Swivel chair chassis surface spraying control method and system

Abstract

The invention relates to the field of data processing, in particular to a swivel chair chassis surface spraying control method and system, and the method comprises the steps: preprocessing a swivel chair chassis three-dimensional model to generate an initial spraying path set, discretizing paths to obtain spatial sampling points and key parameters, determining a path execution time sequence, and generating a path execution sequence. Meanwhile, surface evaluation points are preset, path access intensity and directional discrete gain factors are analyzed, a traditional sedimentary model is corrected, and more accurate coating thickness prediction is obtained. The modified model is embedded into the MPC, and an optimized objective function is constructed to realize closed-loop control, so that the coating thickness prediction precision is remarkably improved, the spraying path and parameters are optimized, the coating waste is reduced, the spraying efficiency and quality are improved, and the universality and adaptability of the system to a complex geometric structure are enhanced. According to the method, the deposition model is corrected through the path access intensity and the directional discrete gain factor, the coating thickness prediction precision is remarkably improved, and the paint film defect caused by the traditional model prediction deviation is avoided.

Description

Technical Field

[0001] This invention relates to the field of data processing. In particular, it relates to a method and system for controlling the spraying of coatings on the surface of a swivel chair chassis. Background Technology

[0002] As a key structural component of office furniture, the surface protection performance of swivel chair chassis directly affects the product's durability and appearance quality. With the trend towards automation and intelligence in office furniture manufacturing, the spraying process for swivel chair chassis is receiving increasing attention.

[0003] Typical spray deposition models are based on the single-spray assumption, assuming that the paint film thickness at each spatial point is determined only by a single spray action along the current path, and that the deposition exhibits an axisymmetric Gaussian distribution. However, on workpieces with multi-arm star-shaped structures, such as swivel chair chassis, the same surface area is often repeatedly covered by multiple spray paths in different directions, especially in the intersection of three or five branches, where a significant "multi-path superposition effect" exists. For example, the central connecting plate area may be swept across from different directions by the spray paths of three arms, resulting in an actual paint film thickness much higher than the prediction of the single-spray model.

[0004] Existing MPC (Model Predictive Control) algorithms rely on deposition models that fail to consider path visit frequency, incident direction differences, and superposition accumulation mechanisms. This leads to underestimation of the predicted thickness, causing the controller to misjudge the need for normal spraying, resulting in defects such as excessively thick paint films, sagging, and poor curing. Furthermore, the nonlinear cumulative nature of model bias means that the more overlapping paths and the more dispersed the angles, the faster the actual deposition amount increases. Traditional models still treat this as linear superposition, further exacerbating control inaccuracies. Summary of the Invention

[0005] To address the shortcomings of existing MPC deposition models, which neglect path visit frequency, incident direction differences, and superposition accumulation mechanisms, leading to underestimation of thickness and controller misjudgment requiring normal spraying, resulting in excessively thick paint films, sagging, and poor curing, and further exacerbating the problem of control inaccuracies caused by the nonlinear cumulative characteristics of model bias (where the more overlapping paths and the more dispersed the angles, the faster the actual deposition amount increases), and the linear superposition processing method of traditional models further aggravates the control inaccuracy issue, this invention provides solutions in the following aspects.

[0006] In a first aspect, a method for controlling the surface coating of a swivel chair chassis includes: acquiring a three-dimensional model of the swivel chair chassis and preprocessing it; generating an initial set of coating paths from the preprocessed three-dimensional model; discretizing each path in the initial set of coating paths to obtain several spatial sampling points and extracting key parameters; determining the execution time sequence of the paths based on the spatial sampling points and key parameters; constructing a time function and generating a path execution sequence; pre-setting multiple surface evaluation points on the swivel chair chassis; analyzing the path access intensity of each surface evaluation point to quantify the frequency and density of coverage by the coating path; calculating the directional discrete gain factor to capture the enhancing effect of the diversity of coating path directions on deposition efficiency; using the ratio of the directional discrete gain factor to the path access intensity as the directional discrete enhancement ratio; correcting the predicted layer thickness of a single path in the traditional deposition model to obtain the corrected predicted coating thickness in the significant deposition model; embedding the significant deposition model into the MPC (Multi-Process Control) and constructing an optimization objective function with the spray gun flow rate and moving speed as control variables to achieve closed-loop control.

