Automatic test panel spraying system based on PLC control

Abstract

The invention relates to the technical field of industrial automatic control and fluid surface treatment, in particular to an automatic test panel spraying system based on PLC control. Comprising a virtual feature analysis module which is used for acquiring three-dimensional digital model data and a preset spraying track of a virtual target curved surface and constructing a virtual geometric feature data set containing curvature features of discrete points and a normal vector field; the equivalent flux calculation module is used for generating a target three-dimensional flux distribution data matrix; the projection mapping compensation module is used for generating a composite compensation instruction sequence containing dynamic attitude deflection data, variable-speed motion data and variable-fan-amplitude air pressure data; and the execution driving control module is used for converting the digital instruction into a multi-axis servo driving signal and an analog quantity adjusting signal, driving an execution mechanism to execute a time-space coordination action on a physical plane base material, and reproducing fluid deposition characteristics of a virtual target curved surface. According to the system, the consumption of trial and error materials is greatly reduced, and the conversion time from a laboratory to a production line is shortened.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control and fluid surface treatment technology, specifically to an automatic test plate spraying system based on PLC control. Background Technology

[0002] Currently, the main methods for researching and validating industrial coating processes are to construct standard planar reciprocating machines or manufacture three-dimensional physical models. Conventional methods involve uniformly spraying coatings on two-dimensional standard test plates to test the basic performance of the coatings, or investing heavily in the production of physical component models for on-site test spraying to determine the process window. Existing systems typically treat test plates as simple physical bearing interfaces, often ignoring the nonlinear influence of curved surface geometry on fluid deposition behavior during operation. They tend to simply extrapolate planar test data to complex workpieces or bear the high cost of physical prototype manufacturing. However, in related technologies, with the increasing complexity of curved surface design in automobiles and industrial parts and the increasing requirements for coating appearance quality, verification modes based on traditional planar or solid models face severe challenges. Standard planar testing cannot simulate the unique fluid dynamic phenomena of complex curved surfaces, such as divergent slip on convex surfaces, focused accumulation on concave surfaces, and flux loss on inclined surfaces, making it difficult to identify defects strongly related to geometric curvature, such as sagging and orange peel, in low-cost testing. At the same time, iterative verification methods relying on solid 3D models have problems such as high trial and error costs, large material consumption, and long R&D cycles, making it difficult to meet the needs of efficient and low-consumption green manufacturing. Therefore, there is an urgent need for a solution to address the problem that existing technologies cannot reproduce the characteristics of complex curved surface spraying processes with high fidelity on a low-cost plane.

[0003] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] To solve the above-mentioned technical problems, this invention discloses an automatic test panel spraying system based on PLC control. Specifically, the technical solution of this invention is as follows: The virtual feature parsing module is configured to run in the processing unit of the PLC. It is used to acquire the three-dimensional digital model data of the virtual target surface and the preset spraying trajectory, and to perform discretization mesh processing on the three-dimensional digital model data to construct a virtual geometric feature dataset containing the curvature features and normal vector fields of each discrete point. The equivalent flux calculation module is configured to, based on the virtual geometric feature dataset, call a pre-stored fluid dynamics algorithm model to calculate the theoretical deposition flux vector at each discrete point along the preset spraying trajectory, and generate a target three-dimensional flux distribution data matrix. The projection mapping compensation module is configured to establish a coordinate dimensionality reduction mapping logic from virtual three-dimensional space to physical two-dimensional plane. Based on the target three-dimensional flux distribution data matrix, it calculates the equivalent control parameters on the physical plane and generates a composite compensation instruction sequence containing dynamic attitude deflection data, variable speed motion data and variable fan-width air pressure data. The execution drive control module is configured to read the composite compensation instruction sequence in real time via fieldbus, convert the digital instructions into multi-axis servo drive signals and analog adjustment signals, and drive the actuator to perform spatiotemporal coordinated actions on the physical planar substrate, so as to reproduce the fluid deposition characteristics of the virtual target surface on the physical planar substrate through program control logic.

[0005] Preferably, the modules are connected through the following program control steps: S1. Data Discretization and Feature Extraction: Import the three-dimensional geometric model data of the virtual target surface, extract the coordinates of discrete points on the preset spraying trajectory, calculate the normal vector of the tangent plane and the local radius of curvature at each discrete point through the PLC computing unit, and construct a virtual geometric feature dataset. S2. Flux distribution model construction: Based on the virtual geometric feature dataset, the wall adhesion effect and divergence characteristics of the fluid on the virtual target surface are analyzed by the algorithm, the theoretical deposition amount per unit area at each discrete point is calculated, and the target three-dimensional flux distribution data matrix is ​​generated. S3. Dimensional Reduction Mapping and Command Generation: The target three-dimensional flux distribution data matrix is ​​mapped to the physical plane coordinate system. The tilt angle and relative velocity of the jet source relative to the physical plane substrate are calculated by the geometric projection algorithm. The fan-shaped forming pressure is solved by the flow conservation algorithm to generate a composite compensation command sequence. S4. Multi-channel signal synchronization and execution: The composite compensation instruction sequence is converted into PLC axis control signals and analog adjustment signals. The axis control signals and analog adjustment signals are subjected to time-domain synchronous interpolation processing to drive the actuator to complete the spraying operation.

[0006] Preferably, S1 specifically includes: S11. Digitize the virtual target surface into a grid, obtain all grid units passed through by the preset spraying trajectory, and determine the geometric center point of each grid unit as a discrete calculation point. S12. For each discrete calculation point, extract its position coordinate data and surface normal vector data in three-dimensional space, and calculate the principal curvature and secondary curvature values ​​of the point along the trajectory tangent direction using differential geometry algorithm. S13. Arrange the position coordinate data, surface normal vector data, principal curvature values ​​and secondary curvature values ​​according to the trajectory time sequence to construct a virtual geometric feature dataset containing geometric topology information.

