A simulation and optimization method, device and equipment for teaching a path of a spraying robot

Abstract

The application discloses a simulation and optimization method, device and equipment for teaching path of a spraying robot, relates to the technical field of simulation analysis, and is used for solving the technical problems that the path is disconnected with simulation, optimization efficiency is low, and simulation reality is insufficient in the prior art. The method comprises the following steps: analyzing and converting a teaching path file; performing simulation modeling on a spraying simulation path; performing particle motion and deposition simulation by using a standard k-epsilon turbulent flow model and a particle-wall interaction model; performing deposition quality analysis and identification on a deposition density distribution cloud picture; performing spraying path correction and process parameter optimization; and if the simulation result meets preset quality requirements, using a spraying optimization scheme to guide actual spraying operation. Therefore, the application can strengthen the connection between the path and simulation, improve optimization efficiency and simulation reality, and the like, through a full-process digital closed loop of "teaching path, simulation verification, defect feedback, path optimization, and re-simulation".

Description

Technical Field

[0001] This application relates to the field of simulation analysis technology, and provides a method, apparatus and equipment for simulating and optimizing the teaching path of a painting robot. Background Technology

[0002] Currently, the operation paths of spraying robots are mainly generated through manual teaching or offline programming. For example, the operator sets the robot's motion trajectory through a teach pendant, and the trajectory data is stored in the robot control system to guide on-site spraying operations. Simultaneously, simplified models can be built using Computational Fluid Dynamics (CFD) simulation technology to simulate the atomization, movement, and deposition processes of paint particles. However, in existing technologies, the robot teaching system and the CFD simulation system are disconnected, lacking an effective data interaction and collaborative optimization mechanism. For instance, the "teaching path generation → on-site test spraying → manual inspection → path adjustment" loop mode described in patent CN104841593A, while incorporating a "simulation system," has its functions limited to kinematic / collision detection, joint velocity and acceleration verification, and visualization of spray volume distribution. Therefore, it suffers from the following significant drawbacks: (1) Decoupling of path and process parameters: The robot teaching path data is only stored in the robot control system and cannot be directly imported into the CFD simulation platform, resulting in a deviation between the spray gun trajectory in the simulation model and the actual teaching path. The simulation results have limited guiding significance for actual production. (2) Lack of data support for process optimization: Traditional spraying process optimization relies on manual trial and error, which consumes a lot of paint, workpieces and time costs. (3) Insufficient realism of particle deposition simulation: The spraying process involves complex multi-physics coupling (e.g., aerodynamics and surface tension), but existing simulation methods mostly use static nozzle models or simplified trajectories, without considering the actual scenario of the spray gun moving dynamically with the teaching path, resulting in a large deviation between the simulation results of particle deposition density and coating thickness and the actual situation. (4) No closed-loop correction capability of the system: The existing robot teaching system cannot receive quality feedback data from simulation or actual detection, and cannot automatically correct path parameters based on coating defects (e.g., missed spraying, recoating, uneven thickness). Manual re-teaching and adjustment are required, resulting in low intelligence. Summary of the Invention

[0003] This application provides a method, apparatus, and equipment for simulating and optimizing the teaching path of a painting robot, which solves the technical problems of existing technologies such as disconnect between the path and the simulation, low optimization efficiency, and insufficient simulation realism.

[0004] On the one hand, a method for simulating and optimizing the teaching path of a painting robot is provided, the method comprising: The teaching path file exported from the target painting robot is parsed and converted to obtain several initial key information. The initial key information is used to perform simulation modeling to obtain the initial spraying simulation path; Based on the initial spraying simulation path, particle motion and deposition simulation are performed using a discrete phase model, a standard k-ε turbulence model, and a particle-wall interaction model to generate a deposition density distribution cloud map. The deposition density distribution cloud map is used to perform deposition quality analysis and identification, and to generate a coating thickness error distribution map and quantitative statistical data. Based on the coating thickness error distribution map and the quantitative statistical data, the spraying path is corrected and the process parameters are optimized to generate an optimized spraying scheme. If the simulation results of the coating optimization scheme meet the preset quality requirements, then the coating optimization scheme is used to guide the target coating robot to perform actual coating operations.

[0005] Optionally, the step of parsing and converting the teaching path file exported from the target painting robot to obtain multiple initial key information includes: Export the teaching path file from the target painting robot; A preset parsing algorithm is used to parse the teaching path file to obtain multiple key pieces of information after parsing; among them, the key pieces of information after parsing include the three-dimensional coordinates of the robtarget point, the spray gun attitude angle, the movement speed, and the dwell time. The format of the multiple parsed key information is converted to obtain multiple initial key information.

[0006] Optionally, the step of using the multiple initial key information to perform simulation modeling and obtain the initial spraying simulation path includes: Import the 3D model of the target workpiece into the simulation platform and set the boundary conditions of the spraying area; An unstructured mesh is used to discretize the surface of the target workpiece. The initial key information is imported into a user-defined function to perform dynamic motion control of the spray gun nozzle, thereby obtaining the initial spraying simulation path; Based on the actual spraying process parameters, particle swarm attributes are defined in the user-defined function; wherein, the particle swarm attributes include initial particle velocity, particle size distribution, mass flow rate, and injection angle.

