting the initial working parameters based on a preset multi-nozzle cooperative operation rule to obtain final working parameters of the sand blasting nozzles; issuing control instructions to the sand blasting nozzles based on the final working parameters, and performing sand blasting operation of the sand blasting nozzles; and the application realizes full-process automation and intelligent decision-making from path planning to parameter setting, reduces processing energy consumption, and improves the wear resistance of the workpiece.

Description

Technical Field

[0001] This invention relates to the field of program control technology, and in particular to a method and apparatus for optimizing the path of multi-nozzle collaborative sandblasting. Background Technology

[0002] In existing technologies, traditional sandblasting operations suffer from problems such as poor processing uniformity, low efficiency, high energy consumption, and poor adaptability to complex curved surfaces. Multi-nozzle collaborative sandblasting is an effective way to improve the efficiency of sandblasting operations. However, the movement trajectories and process parameters between multiple nozzles are prone to lack of coordination, leading to overlapping or omissions in processing areas and making it difficult to guarantee surface uniformity. Secondly, the workpiece surface has significant differences in geometric features, and using uniform processing parameters cannot meet the optimization requirements of each area. This can easily lead to over-processing or under-processing in the feature transition zone, resulting in energy waste or substandard workpieces.

[0003] In terms of path optimization, existing technologies mostly focus on single-nozzle scenarios. Some studies have attempted multi-nozzle approaches, but these only involve simple path planning and lack a systematic solution that deeply integrates path segmentation, process databases, and multi-nozzle collaborative rules. This makes it difficult to achieve comprehensive optimization of efficiency and energy consumption while ensuring processing quality. Therefore, there is an urgent need for a sandblasting path optimization method and device that can intelligently segment paths, accurately allocate parameters, and achieve efficient collaboration among multiple nozzles. Summary of the Invention

[0004] This invention provides a method and apparatus for optimizing the path of multi-nozzle collaborative sandblasting to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a multi-nozzle collaborative sandblasting path optimization method, comprising: S1. Obtain the surface morphology data of the workpiece to be processed; S2. Based on the surface topography data, plan the total sandblasting path covering the workpiece to be processed; S3. Based on the curvature change rate of the surface topography data, the total sandblasting path is divided to obtain the sub-path segments of the workpiece to be processed; S4. Based on the pre-established process database, the parameters of the sandblasting nozzles in the sub-path segment are allocated to obtain the initial working parameters of the sandblasting nozzles. S5. Based on the preset multi-nozzle collaborative operation rules, the initial working parameters are corrected to obtain the final working parameters of the sandblasting nozzle; S6. Based on the final working parameters, issue control commands to the sandblasting nozzle and execute the sandblasting operation of the sandblasting nozzle.

[0006] In a preferred embodiment, acquiring the surface morphology data of the workpiece to be processed includes: Obtain the 3D design model of the workpiece to be processed; The three-dimensional design model is meshed to obtain a discrete mesh of the three-dimensional design model; The center point coordinates and normal vector of the discrete grid are determined, and the center point coordinates and normal vector are used as the surface topography data of the workpiece to be processed.

[0007] In a preferred embodiment, planning the total sandblasting path covering the workpiece to be processed based on the surface topography data includes: The target area of ​​the workpiece to be processed is determined based on the surface morphology data. The sandblasting mode of the workpiece to be processed is set based on the target area; The total sandblasting path of the workpiece to be processed is constructed based on the path and row spacing in the sandblasting mode.

[0008] In a preferred embodiment, the step of segmenting the total sandblasting path based on the curvature change rate of the surface topography data to obtain sub-path segments of the workpiece to be processed includes: The rate of change of curvature of the surface topography data is calculated using the following formula: in, The rate of change of curvature is the i-th index of the surface topography data. This is the index of the total sandblasting path. The first of the total sandblasting paths The curvature value of each index, For the first on the path The curvature value of each index, This is the approximate arc length between two adjacent path points; The curvature change rate is compared with a preset threshold, and the segmentation points of the total sandblasting path are marked based on the comparison result; The total sandblasting path is divided based on the dividing point to obtain the sub-path segments of the workpiece to be processed.

[0009] In a preferred embodiment, the pre-established process database includes: Based on the different characterization sandblasting experiments of the workpiece to be processed, the processing result data of the workpiece to be processed is generated; Determine the degree of quantitative influence of the process parameters of the different characterization sandblasting experiments on the processing result data; The process database for the sub-path segment is constructed based on the processing result data and the quantified degree of influence.

[0010] In a preferred embodiment, the parameter allocation of the sandblasting nozzles for the sub-path segment based on a pre-established process database to obtain the initial operating parameters of the sandblasting nozzles includes: Identify the dominant surface feature type of the sub-path segment; The initial operating parameters of the sandblasting nozzle are obtained by searching the process database based on the information requirements of the workpiece to be processed and the dominant surface feature type.

[0011] In a preferred embodiment, the preset multi-nozzle cooperative operation rules include: The safe distance and area coverage of the sandblasting nozzle are used as the spatial operation rules for the sandblasting nozzle. The operating parameters of the sandblasting nozzle are adjusted based on the surface geometry of the sub-path segment, and the adjusted operating parameters are used as the parameter setting rules for the sandblasting nozzle. The spatial operation rules and the parameter setting rules are integrated into the multi-nozzle collaborative operation rules for the sub-path segment.

[0012] In a preferred embodiment, the step of correcting the initial operating parameters based on preset multi-nozzle collaborative operation rules to obtain the final operating parameters of the sandblasting nozzle includes: The initial working parameters are evaluated based on the multi-nozzle collaborative operation rules, and the evaluated initial working parameters are iteratively adjusted to obtain the final working parameters of the sandblasting nozzle.

[0013] In a preferred embodiment, issuing control commands to the sandblasting nozzle based on the final operating parameters and executing the sandblasting operation by the sandblasting nozzle includes: The final operating parameters are compiled into control commands for the sandblasting nozzle; The control commands are used to allocate and operate the sandblasting nozzles to complete the sandblasting operation.

