Coating tool paint parameter adaptive matching method and system

Abstract

The present application relates to the technical field of industrial coating automatic control, in particular to a kind of coating tool paint parameter self-adapting matching method and system.By locking paint supply branch to establish static stable state and generate initial working condition bottom map, inject reference paint to synchronize collection multistage film-forming characteristics and compare with theoretical film-forming curve cycle, converge to form golden response template, based on golden response template, by high-speed valve array inject fixed reference pulse and gradually solve and cross weight reorganization, generate dynamic compensation matrix, in actual coating operation, according to real-time film-forming uniformity, call dynamic compensation matrix to modify pump frequency, valve opening and nozzle trajectory, finally inject steady-state data stream pulse in equipment standby window and capture transient response decay curve, identify drift by double-shaft comparison and carry out hierarchical self-healing management, realize the self-maintenance and self-adapting iteration of coating tool paint parameter under the condition of uninterrupted production in whole life cycle.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology for industrial coating, specifically to an adaptive matching method and system for paint parameters of coating tooling. Background Technology

[0002] In the field of precision coating and topcoating processes, the stable control of paint parameters directly determines the film-forming quality indicators such as coating uniformity, adhesion strength, and edge leveling. Currently, common topcoating fixtures in the industry typically employ multi-channel paint supply systems, achieving quantitative paint supply and spreading by setting fixed pump frequencies, valve openings, and nozzle trajectories. However, the actual operating environment is not an ideal steady state. Minor fluctuations in ambient temperature, accumulation of residual back pressure in pipelines, differences in paint batch viscosity, and minor nozzle blockages can interfere with the film-forming process over a long period, leading to film thickness distribution deviations, changes in atomization cone angle, and edge leveling defects. To address these issues, some production lines have introduced offline calibration methods based on historical production data or periodic manual sampling compensation. However, these methods cannot detect the dynamic drift of real-time operating conditions within the calibration cycle, and the calibration process itself requires production interruption, affecting production line operating efficiency.

[0003] More importantly, the different physical levels in the coating fixture, such as flow distribution, nozzle pressure drop, and spreading delay, are not independent of each other, but rather have a nonlinear coupling relationship. Existing technologies generally adopt single-layer independent compensation or empirical weighted correction strategies. When the compensation ratio coefficient of a certain level changes, it often causes an imbalance in the response of adjacent levels, making it difficult for the overall compensation strategy to converge under complex working conditions, and even causing spatial distribution compensation conflicts. In addition, due to the lack of a multi-level state synchronous comparison mechanism from macro film thickness, meso atomization morphology to micro leveling characteristics, existing methods can often only make ex-post corrections for a single feature, and cannot identify and calculate a systematic multi-parameter adjustment scheme at the initial stage of deviation. For the gradual attenuation and depth degradation caused by equipment aging and long-term intermittent standby, existing technologies also lack intelligent differentiation and adaptive refresh mechanisms, either causing parameter oscillation due to over-response or batch coating defects due to response lag. Therefore, how to achieve multi-level feature synchronous calibration, multi-channel coupling compensation calculation, and full life cycle self-sustaining iteration throughout the actual coating operation is a technical problem that urgently needs to be solved in the field of paint parameter matching for coating tooling. Summary of the Invention

[0004] To overcome the problems of existing technologies, such as the inability of coating tooling paint parameter matching to achieve autonomous compensation throughout the entire life cycle under complex multivariate coupling, the reliance on offline production interruption for calibration, and the difficulty in simultaneously taking into account the multi-level film-forming characteristics of macroscopic film thickness, mesoscopic atomization, and microscopic leveling, this invention provides an adaptive matching method and system for coating tooling paint parameters, which realizes the self-maintenance and adaptive iteration of coating tooling paint parameters under the condition of uninterrupted production throughout the entire life cycle.

[0005] The technical solution of this application specifically includes: According to one aspect of this application, an adaptive matching method for paint parameters of a coating tooling is provided, comprising: Close each paint supply branch and open the micro-circulation loop to eliminate residual back pressure in the pipeline and environmental thermal disturbance, so that the paint is in a static and stable state. Record the static flow resistance of each channel and the surface temperature of the cavity to generate the initial working condition map. Based on the initial working condition map, a standard cavity is selected to inject reference paint. Simultaneously, multi-level features of macroscopic film thickness distribution, mesoscopic atomization cone angle and microscopic edge leveling are collected and compared with the deviation of theoretical film formation curve. The process is repeated until the multi-level features converge and are solidified into a gold response template. The gold response template is converted into a fixed reference pulse. The fixed reference pulse is injected into each coating channel in sequence through a high-speed valve array. The response sequences of three levels—flow distribution, nozzle pressure drop, and spreading delay—are recorded for each channel. The exclusive compensation ratio coefficient is calculated level by level using the sequence corresponding to the gold response template as a benchmark. If the adjustment of the compensation ratio coefficient of a certain layer causes the response of the adjacent layer to be unbalanced, the calculation is paused and cross-weight reorganization is performed. The reorganization and response balance verification are repeated until all level coefficients achieve spatial self-consistency and a dynamic compensation matrix is ​​generated. In actual coating operations, film uniformity is collected. Based on the film uniformity, the dynamic compensation matrix is ​​called to perform corrections on pump frequency, valve opening and nozzle trajectory until the film uniformity meets the deviation threshold, and then a steady-state data stream is output. The equipment's standby window injects steady-state data stream pulses and captures transient response decay curves. It then compares the transient response decay curves with the initial operating condition map and the golden response template on both axes to identify drift data. Based on the drift data, it divides the decay into smooth decay and deep degradation. For smooth decay, it performs parameter smoothing transition and directly refreshes the dynamic compensation matrix. For deep degradation, it transmits drift data back to trigger the generation of the golden response template and the dynamic compensation matrix, thus achieving self-maintenance and adaptive iteration of the paint parameters of the coating tool throughout its entire life cycle.

[0006] As a further option of the method of the present invention, the step of eliminating residual back pressure in the pipeline and environmental thermal disturbance to bring the paint to a static stable state includes: Simultaneously acquire pressure signals from all M channels and at least one temperature signal at a preset sampling rate to form a pressure vector sequence and a temperature sequence; Calculate the standard deviation of the pressure in the i-th channel within the sliding window. , In the formula, Let be the pressure value of the i-th channel at the k-th sampling time. is the arithmetic mean of the pressure in the i-th channel within the window, and N is the number of sampling points within the sliding window; Calculate the rate of temperature change of the temperature sequence within the window; When both conditions are met And the rate of temperature change When the pipeline system reaches a static steady state, it is determined that the system has reached this state. This is the pressure stability threshold; This is the threshold for the rate of temperature change.

[0007] As a further option of the method of the present invention, the initial working condition base map generation step includes: Arrange the static flow resistance of each channel according to the channel number to form an initial flow resistance vector, and each flow resistance element is associated with the corresponding micro-circulation flow rate. Spatial interpolation is performed on the temperature distribution on the cavity surface to make the temperature data correspond one-to-one with the finite element mesh nodes of the cavity, forming an initial temperature matrix; The initial flow resistance vector, the initial temperature matrix, and the micro-circulation flow rate and ambient temperature and humidity information at the reference time are packaged to generate the initial operating condition base map.

[0008] As a further option of the method of the present invention, the simultaneous acquisition of multi-level features of macroscopic film thickness distribution, mesoscopic atomization cone angle and microscopic edge leveling, and comparison with the deviation of theoretical film formation curve, includes: Calculate the relative deviation of macroscopic film thickness separately In the formula Denotes the Frobenius norm of a matrix. This indicates the relative deviation of the macroscopic film thickness. Indicates the macroscopic film thickness distribution. Represents the ideal macroscopic film thickness distribution; mesoscopic atomization cone angle relative deviation. In the formula, This indicates the relative deviation of the mesoscopic atomization cone angle. Indicates the atomization cone angle, Indicates the theoretical atomization cone angle; microscopic leveling relative deviation. In the formula, Indicates the relative deviation of micro-leveling. Indicates the leveling length. Indicates the theoretical leveling length; Calculate the overall deviation : In the formula, , and The weighting coefficients for the macro, meso, and micro levels, respectively, satisfy the following: .