[0007] Optionally, the step of simulating particle motion and deposition using a discrete phase model, a standard k-ε turbulence model, and a particle-wall interaction model based on the initial spraying simulation path, and generating a deposition density distribution cloud map, includes: Based on the initial spraying simulation path, the discrete phase model is used to simulate the motion trajectory of the paint particles; The standard k-ε turbulence model was used to simulate the airflow field distribution in the spraying area; A particle-wall interaction model was used to simulate the deposition process of coating particles; Based on the motion trajectory, the airflow field distribution, and the deposition process, a deposition density distribution cloud map is generated.

[0008] Optionally, the step of performing deposition quality analysis and identification on the deposition density distribution cloud map to generate a coating thickness error distribution map and quantitative statistical data includes: Based on the deposition density distribution cloud map, several key quality indicators were calculated; among them, the key quality indicators include coating thickness distribution, thickness standard deviation, recoating area ratio, and missed spraying area ratio. Based on the aforementioned key quality indicators, a coating thickness error distribution map and quantitative statistical data are generated; wherein, the quantitative statistical data includes the maximum thickness deviation and the pass rate.

[0009] Optionally, the step of correcting the spraying path and optimizing process parameters based on the coating thickness error distribution map and the quantified statistical data to generate a spraying optimization scheme includes: For the defective areas in the coating thickness error distribution map, a preset path correction strategy is used to perform reverse calculation of optimization parameters to obtain the path correction results; wherein, the defective areas include unsprayed areas and recoated areas; Based on quantitative statistical data, the process parameters are adjusted using a preset parameter optimization strategy to obtain the parameter optimization results; Based on the path correction results and the parameter optimization results, a spraying optimization scheme is generated.

[0010] Optionally, the step of using the spraying optimization scheme to guide the target spraying robot in actual spraying operations if the simulation results of the spraying optimization scheme are determined to meet the preset quality requirements includes: The coating optimization scheme is imported into the simulation platform for secondary simulation verification to obtain the simulation results of the coating optimization scheme. Determine whether the simulation results meet the preset quality requirements; If the simulation results are determined to meet the preset quality requirements, the spraying optimization scheme is used to guide the target spraying robot to perform actual spraying operations. If it is determined that the simulation results do not meet the preset quality requirements, the spraying path will be corrected and the process parameters optimized again based on the defect area and quantitative statistical data corresponding to the simulation results.

[0011] On the one hand, a simulation and optimization device for teaching paths of a painting robot is provided, the device comprising: The data parsing and conversion unit is used to parse and convert the teaching path file exported from the target spraying robot to obtain multiple initial key information. The spraying path simulation modeling unit is used to perform simulation modeling using the multiple initial key information to obtain the initial spraying simulation path. The particle motion and deposition simulation unit is used to perform particle motion and deposition simulation based on the initial spraying simulation path, using a discrete phase model, a standard k-ε turbulence model, and a particle-wall interaction model, and to generate a deposition density distribution cloud map. The deposition quality analysis and identification unit is used to perform deposition quality analysis and identification on the deposition density distribution cloud map, and generate a coating thickness error distribution map and quantitative statistical data. The path correction and parameter optimization unit is used to correct the spraying path and optimize the process parameters based on the coating thickness error distribution map and the quantitative statistical data, and generate an optimized spraying scheme. A closed-loop iteration unit is used to guide the target spraying robot to perform actual spraying operations if the simulation results of the spraying optimization scheme meet the preset quality requirements.

[0012] On one hand, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0013] On the one hand, a storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement any of the methods described above.

[0014] Compared with the prior art, the beneficial effects of this application are as follows: In this application, when simulating and optimizing the teaching path of the spraying robot, firstly, the teaching path file exported from the target spraying robot can be parsed and converted to obtain multiple initial key information; then, simulation modeling can be performed using these initial key information to obtain the initial spraying simulation path; next, based on the initial spraying simulation path, particle motion and deposition simulation can be performed using a discrete phase model, a particle-wall interaction model, and a standard k-ε turbulence model to generate a deposition density distribution cloud map; then, deposition quality analysis and identification can be performed on the deposition density distribution cloud map to generate a coating thickness error distribution map and quantitative statistical data; next, based on the coating thickness error distribution map and quantitative statistical data, spraying path correction and process parameter optimization can be performed to generate a spraying optimization scheme; finally, if the simulation results of the spraying optimization scheme meet the preset quality requirements, the spraying optimization scheme can be used to guide the target spraying robot in actual spraying operations.

