Automatic electrostatic spraying parameter intelligent regulation and control method for special pipe surface treatment

Abstract

The invention relates to a special-shaped pipe surface treatment technology, and provides an automatic electrostatic spraying parameter intelligent regulation and control method for solving the problem that the thickness of an electrostatic spraying coating of a special-shaped pipe is not uniform. Firstly, a special pipe three-dimensional geometric model is constructed by means of three-dimensional laser scanning, and an electrostatic field is simulated to obtain electric field distribution data. And then utilizing an improved genetic algorithm to optimize spraying parameters including spray gun voltage, current and the like. During spraying, parameters are monitored in real time through a sensor and are automatically adjusted after being compared with optimized values, and accurate regulation and control are achieved. According to the method, the accurate spraying process physical model and the coating deposition model are constructed, the uniformity and quality of the coating are effectively improved, material waste is reduced, the production efficiency is improved, an efficient and reliable solution is provided for special pipe surface treatment, and the method has good application prospects in the related industrial production field.

Description

Technical Field

[0001] The present invention relates to the field of surface treatment of special-shaped pipes, and in particular to an automated electrostatic spraying parameter intelligent control method for surface treatment of special-shaped pipes. Background Art

[0002] Electrostatic spraying plays a crucial role in the production of special-shaped pipes. It not only improves the corrosion resistance of special-shaped pipes but also enhances their aesthetics. However, current electrostatic spraying technology presents a significant problem when treating special-shaped pipes. Due to the complex geometries of special-shaped pipes, such as bends, corners, and grooves, it is difficult to maintain consistent distance and angle between the spray gun and different parts of the pipe during the electrostatic spraying process. This leads to uneven electrostatic field distribution in these complex areas, and consequently, inconsistent coating thickness. For example, in the production of special-shaped pipes such as automotive exhaust pipes, coatings are often too thin or too thick at bends and locations with sudden changes in pipe diameter. A coating that is too thin fails to provide effective protection, making these areas susceptible to corrosion; a coating that is too thick results in material waste, increases production costs, and may also affect other properties such as the exhaust pipe's heat dissipation. Existing electrostatic spraying parameter control methods are mostly based on manual experience or simple preset parameters. These methods are unable to accurately adjust spray parameters in real time to the complex geometry of special-shaped pipes, making it difficult to meet the requirements for high-quality and consistent surface coatings on special-shaped pipes. Therefore, there is an urgent need for a method that can automatically and intelligently adjust the electrostatic spraying parameters according to the geometric characteristics of special-shaped tubes to solve the above-mentioned problem of uneven coating. Summary of the Invention

[0003] The purpose of the present invention is to provide an automated electrostatic spraying parameter intelligent control method for surface treatment of special-shaped pipes to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for intelligently controlling parameters of automated electrostatic spraying for surface treatment of special-shaped tubes, comprising the following steps:

[0005] Step 1: Constructing the geometric model of the special-shaped tube: First, use 3D laser scanning technology to scan the special-shaped tube to obtain its accurate 3D point cloud data. Then, use the point cloud processing algorithm to convert the point cloud data into an accurate 3D geometric model.

[0006] Step 2, electrostatic field simulation analysis: Import the constructed three-dimensional geometric model of the special-shaped tube into electrostatic field simulation software (such as professional software such as COMSOL Multiphysics). Combined with the physical principles of electrostatic spraying, the electrostatic spraying parameters of the spray gun are set, namely the position, number, voltage, and current initial parameters of the spray gun. The electrostatic field distribution on the surface of the special-shaped tube under the initial parameters is simulated. Through software simulation analysis, the electric field intensity and electric field direction distribution data of different parts of the special-shaped tube are obtained;

[0007] Step 3: Parameter optimization based on improved genetic algorithm: The electrostatic spraying parameters are optimized using the improved genetic algorithm. Specifically, based on the electric field distribution data obtained from the electrostatic field simulation, the optimized parameters are used as the initial values. The improved genetic algorithm is continuously iterated to find the combination of spray gun voltage, current, spray distance, and spray gun movement speed that achieves the best uniformity of coating thickness on the special-shaped tube surface.