[0007] Preferably, S2 specifically includes: S21. Call the fluid dynamics basic model parameters in the PLC storage area and set the initial fluid injection velocity variable and the injection cone angle variable; S22. For each discrete calculation point, determine the surface concavity / convexity attribute based on its local radius of curvature value. If the logical judgment is that it is a convex surface feature, then apply the divergence attenuation coefficient to correct the deposition amount calculation; if the logical judgment is that it is a concave surface feature, then apply the focusing gain coefficient to correct the deposition amount calculation. S23. By combining the angle data between the jet source vector and the discrete point normal vector, calculate the flux loss caused by the cosine effect, generate the corrected theoretical deposition flux vector, and combine them to form the target three-dimensional flux distribution data matrix.

[0008] Preferably, S3 specifically includes: S31. Establish a two-dimensional Cartesian coordinate system for the physical plane substrate inside the PLC, project the discrete points in the virtual geometric feature dataset onto the two-dimensional Cartesian coordinate system, and determine the reference movement path data of the jet source on the physical plane. S32. For each control node on the path, the dynamic attitude deflection command that makes the angle between the jet source and the physical plane equivalent to the angle between the jet source and the tangential plane in the virtual space is calculated by the inverse kinematics algorithm. S33. Based on the dynamic attitude deflection command, calculate the rate of change of the projected area of ​​the jet beam on the physical plane, and reverse the variable speed motion command of the jet source according to the rate of change of the projected area to maintain constant energy data per unit area. S34. Based on the fluid divergence or focusing demand data corresponding to the local curvature radius, calculate the variable fan width air pressure command used to change the fan width of the jet beam, and integrate the dynamic attitude deflection command, variable speed motion command and variable fan width air pressure command into a composite compensation command sequence.

[0009] Preferably, the generation of the variable fan-width air pressure command in S34 follows the following logical operation rules: If the current discrete point corresponds to virtual convex surface feature data, then a digital instruction to reduce the fan-shaped forming pressure is generated to shrink the width of the jet beam and simulate the boundary slip effect of the fluid on the convex surface. If the current discrete point corresponds to virtual concave surface feature data, then a digital command is generated to increase the fan-shaped forming pressure to expand the width of the jet beam and simulate the vortex accumulation effect of fluid on the concave surface. If the current discrete point corresponds to virtual plane feature data, then the preset reference fan-shaped pressure data is maintained; The adjustment value of the pressure has a non-linear mapping relationship with the reciprocal of the local radius of curvature.

[0010] Preferably, the generation of the variable speed motion command in S33 specifically includes: Obtain the tilt angle data from the dynamic attitude deflection command and calculate the elliptical projection area of ​​the jet beam section on the physical plane. Calculate the ratio of the projected area of ​​the ellipse to the projected area of ​​the standard circle, and use this ratio as a velocity correction factor; The preset baseline spraying speed data is multiplied by the speed correction factor to generate instantaneous target speed data at each control node, and a variable speed motion command is constructed to ensure that the coating thickness distribution data on the physical planar substrate is consistent with the virtual target surface under the tilted spraying state.

[0011] Preferably, S4 specifically includes: S41. Obtain the physical response lag time constant of the fluid control system and the motion response lag time constant of the mechanical execution system; S42. Calculate the time difference between the two, perform time axis translation compensation operation on the variable fan width air pressure command in the composite compensation command sequence, and generate a synchronized control data stream. S43, the PLC controller reads the synchronized control data stream at a fixed scan cycle, drives the robot joint servo motor to perform pose transformation through the bus communication protocol, and drives the proportional pressure regulating valve to perform pressure regulation through the analog output port, realizing nanosecond-level coordination of spatial trajectory control and fluid state control.

[0012] Preferably, the system further includes: The multi-dimensional parameter optimization program module is configured to control the process parameters in the composite compensation instruction sequence to continuously change according to a preset gradient along the long axis of the physical planar substrate during a single spraying stroke, forming a gradient coating on the physical planar substrate that exhibits the evolution characteristics of the parameter matrix, and is used to determine the optimal process window data.

[0013] Preferably, the execution drive control module controls the coating formed by the spray source on the physical planar substrate, which has the same flow critical point feature data, orange peel texture feature data and color difference distribution feature data as the virtual target curved surface spraying process, and is used to verify the feasibility of complex curved surface process formulations on a physical plane.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This system innovatively establishes a coordinate dimensionality reduction mapping logic from virtual three-dimensional space to physical two-dimensional plane through a virtual feature analysis and projection mapping compensation module. By dynamically adjusting the spray gun posture, movement speed, and fan-shaped air pressure on a standard planar substrate, it can reproduce the fluid deposition characteristics of complex curved surfaces with high fidelity. This means that in the coating R&D and process verification stage, there is no need to make expensive 3D physical samples. Only low-cost planar test plates can be used to complete the pre-simulation of complex curved surface flow, leveling, and film thickness distribution, which greatly reduces the consumption of trial and error materials and shortens the conversion time from laboratory to production line. 2. This system integrates equivalent flux calculation logic based on the infinitesimal element method, enabling intelligent compensation for convex surface divergence and concave surface focusing effects in the virtual model. By extracting the local radius of curvature and normal vector of discrete points, the system automatically generates composite commands with variable fan width and variable speed: for convex surface features, it automatically reduces the fan width pressure to shrink the spray width, simulating boundary slippage; for concave surface features, it increases the pressure to expand the spray width, simulating eddy current accumulation. Combined with a velocity correction factor based on the rate of change of projected area, it ensures that the coating reproduced on the physical plane not only corresponds in geometric position, but also maintains a high degree of consistency with the theoretical curved surface in terms of deposition energy per unit area and physical thickness, solving the problem that traditional planar spraying cannot simulate the thick edge or exposed substrate phenomenon of curved surfaces. 3. Addressing the industry pain point of time lag between pneumatic fluid response and robotic arm motion response, this system employs multi-channel signal synchronous interpolation technology. By acquiring the physical response lag time constants of the fluid control system and the mechanical execution system, and performing nanosecond-level time axis translation compensation calculations on the composite compensation command sequence within the PLC, the phase difference between the actuator's movement and the spray pattern change is eliminated. This spatiotemporal coordination mechanism ensures that the spray pattern can respond to changes in motion trajectory in real time during drastic changes in posture or spray pattern, effectively avoiding defects such as gun accumulation at startup, gun trailing at retraction, or uneven coating in the transition section caused by system delay. 4. With the help of the built-in multi-dimensional parameter optimization program module, this system can realize the gradient evolution of process parameters in a single spraying stroke. This means that on the same physical test plate, the coating effect under different combinations of spraying speed, air pressure or flow rate can be continuously presented along the long axis, forming a parameter matrix. Researchers do not need to conduct dozens of discrete experiments to quickly capture the sagging critical point, orange peel texture characteristics and the optimal color difference distribution area on a single plate. This high-throughput experimental mode can not only quickly lock the optimal process window data, but also provide intuitive and reliable physical data support for the fine adjustment of coating formulations by reproducing specific appearance defect characteristics. Attached Figure Description