[0009] As a further option of the method of the present invention, the solidification into a gold response template includes: Setting macro tolerance Meso-level tolerance and micro tolerance When both conditions are met , and At that time, a multi-level feature synchronous convergence flag is generated; If at least one level of feature deviation exceeds the corresponding tolerance, the paint supply flow rate is increased or decreased according to the sign and magnitude of the film thickness deviation, and the atomizing air pressure is adjusted according to the atomization cone angle deviation. The micro-leveling deviation is mapped to the joint adjustment amount of flow rate and air pressure through the sensitivity matrix obtained by offline calibration. After updating the process parameters, the paint is recoated and multi-level feature acquisition and comparison are performed again until the convergence condition or the comprehensive deviation is met. The change is less than the convergence threshold; After convergence, the final cycle's flow rate, atomization pressure, nozzle trajectory, and corresponding film thickness distribution, atomization cone angle, leveling length, along with the reference paint properties and standard cavity number, are stored and solidified into a gold response template.

[0010] As a further option of the method of the present invention, the step-by-step calculation of the dedicated compensation ratio coefficient includes: For the i-th channel, define an initial value for the flow compensation ratio coefficient. This is the ratio of the ideal cumulative traffic volume to the actual cumulative traffic volume. Initial value of nozzle pressure drop compensation proportional coefficient This is the ratio of the ideal root mean square voltage drop to the measured root mean square voltage drop. Initial value of the laying delay compensation ratio coefficient The average value is obtained by calculating the ratio of the delayed characteristic components separately. Substitute the initial coefficient vectors of each channel into the control command correction simulation, and calculate the hierarchical normalization error of each channel after adopting the correction coefficients.

[0011] As a further option of the method of the present invention, the cross-weight reorganization step includes: When it is detected that the adjustment of the compensation coefficient of a certain level in the i-th channel causes the normalization error of adjacent levels to be amplified and exceed the level tolerance threshold, a multi-input multi-output coupling model is introduced to increment the coefficient vector of all channels. With the normalized error vector of all levels The relationship between them is represented as , This is the sensitivity matrix; Construct cost function In the formula, Represents the cost function, Indicates the total number of coating channels. Indicates the channel index number. , , These are the penalty factors for traffic, voltage drop, and latency levels, respectively. These represent the flow distribution level normalization error, nozzle pressure drop level normalization error, and spreading delay level normalization error of the i-th channel, respectively. Solve for the cost function J Minimization We obtain a weighted least squares solution, achieving decoupling compensation for all channels and all levels.

[0012] As a further option of the method of the present invention, the dynamic compensation matrix generation step includes: After adding the obtained coefficient increments to the original coefficients, the errors of each channel and each level are recalculated and a balance check is performed again. This process of coefficient adjustment and cost function optimization is repeated until the absolute value of the normalized error at all levels converges below the preset level tolerance threshold, and the cost function... J The relative change is lower than the set constant; The final compensation ratio coefficients of each channel at this time are used to form a dynamic compensation matrix, and each element in the matrix is ​​a three-dimensional coefficient vector of the corresponding channel.

[0013] As a further option of the method of the present invention, the film uniformity is collected during the actual coating operation, including: K film thickness measurements were obtained from preset positions on the cavity surface, and the average film thickness was calculated. and standard deviation Film uniformity index ; Set minimum threshold for uniformity ,when The compensation and correction process is triggered at that time.

[0014] As a further option of the method of the present invention, the step of calling the corresponding compensation ratio coefficient of the dynamic compensation matrix according to the film uniformity to perform correction on the pumping frequency, valve opening and nozzle trajectory includes: Calculate film uniformity deviation Based on channel uniformity sensitivity Decomposed to each coated channel, the required contribution recovery amount for channel i. ,in This represents the normalized sensitivity coefficient. Retrieve the coefficient vector of the i-th channel in the dynamic compensation matrix, and combine it with... Calculate the pump supply frequency increment, valve opening increment, and nozzle trajectory path offset; The incremental values ​​are gradually superimposed onto the current command value over multiple control cycles using a ramp transition. After the next coating cycle, the film uniformity is collected again and compared with... Compare, iteratively correct until .

[0015] As a further option of the method of the present invention, the step of identifying drift data by comparing the transient response decay curve with the initial operating condition base map and the golden response template on both axes includes: First axis comparison: Compare the static flow resistance vectors of each channel under the current microcirculation steady state. Compared with the initial flow resistance vector in the initial operating condition base diagram Compare and calculate the normalized deviation vector magnitude of the flow resistance. : ; Second axis alignment: The decay time constant vector of the current channel is compared. Reference time constant vector corresponding to the golden response template Perform Pearson correlation coefficient The dynamic response deviation is calculated as follows: ; Constructing the drift index : In the formula, and For normalized weight coefficients, satisfying , The normalized deviation vector magnitude of static flow resistance.

[0016] As a further option of the method of the present invention, the step of dividing smooth decay and deep degradation based on drift data includes: Set segmented thresholds for the drift index. When the drift index is between the first segment threshold and the second segment threshold, it is determined to be a gradual decay. The exponentially weighted moving average method is used to smoothly update the elements in the dynamic compensation matrix. When the drift index reaches or exceeds the second segment threshold, it is determined to be a deep degradation. The drift data, operating condition information and attenuation characteristics are packaged and sent back. In the next gap period, the process of regenerating the golden response template and recalculating the dynamic compensation matrix is ​​automatically started to cover the old parameters and achieve deep self-healing.

[0017] Another aspect of this application provides an adaptive matching system for paint parameters of a coating tooling, the system comprising: The initial working condition base map generation module is used to lock each paint supply branch and open the micro-circulation loop, eliminate the residual back pressure of the pipeline and the environmental thermal disturbance to make the paint in a static and stable state, and record the static flow resistance of each channel and the surface temperature of the cavity to generate the initial working condition base map. The gold response template generation module is used to select a standard cavity for injecting reference paint based on the initial working condition map, simultaneously collect multi-level features of macro film thickness distribution, meso atomization cone angle and micro edge leveling and compare them with the deviation of theoretical film formation curve, and cycle until the multi-level features converge and solidify into a gold response template. The dynamic compensation matrix generation module is used to convert the gold response template into a fixed reference pulse. The fixed reference pulse is injected into each coating channel in sequence through a high-speed valve array. The response sequences of three levels—flow distribution, nozzle pressure drop, and spreading delay—are recorded for each channel. The exclusive compensation ratio coefficient is calculated level by level using the corresponding sequence of the gold response template as a benchmark. If the adjustment of the compensation ratio coefficient of a certain layer causes the response of the adjacent layer to be unbalanced, the calculation is paused and cross-weight reorganization is performed. The reorganization and response balance verification are repeated until all level coefficients achieve spatial self-consistency and a dynamic compensation matrix is ​​generated. The dynamic compensation correction module is used to collect film uniformity during actual coating operations. Based on the film uniformity, it calls the corresponding compensation ratio coefficient of the dynamic compensation matrix to perform corrections on pump frequency, valve opening and nozzle trajectory until the film uniformity meets the deviation threshold and outputs steady-state data stream. The full lifecycle drift suppression module is used to inject steady-state data stream pulses into the equipment standby window and capture transient response decay curves. It compares the transient response decay curves with the initial operating condition base map and the golden response template on both axes to identify drift data. Based on the drift data, it divides the drift data into smooth decay and deep degradation. For smooth decay, it performs parameter smooth transition and directly refreshes the dynamic compensation matrix. For deep degradation, it transmits drift data back to trigger the generation of the golden response template and the dynamic compensation matrix, realizing the self-maintenance and adaptive iteration of the paint parameters of the coating tool throughout the entire lifecycle.