[0008] Step 4. Real-time parameter control: Install sensors on the electrostatic spraying equipment to monitor in real time the distance between the spray gun and the special-shaped tube, the angle of the spray gun, and the current and voltage parameters during the spraying process; compare and analyze the data collected by the sensor with the parameters obtained by optimizing the improved genetic algorithm; when there is a deviation between the actual monitored parameters and the optimized parameters, the control system automatically adjusts the position, angle, movement speed, spray voltage and current parameters of the spray gun through the motor drive actuator according to the deviation value, so as to achieve real-time and precise control of the electrostatic spraying process, ensuring that all parts of the special-shaped tube surface are sprayed under the optimal parameters during the entire spraying process to obtain a uniform coating.

[0009] Preferably, the specific implementation steps for the improved genetic algorithm in electrostatic spraying parameter optimization are as follows:

[0010] Step A, encoding and initialization population: Using real number encoding, the electrostatic spraying parameters voltage V, current I, spraying distance D and spray gun moving speed S parameters of step 2 are combined into a parameter vector As individuals of the genetic algorithm; then randomly generate N initial individuals to form the initial population P(0), where the parameter value of each initial individual is randomly generated within the corresponding preset value range;

[0011] Step B, fitness function calculation: Construct a fitness function based on the evaluation index of the thickness uniformity of the coating on the surface of the special-shaped tube

[0012] Specifically, according to the electric field strength E(x,y,z) and electric field direction obtained by the electrostatic field simulation in step 2 The distribution data of (x, y, z) is combined with the electric field force obtained from the electrostatic field simulation software gravity and airflow force Generate a physical model of the spraying process that affects the trajectory of paint particles. Using the physical model of the spraying process, calculate the coating thickness of each discrete point under different parameter combinations, and then obtain the distribution of coating thickness, that is, calculate the different parameter combinations Coating thickness at various locations on the surface of the lower profile tube Where (x, y, z) is the coordinate of a point on the surface of the special-shaped tube, q is the charge of the coating particles, m is the mass of the coating particles, and g is the acceleration of gravity. It is determined by combining the fluid mechanics model with the actual spraying environment;

[0013] Set the uniformity evaluation standard as the coating thickness standard deviation To minimize, the fitness function is defined as: in is the average thickness of the coating on the surface of the special-shaped tube under the parameter combination X, where n is the number of discrete points on the surface of the special-shaped tube selected when calculating the coating thickness. For each initial individual Calculate its fitness value Repeat this step to complete the calculation of the fitness values of all individuals in the population P(l), where l is the current iteration number;

[0014] Step C, use tournament selection to perform selection operation: After completing the calculation of the fitness values of all individuals in the population P(l), start the selection operation, that is, randomly select k individuals from the population P(l) each time to form a tournament group, and then pass the selected k individuals through the fitness function Calculate and select the individual with the highest fitness value to enter the next generation population P(l+1);

[0015] Step D, crossover operation: After completing the selection operation to obtain the new generation population P(l+1), perform the crossover operation; first set the crossover probability P c , for individuals in the population P(l+1), randomly select two individuals and According to the crossover probability P c Perform arithmetic crossover operation, set the crossover factor to α∈(0,1), and generate new individuals after crossover and The calculation is as follows: According to the above crossover operation, the individuals in the population P(l+1) are crossovered one by one to generate new individuals to form the crossover population;

[0016] Step E, mutation operation: first set the mutation probability P m , for the individuals in the population after crossover If a parameter V i According to the mutation probability P m If it is selected for mutation, it will undergo a random change of ±5% within its value range, that is, Likewise, for I i ,D i ,S i The parameters are also mutated in the same way as above;

[0017] Step F, iterative termination condition judgment: set a maximum number of iterations T, when the number of iterations t reaches T, the iteration is stopped, at this time the parameter combination corresponding to the individual with the highest fitness value in the population p(l) That is, the combination of spray gun voltage, current, spray distance and spray gun moving speed parameters to achieve the best uniformity of coating thickness on the surface of special-shaped tube.