[0014] To address the above problems, the present invention also provides a multi-nozzle collaborative sandblasting path optimization device, the device comprising: 3D data acquisition module: used to acquire surface topography data of the workpiece to be processed; Path planning module: used to plan the total sandblasting path covering the workpiece to be processed based on the surface topography data; Path segmentation module: used to segment the total sandblasting path based on the curvature change rate of the surface topography data to obtain sub-path segments of the workpiece to be processed; Process database module: used to allocate parameters to the sandblasting nozzles of the sub-path segment based on a pre-established process database, and obtain the initial operating parameters of the sandblasting nozzles; Parameter optimization module: used to correct the initial working parameters based on preset multi-nozzle collaborative operation rules to obtain the final working parameters of the sandblasting nozzle; Motion control module: used to issue control commands to the sandblasting nozzle based on the final working parameters and to execute the sandblasting operation of the sandblasting nozzle.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention utilizes intelligent path segmentation technology based on the rate of curvature change to automatically divide complex curved workpieces into geometrically consistent sub-regions. Each sub-region is then matched with optimal initial parameters retrieved from a process database, enabling on-demand allocation of processing parameters. This effectively overcomes the inherent drawbacks of traditional sandblasting operations, such as poor uniformity, low efficiency, high energy consumption, and poor adaptability to complex surfaces. It solves the problem of uneven processing quality caused by surface variations, significantly improving surface uniformity. Simultaneously, the collaborative operation of the multi-nozzle system greatly enhances processing efficiency, and rule-based energy optimization effectively reduces the ineffective consumption of compressed air and abrasive, achieving a unified approach to green, efficient, and high-quality processing.

[0016] This invention achieves end-to-end optimization from macroscopic paths to microscopic parameters by establishing a deeply coupled technical framework of path segmentation, database allocation, and rule coordination. It solves the core challenges of trajectory and parameter mismatch and the lack of a systematic solution in multi-nozzle coordination. Pre-set multi-nozzle coordination operation rules ensure precise spatial and temporal coordination among multiple nozzles, avoiding processing overlap or omissions. Addressing significant differences in workpiece surface features, feature recognition and adaptive parameter correction effectively eliminate over- and under-processing phenomena in feature transition zones, forming a systematic solution that guarantees processing quality while balancing efficiency and energy consumption, overcoming the limitations of fragmented optimization in existing technologies. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a multi-nozzle collaborative sandblasting path optimization method according to an embodiment of the present invention. Figure 2 This is a functional block diagram of a multi-nozzle collaborative sandblasting path optimization device provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for optimizing the path of multi-nozzle collaborative sandblasting. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a multi-nozzle collaborative sandblasting path optimization method according to an embodiment of the present invention. In this embodiment, the multi-nozzle collaborative sandblasting path optimization method includes: S1. Obtain the surface morphology data of the workpiece to be processed; In this embodiment of the invention, obtaining the surface morphology data of the workpiece to be processed includes: Obtain the 3D design model of the workpiece to be processed; The three-dimensional design model is meshed to obtain a discrete mesh of the three-dimensional design model; The center point coordinates and normal vector of the discrete grid are determined, and the center point coordinates and normal vector are used as the surface topography data of the workpiece to be processed.

[0021] Specifically, the workpiece to be processed is a metal stamping or die casting with a complex curved surface, which usually includes a variety of geometric features such as planes, arc surfaces, edges, chamfers, and logo recesses.

[0022] More specifically, for example, the metal casing of a laptop, the mid-frame of a smartphone, and the case of a smartwatch.

[0023] Specifically, a three-dimensional design model is a digital representation of a workpiece in a computer, usually derived from a CAD model file generated during the product design phase.

[0024] More specifically, common formats for 3D design models include, but are not limited to, general-purpose formats such as STEP, IGES, and STL. These formats can accurately store the 3D geometric information of the workpiece, including boundary representations of surfaces, curves, or polygon mesh information.

[0025] Furthermore, the final version of the CAD model file of the workpiece can be directly retrieved from the product data management or product lifecycle management system.

[0026] Furthermore, for workpieces or physical samples on tooling fixtures for which there is no readily available CAD model, a 3D scanner, such as a laser scanner or structured light scanner, can be used to scan them to obtain point cloud data. Subsequently, the point cloud can be reconstructed into a 3D CAD model using reverse engineering software.

[0027] In summary, the 3D design model serves as the geometric foundation and digital source for all subsequent optimization calculations. It provides complete and accurate dimensional and shape information of the workpiece, which is a prerequisite for achieving automated and high-precision path planning, avoiding the errors and inconveniences caused by relying on manual experience or 2D drawings.

[0028] Specifically, meshing is a data processing procedure that discretizes and approximates the surface of a continuous CAD model. It divides the originally smooth curved surface of the model into a mesh-like surface composed of a large number of simple geometric units.

[0029] Specifically, a discrete mesh is a data structure unit obtained after meshing, consisting of vertices, edges, and faces. For a triangular mesh, it consists of a series of points in three-dimensional space and a list of triangular faces connecting these points.

[0030] Furthermore, the mesh density or maximum side length tolerance is set to control the mesh fineness, and each surface of the CAD model is discretized to generate a triangular mesh.

[0031] Furthermore, if the original model is already a mesh model, then it can be used directly or the mesh can be refined to unify the quality.

[0032] In summary, meshing transforms complex mathematical surfaces into simplified data structures that are easier for computers to process and compute. This is a necessary preprocessing step for efficient normal vector calculations, curvature analysis, and other subsequent geometric calculations. Furthermore, the mesh density directly affects the accuracy and computational cost of path planning, making it crucial for achieving a balance between accuracy and efficiency.

[0033] Specifically, surface morphology data is a set of data that can digitally describe the micro-geometry and spatial orientation of a workpiece surface.

[0034] More specifically, for example, it consists of the three-dimensional coordinates of the center point of each mesh patch and the normal vector at that point that is perpendicular to the workpiece surface.

[0035] Furthermore, for a triangular facet, the coordinates of its center point can be calculated by the arithmetic mean of the coordinates of its three vertices, using the following formula: Furthermore, among them, Here are the three-dimensional coordinates of the center point of the triangular patch. , , Let be the three-dimensional coordinate vectors of the three vertices of the triangular facet. To obtain the three-dimensional coordinates of the center point of the triangle by adding the vectors of the coordinates of the three vertices and dividing each component by three. .