[0007] Preferably, the preprocessing step includes: Based on the geometric topology information of the obtained 3D model of the swivel chair chassis, the 3D model is geometrically repaired, including filling holes, eliminating self-intersecting surfaces, and correcting normal consistency. The key areas of the repaired 3D model are marked, including the central connecting plate, the transition area at the root of the support arm, and the area around the threaded hole.

[0008] By performing geometric repairs on the 3D model of the swivel chair chassis, including filling holes, eliminating self-intersecting surfaces, and correcting normal consistency, the geometric integrity and accuracy of the model were ensured. Key areas of the repaired model, such as the central connecting plate, the transition area at the root of the support arm, and the area around the threaded holes, were marked, allowing for special attention and optimization of these defect-prone areas during the painting process. This significantly improved the accuracy of the painting path planning and the uniformity of the painting quality, effectively reducing problems such as uneven coating, excessive thickness, or insufficient thickness, thereby improving overall painting efficiency and product quality.

[0009] Preferably, generating the initial spraying path set includes: The 3D model of the swivel chair chassis is mapped to a 2D parameter domain using the UV unwrapping method. Based on the structural characteristics of the swivel chair chassis, a main spraying direction is selected. Based on the main spraying direction, a set of parallel lines are generated in the 2D parameter domain at preset intervals. The parallel lines are then mapped back to the 3D curved surface and transformed into spatial curves. Each spatial curve is used as an independent spraying path to generate an initial set of spraying paths covering the entire surface of the swivel chair chassis.

[0010] By mapping the 3D model of the swivel chair chassis to the 2D parameter domain and selecting the main spraying direction to generate the initial spraying path set, the uniform distribution and full coverage of the spraying path can be ensured, effectively reducing the overlapping and missed areas in the spraying process, thereby improving the uniformity of the coating and the spraying efficiency. At the same time, it reduces the uneven coating thickness and paint waste caused by unreasonable path planning, and enhances the adaptability and reliability of the spraying system.

[0011] Preferably, the path execution sequence for determining the path based on spatial sampling points and key parameters includes: Based on geometric features and painting requirements, paths are prioritized. After determining the priorities, offline programming tools are used to simulate robot movement, calculate the start and end times of each path, and generate a path execution sequence arranged in ascending order of time.

[0012] Preferably, the analysis of the path access intensity at each surface evaluation point includes: On the surface of the 3D model of the swivel chair chassis, a set of surface evaluation points is set according to the principle of uniform distribution and the principle of densification for the areas corresponding to key parameters. Taking any surface evaluation point in the set as the target evaluation point, the square of the distance between the target evaluation point and the nearest point on each spraying path is calculated. The ratio of the square of the distance to the square of twice the spatial attenuation coefficient is calculated, and exponential attenuation is performed using a negative exponential function. The result of exponential attenuation is the Gaussian function value of the path point closest to the target evaluation point on each spraying path. The Gaussian function values ​​corresponding to all spraying paths are summed to obtain the path access intensity of the target evaluation point.

[0013] By setting a uniformly distributed set of surface evaluation points with denser coverage in key areas on the surface of the swivel chair chassis, and calculating the path access intensity of each evaluation point, the frequency and density of the coating path's coverage of each point on the surface can be accurately quantified. This effectively identifies areas where the coating thickness may be too thick or too thin, thereby significantly improving the uniformity of the coating thickness, reducing coating defects caused by uneven path coverage, and enhancing the stability and reliability of the coating quality.

[0014] Preferably, the discrete gain factor is calculated in the following ways: When the value of the Gaussian function of the nearest path point to each surface evaluation point on each path is greater than a preset threshold, the path is considered to have a significant impact on the surface evaluation points and constitutes a set of significantly influential paths. Based on the nearest path point to the surface evaluation point on each path in the set of significantly influential paths, calculate the angle between the spraying axis direction and the surface normal vector, take the negative value of the cosine of the angle as the cosine weight, multiply the cosine weight by the travel direction vector of the nearest path point, and sum over all paths to obtain the weighted average direction vector. Calculate the Euclidean norm of the weighted average direction vector, subtract the ratio between 1 and the path access intensity, and use it as the direction dispersion enhancement factor. Multiply the preset gain coefficient by the direction dispersion enhancement factor and add 1 to get the direction dispersion gain factor at the surface evaluation point.