[0018] Preferably, the specific steps for generating a physical model of the spraying process that affects the motion trajectory of the paint particles are as follows:

[0019] 3D discretization of the special-shaped tube and the spraying space: Use professional mesh generation software (such as ICEM CFD) to perform 3D meshing of the special-shaped tube surface and the entire spraying space, specifically using tetrahedral mesh units. After meshing is completed, each mesh unit and node is assigned a unique number. At the same time, the 3D coordinates (x, y, z) of each node are accurately recorded, and the area of the mesh unit is set to A.

[0020] Particle motion trajectory model establishment: The Lagrangian method is used to establish the motion equation of the paint particles. According to Newton's second law, the movement of particles in the spraying space is affected by the electric field force. gravity and airflow force The equation of motion is in is the acceleration of the particle;

[0021] Numerical solution: The fourth-order Runge-Kutta method is used to perform numerical integration calculations on the equation of motion. Specifically, the time of the equation of motion is discretized, and an appropriate time step Δt is set. Through iterative calculations, the position and velocity information of the particle at each time step is updated; that is, at each time step t, the current position of the known particle is and speed The position and velocity at the next time t+Δt are calculated by the following steps: Calculate four intermediate values:

[0022]

[0023] Update speed and position: By repeating the above iterative calculation process, a motion trajectory model of the paint particles in the entire spraying process is obtained;

[0024] Coating deposition model construction: After completing particle motion trajectory tracking, the coating deposition model is constructed by setting deposition judgment conditions, calculating deposition efficiency, and calculating coating thickness based on deposition mass. The coating thickness distribution is calculated using the coating deposition model to achieve quantitative analysis of the coating deposition process.

[0025] Construction of the physical model of the spraying process: After completing the particle motion trajectory tracking and deposition judgment, the coating thickness distribution of all grid cells on the surface of the special-shaped tube is calculated to calculate the coating thickness distribution of the entire special-shaped tube surface, thus completing the construction of the physical model of the spraying process.

[0026] Preferably, the specific implementation steps of constructing the coating deposition model are as follows:

[0027] Particle deposition judgment condition: Determine the judgment condition: When the particle impact speed v n If the incident angle θ is greater than 1m / s and greater than 60°, the particles will bounce back; otherwise, the particles will be deposited on the surface of the special-shaped tube, where v n is the velocity component perpendicular to the surface of the special-shaped tube, θ is the angle between the particle motion direction and the normal line of the special-shaped tube surface;

[0028] Deposition efficiency calculation: For particles that meet the deposition conditions, the deposition efficiency model is used to calculate the actual deposition ratio of particles, that is, the deposition efficiency where v c is the critical deposition rate; according to η d , the relationship between particle impact velocity and critical deposition velocity is used to calculate the deposition probability of particles at different impact velocities. Assuming that there are γ particles impacting a grid cell, the actual number of deposited particles is calculated as γ×η based on the deposition efficiency. d ;

[0029] Coating thickness calculation: Based on the known coating density ρ and the area A of the grid unit, the formula Calculate the coating thickness T, that is, the coating thickness distribution of the mesh unit on the surface of the special-shaped tube, where m dep is the total mass of particles deposited on the grid cells, where m dep =γ×η d ×m.

[0030] Preferably, an electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor:

[0031] The processor calls the computer program stored in the memory to execute the automated electrostatic spraying parameter intelligent control method for implementing the surface treatment of the special-shaped pipe.

[0032] Preferably, a computer program product stored on a computer-readable medium includes a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement an automated electrostatic spraying parameter intelligent control method for surface treatment of special-shaped pipes.