[0036] Furthermore, the normal vector of the triangular facet can be obtained by calculating the cross product of its two sides, as shown in the following formula: Furthermore, among them, Let be the normal vector of the triangular facet. From the vertex Pointing to the vertex The edge vector, From the vertex Pointing to the vertex The edge vectors.

[0037] Furthermore, the formula for calculating the normal vector uses the cross product of any two side vectors of the triangle to generate a vector perpendicular to the plane of the triangle.

[0038] In summary, the coordinates of the center point constitute the candidate set of subsequent path points, and the normal vector determines the spray direction that the sandblasting nozzle should have at each path point, ensuring that the abrasive flow is always as perpendicular as possible to the workpiece surface. This is the key physical factor for obtaining a uniform sandblasting effect.

[0039] In summary, the coordinates of the center point represent the physical equilibrium point of the triangle. In path planning, using this point to represent the position of the entire triangle facet is more representative than using a single vertex, resulting in a smoother generated path.

[0040] In summary, this normal vector directly determines the proper orientation of the sandblasting nozzle at that point, i.e., the direction in which the nozzle axis should be aligned, to ensure that the abrasive flow impacts the workpiece surface at the optimal angle. This is the core parameter for ensuring the uniformity and quality of sandblasting.

[0041] In summary, through the above steps, the three-dimensional shape of the workpiece is transformed into a set of digital data containing position and orientation information that the system can directly understand and process. This data is the core input driving the entire optimization process, providing direct data support for path planning and curvature calculation.

[0042] In summary, by acquiring and processing 3D models and transforming them into surface topography data rich in geometric information, we provide accurate and reliable digital basis for all subsequent intelligent decisions, such as path planning, feature segmentation, and parameter allocation. This is the fundamental guarantee for the entire method to move from experience-driven to data-driven and model-driven approaches.

[0043] S2. Based on the surface topography data, plan the total sandblasting path covering the workpiece to be processed; In this embodiment of the invention, the step of planning the total sandblasting path covering the workpiece to be processed based on the surface topography data includes: The target area of ​​the workpiece to be processed is determined based on the surface morphology data. The sandblasting mode of the workpiece to be processed is set based on the target area; The total sandblasting path of the workpiece to be processed is constructed based on the path and row spacing in the sandblasting mode.

[0044] Specifically, the target area is a set of specific surfaces on the workpiece that require sandblasting. It is usually not the entire surface of the workpiece, but rather a portion specified according to design requirements.

[0045] More specifically, for example, for a laptop casing, the target area is a specific decorative area on the outer surface of the top cover or the keyboard surface, while areas such as screw holes and the inside of interfaces are excluded.

[0046] Furthermore, the system analyzes surface topography data and automatically identifies regions with specific geometric features, such as all exposed planes and specified arc surfaces.

[0047] More specifically, for example, users can directly select, click, or choose specific surface features on a 3D model using a human-computer interface to specify target areas. The system records these selections and associates them with the surface's mesh data.

[0048] In summary, defining the target area clarifies the scope of processing, avoids ineffective processing, and saves time and materials. It is a prerequisite for achieving precision sandblasting, ensuring that subsequent path planning and optimization are only performed on the areas that need to be treated, thus improving the targeting and economy of the operation.

[0049] Specifically, the sandblasting mode is the macroscopic strategy and trajectory shape of the sandblasting nozzle scanning motion on the target area, and it serves as a template for path generation.

[0050] Furthermore, based on the geometric features of the target region, the most suitable pattern is selected from a predefined pattern library.

[0051] Furthermore, for example, the main patterns include: Parallel grid line scanning mode generates a set of parallel, reciprocating straight line paths, suitable for large, relatively flat areas.

[0052] Offset contour scanning mode starts from the boundary of the target area and gradually contracts inward to form a continuous, circling path. Suitable for complex contours, cavities, or island-like areas.

[0053] Spiral scanning mode generates a spiral path from the center outwards or from the outside towards the center. Suitable for circular or near-circular workpieces.

[0054] In summary, choosing the appropriate sandblasting mode is key to balancing processing efficiency and surface quality. The correct mode minimizes idle travel, such as the distance the nozzle travels without blasting sand, ensuring a proper scanning direction and thus laying the foundation for achieving a uniform surface finish.

[0055] Specifically, the path in the sandblasting mode is a single scan trajectory generated in the selected mode. For example, in the parallel grid mode, the path is a unidirectional straight line.

[0056] Specifically, the row spacing of the sandblasting mode is the vertical distance between two adjacent scanning paths, which is a key parameter that determines whether the sandblasting coverage is complete and whether there are any omissions.

[0057] Specifically, the total blasting path is a collection of all continuous scanning trajectories generated according to a predetermined pattern and row spacing, covering the entire target area. It consists of a series of dense, ordered path points, each containing three-dimensional coordinates and normal vector information, forming a complete spatial trajectory that the nozzle needs to follow.

[0058] Furthermore, the row spacing is set based on the effective spray width of a single nozzle. To ensure full coverage without excessive overlap, the row spacing is usually set to 50% to 80% of the effective spray width, which ensures that the spray fan surfaces of adjacent paths have a stable overlap area.

[0059] Subsequently, based on the selected pattern, the set line spacing, and the boundary of the target area, the entire scan path covering the area is automatically calculated and generated. Dense path points are inserted along the generated path at fixed intervals or according to curvature variations, and the optimal order in which the nozzle traverses these points is determined, such as the shortest idle path.

[0060] In summary, the overall sandblasting path transforms the abstract machining area into a precise sequence of motion commands that the CNC system can execute. A well-planned overall path is the fundamental guarantee for achieving efficient and complete machining.

[0061] In summary, planning the total sandblasting path covering the workpiece based on the surface topography data serves as a bridge connecting the workpiece geometry data with specific processing actions. Through scientific path planning and row spacing calculation, it ensures that every part of the target area can be blasted, fundamentally avoiding processing omissions.

[0062] In summary, a reasonable path pattern and stable row spacing provide macroscopic assurance for surface uniformity. A uniform path distribution is a prerequisite for achieving a uniform surface texture. Optimized path patterns and traversal sequences can significantly reduce nozzle idle time, thereby directly improving overall operational efficiency.