[0015] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1This is a system structure diagram of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] Example 1

[0018] Please see Figure 1 An automatic test panel spraying system based on PLC control, comprising: The virtual feature parsing module is configured to run in the PLC's processing unit. It is used to acquire the three-dimensional digital model data of the virtual target surface and the preset spraying trajectory, perform discretization mesh processing on the three-dimensional digital model data, and construct a virtual geometric feature dataset containing the curvature features and normal vector fields of each discrete point. The equivalent flux calculation module is configured to use a virtual geometric feature dataset to call a pre-stored fluid dynamics algorithm model to calculate the theoretical deposition flux vector at each discrete point along the preset spraying trajectory and generate a target three-dimensional flux distribution data matrix. The projection mapping compensation module is configured to establish a coordinate dimensionality reduction mapping logic from virtual three-dimensional space to physical two-dimensional plane. Based on the target three-dimensional flux distribution data matrix, it calculates the equivalent control parameters on the physical plane and generates a composite compensation instruction sequence containing dynamic attitude deflection data, variable speed motion data and variable fan width air pressure data. The execution drive control module is configured to read the composite compensation instruction sequence in real time via fieldbus, convert the digital instructions into multi-axis servo drive signals and analog adjustment signals, and drive the actuator to perform spatiotemporal coordinated actions on the physical plane substrate, so as to reproduce the fluid deposition characteristics of the virtual target surface on the physical plane substrate through program control logic; This embodiment provides an automatic test panel spraying system based on PLC control. The system constructs a virtual-physical mapping engine, which aims to solve the technical pain point that traditional planar reciprocating machines cannot verify complex curved surface process windows. The hardware architecture of the system is based on a high-performance industrial PLC, such as Siemens S7-1500T, and connects a multi-axis servo driver and an electrical proportional pressure regulating valve through an EtherCAT real-time Ethernet bus. The system initiates the virtual feature analysis module, which runs in the real-time core of the PLC processing unit and imports CAD / CAM data through the host computer interface. The module employs a discretization mesh processing algorithm to divide the continuous curved surface model into tiny triangular meshes and constructs a virtual geometric feature dataset for each sampling point on the preset spraying trajectory. The virtual geometric feature dataset originates from the CAD model analysis and physically represents a collection containing principal curvature, Gaussian curvature, and normal vector field data for each discrete point, in dimensionless or curvature units. ; The fluid dynamics algorithm model specifically adopts the Gaussian flux distribution function. As the basic model, its expression is:

[0019] in, Nozzle flow rate, unit: ; This represents the standard deviation of the jet distribution at a specific height from the nozzle, and its value is related to the spraying distance. It exhibits a linear functional relationship; Radial distance from the discrete point to the jet center; divergence attenuation coefficient. With focus gain coefficient All are preset characteristic parameters with the dimension of length, in meters, used to ensure the correction terms. and The value is dimensionless. In step S22, the divergence attenuation coefficient is applied. The specific formula for calculating the corrected sediment volume is as follows:

[0020] Application of Focusing Gain Factor The specific formula for calculating the corrected sediment volume is as follows:

[0021] in, The local radius of curvature; The equivalent flux calculation module calls the pre-stored fluid dynamics algorithm model and calculates the physical behavior of fluid particles when they impact discrete points on the virtual surface based on the above dataset, especially the theoretical deposition flux vector of each discrete point along the preset spraying trajectory, and generates the target three-dimensional flux distribution data matrix. The projection mapping compensation module executes the core control logic to establish a virtual three-dimensional space. To the physical two-dimensional plane The module performs coordinate dimensionality reduction mapping logic; it inversely calculates the equivalent control parameters of the spray gun operating on the plane, and generates a composite compensation command sequence containing dynamic attitude deflection data, variable speed motion data and variable fan width air pressure data; the execution drive control module reads the sequence in real time through the fieldbus, converts the digital commands into multi-axis servo drive signals to drive the 6-axis robot, and analog adjustment signals to drive the precision proportional pressure regulating valve, and drives the actuator to perform spatiotemporal coordinated actions on the physical plane substrate; This embodiment achieves high-fidelity reproduction of the three-dimensional complex curved surface spraying process on a low-cost two-dimensional planar test plate by constructing a virtual feature analysis and projection mapping compensation closed loop within the PLC. In the R&D scenario of automotive parts coating, the system uses dimensionality reduction mapping logic to allow engineers to intuitively observe the risk of sagging or orange peel when the spray gun passes through a virtual corner, without the need to manufacture expensive physical 3D models. This significantly reduces the trial and error costs and material consumption in the process development stage and establishes a new process development paradigm of verifying curved surfaces with a planar surface.