[0033] Compared with existing technologies, the present invention achieves the following beneficial effects: Improved coating quality and uniformity: Traditional electrostatic spraying techniques, due to the complex geometry of shaped pipes, make it difficult to maintain consistent distances and angles between the spray gun and different parts, resulting in uneven coating thickness. This present invention constructs a precise three-dimensional geometric model of the shaped pipe and combines it with electrostatic field simulation analysis to accurately determine the electric field distribution at various locations on the pipe's surface. Furthermore, an improved genetic algorithm is used to optimize electrostatic spraying parameters, identifying the parameter combination that optimizes coating thickness uniformity. Real-time parameter control ensures that all parts of the pipe's surface are consistently sprayed with the optimal parameters throughout the spraying process. This effectively resolves the problem of inconsistent coating thickness, significantly improving coating quality and uniformity, and enhancing the corrosion resistance and aesthetics of the shaped pipe. For example, using the present method on automobile exhaust pipes, the coating thickness uniformity at bends and sudden changes in pipe diameter is significantly improved, effectively preventing excessively thin or thick coatings and improving the exhaust pipe's protective performance and service life.

[0034] Improved production efficiency and reduced costs: Existing electrostatic spraying parameter control methods are mostly based on manual experience or simple preset parameters. They are unable to accurately adjust spray parameters in real time according to the geometric characteristics of special-shaped pipes. This not only leads to unstable product quality, but also easily causes material waste and increases production costs. This invention realizes automated and intelligent control of spraying parameters, reducing manual intervention and debugging time, and improving production efficiency. At the same time, the improved coating quality reduces product rework and scrap due to coating failures, thereby reducing production costs.

[0035] Enhanced algorithm optimization and adaptability: The improved genetic algorithm addresses the issues of premature convergence and local optimal solutions that often occur with traditional genetic algorithms when solving complex multivariable optimization problems. By adopting real-number encoding, designing a fitness function based on coating thickness uniformity as the primary evaluation metric, improving the selection strategy, and dynamically adjusting crossover and mutation probabilities, the algorithm is able to more efficiently search for the global optimal solution.

[0036] Real-time monitoring and precise control: Multiple sensors are installed on the electrostatic spray equipment to monitor parameters such as the distance between the spray gun and the shaped tube, the gun's angle, and the current and voltage during the spraying process. The sensor data is compared and analyzed with the optimized parameters. If any deviation is detected, the control system quickly and automatically adjusts the gun's position, angle, movement speed, and spraying voltage and current through the motor drive. This real-time monitoring and precise control mechanism ensures the spraying process is always optimized, improves the stability and consistency of product quality, and provides reliable support for large-scale automated production. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the overall method of the present invention;

[0038] Figure 2 This is a schematic diagram of the implementation process of the improved genetic algorithm of the present invention;

[0039] Figure 3 This is a schematic diagram of the implementation process of the physical model of the spraying process of the present invention;

[0040] Figure 4 The figure is a structural diagram of an electronic device. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Example 1

[0043] See also Figure 1-3 The present invention provides a technical solution: an automated electrostatic spraying parameter intelligent control method for surface treatment of special-shaped tubes, comprising the following steps:

[0044] Step 1: Constructing the geometric model of the special-shaped tube: First, use 3D laser scanning technology to scan the special-shaped tube to obtain its accurate 3D point cloud data. Then, use the point cloud processing algorithm to convert the point cloud data into an accurate 3D geometric model.

[0045] Step 2, electrostatic field simulation analysis: Import the constructed three-dimensional geometric model of the special-shaped tube into electrostatic field simulation software (such as professional software such as COMSOL Multiphysics). Combined with the physical principles of electrostatic spraying, the electrostatic spraying parameters of the spray gun are set, namely the position, number, voltage, and current initial parameters of the spray gun. The electrostatic field distribution on the surface of the special-shaped tube under the initial parameters is simulated. Through software simulation analysis, the electric field intensity and electric field direction distribution data of different parts of the special-shaped tube are obtained;

[0046] Step 3: Parameter optimization based on improved genetic algorithm: The electrostatic spraying parameters are optimized using the improved genetic algorithm. Specifically, based on the electric field distribution data obtained from the electrostatic field simulation, the optimized parameters are used as the initial values. The improved genetic algorithm is continuously iterated to find the combination of spray gun voltage, current, spray distance, and spray gun movement speed that achieves the best uniformity of coating thickness on the special-shaped tube surface.