[0063] S3. Based on the curvature change rate of the surface topography data, the total sandblasting path is divided to obtain the sub-path segments of the workpiece to be processed; In this embodiment of the invention, the step of segmenting the total sandblasting path based on the curvature change rate of the surface topography data to obtain sub-path segments of the workpiece to be processed includes: The rate of change of curvature of the surface topography data is calculated using the following formula: in, The rate of change of curvature is the i-th index of the surface topography data. This is the index of the total sandblasting path. The first of the total sandblasting paths The curvature value of each index, For the first on the path The curvature value of each index, This is the approximate arc length between two adjacent path points; The curvature change rate is compared with a preset threshold, and the segmentation points of the total sandblasting path are marked based on the comparison result; The total sandblasting path is divided based on the dividing point to obtain the sub-path segments of the workpiece to be processed.

[0064] Specifically, the rate of change of curvature is a physical quantity that describes how quickly the curvature of the workpiece surface changes as it moves along the sandblasting path, quantifying the magnitude of the curvature change per unit path length.

[0065] Specifically, the formula for calculating the rate of change of curvature is as follows: Furthermore, among them, For the first on the path The rate of change of curvature calculated at each index is a non-negative real value. The larger the value, the better. The more drastic the change in surface shape near the index, such as a sudden transition from a plane to an edge.

[0066] Furthermore, For the first on the path The curvature value at the index is a geometric quantity that describes the degree of curvature of a curve at that point, and can be directly calculated using a CAD model engine or mesh differential geometry methods.

[0067] Furthermore, For the first on the path The curvature value at the index.

[0068] Furthermore, path point with path points The approximate arc length between the two points is represented by the Euclidean distance between them.

[0069] Furthermore, The absolute value of the curvature difference between two adjacent points is used to obtain the magnitude of the curvature change.

[0070] Furthermore, path point with path points The curvature difference between the two paths divided by the path points with path points The approximate arc lengths between them are normalized to eliminate the influence of uneven sampling density of path points. It becomes a rate of change per unit length.

[0071] In summary, the rate of curvature change transforms the concept of shape change into a calculable and comparable precise value, which serves as an objective and quantitative basis for subsequent automatic path segmentation and is key to achieving intelligent decision-making.

[0072] Specifically, the preset threshold is a critical value pre-set based on workpiece quality requirements and processing experience.

[0073] More specifically, for example, a threshold value of 0.1 mm⁻² can be set. This threshold is an adjustable parameter; the smaller the threshold, the finer the segmentation and the more sub-path segments are generated; the larger the threshold, the coarser the segmentation and the fewer sub-path segments are generated.

[0074] Specifically, the comparison results are for each path point. Determine the rate of change of curvature Whether it is valid or not.

[0075] Furthermore, starting from the second path point, traverse every path point on the total path and calculate the rate of curvature change in turn. and will Compare the size with a preset threshold.

[0076] Furthermore, if the rate of change of curvature If the value is greater than a preset threshold, it is determined to be at a path point. At this point, the geometric features of the workpiece surface undergo significant changes, affecting the path point. Mark it as a split point.

[0077] Specifically, the dividing points of the total sandblasting path are the marked points on the path, which are the boundaries of different geometric feature regions.

[0078] Specifically, for example, at the intersection of a plane and an arc, or at the intersection of an arc and an acute angle, the rate of change of curvature increases significantly, thus being marked as a dividing point.

[0079] Specifically, a sub-path segment is a continuous path segment formed after being divided by the segmentation point. The curvature changes gently within each sub-path segment, and they have the same or similar geometric features, such as a flat plane, a uniform convex arc, or a concave angle.

[0080] Furthermore, all marked split points are arranged sequentially according to their order on the overall path. Subsequently, the continuous path portions contained between two adjacent split points, as well as between the path start point and the first split point, and between the last split point and the path end point, are each defined as an independent sub-path segment.

[0081] In summary, segmenting the total sandblasting path based on the aforementioned dividing points to obtain sub-path segments of the workpiece to be processed achieves segmented processing of complex workpiece surfaces. Through intelligent segmentation based on the rate of curvature change, the originally complex overall processing problem is decomposed into multiple sub-problems targeting simple, uniform features.

[0082] In summary, the total sandblasting path is finely segmented according to its characteristics, and the optimal sandblasting parameters are tailored for each segment, thereby fundamentally solving the problem of poor processing uniformity caused by surface variations.

[0083] Specifically, the curvature sequence of path points is smoothed before calculating the rate of change of curvature.

[0084] More specifically, a sliding window is used to calculate the average or median of the curvature within the window, and the smoothed curvature value is then used. To replace the original The formula for calculating the rate of change of curvature and performing smoothing filtering is as follows: Furthermore, among them, For the smoothed path, the first The rate of change of curvature at the index. Furthermore, This represents the half-width of the sliding window in the smoothing filter.

[0085] Furthermore, This is the index of the path point for which the smooth curvature needs to be calculated.

[0086] Furthermore, This represents the total length of the sliding window in the smoothing filter.

[0087] Furthermore, For the path from Index to The sum of the curvature values ​​at the index.

[0088] Furthermore, the smoothed path points Curvature value at index Replace original path points curvature value at index To calculate the curvature transformation rate.

[0089] In summary, by calculating the average curvature within a local window, random high-frequency fluctuations in curvature caused by model noise, minor imperfections, or numerical instability can be effectively smoothed out, resulting in smoother curvature data that better reflects the macroscopic geometric trend of the surface.

[0090] In summary, this method not only utilizes the rate of curvature change for path segmentation, but also innovatively introduces a preprocessing mechanism based on moving average filtering to calculate the anti-interference rate of curvature change.

[0091] In summary, this method effectively suppresses data fluctuations caused by factors such as 3D model discretization errors, file conversion distortion, surface micro-burrs, or vibration simulation noise, resulting in more accurate and stable feature boundary identification and significantly reducing the probability of missegmentation. This enhances the robustness and reliability of the entire path optimization system. This demonstrates the significant advantages of this invention in practical industrial applications.

[0092] S4. Based on the pre-established process database, the parameters of the sandblasting nozzles in the sub-path segment are allocated to obtain the initial working parameters of the sandblasting nozzles. In this embodiment of the invention, the pre-established process database includes: Based on the different characterization sandblasting experiments of the workpiece to be processed, the processing result data of the workpiece to be processed is generated; Determine the degree of quantitative influence of the process parameters of the different characterization sandblasting experiments on the processing result data; The process database for the sub-path segment is constructed based on the processing result data and the quantified degree of influence.