[0022] Example 2: The modules are connected through the following program control steps: S1. Data Discretization and Feature Extraction: Import the three-dimensional geometric model data of the virtual target surface, extract the coordinates of discrete points on the preset spraying trajectory, calculate the normal vector of the tangent plane and the local radius of curvature at each discrete point through the PLC computing unit, and construct a virtual geometric feature dataset. S2. Flux Distribution Model Construction: Based on the virtual geometric feature dataset, the wall adhesion effect and divergence characteristics of the fluid on the virtual target surface are analyzed by the algorithm, the theoretical deposition amount per unit area at each discrete point is calculated, and the target three-dimensional flux distribution data matrix is ​​generated. S3, Dimensional Reduction Mapping and Command Generation: The target three-dimensional flux distribution data matrix is ​​mapped to the physical plane coordinate system. The tilt angle and relative velocity of the jet source relative to the physical plane substrate are calculated through the geometric projection algorithm. The fan-shaped forming pressure is solved by combining the flow conservation algorithm to generate a composite compensation command sequence. S4. Multi-channel signal synchronization and execution: Convert the composite compensation instruction sequence into PLC axis control signals and analog adjustment signals, perform time-domain synchronous interpolation processing on the axis control signals and analog adjustment signals, and drive the actuator to complete the spraying operation; This embodiment details the data flow and program control steps between the above modules to ensure the timing rigor of the control logic; Step S1 involves data discretization and feature extraction; the system imports the 3D geometric model data of the virtual target surface via an Ethernet interface, and the PLC processing unit extracts the coordinates of discrete points on the preset spraying trajectory. For each The program calculates the normal vector of its tangent plane and the local radius of curvature. A virtual geometric feature dataset was constructed, realizing the digital adaptation of continuous geometric information; then, step S2, flux distribution model construction was carried out; based on the virtual geometric feature dataset, the algorithm analyzed the interaction between the fluid and the virtual surface, especially the wall adhesion effect and divergence characteristics. Wall adhesion effect: Sourced from a fluid dynamics model library, its physical meaning is the physical phenomenon that the amount of sediment is reduced due to the deflection of fluid along a convex surface, and the unit is a dimensionless coefficient; The system calculates the theoretical deposition amount per unit area at each discrete point based on this. The system generates a target three-dimensional flux distribution data matrix; performs step S3, dimensionality reduction mapping and instruction generation; maps the target three-dimensional flux distribution data matrix to a physical plane coordinate system, calculates the tilt angle and relative velocity of the jet source relative to the physical plane substrate using a geometric projection algorithm, and solves the fan-shaped forming pressure using a flow conservation algorithm. In step S4, the PLC unpacks the composite compensation instruction sequence, converts it into axis control signals and analog adjustment signals, and performs time-domain synchronous interpolation processing on these two types of signals to drive the actuator to complete a high-precision spraying operation. This embodiment transforms complex physical simulation problems into deterministic logic executable by PLC through layered processing of program control steps. In the automated spraying experiment scenario, the dimensionality reduction mapping in S3, combined with the synchronous interpolation in S4, ensures the fidelity of the virtual simulation, so that the coating distribution on the plane truly reflects the surface process characteristics. This effectively solves the data mismatch problem caused by dimensional differences between virtual simulation and physical execution, and ensures the reliability of the process verification results.

[0023] Example 3: S1 specifically includes: S11. Digitize the virtual target surface into a grid, obtain all grid units passed through by the preset spraying trajectory, and determine the geometric center point of each grid unit as a discrete calculation point. S12. For each discrete calculation point, extract its position coordinate data and surface normal vector data in three-dimensional space, and calculate the principal curvature and secondary curvature values ​​of the point along the trajectory tangent direction using differential geometry algorithm. S13. Arrange the position coordinate data, surface normal vector data, principal curvature values ​​and secondary curvature values ​​according to the trajectory time sequence to construct a virtual geometric feature dataset containing geometric topology information. This embodiment specifies the data discretization and feature extraction in step S1, focusing on the introduction of differential geometric attribute extraction; performing S11 meshing and center positioning; the system performs digital meshing on the virtual target surface, obtains all mesh units passed through by the preset spraying trajectory, and determines the geometric center point of each mesh unit as the discrete calculation point to reduce computational noise; Perform S12 differential geometric attribute extraction; for each discrete calculation point, extract its position coordinate data and surface normal vector data in three-dimensional space; based on this, calculate the principal curvature value of the point along the trajectory tangent direction using a differential geometric algorithm. The numerical value of the secondary curvature perpendicular to the trajectory direction ; Principal curvature values The source is differential geometric calculation; its physical meaning is a quantity reflecting the degree of surface curvature along the spraying direction, determining the acceleration or deceleration tendency of the fluid in the direction of travel; the unit is... ; numerical value of secondary curvature The source is differential geometric calculation; its physical meaning is a quantity reflecting the degree of surface curvature perpendicular to the spraying direction, determining the lateral expansion or contraction tendency of the fluid fan. The unit is... ; Perform S13 to construct a time-series feature set; Arrange the extracted position coordinates, normal vectors, and principal and secondary curvature values ​​strictly according to the time sequence of the spray gun's movement along the trajectory to construct a virtual geometric feature dataset containing geometric topology information. This embodiment achieves a refined description of the geometric features of the virtual surface by introducing a two-dimensional calculation of principal curvature and secondary curvature. In the simulation scenario of complex surface spraying, the system can not only identify the surface's concavity and convexity properties, but also distinguish the curvature difference along the path direction and perpendicular to the path direction. This refined geometric feature extraction provides a solid mathematical foundation for the subsequent accurate simulation of anisotropic fluid behavior, thereby enabling more accurate prediction of coating defects that may occur on saddle surfaces or tortuous surfaces.