[0047] The specific implementation steps for the improved genetic algorithm in electrostatic spraying parameter optimization are as follows:

[0048] Step A, encoding and initialization population: Using real number encoding, the electrostatic spraying parameters of the spray gun in step 2, voltage V (range 40-80 kV), current I (range 80-120 μA), spraying distance D (range 100-300 mm) and spray gun moving speed S (range 50-150 mm / s) are combined into a parameter vector As individuals of the genetic algorithm; then randomly generate N initial individuals to form the initial population P(0), where the parameter value of each initial individual is randomly generated within the corresponding preset value range; for example, for individual i, its parameter vector Where V i Randomly select values within 40-80 kV, and so on;

[0049] Step B, fitness function calculation: Construct a fitness function based on the evaluation index of the thickness uniformity of the coating on the surface of the special-shaped tube

[0050] Specifically, according to the electric field strength E(x,y,z) and electric field direction obtained by the electrostatic field simulation in step 2 The distribution data of the electric field force is obtained from the electrostatic field simulation software. gravity and airflow force (It can be estimated by combining the fluid mechanics model with the actual spraying environment) to generate a physical model of the spraying process that affects the trajectory of paint particles. Using the above physical model of the spraying process, calculate different parameter combinations The coating thickness of each discrete point is calculated, and then the distribution of coating thickness is obtained, that is, the different parameter combinations are calculated. Coating thickness at various locations on the surface of the lower profile tube Where (x, y, z) is the coordinate of a point on the surface of the special-shaped tube, q is the charge of the coating particles, m is the mass of the coating particles, and g is the acceleration of gravity. It is determined by combining the fluid mechanics model with the actual spraying environment;

[0051] Set the uniformity evaluation standard as the coating thickness standard deviation To minimize, the fitness function is defined as: in is the average thickness of the coating on the surface of the special-shaped tube under the parameter combination X, where n is the number of discrete points on the surface of the special-shaped tube selected when calculating the coating thickness. For each initial individual Calculate its fitness value Repeat this step to complete the calculation of the fitness values of all individuals in the population P(l), where l is the current iteration number;

[0052] Step C, use tournament selection to perform selection operation: After completing the calculation of the fitness values of all individuals in the population P(l), start the selection operation, that is, randomly select k individuals from the population P(l) each time to form a tournament group, and then pass the selected k individuals through the fitness function The individual with the highest fitness value is selected to enter the next generation population P(l+1); because high fitness means that the parameter combination corresponding to the individual can make the coating thickness of the special-shaped tube surface more uniform. The individual with the highest fitness value is selected from the group to enter the next generation population P(l+1). Repeat this operation N times to obtain the new generation population P(l+1); through this selection method, the population as a whole evolves in a better direction, providing a better foundation for subsequent crossover and mutation operations. For example, in a certain selection, an individual is randomly selected from the population P(10) Calculate their fitness values like Maximum, then Select the next generation population P(11).

[0053] Step D, crossover operation: After completing the selection operation to obtain the new generation population P(l+1), perform the crossover operation; first set the crossover probability P c , for individuals in the population P(l+1), randomly select two individuals and According to the crossover probability P c Perform arithmetic crossover operation. Specifically, use a random number generator to generate a random number r between 0 and 1. If r <Pc , then the crossover condition is satisfied, and the two selected individuals and Perform crossover operation, if r≥P c , then no crossover operation is performed, and the two individuals directly enter the next generation population; when the crossover condition is met, the crossover factor is set to α∈(0,1), and new individuals are generated after crossover and The calculation is as follows: According to the above crossover operation, the individuals in the population P(l+1) are crossovered one by one to generate new individuals to form the crossover population;