[0018] The beneficial effects of this application are as follows: This invention overcomes the limitations of existing technologies, such as the need for production interruption for calibration, the susceptibility to imbalance due to single-layer compensation, and the lack of multi-level state synchronous comparison. By eliminating environmental disturbances through micro-circulation and static stable state, it constructs a multi-level feature synchronous closed-loop comparison mechanism for macroscopic film thickness, mesoscopic atomization cone angle, and microscopic flow level, ensuring that the comprehensive deviation is accurately converged within the tolerance, and the film uniformity index approaches the absolute uniformity limit value of 1. Simultaneously, it introduces a cross-weighted recombination decoupling algorithm for three-level responses of flow rate, pressure drop, and delay, based on the global sensitivity matrix minimization cost function, to completely eliminate response imbalance and compensation conflict caused by nonlinear coupling, achieving full-domain spatial self-consistent compensation. Furthermore, it utilizes standby window micro-perturbation dual-axis comparison to construct a drift index to accurately distinguish between gradual decay and deep degradation. Under gradual decay, only a 5% new coefficient weight is used for smooth refresh, resulting in minimal uniformity fluctuations. Under deep degradation, the template is automatically regenerated, thereby achieving high robustness self-sustainability and adaptive iteration of the coating tool's paint parameters throughout the entire life cycle with zero downtime. Attached Figure Description

[0019] Figure 1This is a schematic diagram of the overall control method; Figure 2 Flowchart of control method S100; Figure 3 Flowchart of control method S200; Figure 4 Flowchart of control method S300; Figure 5 Flowchart of control method S400; Figure 6 Flowchart of control method S500; Figure 7 , Figure 8 , Figure 9 , Figure 10 and Figure 11 This is a diagram of the control system's operating interface. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The core theoretical foundation of this invention is based on the non-Newtonian fluid pipeline transport theory in fluid mechanics, the atomization film formation model in computational fluid dynamics, the nonlinear system identification theory, and the adaptive control theory. By combining the pipeline transient flow resistance model and the thermal balance equation to eliminate environmental disturbances, a multi-level feature comparison mechanism is established based on the film formation kinetic equation to identify the dynamic characteristics of each channel using impulse response, and a cross-coupling decoupling and weight reorganization algorithm is introduced to achieve spatial self-consistent compensation. Finally, a complete closed-loop method is formed, from static base map construction, golden response calibration, dynamic compensation generation to full life cycle drift suppression, to achieve high-precision and high-robust adaptive matching of paint parameters for coating tooling.

[0022] The specific embodiments of the present invention will be described in detail below.

[0023] Example 1: Please see Figure 1 The diagram illustrates the overall flowchart of an adaptive matching method for paint parameters in a coating tooling according to an embodiment of the present invention. The method includes: S100: Controls the paint supply branch to lock and open the micro-circulation loop, eliminates residual back pressure in the pipeline and environmental thermal disturbance, and generates the initial operating condition map.

[0024] S200: Based on the initial working condition map, a golden response template is generated by cyclically injecting reference paint and comparing it with multi-level feature deviations.

[0025] S300: Based on the golden response template, a dynamic compensation matrix is ​​generated by injecting a fixed reference pulse through a high-speed valve array and performing step-by-step calculation and cross-weight recombination.

[0026] S400: In actual coating operations, it continuously collects film uniformity data and calls the dynamic compensation matrix to perform multi-level correction, outputting a steady-state data stream.

[0027] S500: Injects steady-state data stream pulses into the device standby window and captures transient response decay curves, identifies drift through dual-axis comparison and performs hierarchical self-healing management.

[0028] The specific plan is as follows: In an adaptive matching method for paint parameters of a coating tooling, S100 eliminates residual back pressure in the pipeline and thermal disturbance caused by changes in ambient temperature by locking the paint supply branch and starting the micro-circulation loop, so that the paint is in a uniform and stable static state. On this basis, the static flow resistance of each coating channel and the temperature field of the cavity surface are collected to generate a base map characterizing the initial working condition.

[0029] Please refer to Figure 2 The diagram illustrates a flowchart of an exemplary adaptive matching method S100 for paint parameters in a coating tooling, comprising: S110: In this embodiment, the main paint supply branch to each coating nozzle is completely closed by controlling the valve group, while the micro-circulation bypass connected in parallel with the main circuit is opened. The micro-circulation bypass consists of a small displacement micro-circulation pump, an adjustable flow valve, and a return pipeline, which can maintain the slow flow of paint in the pipeline when the main branch is closed, avoiding paint settling and local temperature stratification.

[0030] In one possible implementation of this embodiment, the micro-circulation pump is a screw pump or a diaphragm pump, with a displacement set to 5% to 10% of the main paint flow rate to ensure that the paint renewal rate is sufficient to offset the development of the thermal boundary layer without introducing significant pressure pulsations. The adjustable flow valve is adjusted to ensure that the micro-circulation flow rate in each channel is basically balanced, preventing localized skinning in some channels due to insufficient flow.

[0031] S120: In this embodiment, after the locking and microcirculation are activated, static data is not collected immediately, but a static steady-state monitoring stage is entered. During this stage, the time series of pipeline pressure and paint temperature are continuously monitored by pressure sensors installed at the end of each channel main pipeline and thermocouples embedded in the pipe wall.

[0032] In one possible implementation of this embodiment, the determination of the static stable state is achieved through the following sub-steps: S121: Synchronously acquire pressure signals from all M channels and temperature signals from at least one representative location at a preset sampling rate to form a pressure vector sequence and a temperature sequence.

[0033] S122: Calculate the standard deviation of the pressure sequence for each channel within the sliding window, and take the maximum value among all channels as the global pressure fluctuation index. Let the standard deviation of the pressure of the i-th channel within the window be... The calculation formula is expressed as: In the formula, Let be the pressure value of the i-th channel at the k-th sampling time. Let N be the arithmetic mean of the pressure in the i-th channel within the window, and N be the number of sampling points within the sliding window. Simultaneously, the rate of temperature change of the temperature sequence within the window is calculated.

[0034] S123: When both conditions are met And the rate of temperature change At this point, the piping system is determined to have reached a static steady state. This is the pressure stability threshold; This is the temperature change rate threshold. If stability is not achieved within the preset maximum waiting time, further intervention can be made by adjusting the micro-circulation flow rate or activating the pipe wall heating / cooling unit, followed by re-execution of S121 to S123. Once the stability condition is met, the current time is recorded as the baseline time. .

[0035] S130: In this embodiment, after the static steady state is achieved, the static operating condition data acquisition stage begins. The acquired data mainly includes two parts: the static flow resistance of each coating channel and the temperature distribution on the cavity surface.

[0036] In one possible implementation of this embodiment, the static flow resistance acquisition method is as follows: under stable micro-circulation flow conditions, the pressure difference between the two ends of a measurement section of known length and diameter in each channel's micro-circulation loop is measured using a differential pressure sensor. Simultaneously, a micro-flow meter is used to record the micro-circulation volumetric flow rate through the i-th channel. Under the condition that the microcirculation flow is laminar, the static flow resistance of the i-th channel is given in the laminar form according to the Darcy-Weisbach formula, that is... If the microcirculation is in a transitional or turbulent flow state, a mapping table between pressure difference and flow rate is established through calibration, and the equivalent flow resistance is obtained by table lookup interpolation.

[0037] In one possible implementation of this embodiment, the cavity surface temperature is acquired by using a multi-point thermocouple array or infrared thermal imager arranged on the cavity surface to obtain discrete or continuous surface temperature distributions. The temperature data is recorded in the form of a two-dimensional matrix, where the temperature value at the j-th measurement point is... ,in These are the spatial coordinates. If an infrared thermal imager is used, the thermal image is spatially calibrated so that each pixel corresponds to the exact location of the cavity. The resulting temperature distribution is mapped onto the three-dimensional digital model of the cavity for subsequent thermal deformation compensation analysis.

[0038] S140: In this embodiment, the static flow resistance of each channel obtained in S130 is... Arranged according to channel number to form the initial flow resistance vector. The temperature distribution obtained from S130 is rearranged into an initial temperature matrix. The two data points, along with information such as microcirculation flow rate and ambient temperature and humidity at the baseline time, are packaged to form an initial operating condition base map. Initial operating condition base map The data is stored in the controller's non-volatile memory as a structured data file, serving as the basis for all subsequent comparisons and drift identification.

[0039] In one possible implementation of this embodiment, the initial flow resistance vector Each element Each comes with a corresponding microcirculation flow rate. This is to correct for flow dependence in subsequent comparisons. Initial temperature matrix Spatial interpolation ensures a one-to-one correspondence between the nodes and the finite element mesh of the cavity, providing input for thermal deformation compensation.

[0040] In an adaptive matching method for paint parameters of a coating tooling, S200 selects the standard cavity with the highest geometric accuracy and injects a reference paint with known rheological properties based on the initial working condition map. By synchronously collecting multi-level film-forming features from macroscopic film thickness distribution, mesoscopic atomization cone angle to microscopic edge leveling, it performs closed-loop deviation comparison and parameter iteration with the theoretical film-forming curve generated based on the process model until all level features converge simultaneously, and finally solidifies into the golden response template under this working condition.