[0015] By calculating the Gaussian function value of the nearest path point to each surface evaluation point on each path, and selecting paths that significantly affect the surface evaluation points to form a set of significantly influential paths, and then calculating the directional discrete gain factor, the enhancement effect of the diversity of spraying path directions on deposition efficiency can be accurately captured. This significantly improves the uniformity and consistency of spraying quality, reduces the problem of uneven coating thickness caused by directional differences, and enhances the adaptability and reliability of the spraying system.

[0016] Preferably, the step of obtaining the predicted layer thickness for a single path in the conventional deposition model includes: Obtain the set of significantly influential paths and calculate the ratio of paint flow rate to spray gun movement speed for each path; use a negative exponential function to perform attenuation mapping on the actual spraying distance from the nozzle to the surface evaluation point under the spraying path; calculate the angle between the spraying axis direction and the surface normal vector based on the path point closest to the surface evaluation point on each path in the set of significantly influential paths, and use the negative value of the cosine of the angle as the cosine weight. The thickness of the predicted layer for a traditional single path is obtained by multiplying and summing the ratios of each path in the path set that significantly affect the attenuation mapping results and the cosine weights.

[0017] Secondly, a swivel chair chassis surface spraying control system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned swivel chair chassis surface spraying control method is implemented.

[0018] The present invention has the following effects: 1. This invention, by introducing path access intensity and directional discrete gain factors, enables the modified deposition model to dynamically reflect the impact of multipath superposition effects and directional diversity on deposition efficiency, thereby significantly improving the accuracy of coating thickness prediction. It effectively avoids prediction biases caused by traditional models that neglect path access frequency, incident direction differences, and superposition accumulation mechanisms, reducing defects such as excessively thick coatings, sagging, and poor curing.

[0019] 2. This invention utilizes an MPC (Model Predictive Control) framework to predict coating thickness in real time and construct an optimization objective function with spray gun flow rate and travel speed as control variables. A rolling optimization strategy is implemented, re-predicting and optimizing after only the first control action, achieving closed-loop control. Simultaneously, a real-time feedback correction mechanism is introduced to dynamically adjust model parameters based on online thickness measurement data, improving long-term prediction accuracy, significantly reducing paint waste, and increasing spraying efficiency. Attached Figure Description

[0020] Figure 1 This is a flowchart of steps S1-S4 in a method for controlling the spraying of a swivel chair chassis surface according to an embodiment of the present invention.

[0021] Figure 2 This is a structural block diagram of a swivel chair chassis surface spraying control system according to an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0023] Reference Figure 1 A method for controlling the spraying of a swivel chair chassis surface includes steps S1-S4, as detailed below: S1: Obtain the 3D model of the swivel chair chassis and preprocess it. Generate an initial set of spraying paths from the preprocessed 3D model.

[0024] The preprocessing steps include: Based on the geometric topology information of the obtained 3D model of the swivel chair chassis, the 3D model is geometrically repaired, including filling holes, eliminating self-intersecting surfaces, and correcting normal consistency. The key areas of the repaired 3D model are marked, including the central connecting plate, the transition area at the root of the support arm, and the area around the threaded hole.

[0025] The 3D model of the swivel chair chassis is mapped to a 2D parameter domain using the UV unwrapping method. Based on the structural characteristics of the swivel chair chassis, a main spraying direction is selected. Based on the main spraying direction, a set of parallel lines are generated in the 2D parameter domain at preset intervals. The parallel lines are then mapped back to the 3D curved surface and transformed into spatial curves. Each spatial curve is used as an independent spraying path to generate an initial set of spraying paths covering the entire surface of the swivel chair chassis.

[0026] For example, if the swivel chair chassis has a five-pronged symmetrical structure, the main spraying direction can be selected along the axis of one of the arms to ensure uniform distribution and efficient coverage of the spraying path.

[0027] S2: Discretize each path in the initial spraying path set to obtain several spatial sampling points, extract key parameters, determine the execution time order of the path based on the spatial sampling points and key parameters, construct a time function, and generate a path execution sequence.

[0028] Key parameters include: position coordinates, surface normal vector, spray gun direction, spray axis direction, spray distance, and local curvature. Based on geometric features and painting requirements, paths are prioritized. After determining the priorities, offline programming tools are used to simulate robot movement, calculate the start and end times of each path, generate a path execution sequence arranged in ascending order of time, and construct a time-position mapping table for querying the painting status.