[0054] Step E, mutation operation: first set the mutation probability P m , for the individuals in the population after crossover If a parameter V i According to the mutation probability P m If it is selected for mutation, it will undergo a random change of ±5% within its value range, that is, Likewise, for I i ,D i ,S i The parameters are also mutated in the same way as above;

[0055] Step F, iterative termination condition judgment: set a maximum number of iterations T, when the number of iterations t reaches T, the iteration is stopped, at this time the parameter combination corresponding to the individual with the highest fitness value in the population P(l) That is, the combination of spray gun voltage, current, spray distance and spray gun moving speed parameters to achieve the best uniformity of coating thickness on the surface of special-shaped tubes;

[0056] The specific steps for generating a physical model of the spraying process that affects the trajectory of paint particles are as follows:

[0057] Three-dimensional discretization of special-shaped pipes and spraying space: Use professional mesh generation software (such as ICEM CFD) to carry out three-dimensional mesh division of the special-shaped pipe surface and the entire spraying space. In view of the complex geometric shape of the special-shaped pipe, especially the key parts such as bends, corners, grooves, etc., tetrahedral mesh units are selected. The overall mesh size is initially set to 0.5mm to better capture the geometric features. Taking typical special-shaped pipes such as automobile exhaust pipes as an example, the sudden change of pipe diameter (such as expansion or contraction parts) and the area with a bending radius of less than 50mm need to be locally meshed because of the great influence on the uniformity of the coating. The mesh size of this part is refined to 0.2mm to improve the calculation accuracy; after the mesh division is completed, each mesh unit and node is assigned a unique number. At the same time, the three-dimensional coordinates (x, y, z) of each node are accurately recorded, and the area of the mesh unit is set to A;

[0058] Particle motion trajectory model establishment: The Lagrangian method is used to establish the motion equation of the paint particles. According to Newton's second law, the movement of particles in the spraying space is affected by the electric field force. gravity and airflow force The combined effect of the airflow on the paint particles is calculated using the Stokes drag formula ), its equation of motion is in is the acceleration of the particle;

[0059] Numerical solution: The fourth-order Runge-Kutta method is used to perform numerical integration calculations on the equation of motion. Specifically, the time of the equation of motion is discretized, and an appropriate time step Δt is set. Through iterative calculations, the position and velocity information of the particle at each time step is updated; that is, at each time step t, the current position of the known particle is and speed The position and velocity at the next moment t+Δt are calculated by the following steps: Calculate four intermediate values:

[0060]

[0061] Update speed and position: By repeating the above iterative calculation process, a motion trajectory model of the paint particles in the entire spraying process is obtained;

[0062] In actual spraying, when the particle concentration is high, the Coulomb repulsion between particles cannot be ignored. A simplified model is used to consider this repulsion. When the distance between particles is less than 10 times the average particle size, a repulsive force is applied. Where k is the Coulomb constant and r is the interparticle distance. During calculations, the interparticle distance is determined in real time. If the conditions are met, the Coulomb repulsion force is incorporated into the particle force calculation to more accurately simulate the actual particle motion state.

[0063] Coating deposition model construction: After completing particle trajectory tracking, it is necessary to determine the deposition conditions after the particles impact the surface of the special-shaped tube, thereby constructing a coating deposition model and calculating the coating thickness distribution. This model achieves quantitative analysis of the coating deposition process by setting deposition judgment conditions, calculating deposition efficiency, and calculating coating thickness based on deposition mass.

[0064] Construction of the physical model of the spraying process: After completing particle motion trajectory tracking and deposition judgment, the coating thickness distribution of all grid cells on the surface of the special-shaped tube is calculated to complete the construction of the physical model of the spraying process;

[0065] The specific implementation steps for coating deposition model construction are as follows:

[0066] Particle deposition judgment condition: Determine the judgment condition: When the particle impact speed v n If the incident angle θ is greater than 1m / s and greater than 60°, the particles will bounce back; otherwise, the particles will be deposited on the surface of the special-shaped tube, where v n is the velocity component perpendicular to the surface of the special-shaped tube, and θ is the angle between the particle motion direction and the normal of the special-shaped tube surface. This judgment condition is based on the study of the physical characteristics of the interaction between the coating particles and the special-shaped tube surface. It can reasonably distinguish the rebound and deposition of the particles, providing a basis for subsequent calculations.