[0041] Please refer to Figure 3 The diagram illustrates a flowchart of an exemplary adaptive matching method S200 for paint parameters in a coating tooling, comprising: S210: In this embodiment, a standard cavity with surface accuracy confirmed to be better than the preset grade value is selected from the cavity library, installed on the painting fixture, and automatically positioned and clamped. The geometric dimensions and surface roughness of the standard cavity are within the tolerance range given in the calibration certificate. At the same time, a batch of reference paint with a calibration certificate is injected into the paint supply system. The key physical properties of the reference paint, such as viscosity, surface tension, solid content, and density, have been precisely measured by laboratory rheometers and densitometers and entered into the controller parameter table.

[0042] S220: In this embodiment, preset initial process parameters are used, including the initial paint supply flow rate. and initial atomization pressure The system drives the coating nozzle to perform a complete coating operation on a standard cavity along a predetermined trajectory. During the coating process and for a very short time after coating, multiple feature acquisition systems are simultaneously triggered to obtain film-forming features covering macroscopic, mesoscopic, and microscopic scales.

[0043] In one possible implementation of this embodiment, the macroscopic film thickness distribution is acquired as follows: a multi-point laser triangulation displacement sensor mounted on a coating fixture is used to non-contactly scan the surface of the coated cavity according to a preset grid. The laser displacement sensor is pre-calibrated with standard gauge blocks, achieving a measurement resolution at the micrometer level. The wet film thickness of the coating is acquired for each sampling point, forming a discrete thickness dataset. Median filtering and bilinear interpolation algorithms are used to process the discrete data into a continuous and smooth macroscopic film thickness distribution matrix. .

[0044] In one possible implementation of this embodiment, the mesoscopic atomization cone angle acquisition method is as follows: A high-speed industrial camera, mounted in the same direction as the nozzle, is used to capture the transient morphology of the spray image under backlighting conditions. Gaussian filtering and Canny edge detection are applied to the acquired spray image to extract the spray contour, and the spray deployment angle, i.e., the atomization cone angle, is calculated using a least-squares ellipse fitting algorithm. .

[0045] In one possible implementation of this embodiment, the micro-edge leveling acquisition method is as follows: within a preset extremely short time after coating—typically within 1 second after the paint droplet contacts the substrate—a micro-field imaging system integrated into a white light interferometer is used to align with the edge region of the cavity and acquire the micro-three-dimensional morphology of the coating surface. The area near the edge line is automatically scanned to obtain a morphology image with nanometer-level vertical resolution. Leveling features at the edge are extracted from the three-dimensional morphology, specifically the width from the coating body to the transition area to the uncovered substrate, defined as the leveling length. Leveling length Take the average of the measurements from multiple equidistant measuring points along the edge.

[0046] S230: In this embodiment, a film formation theory model based on computational fluid dynamics and atomization theory is pre-set. The theoretical model takes flow rate, atomization pressure, paint properties, and nozzle geometry as inputs and can calculate the ideal macroscopic film thickness distribution that should be produced under the current process parameters. Theoretical atomization cone angle and theoretical leveling length The theoretical model is formally a regression model that has been experimentally modified or an approximate solution to the simplified Navier-Stokes equation, and is obtained through training with offline simulation data.

[0047] In one possible implementation of this embodiment, the multi-level deviation comparison method with the theoretical film formation curve includes: S231: Calculate the relative deviations of the three hierarchical features respectively. The macroscopic film thickness relative deviation is defined as the normalized root mean square error between the measured film thickness distribution and the theoretical film thickness distribution. In the formula This represents the Frobenius norm of the matrix, which is the square root of the sum of the squares of all its elements. (Mesoscopic atomization cone angle relative deviation) Microscopic leveling relative deviation .

[0048] S232: To incorporate the three scale biases into a unified judgment framework, a comprehensive bias is defined. The weighted sum of the relative deviations at each level: In the formula, , and The weighting coefficients for the macro, meso, and micro levels, respectively, satisfy the following: .

[0049] S240: In this embodiment, a macroscopic tolerance is set. Meso-level tolerance and micro tolerance At the end of each data collection and comparison cycle, the following judgment is made: If both conditions are met... , and This indicates that the film-forming characteristics under the current parameters simultaneously match the theoretical expectation at multiple levels, generating a multi-level feature synchronous convergence flag; if at least one level of feature deviation exceeds the corresponding tolerance, then proceed to the S250 parameter fine-tuning stage; if multiple iterations have been performed consecutively and the overall deviation... The change is less than the convergence threshold Even if there is a slight deviation from the limit, it is considered convergence to avoid infinite loops.

[0050] S250: In this embodiment, when S240 determines that the convergence condition is not met, the process parameter adjustment amount for the next cycle is automatically calculated based on the direction and magnitude of the deviation at each level. The adjustment logic is as follows: based on the film thickness deviation... The sign and magnitude indicate the increase or decrease of the paint flow rate. If the measured film thickness is generally lower than the theoretical value, increase the flow rate; conversely, decrease the flow rate. This should be based on the atomization cone angle deviation. Adjust the atomizing air pressure If the measured cone angle is less than the theoretical value, increase the air pressure to expand the spray angle; conversely, decrease the air pressure. Microscopic leveling deviation. It is mainly related to paint viscosity and solvent evaporation after film formation, and is coupled with flow rate and air pressure. The sensitivity matrix obtained through offline calibration will... This is mapped to a combined adjustment of flow rate and gas pressure. The updated process parameters are then calculated. The nozzle is then repainted on the standard cavity, and the multi-level feature acquisition and comparison processes S220 and S230 are executed again. This cycle is repeated until convergence is determined in S240.

[0051] S260: After confirming the synchronous convergence of the multi-level features, the flow rate, atomizing pressure, nozzle trajectory, and corresponding multi-level feature response vectors used in the final cycle—including film thickness distribution—will be used. Atomization cone angle Leveling length The physical properties of the reference paint and the standard cavity number are stored together and solidified into a gold response template for this working condition. The gold response template serves as an ideal benchmark for the current paint and tooling conditions, providing a baseline for subsequent dynamic identification and compensation of each channel.

[0052] In an adaptive matching method for paint parameters of a coating tool, S300 converts the gold response template into a fixed reference pulse sequence that can be injected into the physical system. The reference pulse is applied to each coating channel one by one through a high-speed valve array, and the responses at three levels—flow distribution, nozzle pressure drop, and spreading delay—are recorded. The compensation ratio coefficients specific to each channel are calculated using the corresponding sequence in the gold template as a scale. During the calculation process, if the adjustment of the coefficient at a certain level causes an imbalance in the response of adjacent levels, a cross-weight reorganization mechanism is initiated. After iterative balance verification, a spatially self-consistent dynamic compensation matrix is ​​generated.

[0053] Please refer to Figure 4 The diagram illustrates a flowchart of an exemplary adaptive matching method S300 for paint parameters in a coating tooling, comprising: S310: In this embodiment, the gold response template This includes the optimal static process parameters and desired film formation characteristics under point conditions. To identify the dynamic characteristics of each coating channel, this static reference needs to be converted into a series of time-varying excitation signals. A trapezoidal pulse including a rising edge, a steady-state hold, and a falling edge is selected as the basic waveform of the fixed reference pulse, with the pulse amplitude corresponding to the paint flow rate setpoint in the gold response template. The steady-state duration is preset to ensure fully developed fluid flow within the coated channel. During the rising and falling edges, the valve opening varies according to a smooth S-shaped curve to fully stimulate the transient characteristics of the channel. The resulting fixed reference pulse is then used as a time-series vector. The vector is stored in the form of a vector, where each discrete control time step contains the valve opening command value and the atomizing air pressure command value.

[0054] S320: In this embodiment, the coating fixture is equipped with a high-speed valve array, and each coating channel is equipped with an independent fast-response proportional valve. First, all channels are placed in a micro-circulation standby state. Then, for channels 1, 2, and up to M, a fixed reference pulse signal is sent to the corresponding proportional valve individually, ensuring that the excited channel experiences a complete reference pulse flow, while the other channels maintain a constant micro-circulation flow rate. To avoid pipeline pressure coupling caused by simultaneous excitation of multiple channels, each channel is excited serially according to a preset minimum interval time, with the time interval between excitation starts of adjacent channels being greater than five times the steady-state pulse time.

[0055] S330: In this embodiment, during the entire process of applying a fixed reference pulse to the i-th channel, the three levels of physical response of the channel are acquired at high speed and synchronously using distributed sensors. Specifically: S331: Using a Coriolis mass flow meter installed upstream of the nozzle in the i-th channel, record the instantaneous mass flow rate and convert it into volumetric flow rate to obtain the time series. .

[0056] S332: Records the real-time pressure drop time series across the nozzle via high-frequency differential pressure sensors extended before and after the nozzle. The differential pressure sensor has a range covering the maximum pressure drop and features temperature compensation.