[0093] The parameter allocation of the sandblasting nozzles for the sub-path segment based on the pre-established process database yields the initial operating parameters of the sandblasting nozzles, including: Identify the dominant surface feature type of the sub-path segment; The initial operating parameters of the sandblasting nozzle are obtained by searching the process database based on the information requirements of the workpiece to be processed and the dominant surface feature type.

[0094] Specifically, the different characterization sandblasting experiments are a series of scientifically designed sandblasting tests, such as those using the Design of Experiments (DOE) method. These experiments systematically change different process parameters to observe their impact on the processing effect. The experimental objects are standard test pieces or workpiece scraps containing various typical features, such as flat surfaces, convex arcs, concave corners, and sharp edges, and their material is the same as the workpiece to be processed, for example, aluminum alloy 6061.

[0095] Specifically, the processing result data are quantitative quality indicators obtained by precision measuring instruments after each experiment, including surface roughness, surface morphology, and material removal amount.

[0096] Furthermore, an experimental matrix is ​​designed to determine key process parameters, such as nozzle speed V, sand output Q, air pressure P, angle θ and their variation levels. Full factorial experiments or orthogonal arrays are used to arrange experimental combinations to obtain the most comprehensive information with the fewest number of experiments.

[0097] Subsequently, under strict control of other environmental factors, the standard test pieces were sandblasted according to the experimental matrix.

[0098] Next, the results of each experiment were measured using equipment such as a surface roughness tester and a three-dimensional profilometer, and the corresponding combination of process parameters was recorded in detail.

[0099] Furthermore, the process parameters input for each experiment and the processing results obtained from the output measurement are recorded one by one to generate the processing result data of the workpiece to be processed.

[0100] In summary, different characterization sandblasting experiments obtained systematic and reliable raw data covering the entire process window, revealing the causal relationship between process parameters and processing results, rather than relying on fragmented experience.

[0101] In summary, processing result data transforms abstract technological knowledge into structured, quantifiable data assets, laying the foundation for building intelligent decision-making systems.

[0102] Specifically, the process parameters for different characterization sandblasting experiments are the parameters that are actively changed as input variables in the experiment, namely the working parameters of the sandblasting nozzle.

[0103] More specifically, for example: nozzle movement speed (V), sand output (Q), injection air pressure (P), injection angle (θ), injection distance (H), and abrasive type, etc.

[0104] Specifically, the degree of influence quantification is a mathematical measure of the impact of each process parameter on the processing result.

[0105] Furthermore, group the complete experimental data according to different levels of a specific process parameter. For example, group all the experimental data with a low nozzle speed V into one group, all the experimental data with a medium nozzle speed V into one group, and all the experimental data with a high nozzle speed V into one group.

[0106] Subsequently, for each group of data, calculate the processing result data within the group, such as the average value of the average roughness Ra.

[0107] Even further, calculate the range of the average values of the result data between different levels of this parameter. This range intuitively reflects the maximum result fluctuation caused by the change of this parameter.

[0108] Subsequently, repeat the above steps for each process parameter to be evaluated, such as speed V, sand output Q, air pressure P, etc., and calculate their respective ranges.

[0109] Furthermore, divide the range of each process parameter by the total range sum and percentage the result to obtain the contribution rate CR of each process parameter and the interaction between parameters to the result variation. For example, the contribution rate CR of the nozzle speed V to the surface roughness Ra value is 50%, and the contribution rate CR of the sand output Q is 20%.

[0110] Furthermore, sort all the process parameters from large to small according to the calculated contribution rates.

[0111] Even further, the decision rule is: High-influence parameters, one or several parameters with the highest contribution rates. For example, if CR > 20%, they are considered to have a significant impact on the processing result and are the key control variables for process optimization.

[0112] Medium-influence parameters, parameters with a medium contribution rate. For example, if 5% < CR < 20%, they are considered to have a medium impact. Attention is needed, but their importance is secondary to high-influence parameters.

[0113] Low-influence parameters: parameters with a very small contribution rate. For example, if CR < 5%, they are considered to have a slight impact. They can be set as fixed values in the preliminary optimization to simplify the problem.

[0114] In summary, this approach transforms engineers' qualitative experience into precise speed contribution rates, indicating which core parameters should be prioritized for optimization, and also provides a weighting basis for subsequent intelligent database retrieval and recommendation.

[0115] In summary, this contribution rate calculation method based on range analysis eliminates the need for complex mathematical models. By simply grouping, averaging, and comparing, the relative influence of each process parameter on the processing results can be clearly and quantitatively determined.

[0116] Specifically, the process database is a structured relational database, the core of which stores the mapping relationship of which parameters should be used under specific conditions.

[0117] Furthermore, a table structure centered on query keys and output values ​​is determined, with the query keys serving as retrieval conditions, typically including: workpiece material, surface feature type, and target roughness.

[0118] The output value is then used as the recommended result, i.e., the optimized combination of process parameters.

[0119] Furthermore, based on experimental data and quantitative analysis results, the optimal parameter combinations for each material-feature-target combination are entered into the database.

[0120] In summary, the process database encapsulates complex process knowledge and experimental results into a central hub that can be quickly and accurately accessed by computer systems. It enables a shift from experience-based trial and error to data-driven, precise recommendations.

[0121] Specifically, the dominant surface feature type is the category of geometric features that dominates within the sub-path segment. For example, a path may be classified as a convex surface with high curvature, a plane with low curvature, or a concave angle.

[0122] Furthermore, statistical analysis is performed on the geometric properties of all path points within the sub-path segment, such as curvature and normal vector, to calculate the average curvature and curvature variance.

[0123] Subsequently, according to the preset classification rules, if the average curvature is close to 0 and the variance is small, it is judged as a plane; if the average curvature is positive and large, it is judged as a convex surface, and it is classified into a predefined feature category.

[0124] In summary, identifying the dominant surface feature type is a crucial step in transforming continuous geometric paths into retrieval keywords that can be recognized by the database, serving as a bridge connecting the geometric world and the world of technological knowledge.

[0125] Specifically, the information requirements for the workpiece to be processed include the workpiece material and the target processing requirements.

[0126] More specifically, for example, the workpiece material is aluminum alloy 6061, and the target machining requirement is a target surface roughness Ra of 1.6 μm.