[0024] Example 4: S2 specifically includes: S21. Call the fluid dynamics basic model parameters in the PLC storage area and set the initial fluid injection velocity variable and the injection cone angle variable; S22. For each discrete calculation point, determine the surface concavity / convexity attribute based on its local radius of curvature value. If the logical judgment is that it is a convex surface feature, then apply the divergence attenuation coefficient to correct the deposition amount calculation; if the logical judgment is that it is a concave surface feature, then apply the focusing gain coefficient to correct the deposition amount calculation. S23. Combine the angle data between the jet source vector and the discrete point normal vector to calculate the flux loss caused by the cosine effect, generate the corrected theoretical deposition flux vector, and combine them to form the target three-dimensional flux distribution data matrix. This embodiment refines the construction logic of the flux distribution model in S2, focusing on the simplification and correction of fluid dynamics; it executes fluid parameter initialization in S21; and it calls the basic fluid dynamics model parameters stored in the PLC data block to set the initial fluid injection velocity variable.

[0025] With the variable injection cone angle ; Perform S22 curvature-based deposition correction; for each discrete calculation point, read its local radius of curvature value. ; In response to the logical judgment being a convex feature, the system applies a divergent attenuation coefficient. The deposition rate calculation is revised to simulate the decrease in deposition rate per unit area caused by the increased surface curvature of the fluid impact point; in response to the logical judgment that the surface is concave, a focusing gain coefficient is applied. The sedimentation calculation was revised to simulate the fluid convergence towards the center and the eddy effect; Perform S23 cosine effect correction; combine the angle data between the jet source vector and the discrete point normal vector. The flux loss caused by the cosine effect is calculated using a variant of Lambert's cosine law. Theoretical deposition flux The source is a corrected calculation; the physical meaning is the actual effective coating deposition rate after considering geometric curvature and incident angle, and the unit is... ; Generate the corrected theoretical deposition flux vector and combine them to form the target three-dimensional flux distribution data matrix; This embodiment mathematically expresses the physical laws that make it difficult to spray thick coatings on convex surfaces, easy to accumulate paint on concave surfaces, and less deposition on inclined surfaces by introducing a curvature-based correction coefficient and cosine effect calculation. In the virtual spraying process simulation scenario, this method enables the control commands generated by the PLC to pre-compensate for these physical phenomena, thereby accurately simulating the uneven film thickness distribution caused by the curved surface geometry on the flat test plate and avoiding the verification deviation caused by the idealized model.

[0026] Example 5: S3 specifically includes: S31. Establish a two-dimensional Cartesian coordinate system for the physical plane substrate inside the PLC, project the discrete points in the virtual geometric feature dataset onto the two-dimensional Cartesian coordinate system, and determine the reference movement path data of the jet source on the physical plane. S32. For each control node on the path, the dynamic attitude deflection command that makes the angle between the jet source and the physical plane equivalent to the angle between the jet source and the tangential plane in the virtual space is calculated by the inverse kinematics algorithm. S33. Based on the dynamic attitude deflection command, calculate the rate of change of the projected area of ​​the jet beam on the physical plane, and reverse the variable speed motion command of the jet source according to the rate of change of the projected area to maintain constant energy data per unit area. S34. Based on the fluid divergence or focusing demand data corresponding to the local curvature radius, calculate the variable fan width air pressure command used to change the fan width of the jet beam, and integrate the dynamic attitude deflection command, variable speed motion command and variable fan width air pressure command into a composite compensation command sequence. This embodiment describes in detail how S3 reduces three-dimensional requirements to two-dimensional control instructions, which constitutes the core control strategy of the system; Perform S31 reference path projection; establish a two-dimensional Cartesian coordinate system for the physical plane substrate within the PLC. Discrete points in the virtual geometric feature dataset are orthogonally projected onto a two-dimensional Cartesian coordinate system along the inverse direction of the normal vector of the physical plane substrate to establish discrete points in virtual space. physical plane coordinates One-to-one mapping relationship; Perform S32 attitude equivalent mapping; for each control node on the path, calculate the dynamic attitude deflection command using inverse kinematics algorithm; this command aims to deflect the physical spray gun, setting the angle between the spray axis and the physical plane. Equivalent to the angle between the jet axis and the tangent plane of the virtual surface in virtual space. ; S33 variable velocity energy conservation is executed; based on the dynamic attitude deflection command, the rate of change of the projected area of ​​the jet beam on the physical plane is calculated, and the variable velocity motion command is solved in reverse accordingly, aiming to maintain constant energy data per unit area. S34 variable sector pressure modulation is implemented; the system is based on local radius of curvature. The corresponding fluid divergence or focusing demand data is used to calculate the variable fan width air pressure command used to change the width of the jet beam, and all commands are integrated into a composite compensation command sequence. Fan width With fan-shaped pressure The two functions satisfy a predefined quadratic function relationship:

[0027] in, The fitting constant is related to the fluid viscosity; the system calculates the required fan width based on the target coverage area. Then substitute the above formula to find the answer. ; This embodiment reconstructs the dynamic environment of three-dimensional spraying on a two-dimensional plane through a multivariable coupling control strategy. In the test panel verification scenario, by simultaneously adjusting the attitude, speed and fan width, the system can make the deposition behavior of each drop of paint as if it were sprayed on a curved surface, even when spraying on a flat surface. This dimension reduction mapping mechanism successfully preserves the process characteristics of the virtual curved surface on the physical plane, providing a technical implementation path for low-cost process verification of complex curved surfaces.