[0015] Based on this, this application, by uniformly parsing and converting the teaching path files, can overcome the format barriers of teaching files from different robot brands (ABB, KUKA, FANUC, etc.), enabling accurate import of path data into the simulation platform and cross-platform path integration, thus solving the problem of path and simulation disconnect in existing technologies. Furthermore, by introducing a particle-wall interaction model and a standard k-ε turbulence model to simulate particle motion and deposition, it can model particle deposition behavior with high precision, accurately reproducing the deposition patterns of particles on complex curved surfaces, thus solving the problem of insufficient simulation realism in existing technologies. In addition, by correcting the spraying path and optimizing process parameters, and by performing secondary simulation on the generated optimized spraying scheme, this application clearly achieves a fully digital closed loop of "teaching path → simulation verification → defect feedback → path optimization → re-simulation" compared to existing technologies. Process optimization can be completed without on-site trial spraying, solving problems such as path and simulation disconnect, low optimization efficiency, and insufficient simulation realism in existing technologies, saving more than 60% of trial spraying costs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application; Figure 2A schematic diagram illustrating a simulation and optimization method for a painting robot teaching path provided in an embodiment of this application; Figure 3 A schematic diagram illustrating the parsing and conversion of the teaching path file provided in the embodiments of this application; Figure 4 A schematic diagram of the dynamic linear trajectory of a nozzle provided in an embodiment of this application; Figure 5 A schematic diagram of the coating thickness error distribution provided in the embodiments of this application; Figure 6 This is a schematic diagram of a simulation and optimization device for teaching paths of a painting robot provided in an embodiment of this application.

[0018] The diagram is labeled as follows: 10-Simulation and optimization equipment for teaching path of spraying robot, 101-Processor, 102-Memory, 103-I / O interface, 104-Database, 60-Simulation and optimization device for teaching path of spraying robot, 601-Data parsing and conversion unit, 602-Spraying path simulation modeling unit, 603-Particle motion and deposition simulation unit, 604-Deposition quality analysis and identification unit, 605-Path correction and parameter optimization unit, 606-Closed-loop iteration unit. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0020] Currently, the operation paths of spraying robots are mainly generated through manual teaching or offline programming. For example, the operator sets the robot's motion trajectory through a teach pendant, and the trajectory data is stored in the robot control system to guide on-site spraying operations. Simultaneously, simplified models can be built using Computational Fluid Dynamics (CFD) simulation technology to simulate the atomization, movement, and deposition processes of paint particles. However, in existing technologies, the robot teaching system and the CFD simulation system are disconnected, lacking an effective data interaction and collaborative optimization mechanism. For instance, the "teaching path generation → on-site test spraying → manual inspection → path adjustment" loop mode described in patent CN104841593A, while incorporating a "simulation system," has its functions limited to kinematic / collision detection, joint velocity and acceleration verification, and visualization of spray volume distribution. Therefore, it suffers from the following significant drawbacks: (1) Decoupling of path and process parameters: The robot teaching path data is only stored in the robot control system and cannot be directly imported into the CFD simulation platform, resulting in a deviation between the spray gun trajectory in the simulation model and the actual teaching path. The simulation results have limited guiding significance for actual production. (2) Lack of data support for process optimization: Traditional spraying process optimization relies on manual trial and error, which consumes a lot of paint, workpieces and time costs. (3) Insufficient realism of particle deposition simulation: The spraying process involves complex multi-physics coupling (e.g., aerodynamics and surface tension), but existing simulation methods mostly use static nozzle models or simplified trajectories, without considering the actual scenario of the spray gun moving dynamically with the teaching path, resulting in a large deviation between the simulation results of particle deposition density and coating thickness and the actual situation. (4) No closed-loop correction capability of the system: The existing robot teaching system cannot receive quality feedback data from simulation or actual detection, and cannot automatically correct path parameters based on coating defects (e.g., missed spraying, recoating, uneven thickness). Manual re-teaching and adjustment are required, resulting in low intelligence.

[0021] Based on this, this application provides a simulation and optimization method for the teaching path of a painting robot. In this method, firstly, the teaching path file exported from the target painting robot can be parsed and converted to obtain multiple initial key information. Then, simulation modeling can be performed using these initial key information to obtain an initial painting simulation path. Next, based on the initial painting simulation path, particle motion and deposition simulation can be performed using a discrete phase model, a standard k-ε turbulence model, and a particle-wall interaction model to generate a deposition density distribution cloud map. Then, deposition quality analysis and identification can be performed on the deposition density distribution cloud map to generate a coating thickness error distribution map and quantitative statistical data. Next, based on the coating thickness error distribution map and quantitative statistical data, painting path correction and process parameter optimization can be performed to generate a painting optimization scheme. Finally, if the simulation results of the painting optimization scheme meet the preset quality requirements, the painting optimization scheme can be used to guide the target painting robot in actual painting operations. Based on this, this application, by uniformly parsing and converting the teaching path files, can overcome the format barriers of teaching files from different robot brands (ABB, KUKA, FANUC, etc.), enabling accurate import of path data into the simulation platform and cross-platform path integration, thus solving the problem of path and simulation disconnect in existing technologies. Furthermore, by introducing a particle-wall interaction model and a standard k-ε turbulence model to simulate particle motion and deposition, it can model particle deposition behavior with high precision, accurately reproducing the deposition patterns of particles on complex curved surfaces, thus solving the problem of insufficient simulation realism in existing technologies. In addition, by correcting the spraying path and optimizing process parameters, and by performing secondary simulation on the generated optimized spraying scheme, this application clearly achieves a fully digital closed loop of "teaching path → simulation verification → defect feedback → path optimization → re-simulation" compared to existing technologies. Process optimization can be completed without on-site trial spraying, solving problems such as path and simulation disconnect, low optimization efficiency, and insufficient simulation realism in existing technologies, saving more than 60% of trial spraying costs.