[0127] Specifically, the initial working parameters are: a set of recommended process parameters retrieved from the process database for specific conditions of the current sub-path segment, including speed, sand output, air pressure, etc.

[0128] Furthermore, for example, the material: aluminum alloy 6061, feature type: convex surface, target Ra: 1.6μm is used as a joint query key to perform a matching search in the process database, and finally the corresponding parameter combination stored in the database is returned as the initial working parameters for this sub-path segment.

[0129] In summary, the retrieved initial working parameters enable feature-based spraying, automatically assigning appropriate processing parameters to surfaces with different geometric features. This provides the first and most important layer of assurance for obtaining a uniform and high-quality surface. This method is fast, accurate, and does not rely on the operator's instantaneous judgment.

[0130] In summary, the above steps transform historical process data into a core element of current production decision-making. Through a pre-built process database, the ineffable experience of skilled craftsmen is solidified into replicable, scalable, and optimizable digital assets. This ensures that the setting of processing parameters is no longer blind or purely based on personal experience, but rather scientific, systematic, and traceable, greatly improving the stability and repeatability of the process and representing a typical manifestation of intelligent manufacturing.

[0131] S5. Based on the preset multi-nozzle collaborative operation rules, the initial working parameters are corrected to obtain the final working parameters of the sandblasting nozzle; In this embodiment of the invention, the preset multi-nozzle cooperative operation rules include: The safe distance and area coverage of the sandblasting nozzle are used as the spatial operation rules for the sandblasting nozzle. The operating parameters of the sandblasting nozzle are adjusted based on the surface geometry of the sub-path segment, and the adjusted operating parameters are used as the parameter setting rules for the sandblasting nozzle. The spatial operation rules and the parameter setting rules are integrated into the multi-nozzle collaborative operation rules for the sub-path segment.

[0132] The initial operating parameters are corrected based on preset multi-nozzle collaborative operation rules to obtain the final operating parameters of the sandblasting nozzle, including: The initial working parameters are evaluated based on the multi-nozzle collaborative operation rules, and the evaluated initial working parameters are iteratively adjusted to obtain the final working parameters of the sandblasting nozzle.

[0133] Specifically, the safe distance between sandblasting nozzles is the minimum three-dimensional spatial interval that must be maintained to ensure that the mechanical structures of any two nozzles and their high-speed abrasive jets do not collide or cause harmful interference during movement. This distance must take into account the physical dimensions of the nozzles, their installation orientation, and the jet diffusion angle.

[0134] Specifically, the spatial operation rules are a series of logical conditions used to constrain the relative positions and trajectories of multiple nozzles in space, with the core objective of avoiding interference and ensuring coverage.

[0135] Furthermore, the three-dimensional Euclidean distance D between any two nozzles is calculated in real time. D is ensured to always be greater than or equal to a preset safety distance. If the assessment finds that D is less than the safety distance, the path must be adjusted or a particular nozzle must be paused.

[0136] Furthermore, the relationship between the row spacing S of adjacent nozzle paths and the effective spray width W of a single nozzle is calculated. The formula for calculating the overlap rate is as follows: in, This represents the jet overlap rate between adjacent sandblasting nozzles. The effective spray width of a single nozzle. This represents the row spacing between adjacent nozzle paths. The spray overlap rate of adjacent sandblasting nozzles is obtained by dividing the result of subtracting the row spacing of adjacent nozzle paths from the effective spray width of a single nozzle by the effective spray width of a single nozzle. .

[0137] Furthermore, if the jet overlap rate of adjacent sandblasting nozzles If the overlap rate deviates from the preset target, for example, 30%, the row spacing of adjacent nozzle paths will be dynamically adjusted. .

[0138] In summary, spatial operation rules are the foundation for the safe and orderly operation of multi-nozzle systems. Geometrically, they ensure that multiple actuators do not collide with each other or leave blind spots in the shared workspace, serving as the traffic rules for achieving physical collaboration.

[0139] Specifically, the operating parameters of the sandblasting nozzle are directly controllable processing parameters, namely the contents of the initial working parameters, such as moving speed V, sand output Q, jetting air pressure P, jetting angle θ, etc.

[0140] Specifically, the parameter setting rules are a series of empirical logic or functional relationships based on the dynamic fine-tuning of operating parameters according to surface geometric features. Its core objective is to compensate for geometric effects and ensure the consistency of impact energy across different feature regions.

[0141] Furthermore, the parameter setting rules include energy compensation rules and constant coverage rules.

[0142] Furthermore, according to the energy compensation rule, for concave angles, due to the shielding effect, the impact energy is attenuated, so the air pressure of the sandblasting nozzle is increased or the speed of the sandblasting nozzle is decreased to compensate for the energy.

[0143] Furthermore, according to the energy compensation rule, for convex angles, energy is concentrated and over-processing is easy, so the air pressure of the sandblasting nozzle should be reduced or the speed of the sandblasting nozzle should be increased.

[0144] Furthermore, according to the constant coverage rule, to ensure consistent number of blows per unit area, the speed parameters of the sandblasting nozzle need to be adjusted for areas with high curvature. The greater the curvature, the lower the sandblasting nozzle speed.

[0145] In summary, parameter setting rules are the essence of achieving high-quality machining. Precise spraying targeting different geometric features at the physical process level eliminates quality fluctuations caused by changes in workpiece shape, representing expert process rules for achieving high-quality uniformity.

[0146] Specifically, the multi-nozzle collaborative operation rule is a unified rule base or decision logic set, which includes the aforementioned spatial operation rules and parameter setting rules.

[0147] Furthermore, the rules are encoded, transforming each rule described in text into conditional statements in the program code.

[0148] Next, execution priorities are assigned to the rules. Safety-related rules, such as non-interference rules, have the highest priority, followed by rules directly related to quality, such as energy compensation rules, and finally efficiency optimization rules.

[0149] Next, all the encoded rules are integrated into the parameter optimization module of the path optimization system to form a complete rule engine.

[0150] In summary, the multi-nozzle collaborative operation rules systematize and modularize the dispersed strategies for different problems, forming a collaborative decision-making mechanism capable of handling complex optimization problems with multiple constraints and objectives.