[0029] In other words, key parameters are extracted from spatial sampling points using Robot Studio robot simulation software; for example, path priority allocation: high curvature areas and deep groove areas are painted first; the central connecting plate area is painted first than the end of the arm; adjacent paths are executed as continuously as possible to reduce idle travel.

[0030] It's important to note that the key issue causing inaccurate predictions by traditional spray deposition models for the multi-arm star-shaped structure of a swivel chair chassis is the complexity of the chassis's shape. Due to the numerous arms forming a star shape, areas where the arms intersect, such as the central connecting plate, are actually painted multiple times from different directions. This results in the central connecting plate having a significantly thicker paint layer than other areas. Traditional spray deposition models don't account for this multi-layered painting and the different painting directions, leading to inaccurate paint film thickness predictions. They assume the paint film thickness in each area is simply added together, but in reality, multiple painting layers result in a faster increase in paint film thickness, and the different painting directions also contribute to a more uniform paint coverage. This leads to a significant discrepancy between the actual paint film thickness and the traditional model's prediction, potentially resulting in excessively thick paint films, runs, or uneven coverage.

[0031] To address the multipath superposition effect in the intersection area of ​​the swivel chair chassis, a dynamic deposition model is needed to accurately predict the paint film thickness on the surface of complex geometries. The specific steps are as follows: S3: Multiple surface evaluation points are preset on the swivel chair chassis. The path access intensity of each surface evaluation point is analyzed to quantify the frequency and density of the sprayed path coverage. The directional discrete gain factor is calculated to capture the enhancing effect of the diversity of sprayed path direction on deposition efficiency. The ratio of the directional discrete gain factor to the path access intensity is used as the directional discrete enhancement ratio. The predicted layer thickness of a single path in the traditional deposition model is corrected to obtain the corrected predicted coating thickness in the significant deposition model.

[0032] Path access strength includes: On the surface of the 3D model of the swivel chair chassis, a set of surface evaluation points is set according to the principle of uniform distribution and the principle of densification for the areas corresponding to key parameters. Taking any surface evaluation point in the set as the target evaluation point, the square of the distance between the target evaluation point and the nearest point on each spraying path is calculated. The ratio of the square of the distance to the square of twice the spatial attenuation coefficient is calculated, and exponential attenuation is performed using a negative exponential function. The result of exponential attenuation is the Gaussian function value of the path point closest to the target evaluation point on each spraying path. The Gaussian function values ​​corresponding to all spraying paths are summed to obtain the path access intensity of the target evaluation point.

[0033] Furthermore, the construction of path access intensity comprehensively considers key factors such as the spatial distribution density of paths, the spraying influence range, and process priority, thereby accurately identifying areas with high overlap risks. When multiple spraying paths densely pass through the same area, the path access intensity value increases significantly, reflecting the high path access frequency and density in that area; conversely, when the path distribution is sparse, the path access intensity value decreases rapidly. This makes path access intensity a core parameter for predicting the multi-path superposition effect, and it is of great significance for optimizing spraying path planning and controlling spraying quality.

[0034] In other words, a set of surface evaluation points is set on the surface of the 3D model of the swivel chair chassis, based on the principle of uniform distribution and the principle of densification for key areas. , Specifically, the basic mesh spacing is set to 20mm to achieve uniform coverage of the model surface. For critical areas such as the central connecting plate and the transition zone at the root of the support arm, considering that these areas are prone to uneven coating thickness during spraying, the mesh spacing is automatically increased to 8mm. Through these settings, approximately 500 surface evaluation points are generated, ensuring that each potentially high-overlapping area has a sufficient density of evaluation points, thus providing strong support for accurate coating thickness prediction. In practical applications, the layout of the surface evaluation points can be flexibly adjusted according to specific spraying process requirements and the geometry of the swivel chair chassis. These evaluation points are fixed to the surface of the swivel chair chassis, independent of the spraying path, and are specifically set for coating thickness prediction; they are the sole target locations for the prediction work.