[0022] After introducing the design concept of the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions of the embodiments of this application can be applied. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0023] like Figure 1 The diagram shown is an application scenario illustration provided by an embodiment of this application. This application scenario may include a simulation and optimization device 10 for teaching the teaching path of a painting robot.

[0024] The simulation and optimization device 10 for the teaching path of the painting robot can be used to simulate and optimize the teaching path of the painting robot. For example, it can be an on-board computer, a personal computer (PC), a server, or a laptop. The simulation and optimization device 10 for the teaching path of the painting robot may include one or more processors 101, memory 102, I / O interface 103, and database 104. Specifically, the processor 101 can be a central processing unit (CPU) or a digital processing unit, etc. The memory 102 can be volatile memory, such as random-access memory (RAM); the memory 102 can also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or the memory 102 can be any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 102 can be a combination of the above-mentioned memories. The memory 102 can store some program instructions of the simulation and optimization method for the teaching path of the painting robot provided in the embodiments of this application. When these program instructions are executed by the processor 101, they can be used to implement the steps of the simulation and optimization method for the teaching path of the painting robot provided in the embodiments of this application, so as to solve the technical problems of path and simulation being disconnected, low optimization efficiency, and insufficient simulation realism in the prior art. The database 104 can be used to store data such as teaching path files, multiple initial key information, deposition density distribution cloud maps, quantitative statistical data, and coating thickness error distribution maps involved in the solution provided in the embodiments of this application.

[0025] In this embodiment, the simulation and optimization device 10 for teaching the painting robot path can obtain path simulation and optimization instructions through the I / O interface 103. Then, the processor 101 of the simulation and optimization device 10 will solve the technical problems existing in the prior art, such as the disconnect between the path and the simulation, low optimization efficiency, and insufficient simulation realism, according to the program instructions of the simulation and optimization method for teaching the painting robot path provided in this embodiment, which are stored in the memory 102. In addition, the teaching path file, multiple initial key information, deposition density distribution cloud map, quantitative statistical data, and coating thickness error distribution map can be stored in the database 104.

[0026] Of course, the methods provided in the embodiments of this application are not limited to... Figure 1The application scenarios shown can also be used in other possible scenarios, and this application embodiment does not impose any limitations. Figure 1 The functions that the various devices in the application scenarios shown can achieve will be described in subsequent method embodiments, and will not be elaborated on here. Below, the methods of the embodiments of this application will be described in conjunction with the accompanying drawings.

[0027] like Figure 2 The diagram shown is a flowchart illustrating a simulation and optimization method for the teaching path of a painting robot provided in this application embodiment. This method can... Figure 1 The simulation and optimization device 10 for teaching the painting robot's teaching path is used to execute the process. Specifically, the process of this method is described below.

[0028] Step 201: Parse and convert the teaching path file exported from the target painting robot to obtain multiple initial key information.

[0029] For example, such as Figure 3 The diagram illustrates the parsing and conversion of a teaching path file provided in this application embodiment. The original teaching path file is in RobotStudio.mod format, containing 160 teaching points in X / Y / Z coordinate system. After data parsing and conversion, the original teaching path file can be converted into multiple initial key information in standard simulation format. At this time, the initial key information is in AnsysFluent.csv format, containing 160 trajectory point coordinates in standardized X / Y / Z coordinate system.

[0030] Specifically, firstly, "data export" can be performed, that is, the teach path file can be exported from the controller of the target painting robot; the controller of the target painting robot can be the IRC5 controller of an ABB robot or the KRC4 controller of a KUKA robot, etc. The data formats supported by the teach path file include, but are not limited to, .mod (ABB), .ls (KUKA), and .prg (FANUC).

[0031] Then, "data parsing" can be performed, that is, a preset parsing algorithm can be used to parse the teaching path file to obtain multiple key pieces of information after parsing. Among them, the key information after parsing includes parameters such as the three-dimensional coordinates (X, Y, Z) of the robtarget point, the spray gun attitude angle (Euler angles or quaternions), the movement speed (mm / s), and the dwell time / time step (ms).

[0032] Finally, a "format conversion" can be performed, that is, the format of multiple parsed key information is converted to obtain multiple initial key information; wherein, the data format of the initial key information is a standard three-dimensional coordinate data format that can be read by the CFD simulation platform, such as CSV (comma-separated values), XML (Extensible Markup Language), or JSON (Lightweight Data Interchange Format), etc. During the format conversion process, the consistency of the coordinate system must be maintained (such as unifying it to the workpiece coordinate system).