[0151] In summary, the multi-nozzle collaborative operation rules are the brain of the entire multi-nozzle collaborative sandblasting system. This enables the system to go beyond simple single-nozzle parameter replication, achieving intelligent coordination and optimization of multiple nozzles in space and time, thereby achieving the synergistic effect of multi-nozzle sandblasting.

[0152] Specifically, the final working parameters are the globally optimal or suboptimal set of sandblasting nozzle working parameters that are actually issued to the equipment for execution after optimization and correction by the collaborative rules. This satisfies both local processing quality requirements and global collaborative constraints.

[0153] Furthermore, the initial operating parameters and planned paths of all nozzles are simulated and loaded.

[0154] Then, the rules engine checks each rule in order of priority to see if it is satisfied.

[0155] Next, a violation report is generated, indicating which rules were not met.

[0156] Furthermore, for example: nozzle 1 and nozzle 2 are too close together at time t, the estimated coverage of region A is only 80%, and the parameters at the convex corner B may lead to over-processing, etc.

[0157] Furthermore, based on the violation report, the initial parameters are modified according to the preset adjustment strategy in the rule base.

[0158] Furthermore, for example, if the distance is too close, the path of one of the nozzles is fine-tuned; if the coverage is insufficient, the row spacing is reduced.

[0159] Furthermore, the adjusted new parameter set is evaluated again according to the rules.

[0160] The evaluation-adjustment process is then repeated until all rules are met or the maximum number of iterations is reached.

[0161] Next, the set of parameters that satisfies all rule constraints after the last adjustment is determined as the final working parameters.

[0162] In summary, evaluating initial working parameters is a process of virtual simulation and feasibility verification. It can identify and locate coordination conflicts in the parameter set before actual processing, preventing problems from arising in advance.

[0163] In summary, iterative adjustment to obtain the final working parameters is an automated parameter optimization process. Through continuous feedback and correction, an initial parameter set that only considers local optimization and may have conflicts is converged to a feasible, globally optimized final solution that satisfies all coordination requirements.

[0164] In summary, the above steps are key to achieving synergy in this invention. By transforming human collaborative control experience into automated machine decision-making logic, a global optimization of initial parameters is performed. This not only solves the problems of physical interference and processing coverage between multiple nozzles but also improves the uniformity of the final processing through feature compensation rules. This enables the system to move from automation to intelligence, ensuring the full utilization of the advantages of the multi-nozzle system and providing a core guarantee for achieving high-quality, high-efficiency, and high-reliability sandblasting.

[0165] S6. Based on the final working parameters, issue control commands to the sandblasting nozzle and execute the sandblasting operation of the sandblasting nozzle; In this embodiment of the invention, issuing control commands to the sandblasting nozzle based on the final operating parameters and executing the sandblasting operation of the sandblasting nozzle includes: The final operating parameters are compiled into control commands for the sandblasting nozzle; The control commands are used to allocate and operate the sandblasting nozzles to complete the sandblasting operation.

[0166] Specifically, the control commands for the sandblasting nozzle are a standardized set of commands that can be directly recognized and executed by the underlying sandblasting equipment's CNC system or robot controller. These commands include not only the coordinates of the motion path points but also synchronous process commands.

[0167] Furthermore, the final working parameters obtained from the optimization are mapped to standard instructions.

[0168] Subsequently, the path point sequence of the sub-path segment, such as coordinates and normal vectors, is precisely synchronized with the above-mentioned process parameter commands to generate a continuous and complete control program segment.

[0169] Next, an independent control command stream is generated for each sandblasting nozzle, and a synchronization command is inserted to ensure that the movement of multiple nozzles and sandblasting actions are coordinated in time, avoiding waiting or conflict.

[0170] In summary, compiler control instructions translate the optimization results output by upper-level optimization algorithms into instructions that the underlying hardware driver system can understand and execute. They serve as a bridge connecting the virtual information world and the physical manufacturing world.

[0171] Specifically, the sandblasting operation is a process in which a multi-axis motion platform or industrial robot drives the sandblasting nozzles and performs coordinated sandblasting treatment on the surface of the workpiece to be processed, strictly following the optimized path and parameters.

[0172] Furthermore, the main control computer sends the compiled control instructions to the corresponding motion control cards and sandblasting valve controllers via the communication bus.

[0173] Furthermore, the multi-axis motion platform or robot precisely follows the path points and adjusts the nozzle posture in real time to ensure the spray angle.

[0174] The abrasive supply system and air pressure regulating valve precisely control the amount of abrasive output and air pressure according to instructions, and are precisely synchronized with the movement position.

[0175] Subsequently, the equipment status, such as actual location and air pressure value, is monitored in real time and compared with the command value to perform closed-loop fine-tuning to ensure stable and reliable processing.

[0176] In summary, the sandblasting nozzles are allocated and controlled based on the control commands to complete the sandblasting operation. Through precise allocation and control, the optimization strategy is ensured to be stably executed in actual processing.

[0177] In summary, this invention utilizes 3D model data to accurately perceive the shape of the workpiece; through path planning and intelligent segmentation based on the rate of curvature change, it achieves divide-and-conquer for complex workpieces, providing a strategic blueprint for precision processing; through a process database and collaborative rules, it digitizes craftsmanship skills, enabling intelligent decision-making from local optimization to global collaboration of parameters; and through digital instructions driving equipment, it ensures that decision results are executed accurately and collaboratively.

[0178] In summary, this invention proposes a comprehensive solution that integrates computer graphics, process modeling, optimization algorithms, and automatic control, breaking through the efficiency bottleneck of traditional sandblasting and even surface treatment industries that rely on manual experience and have unstable quality. It provides a brand-new technical paradigm for the automated and intelligent precision machining of complex curved parts.

[0179] like Figure 2 The diagram shown is a functional block diagram of a multi-nozzle collaborative sandblasting path optimization device provided in an embodiment of the present invention.