[0035] Specifically, path access strength satisfies the following relationship: ; In the formula, Indicates the first Surface evaluation points Path access strength, Indicates the total number of spray paths. Indicates the first Surface evaluation points The spatial position vector represents a discretized analysis point on the surface of the swivel chair chassis, i.e., a location to be evaluated as to whether it will be sprayed multiple times. The position coordinates of the surface evaluation point are represented as follows: , is the target location for coating thickness prediction. For example, if the coordinates of the center point of a certain intersection area are , ,but ; Indicates the first Distance point on the spraying path closest path point Spatial position vector, This represents the spatial attenuation coefficient, indicating the effective radius of the spray's influence range. It determines how far away a surface point is from a certain path will still be significantly affected by the spraying action along that path. The value depends on the nozzle type and is typically 15-25mm. Represented by natural numbers An exponential function with base 0.

[0036] In other words, when the path access intensity of the surface evaluation point approaches 0, it indicates that the distance between the nearest path point to the surface evaluation point on each spraying path and the surface evaluation point is relatively far. At this time, The value is large, resulting in The intensity approaches 0. Therefore, the sum of the path access intensity also approaches 0, meaning that the surface evaluation point is almost completely covered by the spray path and can be identified as an under-sprayed area. In this case, the area can be touched up in subsequent spraying cycles, or the spray flow rate can be locally increased and the spray gun movement speed reduced in the area to ensure that the coating thickness meets the requirements.

[0037] When the path access intensity approaches 1, it indicates that only one spraying path significantly contributes to the point, while other paths have a smaller impact. In this case, the intensity of each path... The value approaches 1, while the value for other paths approaches 0. Therefore, the sum of path access intensities approaches 1, indicating that the surface evaluation point is in a normal spraying area, and its coating thickness is basically consistent with the prediction of the traditional single-path model. For such areas, the established spraying flow rate and speed should be maintained, and the normal spraying process should be followed.

[0038] When the path access intensity is greater than 1, it indicates that multiple spraying paths significantly contribute to that point. In this case, the intensity of each relevant path is... The values ​​all approach 1, resulting in a total path access intensity greater than 1. This indicates that the surface evaluation point is in an overspray zone, where the actual coating thickness will increase, easily leading to excessive coating thickness or sagging. To address this, the spray flow rate should be automatically reduced or the spray gun movement speed increased during subsequent spraying processes, or even the spray gun should be shut off early to suppress overspray and ensure coating thickness uniformity and spraying quality.

[0039] By calculating the distance between each surface evaluation point and the nearest point on all spray paths, and using a Gaussian kernel function for weighting, the frequency and density of the surface evaluation point being covered by the spray path are quantified. The higher the path access intensity, the more times the point is covered by the spray path, and the greater the spray density. This allows for precise identification of which areas may have excessively thick coatings due to path overlap, and which areas may have excessively thin coatings due to insufficient path coverage.

[0040] In the star-shaped confluence area of ​​the swivel chair chassis, due to the large number and dispersed orientation of atomized particles from different spraying paths, when multiple jets impact the same point at different azimuth angles, two key effects occur: local turbulence suppression and leveling gain. Local turbulence suppression reduces particle scattering losses and improves deposition efficiency; leveling gain further improves coating uniformity and thickness by filling the microscopic depressions left by the previous spraying with droplets from different directions. Traditional spray deposition models, due to simple linear addition of deposition in each direction, ignore these efficiency improvements brought about by directional diversity, resulting in an underestimation of coating thickness. The specific operation is as follows: The methods for calculating the discrete gain factor include: When the value of the Gaussian function of the nearest path point to each surface evaluation point on each path is greater than a preset threshold, the path is considered to have a significant impact on the surface evaluation points and constitutes a set of significantly influential paths. Based on the nearest path point to the surface evaluation point on each path in the set of significantly influential paths, calculate the angle between the spraying axis direction and the surface normal vector, take the negative value of the cosine of the angle as the cosine weight, multiply the cosine weight by the travel direction vector of the nearest path point, and sum over all paths to obtain the weighted average direction vector. Calculate the Euclidean norm of the weighted average direction vector, subtract the ratio between 1 and the path access intensity, and use it as the direction dispersion enhancement factor. Multiply the preset gain coefficient by the direction dispersion enhancement factor and add 1 to get the direction dispersion gain factor at the surface evaluation point.