[0033] Step 202: Use multiple initial key information to perform simulation modeling and obtain the initial spraying simulation path.

[0034] Specifically, firstly, "workpiece geometry model construction" can be performed, that is, the three-dimensional model of the target workpiece can be imported into a CFD simulation platform such as ANSYS Fluent, for example, a three-dimensional model in STL or STEP format; and according to the actual production environment, the boundary conditions of the spraying area can be set. For example, when the ambient temperature is 25℃ and the atmospheric pressure is 1atm, the boundary conditions of the spraying area can focus on temperature (controlling the temperature of the spraying area at 20-25℃), humidity (keeping the humidity of the spraying area at 50%-60%RH), air cleanliness (controlling the humidity of the spraying area at SO8 level cleanliness) and safety configuration, etc.

[0035] Then, "mesh generation" can be performed, that is, unstructured meshes can be used to discretize the surface of the target workpiece (including the spraying area), thereby improving the mesh quality of complex curved surfaces and controlling the mesh size within 0.5-2mm (adjusted according to the workpiece accuracy requirements) to ensure the mesh resolution of the particle deposition area.

[0036] Next, "dynamic trajectory driving" can be implemented. This involves importing multiple initial key information points into a user-defined function (UDF) for programming, causing the nozzle to move along a linear trajectory of "previous point → current point → next point," with its speed and attitude angle consistent with the taught path. This achieves dynamic motion control of the spray gun nozzle, obtaining the initial spraying simulation path, such as... Figure 4 The diagram shown is a schematic of a nozzle dynamic linear trajectory provided in an embodiment of this application, wherein the nozzle dynamic linear trajectory is the initial spraying simulation path.

[0037] Finally, "particle injection settings" can be configured, which means that particle swarm attributes can be defined in a user-defined function (UDF) based on actual spraying process parameters (e.g., paint type and nozzle model). These particle swarm attributes include initial particle velocity (10-50 m / s), particle size distribution (10-100 μm, conforming to the Rosin-Rammler distribution model), mass flow rate (0.1-1 kg / min), and injection angle (aligned with the nozzle axis), thereby simulating a realistic atomization effect.

[0038] Step 203: Based on the initial spraying simulation path, use the discrete phase model, the standard k-ε turbulence model, and the particle-wall interaction model to simulate particle motion and deposition, and generate a deposition density distribution cloud map.

[0039] Specifically, firstly, a "physical model selection" can be performed. That is, based on the initial spraying simulation path, a discrete phase model can be used during the simulated spraying process. DPM) The motion trajectory of paint particles can be simulated; and considering the effects of gravity, air resistance, turbulent diffusion force, etc. on the particles, the standard k-ε turbulence model (i.e., particle size distribution model) can also be used to simulate the airflow field distribution in the spraying area.

[0040] Then, “wall interaction modeling” can be performed, that is, a particle-wall interaction model can be used to simulate the deposition process of coating particles, and for different workpiece materials, the judgment conditions for particle adhesion, rebound, and trap can be set. For example, when the particle velocity is lower than the critical value (such as 5m / s) and the impact angle is less than 60°, the particle is judged to be adhesive deposition; when the particle velocity is too high or the angle is too large, the particle is judged to be rebound.

[0041] Finally, "data recording" can be performed, that is, during the simulated spraying process, based on the motion trajectory, deposition process and airflow distribution, the deposition position, deposition time and deposition amount (mass / area) of the particles on the surface of the target workpiece can be recorded in real time, thereby generating a deposition density distribution cloud map.

[0042] Step 204: Perform deposition quality analysis and identification on the deposition density distribution cloud map to generate a coating thickness error distribution map and quantitative statistical data.

[0043] Specifically, firstly, "key quality indicators can be extracted," that is, multiple key quality indicators can be calculated based on the deposition density distribution cloud map. Among them, key quality indicators include coating thickness distribution (converted from deposition amount to coating density), thickness standard deviation (evaluating uniformity), recoating area ratio (areas with thickness exceeding the threshold), and missed spraying area ratio (areas with thickness below the threshold).

[0044] Then, "defect visualization" can be performed, that is, a coating thickness error distribution map and quantitative statistics can be generated based on multiple key quality indicators. The coating thickness error distribution map uses the target thickness as a benchmark, displays positive / negative deviations, and marks high-risk defect areas (e.g., missed spray points and accumulation points). The quantitative statistics include data such as maximum thickness deviation and pass rate. Figure 5 The image shown is a schematic diagram of a coating thickness error distribution provided in an embodiment of this application.

[0045] Step 205: Based on the coating thickness error distribution map and quantitative statistical data, correct the spraying path and optimize the process parameters to generate an optimized spraying scheme.