[0028] Example 6: The generation of the S34 variable fan-width air pressure command follows the following logical operation rules: If the current discrete point corresponds to virtual convex surface feature data, a digital instruction to reduce the fan-shaped forming pressure is generated to shrink the jet beam width and simulate the boundary slip effect of fluid on the convex surface; if the current discrete point corresponds to virtual concave surface feature data, a digital instruction to increase the fan-shaped forming pressure is generated to expand the jet beam width and simulate the vortex accumulation effect of fluid on the concave surface. If the current discrete point corresponds to virtual plane feature data, then the preset reference fan-shaped pressure data is maintained; wherein, the adjustment amount of pressure has a non-linear mapping relationship with the reciprocal of the local radius of curvature. This embodiment specifically defines the generation logic of the variable fan width air pressure command in S34, and uses aerodynamic characteristics to simulate geometric characteristics; the system performs the following logical operation based on the geometric attributes of the current discrete point: in response to the virtual convex surface feature data corresponding to the current discrete point, a digital command to reduce the fan width forming pressure is generated; this operation aims to shrink the width of the jet beam, thereby simulating the change in the effective coverage area caused by the boundary slip effect of the fluid on the convex surface on the plane; In response to the virtual concave surface feature data corresponding to the current discrete point, a digital command is generated to increase the fan-shaped forming pressure to expand the jet width and simulate the vortex accumulation effect and multiple reflections of fluid on the concave surface; in response to the virtual planar feature data corresponding to the current discrete point, the preset baseline fan-shaped forming pressure data is maintained; during this process, the pressure adjustment amount is... The reciprocal of the local radius of curvature, i.e., curvature It exhibits a non-linear mapping relationship; pressure adjustment amount The source is nonlinear mapping calculation; its physical meaning is the fan-shaped air pressure compensation value required to simulate the effect of geometric curvature, with units of... or The formula is as follows:

[0029] in, Specifically, it refers to the sub-curvature value perpendicular to the trajectory direction extracted in step S12. This is because the adjustment of the fan width is mainly used to compensate for the geometric distortion of the spray band in the width direction; while The preset adjustment coefficient is designed with pressure and length as its physical dimensions. ,For example This is to ensure the homogeneity of the physical dimensions on both sides of the formula; Preset adjustment coefficient The steps to obtain the data are as follows: During the system initialization phase, a variable pressure spraying test is conducted on a standard flat test plate using the current coating. The change in spray width under different forming pressures is recorded, and the pressure change is fitted using linear regression analysis. The slope proportional to the geometric deformation eigenvalue is set as an adjustment coefficient. .

[0030] Example 7: The generation of variable speed motion commands in S33 specifically includes: acquiring tilt angle data from dynamic attitude deflection commands and calculating the elliptical projection area of ​​the jet beam cross-section on the physical plane. Calculate the ratio of the elliptical projected area to the standard circular projected area, and use this ratio as a speed correction factor; multiply the preset baseline spraying speed data by the speed correction factor to generate instantaneous target speed data at each control node, construct variable speed motion commands, and ensure that the coating thickness distribution data on the physical plane substrate is consistent with the virtual target surface under inclined spraying conditions. This embodiment details the algorithm for generating variable speed motion commands, aiming to eliminate the interference of uneven film thickness caused by tilted spraying; the system parses dynamic attitude deflection commands and extracts the tilt angle data. That is, the angle between the central axis of the spray gun and the normal to the physical plane; based on the geometric projection model, calculate the numerical value of the elliptical projection area of ​​the spray beam section on the physical plane. , ; Ellipse projected area value The source is geometric projection calculation; its physical meaning is the actual coverage area of ​​the spray on a plane when the spray gun is tilted; the unit is... Standard circular projected area value The source is the preset process parameters; its physical meaning is the spray area when the spray gun is perpendicular to the plane and maintains the standard construction distance, and the unit is... Tilt angle data The source is inverse kinematics solution, and the unit is degrees; Calculate the geometric ratio between the projected area of ​​the ellipse and the projected area of ​​the standard circle; to compensate for the decrease in flow rate per unit area caused by the increase in projected area, the system will use a velocity correction factor. It is set to the reciprocal of this geometric expansion ratio, that is, the ratio of the projected area of ​​a standard circle to the projected area of ​​an ellipse; Specifically, tilting will increase the projected area ( To conform to the law of conservation of fluid deposition, the following is defined here: Therefore, the calculated Therefore, the calculated Simultaneously set the maximum effective tilt angle threshold. Preferred ,like Then a mandatory order To prevent due to This causes the spray gun to stop moving; Instantaneous target velocity data The source is real-time calculation; its physical meaning is the robot's TCP movement speed, which is reduced to compensate for the flux dispersion caused by the increase in area; the unit is... A variable speed motion command is constructed, which increases the spraying time per unit length by reducing the spray gun movement speed, thereby compensating for the decrease in paint throughput per unit area caused by the expansion of the spray area, and ensuring that the coating thickness distribution data on the physical planar substrate is consistent with the virtual target surface under inclined spraying conditions.

[0031] Example 8: S4 specifically includes: S41. Obtain the physical response lag time constant of the fluid control system and the motion response lag time constant of the mechanical execution system; S42. Calculate the time difference between the two, perform time axis translation compensation operation on the variable fan pressure command in the composite compensation command sequence, and generate a synchronized control data stream. The S43 and PLC controllers read the synchronized control data stream at a fixed scan cycle, drive the robot joint servo motors to perform pose transformations through the bus communication protocol, and drive the proportional pressure regulating valve to perform pressure regulation through the analog output port, thereby achieving nanosecond-level coordination between spatial trajectory control and fluid state control. This embodiment solves the problem of mismatch in response speed between the fluid control system and the mechanical motion system; it executes S41 to obtain the time constant; and the system pre-measures and stores the physical response lag time constant of the fluid control system. Motion response lag time constant of mechanical actuator system ; Physical response lag time constant The source is system calibration; its physical meaning is the delay from signal transmission to actual change in nozzle pressure, measured in units of... ; Perform S42 time axis translation compensation; calculate the time difference between the two. Furthermore, time axis translation compensation calculations are performed on the variable fan-amplitude air pressure commands in the composite compensation command sequence, i.e., in advance. It continuously issues air pressure commands, generating synchronized control data streams; executes S43 nanosecond-level coordination; the PLC controller reads the synchronized control data streams at fixed scan cycles. The robot joint servo motors are driven by the EtherCAT bus to perform pose transformation, and the proportional pressure regulating valve is driven by the high-speed analog output port to perform pressure regulation, realizing nanosecond-level coordination between spatial trajectory control and fluid state control. This embodiment overcomes the physical limitation that the gas path response lags behind the circuit response by using time axis translation compensation technology. In high-speed dynamic spraying scenarios, this method eliminates the positional shift of simulated features caused by response time difference, ensuring that the application position of process parameters strictly matches the preset trajectory in high dynamic processes with varying speeds and angles, thereby improving the spatiotemporal coordination accuracy of the system.