[0041] Specifically, the discrete gain factor satisfies the following polynomial: ; ; In the formula, This represents the weighted average direction vector. Indicates the surface evaluation points The set of paths with significant influence indicates that the contribution of a path to the surface evaluation point is calculated using a Gaussian function. When the contribution value is greater than a threshold... In this case, the path is considered to have a significant impact on the surface evaluation points. The specific value can be set according to the actual application scenario; Indicates the first Distance from surface evaluation point on the spraying path closest path point The spray gun spraying axis direction, Indicates the first Surface evaluation points Surface normal vector, Indicates the first Distance from surface evaluation point on the spraying path closest path point The direction vector of travel; This indicates the angle between the spraying axis and the surface normal. This indicates the effective spray component weight; a larger value indicates a closer proximity to verticality and higher deposition efficiency.

[0042] Indicates the first The directional discrete gain factor at each surface evaluation point. This is used to quantify the deposition efficiency enhancement effect caused by directional disorder when multiple spraying paths spray the point from different directions. This represents the empirical gain coefficient, reflecting the degree to which directional dispersion enhances coating deposition efficiency. It is used to control... The maximum enhancement range, to avoid excessive amplification, is affected by factors such as coating type, nozzle atomization particle size, and workpiece material, and the value range is [value missing]. The specific value can be determined based on the actual application scenario; Representing vectors The Euclidean norm of a vector The modulus reflects the degree of directional consistency of all paths in a local region. Indicates the first Surface evaluation points Path access strength.

[0043] In other words, when Proximity path access strength When the direction consistency is high, the direction discrete gain factor is close to 1; when Much smaller than path access strength When the directional dispersion is high, the directional dispersion gain factor is significantly greater than 1, indicating that directional diversity has a significant enhancing effect on deposition efficiency.

[0044] Since path access intensity represents how many paths cover a surface evaluation point and at what density, directional gain is only significant when the paths are truly superimposed, i.e., when... When the direction is random, there will be no gain because there is no superposition; while when Only when the direction is discrete will additional deposition occur. Divide by This is equivalent to normalizing the directional dispersion to the scale of the superposition intensity, avoiding [the following]. Amplify occasional directional differences when they are very small.

[0045] For direction discrete gain factor In general, when there is only one path to the surface evaluation point, such as a straight line segment, ,therefore ,thereby ,at this time The prediction of coating thickness is not enhanced and is handled according to the conventional deposition model. When there are multiple paths at the surface evaluation point and their directions are consistent, ,in , At this point, the directional discrete gain factor is appropriately amplified. When there are multiple paths and their directions are dispersed at the surface evaluation point, such as at a five-way intersection, ,therefore ,thereby ,at this time This significantly improves the prediction of coating thickness, triggering control measures to reduce flow rate and slow down speed.

[0046] The steps for obtaining the predicted layer thickness in a traditional single-path method include: Obtain the set of significantly influential paths and calculate the ratio of paint flow rate to spray gun movement speed for each path; use a negative exponential function to perform attenuation mapping on the actual spraying distance from the nozzle to the surface evaluation point under the spraying path; calculate the angle between the spraying axis direction and the surface normal vector based on the path point closest to the surface evaluation point on each path in the set of significantly influential paths, and use the negative value of the cosine of the angle as the cosine weight. The thickness of the predicted layer for a traditional single path is obtained by multiplying and summing the ratios of each path in the path set that significantly affect the attenuation mapping results and the cosine weights.

[0047] Specifically, the prediction layer thickness of a traditional single path satisfies the following relationship: ; In the formula, Indicates the first Surface evaluation points Traditional single-path prediction layer thickness, in units of This indicates the surface evaluation point under the current spraying parameters. The cumulative paint film thickness after spraying through all relevant paths; Indicates the surface evaluation points The set of paths with significant impact Indicates the first The paint flow rate for each path, in units of This directly affects the amount of paint sprayed per unit time and is used as a proportional input in the model; Indicates the first The speed of the spray gun along the path, in units of The faster the speed, the shorter the residence time per unit area, and the less deposition. The specific value can be determined according to the complexity of the workpiece in the actual application scenario. This indicates the amount of paint supplied per unit area, which determines the basic deposition potential of a single spray. This represents the distance attenuation factor, in units of... , indicating the first Nozzle to surface evaluation point under each spraying path The actual spraying distance is crucial; the greater the distance, the more severe the dispersion of atomized particles and the lower the deposition rate per unit area. Using an exponential method aligns with the actual mass diffusion laws in spraying and can effectively address this. Add hyperparameters ,like: Control the intensity of distance attenuation; for example, set it to 0.001. Indicates the first Distance from surface evaluation point on the spraying path closest path point The spray gun spraying axis direction, Indicates the first Surface evaluation points Surface normal vector, This indicates the angle between the spraying axis and the surface normal. This indicates the weight of the effective spray component; a larger value indicates a closer proximity to verticality and higher deposition efficiency. It reflects the principle of prioritizing normal spraying.