[0180] The multi-nozzle collaborative sandblasting path optimization device 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the multi-nozzle collaborative sandblasting path optimization device 100 may include a three-dimensional data acquisition module 101, a path planning module 102, a path segmentation module 103, a process database module 104, a parameter optimization module 105, and a motion control module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0181] In this embodiment, the functions of each module / unit are as follows: The three-dimensional data acquisition module 101 is used to acquire the surface morphology data of the workpiece to be processed. The path planning module 102 is used to plan the total sandblasting path covering the workpiece to be processed based on the surface topography data. The path segmentation module 103 is used to segment the total sandblasting path based on the curvature change rate of the surface topography data to obtain sub-path segments of the workpiece to be processed. The process database module 104 is used to allocate parameters to the sandblasting nozzles of the sub-path segment based on the pre-established process database, so as to obtain the initial working parameters of the sandblasting nozzles. The parameter optimization module 105 is used to correct the initial working parameters based on preset multi-nozzle collaborative operation rules to obtain the final working parameters of the sandblasting nozzle. The motion control module 106 is used to issue control commands to the sandblasting nozzle based on the final working parameters and to execute the sandblasting operation of the sandblasting nozzle.

[0182] In the several embodiments provided by this invention, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0183] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0185] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0186] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the path of multi-nozzle collaborative sandblasting, characterized in that, The method includes: S1. Obtain the surface morphology data of the workpiece to be processed; S2. Based on the surface topography data, plan the total sandblasting path covering the workpiece to be processed; S3. Based on the curvature change rate of the surface topography data, the total sandblasting path is divided to obtain the sub-path segments of the workpiece to be processed; S4. Based on the pre-established process database, the parameters of the sandblasting nozzles in the sub-path segment are allocated to obtain the initial working parameters of the sandblasting nozzles. S5. Based on the preset multi-nozzle collaborative operation rules, the initial working parameters are corrected to obtain the final working parameters of the sandblasting nozzle; S6. Based on the final working parameters, issue control commands to the sandblasting nozzle and execute the sandblasting operation of the sandblasting nozzle.

2. The multi-nozzle collaborative sandblasting path optimization method as described in claim 1, characterized in that, The acquisition of surface morphology data of the workpiece to be processed includes: Obtain the 3D design model of the workpiece to be processed; The three-dimensional design model is meshed to obtain a discrete mesh of the three-dimensional design model; The center point coordinates and normal vector of the discrete grid are determined, and the center point coordinates and normal vector are used as the surface topography data of the workpiece to be processed.

3. The multi-nozzle collaborative sandblasting path optimization method as described in claim 1, characterized in that, The step of planning the total sandblasting path covering the workpiece to be processed based on the surface topography data includes: The target area of ​​the workpiece to be processed is determined based on the surface morphology data. The sandblasting mode of the workpiece to be processed is set based on the target area; The total sandblasting path of the workpiece to be processed is constructed based on the path and row spacing in the sandblasting mode.

4. The multi-nozzle collaborative sandblasting path optimization method as described in claim 1, characterized in that, The process of segmenting the total sandblasting path based on the curvature change rate of the surface topography data to obtain sub-path segments of the workpiece to be processed includes: The rate of change of curvature of the surface topography data is calculated using the following formula: in, The rate of change of curvature is the i-th index of the surface topography data. This is the index of the total sandblasting path. The first of the total sandblasting paths The curvature value of each index, For the first on the path The curvature value, This is the approximate arc length between two adjacent path points; The curvature change rate is compared with a preset threshold, and the segmentation points of the total sandblasting path are marked based on the comparison result; The total sandblasting path is divided based on the dividing point to obtain the sub-path segments of the workpiece to be processed.

5. The multi-nozzle collaborative sandblasting path optimization method as described in claim 1, characterized in that, The pre-established process database includes: Based on the different characterization sandblasting experiments of the workpiece to be processed, the processing result data of the workpiece to be processed is generated; Determine the degree of quantitative influence of the process parameters of the different characterization sandblasting experiments on the processing result data; The process database for the sub-path segment is constructed based on the processing result data and the quantified degree of influence.

6. The multi-nozzle collaborative sandblasting path optimization method as described in claim 5, characterized in that, The parameter allocation of the sandblasting nozzles for the sub-path segment based on the pre-established process database yields the initial operating parameters of the sandblasting nozzles, including: Identify the dominant surface feature type of the sub-path segment; The initial operating parameters of the sandblasting nozzle are obtained by searching the process database based on the information requirements of the workpiece to be processed and the dominant surface feature type.

7. The multi-nozzle collaborative sandblasting path optimization method as described in claim 1, characterized in that, The preset multi-nozzle collaborative operation rules include: The safe distance and area coverage of the sandblasting nozzle are used as the spatial operation rules for the sandblasting nozzle. The operating parameters of the sandblasting nozzle are adjusted based on the surface geometry of the sub-path segment, and the adjusted operating parameters are used as the parameter setting rules for the sandblasting nozzle. The spatial operation rules and the parameter setting rules are integrated into the multi-nozzle collaborative operation rules for the sub-path segment.

8. The multi-nozzle collaborative sandblasting path optimization method as described in claim 7, characterized in that, The initial operating parameters are corrected based on preset multi-nozzle collaborative operation rules to obtain the final operating parameters of the sandblasting nozzle, including: The initial working parameters are evaluated based on the multi-nozzle collaborative operation rules, and the evaluated initial working parameters are iteratively adjusted to obtain the final working parameters of the sandblasting nozzle.

9. The multi-nozzle collaborative sandblasting path optimization method as described in claim 1, characterized in that, The step of issuing control commands to the sandblasting nozzle based on the final operating parameters and executing the sandblasting operation of the sandblasting nozzle includes: The final operating parameters are compiled into control commands for the sandblasting nozzle; The control commands are used to allocate and operate the sandblasting nozzles to complete the sandblasting operation.

10. A multi-nozzle collaborative sandblasting path optimization device, characterized in that, The device includes: 3D data acquisition module: used to acquire surface topography data of the workpiece to be processed; Path planning module: used to plan the total sandblasting path covering the workpiece to be processed based on the surface topography data; Path segmentation module: used to segment the total sandblasting path based on the curvature change rate of the surface topography data to obtain sub-path segments of the workpiece to be processed; Process database module: used to allocate parameters to the sandblasting nozzles of the sub-path segment based on a pre-established process database, and obtain the initial operating parameters of the sandblasting nozzles; Parameter optimization module: used to correct the initial working parameters based on preset multi-nozzle collaborative operation rules to obtain the final working parameters of the sandblasting nozzle; Motion control module: used to issue control commands to the sandblasting nozzle based on the final working parameters and to execute the sandblasting operation of the sandblasting nozzle.

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