[0046] Specifically, firstly, "path correction" can be performed. That is, for the defective areas in the coating thickness error distribution map, a preset path correction strategy can be used to perform reverse calculation of optimization parameters, thereby obtaining the path correction result. The defective areas include missed spraying areas and recoating areas. For example, for missed spraying areas, the overlap rate of the spray gun path can be adjusted (increased from 50% to 60%) and the movement speed can be reduced (from 300mm / s to 200mm / s); for recoating areas, the spray gun spacing can be increased or the movement speed can be increased, etc.

[0047] Then, "parameter optimization" can be performed. That is, based on the thickness error data in the quantitative statistics, the process parameters can be adjusted using a preset parameter optimization strategy to obtain the parameter optimization results. For example, when the local thickness is insufficient, the coating mass flow rate can be increased (from 0.3 kg / min to 0.4 kg / min) or the spray distance can be reduced (from 300 mm to 250 mm); when there is too much particle rebound, the nozzle pressure can be optimized (from 0.5 MPa to 0.6 MPa) to improve the atomization effect.

[0048] Finally, "optimization scheme generation" can be performed, that is, based on the path correction results and parameter optimization results, a spraying optimization scheme can be generated (generating optimized teaching path data and process parameter combinations that can be directly imported into the robot control system).

[0049] Step 206: If the simulation results of the spraying optimization scheme meet the preset quality requirements, the spraying optimization scheme is used to guide the target spraying robot to perform actual spraying operations.

[0050] Specifically, firstly, the spraying optimization scheme (optimized path and parameters) can be imported into the CFD simulation platform for secondary simulation verification to obtain the simulation results of the spraying optimization scheme.

[0051] Then, the optimized key quality indicators can be judged by determining whether the simulation results meet the preset quality requirements. For example, it can be determined whether the pass rate in the simulation results is not less than ≥95%.

[0052] Next, if the simulation results are determined to meet the preset quality requirements, the spraying optimization scheme can be used to guide the target spraying robot to perform actual spraying operations.

[0053] Conversely, if the simulation results do not meet the preset quality requirements, the spraying path needs to be corrected and the process parameters optimized again based on the defect area and quantitative statistical data corresponding to the simulation results, until the simulation results meet the preset quality requirements. Finally, the optimal path data and process parameters verified by the simulation are output to guide the actual spraying operation.

[0054] In summary, this application has the following advantages: (1) Strengthened the connection between path and simulation: Since the teaching path file has been uniformly parsed and converted, this application can break through the teaching file format barrier of different brands of robots (ABB, KUKA, FANUC, etc.), realize the accurate import of path data into the simulation platform and cross-platform path integration, and solve the problem of path and simulation being disconnected in the prior art.

[0055] (2) Reduced deposition simulation error: In the simulation modeling process of the spraying simulation path, the dynamic driving method of "trajectory point sequence + linear motion vector" is adopted for the first time, so that the spray gun nozzle can completely reproduce the motion state (position, attitude and speed, etc.) of the teaching path in the simulation. Therefore, compared with the traditional static nozzle model, the deposition simulation error of this application is reduced to less than 5%.

[0056] (3) Improved the simulation realism of deposition: Since the standard k-ε turbulence model and particle-wall interaction model are comprehensively introduced, the deposition law of particles on complex curved surfaces can be accurately restored, solving the problem of insufficient simulation realism in the existing simulation.

[0057] (4) Reduced costs: Since the path and parameter correction can be calculated in reverse based on the simulation defect data, forming a closed loop of "simulation results → optimization scheme → re-simulation", this application can complete the process optimization without on-site test spraying based on the simulation back-reasoning optimization mechanism, saving more than 60% of the test spraying cost.

[0058] (5) Improved multi-scenario expansion capability: This application can support complex scenarios such as multi-axis collaborative spraying (e.g., dual-arm robot) and multi-spray gun combination, and meet the spraying needs of complex structures such as automobiles and aerospace large components through path synchronous control and particle field coupling simulation.

[0059] Furthermore, this application has significant effects in terms of technology, economy, society, and application value, as detailed below: (1) Technical effect: The uniformity of the coating thickness is improved by more than 30%, and the defect rate of missed spraying / recoating is reduced to less than 1%; the deviation between the simulation results and the actual spraying is controlled within 5%, providing accurate data support for process optimization.

[0060] (2) Economic benefits: Reduces the number of manual test sprays by more than 80%, and saves hundreds of thousands of yuan in paint and workpiece costs per production line per year; shortens the process optimization cycle from the traditional 7-10 days to 1-2 days, significantly improving production efficiency.

[0061] (3) Social impact: Promote the transformation of spray painting manufacturing to the "digital twin" model, reduce reliance on operator experience, and improve the level of intelligent manufacturing; reduce paint waste and volatile organic compound (VOC) emissions, which meets the needs of green manufacturing development.

[0062] (4) Application value: It has broad application prospects in high-precision spraying fields such as automobile body, aerospace engine parts, and high-speed rail carriages, and can meet the stringent requirements of different industries for coating quality.