[0032] Example 9: The system also includes: The multi-dimensional parameter optimization program module is configured to control the process parameters in the composite compensation instruction sequence to change continuously according to a preset gradient along the long axis of the physical planar substrate during a single spraying stroke, forming a gradient coating on the physical planar substrate that exhibits parameter matrix evolution characteristics, and is used to determine the optimal process window data. This embodiment introduces a multi-dimensional parameter optimization module to improve the efficiency of process window determination. This module is configured to, during a single spraying stroke, control the process parameters in a composite compensation command sequence to continuously change according to a preset gradient along the long axis of the physical planar substrate, instead of executing a single parameter. For example, a low flow rate is set at the start of the stroke, a high flow rate is set at the end, and linear interpolation is performed in between. This forms a gradient coating on the physical planar substrate exhibiting parameter matrix evolution characteristics. Engineers can determine the optimal process window data—the parameter range corresponding to acceptable coating quality—in one go by observing the change in coating quality along the length of the substrate. This embodiment utilizes gradient coating technology to transform traditional discrete point testing into continuous domain testing. In formulation development and process debugging scenarios, this module can continuously present the results of parameter changes on a single plate, compressing the experiment that originally required spraying dozens of plates into a single stroke, increasing experimental efficiency by dozens of times, and accelerating the development cycle of coating processes for new materials or new vehicle models.

[0033] Example 10: The execution drive control module controls the coating formed by the spray source on the physical plane substrate. It has the same flow critical point characteristic data, orange peel texture characteristic data and color difference distribution characteristic data as the virtual target curved surface spraying process, and is used to verify the feasibility of complex curved surface process formulas on physical planes. This embodiment describes the final verification metrics and applications of the system, confirming the consistency between the physical plane results and the virtual surface features; The execution drive control module controls the coating formed by the spray source on a physical planar substrate, which has the same key feature data as the virtual target curved surface spraying process; Sagging critical point characteristic data: sourced from coating observation, its physical meaning is the critical thickness position at which the coating begins to flow downwards under the influence of gravity, used to evaluate anti-sagging performance; Orange peel texture characteristic data: sourced from wave scanner measurement, its physical meaning is long and short wave data reflecting the microscopic smoothness of the coating surface; Color difference distribution characteristic data: sourced from multi-angle colorimeter, its physical meaning is the angle-dependent color difference phenomenon reflecting the change of aluminum powder arrangement in metallic paint with the spraying angle; The system uses the comparison of these feature data to verify the feasibility of complex curved surface process formulations on a physical plane; This embodiment establishes a verification standard of verifying three dimensions using two dimensions. In the final process acceptance scenario, by reproducing the three core appearance indicators of sagging, orange peel, and color difference, it is confirmed that the system successfully reproduced the process challenges of complex curved surfaces on the flat test board, realizing a deep integration of physical experiments and virtual simulation, and providing a reliable physical verification terminal for the digital transformation of coating processes.

[0034] 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. An automatic test panel spraying system based on PLC control, characterized in that, include: The virtual feature parsing module is configured to run in the processing unit of the PLC. It is used to acquire the three-dimensional digital model data of the virtual target surface and the preset spraying trajectory, and to perform discretization mesh processing on the three-dimensional digital model data to construct a virtual geometric feature dataset containing the curvature features and normal vector fields of each discrete point. The equivalent flux calculation module is configured to, based on the virtual geometric feature dataset, call a pre-stored fluid dynamics algorithm model to calculate the theoretical deposition flux vector at each discrete point along the preset spraying trajectory, and generate a target three-dimensional flux distribution data matrix. The projection mapping compensation module is configured to establish a coordinate dimensionality reduction mapping logic from virtual three-dimensional space to physical two-dimensional plane. Based on the target three-dimensional flux distribution data matrix, it calculates the equivalent control parameters on the physical plane and generates a composite compensation instruction sequence containing dynamic attitude deflection data, variable speed motion data and variable fan-width air pressure data. The execution drive control module is configured to read the composite compensation instruction sequence in real time via fieldbus, convert the digital instructions into multi-axis servo drive signals and analog adjustment signals, and drive the actuator to perform spatiotemporal coordinated actions on the physical planar substrate, so as to reproduce the fluid deposition characteristics of the virtual target surface on the physical planar substrate through program control logic.

2. The automatic test panel spraying system based on PLC control according to claim 1, characterized in that, The modules are interconnected through the following program control steps: S1. Data Discretization and Feature Extraction: Import the three-dimensional geometric model data of the virtual target surface, extract the coordinates of discrete points on the preset spraying trajectory, calculate the normal vector of the tangent plane and the local radius of curvature at each discrete point through the PLC computing unit, and construct a virtual geometric feature dataset. S2. Flux distribution model construction: Based on the virtual geometric feature dataset, the wall adhesion effect and divergence characteristics of the fluid on the virtual target surface are analyzed by the algorithm, the theoretical deposition amount per unit area at each discrete point is calculated, and the target three-dimensional flux distribution data matrix is ​​generated. S3. Dimensional Reduction Mapping and Command Generation: The target three-dimensional flux distribution data matrix is ​​mapped to the physical plane coordinate system. The tilt angle and relative velocity of the jet source relative to the physical plane substrate are calculated by the geometric projection algorithm. The fan-shaped forming pressure is solved by the flow conservation algorithm to generate a composite compensation command sequence. S4. Multi-channel signal synchronization and execution: The composite compensation instruction sequence is converted into PLC axis control signals and analog adjustment signals. The axis control signals and analog adjustment signals are subjected to time-domain synchronous interpolation processing to drive the actuator to complete the spraying operation.