[0057] S333: Utilizing a fixed-position, high-response laser displacement sensor, the coating thickness growth curve at a remote key point on the mold cavity is measured. Two characteristic quantities are extracted from this curve: the hysteresis time from the issuance of the valve opening command to the start of film thickness increase at the key point. And the time constant required for the film thickness to rise from its initial value to its steady-state value. Define the spreading delay vector. If there are multiple measurement points, the spread delay vector is a weighted combination of the delay features of each point.

[0058] S340: In this embodiment, after completing the reference pulse excitation and response recording for all M channels, the measured response data for each channel is obtained. Simultaneously, the golden response template includes an ideal response sequence obtained based on a standard cavity and optimal parameters: ideal flow response. Ideal nozzle pressure drop and ideal unfolding delay vector .

[0059] In one possible implementation of this embodiment, the detailed process of calculating the compensation ratio coefficient layer by layer and performing global balancing includes: S341: For the traffic allocation level, define the initial value of the traffic compensation ratio coefficient for the i-th channel. This is the ratio of the ideal cumulative traffic volume to the actual cumulative traffic volume of the channel. Where T is the total duration of the reference pulse. Similarly, the initial value of the nozzle pressure drop compensation proportional coefficient. Taken as the ratio of the ideal root mean square value of the pressure drop to the measured root mean square value of the pressure drop, i.e. Initial value of the spreading delay compensation ratio coefficient. The result is obtained by taking the average of the ratios of the two delayed feature components.

[0060] S342: Initialize the coefficient vector In the control command correction simulation, the hierarchical response error of each channel after applying the correction coefficient is calculated using digital simulation or a small-signal linearization model. The normalized error is defined as follows: The spreading delay error is compared with a threshold based on the vector magnitude. If the absolute value of the normalized error is less than the preset level tolerance threshold for all channels and all levels... If the response is balanced, the compensation coefficient can be directly determined.

[0061] S343: Once it is detected that the compensation coefficient of a certain level in the i-th channel has been adjusted, the normalization error of its adjacent levels will amplify and exceed the tolerance threshold. This means that a response imbalance is determined.

[0062] S344: In this sub-step, a multi-input multi-output coupling model is introduced to describe the mutual influence between coefficients at different levels and in different channels. The coefficient increment vectors of all channels are then used. With the normalized error vector of all levels The relationship between them is represented as ,in This is the sensitivity matrix. The following method was used for offline finite-difference perturbation experiments: A small perturbation was applied sequentially to each coefficient of each channel, and the changes in all error vectors were recorded and assembled into a global sensitivity matrix. To obtain the coefficient adjustment amount that minimizes the global error, a cost function was constructed: In the formula, M represents the total number of channels. , , These are penalty factors for flow rate, voltage drop, and latency levels, respectively. Larger values ​​indicate a heavier penalty for errors at that level, and these levels are prioritized for reduction during optimization. Substituting the relationship between the error vector and the coefficient increment, the cost function... J Can be transformed into about The quadratic form. Solving for it in each iteration step makes... J extremely small This is equivalent to solving the weighted least squares problem, whose analytical solution involves The inverse operation, where For This is the diagonal weight matrix of the elements. Through this process, decoupling compensation for all channels and all levels can be automatically achieved under the constraint of the sensitivity matrix, ensuring that the coefficient adjustments are spatially coordinated and consistent, and avoiding compensation conflicts.

[0063] S345: After superimposing the obtained coefficient increments with the original coefficients, recalculate the errors of each level in each channel and perform a balance check again. Repeat S342 to S344 until the absolute value of the normalized error of all levels converges to... Below, and the cost function J The relative change is lower than the set constant. At this point, the final compensation ratio coefficients of all channels form a dynamic compensation matrix. ,in Let be the three-dimensional coefficient vector of the i-th channel. This matrix already possesses spatial self-consistency.

[0064] In an adaptive matching method for paint parameters of a coating tooling, S400 inputs the established dynamic compensation matrix with spatial self-consistency into the actual coating operation. Based on the online real-time acquisition of film uniformity, it dynamically calls the coefficients in the compensation matrix to successively correct the pump frequency, valve opening and nozzle trajectory until the film uniformity meets the preset deviation threshold, and outputs the corrected steady-state data stream.

[0065] Please refer to Figure 5 The diagram illustrates a flowchart of an exemplary adaptive matching method S400 for paint parameters in a coating tooling, comprising: S410: In this embodiment, during normal production, the workpieces to be coated continuously enter the coating station and are coated according to preset basic process parameters. Simultaneously, an online film thickness measurement system deployed on the production line acquires the overall apparent uniformity of the coating in real time. Specifically: S411: K film thickness measurements obtained from preset positions on the cavity surface ( Statistical processing was performed to calculate the mean film thickness. and standard deviation Film uniformity index Defined as the complement of the coefficient of variation of film thickness, i.e.: ; The closer the value is to 1, the better the uniformity; 1 indicates absolute uniformity.

[0066] S412: Set the minimum threshold for the uniformity required in production. When detected If the current film uniformity does not meet the requirements, a compensation and correction process is triggered.

[0067] S420: In this embodiment, the film uniformity deviation As the input signal. Based on the channel uniformity sensitivity vector established in the previous calibration phase, Decomposed to each coating channel. For the i-th channel, its required contribution to uniformity recovery is... ,in For the normalized sensitivity coefficient, satisfying Then, the dynamic compensation matrix is ​​retrieved. The coefficient vector of the i-th channel, combined with Calculate the pump frequency increment for the current channel. Valve opening increment and nozzle trajectory path offset Incremental calculations are based on a linearized inverse mapping model, and amplitude limiting is performed in the software.

[0068] S430: The calculated increment is smoothly superimposed onto the current command value for pump frequency updates, valve opening updates, and nozzle trajectory translation based on the offset. To avoid abrupt changes, a ramp transition method is used within three control cycles. After applying the correction, the film uniformity is collected again at the end of the next coating cycle. and with Compare. If the condition is still not met, repeat S420 for iteration. If after multiple consecutive iterations... Still not up to standard The improvement amount has fallen below the preset lower limit, triggering an alert and maintaining the current optimal parameters. When When the correction ends, the corresponding set of pump supply frequency, valve opening, nozzle trajectory, film uniformity, and environmental variables are recorded as a steady-state data stream. .

[0069] S440: Encapsulates all process parameters, film thickness statistics, and environmental sensor readings that meet the uniformity threshold conditions into a steady-state data stream. The steady-state data stream is not only used to guide current production, but also serves as a baseline for equipment status monitoring in the S500 process, providing a comparative basis for diagnosing drift in the standby window.

[0070] In an adaptive matching method for paint parameters of a coating tooling, the S500 utilizes the standby window during production breaks to actively inject micro-perturbation test pulses generated based on steady-state data streams, captures the transient response decay curves of each channel, and compares them with the initial working condition map and the golden response template to identify parameter drift patterns, distinguish between gradual decay and deep degradation, and adopt corresponding strategies of smooth refresh and regeneration of the golden template and compensation matrix, achieving self-sustaining iteration without downtime throughout the entire life cycle.

[0071] Please refer to Figure 6 The diagram illustrates a flowchart of an exemplary adaptive matching method S500 for paint parameters in a coating tooling, the contents of which include: S510: In this embodiment, the production cycle time is continuously monitored. When a preset workpiece conveying gap or equipment standby state is detected, the online health diagnosis mode is entered. The steady-state data stream obtained in S440 is used as described above. Based on this, a weakened test pulse is constructed. The waveform of the test pulse is similar to the fixed reference pulse in S300, being a trapezoidal wave, but its amplitude is only 5% to 10% of the valve opening amplitude in the steady-state data stream. This ensures that there will be no accidental spraying in standby mode, and only slight flow and pressure disturbances are generated through the micro-circulation loop. The pulse duration is kept at the same order as S300 to fully excite the channel dynamics.

[0072] S520: After applying a test pulse to the high-speed valve array, the sensors in each channel collect the dynamic responses of flow and pressure, focusing on recording the time history of the response returning to the microcirculation baseline after the pulse ends. Exponential envelope fitting is performed on the attenuation data collected from each channel to extract the attenuation time constant. Simultaneously, the decay oscillation of the pressure signal is analyzed, and the natural logarithm of the ratio of adjacent peak values ​​is calculated to obtain the logarithmic decay rate. This allows us to construct the attenuation feature vector for each channel. .