[0063] Based on the same inventive concept, embodiments of this application provide a simulation and optimization device 60 for teaching paths of a painting robot, such as... Figure 6 As shown, the simulation and optimization device 60 for the teaching path of the painting robot includes: The data parsing and conversion unit 601 is used to parse and convert the teaching path file exported from the target spraying robot to obtain multiple initial key information. The spraying path simulation modeling unit 602 is used to perform simulation modeling using multiple initial key information to obtain the initial spraying simulation path. The particle motion and deposition simulation unit 603 is used to simulate particle motion and deposition based on the initial spraying simulation path, using a discrete phase model, a standard k-ε turbulence model, and a particle-wall interaction model, and to generate a deposition density distribution cloud map. The deposition quality analysis and identification unit 604 is used to perform deposition quality analysis and identification on the deposition density distribution cloud map, and generate a coating thickness error distribution map and quantitative statistical data. The path correction and parameter optimization unit 605 is used to correct the spraying path and optimize the process parameters based on the coating thickness error distribution map and quantitative statistical data, and generate an optimized spraying scheme. The closed-loop iteration unit 606 is used to guide the target spraying robot to perform actual spraying operations if the simulation results of the spraying optimization scheme meet the preset quality requirements.

[0064] Optionally, the data parsing and transformation unit 601 is also used for: Export the teaching path file from the target painting robot; A preset parsing algorithm is used to parse the teaching path file and obtain several key pieces of information after parsing. Among them, the key information after parsing includes the three-dimensional coordinates of the robtarget point, the spray gun attitude angle, the movement speed, and the dwell time. The format of multiple parsed key information is converted to obtain multiple initial key information.

[0065] Optionally, the spray path simulation modeling unit 602 is also used for: Import the 3D model of the target workpiece into the simulation platform and set the boundary conditions of the spraying area; An unstructured mesh is used to discretize the surface of the target workpiece. Multiple initial key information is imported into a user-defined function to perform dynamic motion control of the spray gun nozzle and obtain the initial spraying simulation path; Based on the actual spraying process parameters, particle swarm attributes are defined in the user-defined function; among them, particle swarm attributes include initial particle velocity, particle size distribution, mass flow rate, and injection angle.

[0066] Optionally, the particle motion and deposition simulation unit 603 is also used for: Based on the initial spraying simulation path, a discrete phase model is used to simulate the motion trajectory of paint particles; The standard k-ε turbulence model was used to simulate the airflow field distribution in the spraying area; A particle-wall interaction model was used to simulate the deposition process of coating particles; Based on the motion trajectory, airflow field distribution, and deposition process, a cloud map of deposition density distribution is generated.

[0067] Optionally, the sedimentation quality analysis and identification unit 604 is also used for: Based on the deposition density distribution cloud map, several key quality indicators were calculated; among them, the key quality indicators include coating thickness distribution, thickness standard deviation, recoating area ratio, and missed spraying area ratio. Based on multiple key quality indicators, a coating thickness error distribution map and quantitative statistics are generated; among them, the quantitative statistics include the maximum thickness deviation and the pass rate.

[0068] Optionally, the path correction and parameter optimization unit 605 is also used for: For the defect areas in the coating thickness error distribution map, a preset path correction strategy is used to perform reverse calculation of optimization parameters to obtain the path correction results; among them, the defect areas include the missed spray area and the recoating area. Based on quantitative statistical data, process parameters are adjusted using a preset parameter optimization strategy to obtain parameter optimization results; Based on the path correction results and parameter optimization results, a spraying optimization scheme is generated.

[0069] Optionally, the closed-loop iteration unit 606 is also used for: The spraying optimization scheme was imported into the simulation platform for secondary simulation verification to obtain the simulation results of the spraying optimization scheme. Determine whether the simulation results meet the preset quality requirements; If the simulation results are determined to meet the preset quality requirements, the spraying optimization scheme will be used to guide the target spraying robot to perform actual spraying operations. If the simulation results are determined to be below the preset quality requirements, the spraying path will be corrected and the process parameters optimized again based on the defect areas and quantitative statistical data corresponding to the simulation results.

[0070] The simulation and optimization device 60 for the teaching path of the painting robot can be used to execute... Figures 2-5 The method executed by the simulation and optimization device for teaching the painting robot in the illustrated embodiment is described above. Therefore, the functions that each functional module of the simulation and optimization device 60 for teaching the painting robot can achieve can be found by referring to [the relevant documentation / reference]. Figures 2-5 The embodiments shown are described in detail below.

[0071] In some possible implementations, various aspects of the methods provided in this application can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps of the methods according to the various exemplary embodiments of this application described above. For example, the computer device may perform actions such as... Figures 2-5 The method performed by the simulation and optimization device for teaching the painting robot path in the illustrated embodiment.