[0067] Deposition efficiency calculation: For particles that meet the deposition conditions, the deposition efficiency model is used to calculate the actual deposition ratio of particles, that is, the deposition efficiency where v c is the critical deposition velocity; this model reflects the exponential relationship between the particle impact velocity and the deposition probability, that is, the smaller the particle impact velocity, the higher the deposition efficiency. Through this formula, the deposition efficiency can be accurately predicted based on the particle velocity state at the moment of impact, providing a basis for calculating the number of deposited particles; according to η d , the relationship between particle impact velocity and critical deposition velocity is used to calculate the deposition probability of particles at different impact velocities. Assuming that there are γ particles impacting a grid cell, the actual number of deposited particles is calculated as γ×η based on the deposition efficiency. d ;

[0068] Coating thickness calculation: Based on the known coating density ρ and the area A of the grid unit, the formula Calculate the coating thickness T, that is, the coating thickness distribution of the mesh unit on the surface of the special-shaped tube, where m dep is the total mass of particles deposited on the grid cells, where mdep =γ×η d ×m.

[0069] The particle trajectory model and coating deposition model are closely linked. The former provides the latter with information about the motion state of particles as they impact the surface of the shaped tube, and the latter calculates the coating deposition based on the former's results. The collaborative work of these two models accurately simulates the movement of paint particles and the coating deposition process under different parameter combinations, providing a reliable calculation basis for optimizing electrostatic spraying parameters based on an improved genetic algorithm, thereby improving the uniformity of coating thickness on the surface of the shaped tube.

[0070] Step 4. Real-time parameter control: Install sensors on the electrostatic spraying equipment to monitor in real time the distance between the spray gun and the special-shaped tube, the angle of the spray gun, and the current and voltage parameters during the spraying process; compare and analyze the data collected by the sensor with the parameters obtained by optimizing the improved genetic algorithm; when there is a deviation between the actual monitored parameters and the optimized parameters, the control system automatically adjusts the position, angle, movement speed, spray voltage and current parameters of the spray gun through the motor drive actuator according to the deviation value, so as to achieve real-time and precise control of the electrostatic spraying process, ensuring that all parts of the special-shaped tube surface are sprayed under the optimal parameters during the entire spraying process to obtain a uniform coating.

[0071] The following is a brief explanation of the specific examples:

[0072] Implementation steps for building the geometric model of special-shaped tubes:

[0073] A 3D laser scanner with an accuracy of 0.05mm was used to scan the special-shaped automobile exhaust pipe. The scanning resolution was set to 0.5mm, and the scanning range covered the entire special-shaped pipe. The resulting point cloud data was imported into professional point cloud processing software. After pre-processing operations such as denoising and filtering, the point cloud data was converted into a triangular mesh model using the Poisson surface reconstruction algorithm. After mesh optimization, an accurate 3D geometric model of the automobile exhaust pipe was obtained. This model accurately represents geometric features such as the curvature of the exhaust pipe's bends, changes in pipe diameter, and corner angles.

[0074] Implementation steps of electrostatic field simulation analysis:

[0075] The constructed three-dimensional geometric model of the automobile exhaust pipe was imported into the COMSOL Multiphysics electrostatic field simulation software. Two spray guns were set as cylindrical electrodes, initially positioned symmetrically on either side of the shaped pipe. The initial voltage of the spray guns was set to 60 kV, and the current to 100 μA. In the simulation software, air was defined as the dielectric, with a relative dielectric constant of 1.00059. Finite element analysis was used to simulate the electrostatic field distribution on the surface of the automobile exhaust pipe under these initial parameters. The electric field intensity and direction distribution cloud maps were obtained, and the electric field intensity values at different locations were extracted.