[0073] S530: In this embodiment, the extracted attenuation features are subjected to biaxial comparison. Specifically: First axis comparison: Compare the static flow resistance of each channel under the current microcirculation steady state. Compared with the initial working condition base map The initial flow resistance vector in Compare and calculate the magnitude of the normalized deviation vector of the flow resistance: .

[0074] Second-axis alignment: The similarity between the dynamic response characteristics of the current channel to the test pulse and the reference dynamic response characteristics of each channel recorded during the golden response template generation stage is calculated. The Pearson correlation coefficient is used. Measurement decay time constant vector With reference vector Similarity between them This is the ratio of the product of the covariance and the respective standard deviation of the two variables. The dynamic response deviation is defined as... When they are completely consistent, the deviation is 0.

[0075] Combining the two types of deviations mentioned above, a drift index is constructed. : In the formula, and For normalized weight coefficients, satisfying Drift Index It approaches 0 when it is in brand new or good condition, and increases monotonically as the pipeline ages, scale builds up, and nozzle wear deepens.

[0076] S540: Set the drift index segmentation threshold and ( ).when When it is confirmed to be in a normal state, the dynamic compensation matrix maintains its existing value; when When the decay is judged to be gradual, it is usually caused by uniform scaling on the inner wall of the pipe, a small amount of paint accumulation, or slow zero-point drift of the sensor; when When this is determined to be deep degradation, it means that the nozzle is severely worn, the seal is in failure, or the paint supply characteristics have changed.

[0077] S550: After determining that the decay is gradual, this embodiment uses an exponentially weighted moving average method to adjust the dynamic compensation matrix. The elements in the table are updated smoothly. New coefficients... Take the original coefficient 95% plus the newly identified ideal coefficient 5% of Ideal coefficient The dynamic compensation matrix was obtained by performing a simplified single-channel pulse identification within the micro-perturbation test window. The refreshed dynamic compensation matrix directly overwrites the original matrix and takes effect immediately, without any production interruption, and the film uniformity only shows slight fluctuations.

[0078] S560: When the drift index Reaching or exceeding At this time, an internal trigger command is automatically generated, packaging and sending back the drift data captured in the current standby window, current operating condition information, and complete attenuation characteristics to the initialization interfaces of S200 and S300. During the next downtime allowed by the production cycle, the regeneration process of the golden response template and the recalculation process of the dynamic compensation matrix are automatically initiated. The regeneration process uses the current actual paint and cavity, but follows the same S200 closed-loop iteration and S300 cross-decoupling steps as the offline template creation, ultimately producing a refreshed product. and The new parameter matrix overwrites the old parameters before subsequent coating begins, achieving deep self-healing. During this process, although template regeneration is performed, it is done during the standby window, minimizing the impact on normal production. It only briefly occupies one coating cycle when necessary to complete the reference paint injection and data acquisition.

[0079] Through continuous monitoring and graded response of S500, the coating tooling paint parameter matching has achieved a full life cycle self-sustaining and adaptive capability covering normal operation, gradual degradation and sudden conditions, and the consistency of tooling capacity and paint film quality is fundamentally guaranteed.

[0080] Example 2: This embodiment illustrates a specific case of applying the adaptive matching method for paint parameters of the coating tooling of the present invention to an automated coating line for automotive body panels. The coating line includes six robotic arms for spraying, corresponding to 12 independent paint supply channels. The paint used is a high-solids-content water-based metallic paint, and the cavity is a typical door inner panel made of lightweight aluminum alloy. The intelligent controller is interconnected with various sensors, valve arrays, and robotic arms via industrial Ethernet, and the data is processed in real time through an edge computing module.

[0081] I. System Configuration and Parameter Settings; Each channel is equipped with a Coriolis mass flow meter with an accuracy class of 0.1% of the actual mass flow rate; the differential pressure sensor has a total error band of 0.075% of the range; the high-frequency proportional valve has a response time of less than 10ms. The laser displacement sensor has a measurement resolution of 2μm and a sampling frequency of 200Hz. The infrared thermal imager has a spatial resolution of 640×480 pixels and a temperature sensitivity of 0.03℃. The core parameter settings are shown in Table 1.

[0082] Table 1 System Core Parameter Settings II. Establishment of the Golden Template and Generation of the Compensation Matrix; Before the production line transfers the coating to the inner door panels of this batch, a precision standard cavity with a surface accuracy of ±15μm, confirmed by coordinate measuring machine, and a reference paint corresponding to the batch number are used to run the entire process from S100 to S300 offline.

[0083] In the S100 stage, the micro-circulation pump displacement was set to 2.4 L / min, and the sliding window had N=60 sampling points. After three adjustments to the micro-circulation flow rate, the maximum standard deviation of pressure across the 12 channels decreased to 9.8 Pa, and the temperature change rate decreased to 0.05℃ / min, meeting the stability conditions. The initial flow resistance vector was recorded. The range of the 12 element values ​​is 1.82 × 10. 9 ~2.15×10 9 Pa·s / m 3 .

[0084] In the S200 stage, at the initial flow rate =0.42m / s and initial atomizing pressure The reference paint was injected at a pressure of 0.18 MPa. The multi-level feature deviation data for each iteration are shown in Table 2.

[0085] Table 2 S200 Multi-level Feature Deviation Iteration Process After the fourth iteration, the deviations at all three levels were less than the corresponding tolerances, and the overall deviation... =0.013. Fixed gold response template. Medium: Atomization cone angle =23.5°, leveling length =0.38mm, average film thickness distribution =18.2μm.

[0086] In the S300 phase, trapezoidal reference pulses with an amplitude of 0.42 m / s are sequentially injected into all 12 channels, with a steady-state duration of 1.5 s and rise / fall times of 0.3 s each. After the initial proportional coefficient is calculated, a balance check is performed, and the flow coefficient of channel 7 is... =1.18 results in a corresponding pressure error. =-0.071, exceeding the ±0.03 tolerance threshold. This triggers cross-weight reconfiguration; the cost function after three iterations of decoupling... The normalized error decreased from an initial value of 0.0372 to a final value of 0.0092, with all channels and all levels converging to within ±0.03. Dynamic compensation matrix. The range of each component of the 12 sets of coefficient vectors is as follows: The range is 0.91 to 1.24. The range is 0.87 to 1.19. The range is 0.82 to 1.31.

[0087] III. Continuous coating verification on the production line; 2100 car door inner panels were continuously coated, with real-time uniformity monitoring and correction performed on each panel according to S400. K=15 detection points were preset on the cavity surface, and a control group was established using a fixed PID compensation method. Batch evaluation indicators were calculated after every 100 coatings, and the summarized results are shown in Table 3.

[0088] Table 3 Comparison of coating quality between the two methods (cumulative statistics of 2100 pieces) Table 3 shows that the method of this invention reduces the standard deviation of film thickness from 1.52 μm in the traditional method to 0.47 μm, a reduction of 69.1%; the average uniformity value increases from 0.921 to 0.974; and no workpiece has a uniformity below the 0.96 threshold, while 16.3% of workpieces using the traditional method fail to meet the standard. Paint and solvent consumption are also reduced by 6.4% and 15.2%, respectively.

[0089] IV. Standby window drift diagnosis and self-healing verification; After 312 hours of cumulative equipment operation, the system automatically triggered S500 diagnostics during the standby window of the production scheduling interval. The test pulse amplitude was set to 8% of the steady-state valve opening, i.e., an equivalent air pressure disturbance of 0.034 MPa. After the pulse ended, 200ms attenuation data were collected from each channel, and the attenuation feature vector was captured. The diagnostic results are shown in Table 4.

[0090] Table 4. Drift diagnosis and treatment results at different operational stages Diagnosis results at 312 hours: =0.09, =0.94, drift index =0.16, falling into the gradual decay range. The system performed a smooth refresh over 64 seconds, and the average uniformity of the subsequent 100 workpieces remained at 0.972.

[0091] After 718 hours of cumulative operation, a second diagnostic was performed: The dynamic response similarity increased to 0.27. The drift index dropped to 0.81. =0.41, exceeding =0.35 threshold, the system judged it as deep degradation and automatically issued a maintenance warning. Disassembly and inspection revealed that the throat diameter of nozzle 9 had increased from the design value of 0.80mm to 0.83mm (an increase of 3.75%), and there were traces of leakage in the sealing ring. The system automatically triggered the regeneration of the golden template and reconstruction of the compensation matrix during the early morning production interval, with the entire process taking 24 minutes. The uniformity of the first door inner panel completed after regeneration was... =0.968, and production subsequently returned to a stable level.