[0072] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Alternatively, if the integrated units of this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0073] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0074] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A simulation and optimization method for the teaching path of a painting robot, characterized in that, The method includes: The teaching path file exported from the target painting robot is parsed and converted to obtain several initial key information. The initial key information is used to perform simulation modeling to obtain the initial spraying simulation path; Based on the initial spraying simulation path, a discrete phase model is used to simulate the motion trajectory of paint particles; a standard k-ε turbulence model is used to simulate the airflow field distribution in the spraying area; a particle-wall interaction model is used to simulate the deposition process of paint particles; and a deposition density distribution cloud map is generated based on the motion trajectory, the airflow field distribution, and the deposition process. Based on the deposition density distribution cloud map, several key quality indicators are calculated; among them, the key quality indicators include coating thickness distribution, thickness standard deviation, recoating area ratio, and missed spraying area ratio; based on the multiple key quality indicators, a coating thickness error distribution map and quantitative statistical data are generated; among them, the quantitative statistical data include maximum thickness deviation and pass rate; Based on the coating thickness error distribution map and the quantitative statistical data, the spraying path is corrected and the process parameters are optimized to generate an optimized spraying scheme. If the simulation results of the coating optimization scheme meet the preset quality requirements, then the coating optimization scheme is used to guide the target coating robot to perform actual coating operations.

2. The method as described in claim 1, characterized in that, The steps of parsing and converting the teaching path file exported from the target painting robot to obtain multiple initial key information include: Export the teaching path file from the target painting robot; A preset parsing algorithm is used to parse the teaching path file to obtain multiple key pieces of information after parsing; among them, the key pieces of information after parsing include the three-dimensional coordinates of the robtarget point, the spray gun attitude angle, the movement speed, and the dwell time. The format of the multiple parsed key information is converted to obtain multiple initial key information.

3. The method as described in claim 1, characterized in that, The step of using the multiple initial key information to perform simulation modeling and obtain the initial spraying simulation path includes: Import the 3D model of the target workpiece into the simulation platform and set the boundary conditions of the spraying area; An unstructured mesh is used to discretize the surface of the target workpiece. The initial key information is imported into a user-defined function to perform dynamic motion control of the spray gun nozzle, thereby obtaining the initial spraying simulation path; Based on the actual spraying process parameters, particle swarm attributes are defined in the user-defined function; wherein, the particle swarm attributes include initial particle velocity, particle size distribution, mass flow rate, and injection angle.

4. The method as described in claim 1, characterized in that, The step of correcting the spraying path and optimizing process parameters based on the coating thickness error distribution map and the quantified statistical data to generate an optimized spraying scheme includes: For the defective areas in the coating thickness error distribution map, a preset path correction strategy is used to perform reverse calculation of optimization parameters to obtain the path correction results; wherein, the defective areas include unsprayed areas and recoated areas; Based on quantitative statistical data, the process parameters are adjusted using a preset parameter optimization strategy to obtain the parameter optimization results; Based on the path correction results and the parameter optimization results, a spraying optimization scheme is generated.

5. The method as described in claim 3, characterized in that, The step of using the spraying optimization scheme to guide the target spraying robot in actual spraying operations if the simulation results of the spraying optimization scheme meet the preset quality requirements includes: The coating optimization scheme is imported into the simulation platform for secondary simulation verification to obtain the simulation results of the coating optimization scheme. Determine whether the simulation results meet the preset quality requirements; If the simulation results are determined to meet the preset quality requirements, the spraying optimization scheme is used to guide the target spraying robot to perform actual spraying operations. If it is determined that the simulation results do not meet the preset quality requirements, the spraying path will be corrected and the process parameters optimized again based on the defect area and quantitative statistical data corresponding to the simulation results.

6. A simulation and optimization device for teaching paths of a painting robot, characterized in that, The device includes: The data parsing and conversion unit is used to parse and convert the teaching path file exported from the target spraying robot to obtain multiple initial key information. The spraying path simulation modeling unit is used to perform simulation modeling using the multiple initial key information to obtain the initial spraying simulation path. The particle motion and deposition simulation unit is used to simulate the motion trajectory of paint particles using a discrete phase model based on the initial spraying simulation path; to simulate the airflow field distribution in the spraying area using a standard k-ε turbulence model; to simulate the deposition process of paint particles using a particle-wall interaction model; and to generate a deposition density distribution cloud map based on the motion trajectory, the airflow field distribution, and the deposition process. The deposition quality analysis and identification unit is used to calculate multiple key quality indicators based on the deposition density distribution cloud map. These key quality indicators include coating thickness distribution, thickness standard deviation, recoating area ratio, and missed spraying area ratio. Based on these multiple key quality indicators, a coating thickness error distribution map and quantitative statistical data are generated. The quantitative statistical data includes the maximum thickness deviation and the pass rate. The path correction and parameter optimization unit is used to correct the spraying path and optimize the process parameters based on the coating thickness error distribution map and the quantitative statistical data, and generate an optimized spraying scheme. A closed-loop iteration unit is used to guide the target spraying robot to perform actual spraying operations if the simulation results of the spraying optimization scheme meet the preset quality requirements.

7. An electronic device, characterized in that, The device includes: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method described in any one of claims 1-5 according to the obtained program instructions.

8. A storage medium, characterized in that, The storage medium stores computer-executable instructions for causing a computer to perform the method described in any one of claims 1-5.

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