3. The automatic test panel spraying system based on PLC control according to claim 2, characterized in that, S1 specifically includes: S11. Digitize the virtual target surface into a grid, obtain all grid units passed through by the preset spraying trajectory, and determine the geometric center point of each grid unit as a discrete calculation point. S12. For each discrete calculation point, extract its position coordinate data and surface normal vector data in three-dimensional space, and calculate the principal curvature and secondary curvature values ​​of the point along the trajectory tangent direction using differential geometry algorithm. S13. Arrange the position coordinate data, surface normal vector data, principal curvature values ​​and secondary curvature values ​​according to the trajectory time sequence to construct a virtual geometric feature dataset containing geometric topology information.

4. The automatic test panel spraying system based on PLC control according to claim 3, characterized in that, S2 specifically includes: S21. Call the fluid dynamics basic model parameters in the PLC storage area and set the initial fluid injection velocity variable and the injection cone angle variable; S22. For each discrete calculation point, determine the surface concavity / convexity attribute based on its local radius of curvature value. If the logical judgment is that it is a convex surface feature, then apply the divergence attenuation coefficient to correct the deposition amount calculation; if the logical judgment is that it is a concave surface feature, then apply the focusing gain coefficient to correct the deposition amount calculation. S23. By combining the angle data between the jet source vector and the discrete point normal vector, calculate the flux loss caused by the cosine effect, generate the corrected theoretical deposition flux vector, and combine them to form the target three-dimensional flux distribution data matrix.

5. The automatic test panel spraying system based on PLC control according to claim 4, characterized in that, S3 specifically includes: S31. Establish a two-dimensional Cartesian coordinate system for the physical plane substrate inside the PLC, project the discrete points in the virtual geometric feature dataset onto the two-dimensional Cartesian coordinate system, and determine the reference movement path data of the jet source on the physical plane. S32. For each control node on the path, the dynamic attitude deflection command that makes the angle between the jet source and the physical plane equivalent to the angle between the jet source and the tangential plane in the virtual space is calculated by the inverse kinematics algorithm. S33. Based on the dynamic attitude deflection command, calculate the rate of change of the projected area of ​​the jet beam on the physical plane, and reverse the variable speed motion command of the jet source according to the rate of change of the projected area to maintain constant energy data per unit area. S34. Based on the fluid divergence or focusing demand data corresponding to the local curvature radius, calculate the variable fan width air pressure command used to change the fan width of the jet beam, and integrate the dynamic attitude deflection command, variable speed motion command and variable fan width air pressure command into a composite compensation command sequence.

6. The automatic test panel spraying system based on PLC control according to claim 5, characterized in that, The generation of the variable fan-width air pressure command in S34 follows the following logical operation rules: If the current discrete point corresponds to virtual convex surface feature data, then a digital instruction to reduce the fan-shaped forming pressure is generated to shrink the width of the jet beam and simulate the boundary slip effect of the fluid on the convex surface. If the current discrete point corresponds to virtual concave surface feature data, then a digital command is generated to increase the fan-shaped forming pressure to expand the width of the jet beam and simulate the vortex accumulation effect of fluid on the concave surface. If the current discrete point corresponds to virtual plane feature data, then the preset reference fan-shaped pressure data is maintained; The adjustment value of the pressure has a non-linear mapping relationship with the reciprocal of the local radius of curvature.

7. The automatic test panel spraying system based on PLC control according to claim 5, characterized in that, The generation of the variable speed motion command in S33 specifically includes: Obtain the tilt angle data from the dynamic attitude deflection command and calculate the elliptical projection area of ​​the jet beam section on the physical plane. Calculate the ratio of the projected area of ​​the ellipse to the projected area of ​​the standard circle, and use this ratio as a velocity correction factor; The preset baseline spraying speed data is multiplied by the speed correction factor to generate instantaneous target speed data at each control node, and a variable speed motion command is constructed to ensure that the coating thickness distribution data on the physical planar substrate is consistent with the virtual target surface under the tilted spraying state.

8. The automatic test panel spraying system based on PLC control according to claim 2, characterized in that, S4 specifically includes: S41. Obtain the physical response lag time constant of the fluid control system and the motion response lag time constant of the mechanical execution system; S42. Calculate the time difference between the two, perform time axis translation compensation operation on the variable fan width air pressure command in the composite compensation command sequence, and generate a synchronized control data stream. S43, the PLC controller reads the synchronized control data stream at a fixed scan cycle, drives the robot joint servo motor to perform pose transformation through the bus communication protocol, and drives the proportional pressure regulating valve to perform pressure regulation through the analog output port, realizing nanosecond-level coordination of spatial trajectory control and fluid state control.

9. The automatic test panel spraying system based on PLC control according to claim 1, characterized in that, The system also includes: The multi-dimensional parameter optimization program module is configured to control the process parameters in the composite compensation instruction sequence to continuously change according to a preset gradient along the long axis of the physical planar substrate during a single spraying stroke, forming a gradient coating on the physical planar substrate that exhibits the evolution characteristics of the parameter matrix, and is used to determine the optimal process window data.

10. The automatic test panel spraying system based on PLC control according to claim 1, characterized in that, The execution drive control module controls the coating formed by the spray source on the physical planar substrate. It has the same flow critical point feature data, orange peel texture feature data and color difference distribution feature data as the virtual target curved surface spraying process, and is used to verify the feasibility of complex curved surface process formulas on physical planes.

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