[0092] V. Conclusion; This embodiment fully validated the practical effectiveness of the method of the present invention on an automotive door inner panel coating line. In a continuous production run of 2100 pieces, a quality level of 0.974 uniformity and zero defective products was achieved, representing a 69.1% improvement in film thickness consistency compared to traditional methods. During a 718-hour operating cycle, one instance of gradual degradation and one instance of deep degradation were successfully identified and addressed, all automatically completed within the standby window with zero downtime. Long-term operational statistics show that the first-pass yield of finished products increased from 94.0% before project implementation to 99.2%, overall paint consumption decreased by 8.1%, and unplanned downtime was reduced by approximately 120 hours per year. Experimental data fully demonstrates that the method of the present invention possesses superior multi-level film-forming characteristic synchronous calibration capability, multi-channel coupling compensation accuracy, and full life-cycle self-maintaining adaptive capability in complex industrial coating scenarios.

[0093] Example 3: An adaptive matching system for paint parameters in a coating tooling, with an interface as shown below. Figure 7 , Figure 8 , Figure 9 , Figure 10 and Figure 11 As shown, the system includes five interfaces: The initial working condition base map generation module is used to lock each paint supply branch and open the micro-circulation loop, eliminate the residual back pressure of the pipeline and the environmental thermal disturbance to make the paint in a static and stable state, and record the static flow resistance of each channel and the surface temperature of the cavity to generate the initial working condition base map. The gold response template generation module is used to select a standard cavity for injecting reference paint based on the initial working condition map, simultaneously collect multi-level features of macro film thickness distribution, meso atomization cone angle and micro edge leveling and compare them with the deviation of theoretical film formation curve, and cycle until the multi-level features converge and solidify into a gold response template. The dynamic compensation matrix generation module is used to convert the gold response template into a fixed reference pulse. The fixed reference pulse is injected into each coating channel in sequence through a high-speed valve array. The response sequences of three levels—flow distribution, nozzle pressure drop, and spreading delay—are recorded for each channel. The exclusive compensation ratio coefficient is calculated level by level using the corresponding sequence of the gold response template as a benchmark. If the adjustment of the compensation ratio coefficient of a certain layer causes the response of the adjacent layer to be unbalanced, the calculation is paused and cross-weight reorganization is performed. The reorganization and response balance verification are repeated until all level coefficients achieve spatial self-consistency and a dynamic compensation matrix is ​​generated. The dynamic compensation correction module is used to collect film uniformity during actual coating operations. Based on the film uniformity, it calls the corresponding compensation ratio coefficient of the dynamic compensation matrix to perform corrections on pump frequency, valve opening and nozzle trajectory until the film uniformity meets the deviation threshold and outputs steady-state data stream. The full lifecycle drift suppression module is used to inject steady-state data stream pulses into the equipment standby window and capture transient response decay curves. It compares the transient response decay curves with the initial operating condition base map and the golden response template on both axes to identify drift data. Based on the drift data, it divides the drift data into smooth decay and deep degradation. For smooth decay, it performs parameter smooth transition and directly refreshes the dynamic compensation matrix. For deep degradation, it transmits drift data back to trigger the generation of the golden response template and the dynamic compensation matrix, realizing the self-maintenance and adaptive iteration of the paint parameters of the coating tool throughout the entire lifecycle.

[0094] Those skilled in the art will understand that the embodiments of this application are provided as methods, systems, or computer program products. Therefore, this application takes the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application takes the form of a computer program product implemented on one or more computer storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code. The solutions in the embodiments of this application are implemented using various computer languages, exemplified by the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, are implemented by computer program instructions. These computer program instructions are provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams.

[0096] These computer program instructions are also stored in a computer read-memory that can direct a computer or other programmed data processing device to operate in a particular manner, such that the instructions stored in the computer read-memory produce an article of manufacture including instruction means that implement the functions specified in the flowchart or multiple flowcharts and / or block diagram blocks or multiple block diagrams.

[0097] These computer program instructions are also loaded onto a computer or other programming data processing device to cause a series of operational steps to be performed on the computer or other programming device to produce a computer-implemented process, such that the instructions, which execute on the computer or other programming device, provide steps for implementing the functions specified in the flowchart flow or multiple flows and / or the block diagram blocks or multiple blocks.

[0098] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0099] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An adaptive matching method for paint parameters of a coating tooling, characterized in that, include: Close each paint supply branch and open the micro-circulation loop to eliminate residual back pressure in the pipeline and environmental thermal disturbance, so that the paint is in a static and stable state. Record the static flow resistance of each channel and the surface temperature of the cavity to generate the initial working condition map. Based on the initial working condition map, a standard cavity is selected to inject reference paint. Simultaneously, multi-level features of macroscopic film thickness distribution, mesoscopic atomization cone angle and microscopic edge leveling are collected and compared with the deviation of theoretical film formation curve. The process is repeated until the multi-level features converge and are solidified into a gold response template. The gold response template is converted into a fixed reference pulse. The fixed reference pulse is injected into each coating channel in sequence through a high-speed valve array. The response sequences of three levels—flow distribution, nozzle pressure drop, and spreading delay—are recorded for each channel. The exclusive compensation ratio coefficient is calculated level by level using the sequence corresponding to the gold response template as a benchmark. If the adjustment of the compensation ratio coefficient of a certain layer causes the response of the adjacent layer to be unbalanced, the calculation is paused and cross-weight reorganization is performed. The reorganization and response balance verification are repeated until all level coefficients achieve spatial self-consistency and a dynamic compensation matrix is ​​generated. In actual coating operations, film uniformity is collected. Based on the film uniformity, the dynamic compensation matrix is ​​called to perform corrections on pump frequency, valve opening and nozzle trajectory until the film uniformity meets the deviation threshold, and then a steady-state data stream is output. The equipment's standby window injects steady-state data stream pulses and captures transient response decay curves. It then compares the transient response decay curves with the initial operating condition map and the golden response template on both axes to identify drift data. Based on the drift data, it divides the decay into smooth decay and deep degradation. For smooth decay, it performs parameter smoothing transition and directly refreshes the dynamic compensation matrix. For deep degradation, it transmits drift data back to trigger the generation of the golden response template and the dynamic compensation matrix, thus achieving self-maintenance and adaptive iteration of the paint parameters of the coating tool throughout its entire life cycle.

2. The adaptive matching method for paint parameters of coating tooling according to claim 1, characterized in that, The steps for eliminating residual back pressure in the pipeline and environmental thermal disturbance to bring the paint to a static and stable state include: Simultaneously acquire pressure signals from all M channels and at least one temperature signal at a preset sampling rate to form a pressure vector sequence and a temperature sequence; Calculate the standard deviation of the pressure in the i-th channel within the sliding window. , In the formula, Let be the pressure value of the i-th channel at the k-th sampling time. is the arithmetic mean of the pressure in the i-th channel within the window, and N is the number of sampling points within the sliding window; Calculate the rate of temperature change of the temperature sequence within the window; When both conditions are met And the rate of temperature change When the pipeline system reaches a static steady state, it is determined that the system has reached this state. This is the pressure stability threshold; This is the threshold for the rate of temperature change.

3. The adaptive matching method for paint parameters of coating tooling according to claim 2, characterized in that, The initial working condition base map generation step includes: Arrange the static flow resistance of each channel according to the channel number to form an initial flow resistance vector, and each flow resistance element is associated with the corresponding micro-circulation flow rate. Spatial interpolation is performed on the temperature distribution on the cavity surface to make the temperature data correspond one-to-one with the finite element mesh nodes of the cavity, forming an initial temperature matrix; The initial flow resistance vector, the initial temperature matrix, and the micro-circulation flow rate and ambient temperature and humidity information at the reference time are packaged to generate the initial operating condition base map.

4. The adaptive matching method for paint parameters of coating tooling according to claim 1, characterized in that, The simultaneous acquisition of multi-level features of macroscopic film thickness distribution, mesoscopic atomization cone angle, and microscopic edge leveling, and comparison with deviations from theoretical film formation curves, includes: Calculate the relative deviation of macroscopic film thickness separately In the formula Denotes the Frobenius norm of a matrix. This indicates the relative deviation of the macroscopic film thickness. Indicates the macroscopic film thickness distribution. Represents the ideal macroscopic film thickness distribution; mesoscopic atomization cone angle relative deviation. In the formula, This indicates the relative deviation of the mesoscopic atomization cone angle. Indicates the atomization cone angle, Indicates the theoretical atomization cone angle; microscopic leveling relative deviation. In the formula, Indicates the relative deviation of micro-leveling. Indicates the leveling length. Indicates the theoretical leveling length; Calculate the overall deviation : In the formula, , and The weighting coefficients for the macro, meso, and micro levels, respectively, satisfy the following: .