[0048] Specifically, the significantly predicted coating thickness satisfies the following relationship: ; In the formula, Indicates the first Surface evaluation points Significant predicted layer thickness at the location, Indicates the first Surface evaluation points The direction discrete gain factor at that location, Indicates the first Surface evaluation points Path access strength, Indicates the first Surface evaluation points The traditional single-path prediction layer thickness at that location.

[0049] When the central connecting plate or the intersection area of ​​the support arms of a swivel chair chassis is repeatedly covered by multiple spray paths in different directions, the following three key effects occur: Multiple sprays prolong the surface wetting time, making subsequent coatings adhere better. Atomized particles from different directions create airflow disturbances in the intersection area, reducing scattering and improving deposition efficiency. A leveling effect occurs: coatings incident at certain angles can fill the microscopic depressions formed by previous sprays, producing a "leveling effect." These effects interact, resulting in the actual film thickness not being a simple linear summation of each spray, but exhibiting an accelerated, non-linear growth characteristic.

[0050] The path access intensity is defined by a weighted Gaussian kernel function to quantify the frequency and density of surface points covered by the spraying path; the directional discrete gain factor is calculated based on the vector analysis method to capture the enhancing effect of spraying direction diversity on deposition efficiency; and the two are combined to construct a multi-path superposition correction factor to dynamically reflect spraying scenarios with different degrees of overlap and directional diversity.

[0051] S4: Embed the significant deposition model into MPC and construct an optimization objective function with spray gun flow rate and moving speed as control variables to achieve closed-loop control.

[0052] Furthermore, this correction factor is embedded into the framework of a traditional deposition model to form a modified dynamic deposition model. This allows the modified dynamic deposition model to retain the basic elements of the traditional model while introducing a correction factor to achieve adaptive adjustment. When the path access intensity is 1, it degenerates into the traditional model, while when the path access intensity is greater than 1 and the direction is dispersed, it accurately predicts the accelerated growth characteristics of the paint film thickness through a nonlinear enhancement mechanism, thereby significantly improving the accuracy of paint film thickness prediction for complex geometric surfaces.

[0053] Specifically, the objective function satisfies the following relationship: ; In the formula, This represents the paint flow rate and spray gun movement speed corresponding to the minimum value of the objective function. Indicates the first Surface evaluation points Significant predicted layer thickness at the location, This indicates the target coating thickness, which is set according to the coating process requirements.

[0054] It should be noted that the rolling optimization strategy involves re-predicting and optimizing after only the first control action is executed, thus achieving closed-loop control. MPC and the rolling optimization strategy are well-known technologies in the field and will not be described in detail here.

[0055] This invention also provides a control system for spraying coating on the surface of a swivel chair chassis. For example... Figure 2 As shown, the system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a method for controlling the spraying of paint on the surface of a swivel chair chassis according to the first aspect of the present invention. The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, the setup and functions of which are known in the art and will not be described further here.

[0056] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for controlling the spray coating on the surface of a swivel chair chassis, characterized in that, include: Obtain the 3D model of the swivel chair chassis and preprocess it; then generate an initial set of spraying paths from the preprocessed 3D model. Each path in the initial spraying path set is discretized to obtain several spatial sampling points, and key parameters are extracted. Based on the spatial sampling points and key parameters, the execution time order of the path is determined, a time function is constructed, and a path execution sequence is generated. Multiple surface evaluation points are preset on the swivel chair chassis. The path access intensity of each surface evaluation point is analyzed to quantify the frequency and density of the sprayed path coverage. The direction discrete gain factor is calculated to capture the enhancing effect of the diversity of sprayed path direction on deposition efficiency. The ratio of the directional discrete gain factor to the path access intensity is used as the directional discrete enhancement ratio, and the predicted layer thickness of a single path in the traditional deposition model is corrected to obtain the corrected predicted coating thickness in the significant deposition model. The significant deposition model is embedded into MPC, and an optimization objective function with spray gun flow rate and moving speed as control variables is constructed to achieve closed-loop control.