5. The adaptive matching method for paint parameters of coating tooling according to claim 1, characterized in that, The solidification into a gold-response template includes: Setting macro tolerance Meso-level tolerance and micro tolerance When both conditions are met , and At that time, a multi-level feature synchronous convergence flag is generated; If at least one level of feature deviation exceeds the corresponding tolerance, the paint supply flow rate is increased or decreased according to the sign and magnitude of the film thickness deviation, and the atomizing air pressure is adjusted according to the atomization cone angle deviation. The micro-leveling deviation is mapped to the joint adjustment amount of flow rate and air pressure through the sensitivity matrix obtained by offline calibration. After updating the process parameters, the paint is recoated and multi-level feature acquisition and comparison are performed again until the convergence condition or the comprehensive deviation is met. The change is less than the convergence threshold; After convergence, the final cycle's flow rate, atomization pressure, nozzle trajectory, and corresponding film thickness distribution, atomization cone angle, leveling length, along with the reference paint properties and standard cavity number, are stored and solidified into a gold response template.

6. The adaptive matching method for paint parameters of coating tooling according to claim 1, characterized in that, The step-by-step calculation steps for the specific compensation ratio coefficient include: For the i-th channel, define an initial value for the flow compensation ratio coefficient. This is the ratio of the ideal cumulative traffic volume to the actual cumulative traffic volume. Initial value of nozzle pressure drop compensation proportional coefficient This is the ratio of the ideal root mean square voltage drop to the measured root mean square voltage drop. Initial value of the laying delay compensation ratio coefficient The average value is obtained by calculating the ratio of the delayed characteristic components separately. Substitute the initial coefficient vectors of each channel into the control command correction simulation, and calculate the hierarchical normalization error of each channel after adopting the correction coefficients.

7. The adaptive matching method for paint parameters of coating tooling according to claim 6, characterized in that, The cross-weight reorganization step includes: When it is detected that the adjustment of the compensation coefficient of a certain level in the i-th channel causes the normalization error of adjacent levels to be amplified and exceed the level tolerance threshold, a multi-input multi-output coupling model is introduced to increment the coefficient vector of all channels. With the normalized error vector of all levels The relationship between them is represented as , This is the sensitivity matrix; Construct cost function In the formula, Represents the cost function, Indicates the total number of coating channels. Indicates the channel index number. , , These are the penalty factors for traffic, voltage drop, and latency levels, respectively. These represent the flow distribution level normalization error, nozzle pressure drop level normalization error, and spreading delay level normalization error of the i-th channel, respectively. Solve for the cost function J Minimization We obtain a weighted least squares solution, achieving decoupling compensation for all channels and all levels.

8. The adaptive matching method for paint parameters of coating tooling according to claim 7, characterized in that, The dynamic compensation matrix generation step includes: After adding the obtained coefficient increments to the original coefficients, the errors of each channel and each level are recalculated and a balance check is performed again. This process of coefficient adjustment and cost function optimization is repeated until the absolute value of the normalized error at all levels converges below the preset level tolerance threshold, and the cost function... J The relative change is lower than the set constant; The final compensation ratio coefficients of each channel at this time are used to form a dynamic compensation matrix, and each element in the matrix is ​​a three-dimensional coefficient vector of the corresponding channel.

9. The adaptive matching method for paint parameters of coating tooling according to claim 1, characterized in that, The film uniformity measured during the actual coating operation includes: K film thickness measurements were obtained from preset positions on the cavity surface, and the average film thickness was calculated. and standard deviation Film uniformity index ; Set minimum threshold for uniformity ,when The compensation and correction process is triggered at that time.

10. The adaptive matching method for paint parameters of coating tooling according to claim 9, characterized in that, The step of calling the corresponding compensation ratio coefficient of the dynamic compensation matrix based on the film uniformity to perform corrections on the pump supply frequency, valve opening, and nozzle trajectory includes: Calculate film uniformity deviation Based on channel uniformity sensitivity Decomposed to each coated channel, the required contribution recovery amount for channel i. ,in This represents the normalized sensitivity coefficient. Retrieve the coefficient vector of the i-th channel in the dynamic compensation matrix, and combine it with... Calculate the pump supply frequency increment, valve opening increment, and nozzle trajectory path offset; The incremental values ​​are gradually superimposed onto the current command value over multiple control cycles using a ramp transition. After the next coating cycle, the film uniformity is collected again and compared with... Compare, iteratively correct until .

11. The adaptive matching method for paint parameters of coating tooling according to claim 1, characterized in that, The step of identifying drift data by comparing the transient response decay curve with the initial operating condition base map and the golden response template on both axes includes: First axis comparison: Compare the static flow resistance vectors of each channel under the current microcirculation steady state. Compared with the initial flow resistance vector in the initial operating condition base diagram Compare and calculate the normalized deviation vector magnitude of the flow resistance. : ; Second axis alignment: The decay time constant vector of the current channel is compared. Reference time constant vector corresponding to the golden response template Perform Pearson correlation coefficient The dynamic response deviation is calculated as follows: ; Constructing the drift index : In the formula, and For normalized weight coefficients, satisfying , The normalized deviation vector magnitude of static flow resistance.

12. The adaptive matching method for paint parameters of coating tooling according to claim 11, characterized in that, The classification of gradual decay and deep degradation based on drift data includes: Set segmented thresholds for the drift index. When the drift index is between the first segment threshold and the second segment threshold, it is determined to be a gradual decay. The exponentially weighted moving average method is used to smoothly update the elements in the dynamic compensation matrix. When the drift index reaches or exceeds the second segment threshold, it is determined to be a deep degradation. The drift data, operating condition information and attenuation characteristics are packaged and sent back. In the next gap period, the process of regenerating the golden response template and recalculating the dynamic compensation matrix is ​​automatically started to cover the old parameters and achieve deep self-healing.

13. The adaptive matching system for paint parameters of a coating tooling as described in any one of claims 1-12, characterized in that the system... include: The initial working condition base map generation module is used to lock each paint supply branch and open the micro-circulation loop, eliminate the residual back pressure of the pipeline and the environmental thermal disturbance to make the paint in a static and stable state, and record the static flow resistance of each channel and the surface temperature of the cavity to generate the initial working condition base map. The gold response template generation module is used to select a standard cavity for injecting reference paint based on the initial working condition map, simultaneously collect multi-level features of macro film thickness distribution, meso atomization cone angle and micro edge leveling and compare them with the deviation of theoretical film formation curve, and cycle until the multi-level features converge and solidify into a gold response template. The dynamic compensation matrix generation module is used to convert the gold response template into a fixed reference pulse. The fixed reference pulse is injected into each coating channel in sequence through a high-speed valve array. The response sequences of three levels—flow distribution, nozzle pressure drop, and spreading delay—are recorded for each channel. The exclusive compensation ratio coefficient is calculated level by level using the corresponding sequence of the gold response template as a benchmark. If the adjustment of the compensation ratio coefficient of a certain layer causes the response of the adjacent layer to be unbalanced, the calculation is paused and cross-weight reorganization is performed. The reorganization and response balance verification are repeated until all level coefficients achieve spatial self-consistency and a dynamic compensation matrix is ​​generated. The dynamic compensation correction module is used to collect film uniformity during actual coating operations. Based on the film uniformity, it calls the corresponding compensation ratio coefficient of the dynamic compensation matrix to perform corrections on pump frequency, valve opening and nozzle trajectory until the film uniformity meets the deviation threshold and outputs steady-state data stream. The full lifecycle drift suppression module is used to inject steady-state data stream pulses into the equipment standby window and capture transient response decay curves. It compares the transient response decay curves with the initial operating condition base map and the golden response template on both axes to identify drift data. Based on the drift data, it divides the drift data into smooth decay and deep degradation. For smooth decay, it performs parameter smooth transition and directly refreshes the dynamic compensation matrix. For deep degradation, it transmits drift data back to trigger the generation of the golden response template and the dynamic compensation matrix, realizing the self-maintenance and adaptive iteration of the paint parameters of the coating tool throughout the entire lifecycle.

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