2. The method for controlling the spraying of a swivel chair chassis surface according to claim 1, characterized in that, The preprocessing steps include: Based on the geometric topology information of the obtained 3D model of the swivel chair chassis, the 3D model is geometrically repaired, including filling holes, eliminating self-intersecting surfaces, and correcting normal consistency. The key areas of the repaired 3D model are marked, including the central connecting plate, the transition area at the root of the support arm, and the area around the threaded hole.

3. The method for controlling the spraying of a swivel chair chassis surface according to claim 1, characterized in that, The generation of the initial spraying path set includes: The 3D model of the swivel chair chassis is mapped to a 2D parameter domain using the UV unwrapping method. Based on the structural characteristics of the swivel chair chassis, a main spraying direction is selected. Based on the main spraying direction, a set of parallel lines are generated in the 2D parameter domain at preset intervals. The parallel lines are then mapped back to the 3D curved surface and transformed into spatial curves. Each spatial curve is used as an independent spraying path to generate an initial set of spraying paths covering the entire surface of the swivel chair chassis.

4. The method for controlling the spraying of a swivel chair chassis surface according to claim 1, characterized in that, The path execution sequence, which determines the path based on spatial sampling points and key parameters, includes: Based on geometric features and painting requirements, paths are prioritized. After determining the priorities, offline programming tools are used to simulate robot movement, calculate the start and end times of each path, and generate a path execution sequence arranged in ascending order of time.

5. The method for controlling the spraying of a swivel chair chassis surface according to claim 1, characterized in that, The analysis of path access strength at each surface evaluation point includes: On the surface of the 3D model of the swivel chair chassis, a set of surface evaluation points is set according to the principle of uniform distribution and the principle of densification for the areas corresponding to key parameters. Taking any surface evaluation point in the set as the target evaluation point, the square of the distance between the target evaluation point and the nearest point on each spraying path is calculated. The ratio of the square of the distance to the square of twice the spatial attenuation coefficient is calculated, and exponential attenuation is performed using a negative exponential function. The result of exponential attenuation is the Gaussian function value of the path point closest to the target evaluation point on each spraying path. The Gaussian function values ​​corresponding to all spraying paths are summed to obtain the path access intensity of the target evaluation point.

6. The method for controlling the spraying of a swivel chair chassis surface according to claim 1, characterized in that, The calculation method for the discrete gain factor includes: When the value of the Gaussian function of the nearest path point to each surface evaluation point on each path is greater than a preset threshold, the path is considered to have a significant impact on the surface evaluation points and constitutes a set of significantly influential paths. Based on the nearest path point to the surface evaluation point on each path in the set of significantly influential paths, calculate the angle between the spraying axis direction and the surface normal vector, take the negative value of the cosine of the angle as the cosine weight, multiply the cosine weight by the travel direction vector of the nearest path point, and sum over all paths to obtain the weighted average direction vector. Calculate the Euclidean norm of the weighted average direction vector, subtract the ratio between 1 and the path access intensity, and use it as the direction dispersion enhancement factor. Multiply the preset gain coefficient by the direction dispersion enhancement factor and add 1 to get the direction dispersion gain factor at the surface evaluation point.

7. The method for controlling the spraying of a swivel chair chassis surface according to claim 1, characterized in that, The steps for obtaining the predicted layer thickness for a single path in the traditional deposition model include: Obtain the set of significantly influential paths and calculate the ratio of paint flow rate to spray gun movement speed for each path; use a negative exponential function to perform attenuation mapping on the actual spraying distance from the nozzle to the surface evaluation point under the spraying path; calculate the angle between the spraying axis direction and the surface normal vector based on the path point closest to the surface evaluation point on each path in the set of significantly influential paths, and use the negative value of the cosine of the angle as the cosine weight. The thickness of the predicted layer for a traditional single path is obtained by multiplying and summing the ratios of each path in the path set that significantly affect the attenuation mapping results and the cosine weights.

8. A swivel chair chassis surface spraying control system, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the swivel chair chassis surface spraying control method according to any one of claims 1-7.

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