[0076] Parameter optimization implementation steps based on improved genetic algorithm:

[0077] Encoding and Initialization: Parameters such as the spray gun voltage (range 40-80 kV), current (range 80-120 μA), spray distance (range 100-300 mm), and spray gun speed (range 50-150 mm / s) were encoded using real numbers to form a parameter vector that served as individuals in the genetic algorithm. 100 initial individuals were randomly generated to form the initial population.

[0078] Fitness Calculation: Based on the electric field distribution data obtained from electrostatic field simulation and an established physical model of the spraying process (taking into account the influence of factors such as electric field forces, gravity, and airflow on the motion trajectory of paint particles), the coating thickness at each location on the vehicle exhaust pipe surface is calculated for each parameter combination corresponding to each individual. The standard deviation of the coating thickness is calculated as the fitness value. The smaller the standard deviation, the higher the fitness value.

[0079] Selection operation: Use the tournament selection method to randomly select 5 individuals from the population each time, select the individual with the highest fitness value to enter the next generation population, repeat this operation 100 times to obtain a new generation population.

[0080] Crossover and mutation operations: At the beginning of the algorithm, the crossover probability is set to 0.8 and the mutation probability is set to 0.05. For the crossover operation, the arithmetic crossover method is used to randomly select two individuals, and the parameter vector is crossovered according to the crossover probability to generate a new individual. For the mutation operation, the parameter value of the individual is randomly perturbed according to the mutation probability. For example, the spray gun voltage parameter is randomly changed by ±5% within its value range. As the algorithm iterates, when the optimal fitness value of the population changes by less than 0.01 for five consecutive generations, the crossover probability is adjusted to 0.6 and the mutation probability is adjusted to 0.1, and the iterative calculation continues. After 500 iterations, the parameter combination that minimizes the standard deviation of the coating thickness is obtained, namely, the spray gun voltage is 65kV, the current is 105μA, the spray distance is 200mm, and the spray gun movement speed is 100mm / s.

[0081] Real-time parameter control implementation steps:

[0082] A laser rangefinder is installed on the electrostatic spray gun to monitor the distance between the gun and the vehicle exhaust pipe in real time. An angle sensor is also installed to monitor the gun's angle. Furthermore, current and voltage sensors are connected to the spray circuit to monitor the current and voltage during the spraying process in real time. The data collected by these sensors is transmitted to an industrial control computer via a data acquisition card.

[0083] A control program is written on an industrial control computer to compare real-time data collected by the sensors with the parameters optimized using a modified genetic algorithm. If the laser rangefinder detects a deviation of more than ±5mm between the spray gun and the exhaust pipe, the control program uses the motor drive to adjust the spray gun's position to restore it to the optimized spray distance. If the angle sensor detects a deviation of more than ±3°, the motor adjusts the spray gun angle. If the current and voltage values measured by the current and voltage sensors deviate by more than ±5% from the optimized values, the control program automatically adjusts the spray power supply output to restore them to their optimized values. This real-time parameter control ensures that the exhaust pipe surface is sprayed with optimal parameters throughout the electrostatic spraying process, resulting in a uniform, high-quality coating.

[0084] Through the above specific implementation methods, the automated electrostatic spraying parameter intelligent control method for the surface treatment of special-shaped tubes of the present invention can effectively solve the problem of uneven coating on complex parts of special-shaped tubes, improve the surface treatment quality and production efficiency of special-shaped tubes, and has good application prospects.

[0085] Example 2

[0086] According to an exemplary embodiment, an electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0087] The processor executes the above-mentioned automatic process processing system for accounting data by calling the computer program stored in the memory.

[0088] Figure 4 This is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement the automated electrostatic spraying parameter intelligent control method for the surface treatment of special-shaped tubes provided in the above-mentioned various method embodiments.

[0089] The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also include components such as a wired or wireless network interface and an input / output interface for input and output.

[0090] This embodiment also provides a computer program product stored on a computer-readable medium, including a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the automated electrostatic spraying parameter intelligent control method for special-shaped pipe surface treatment.

[0091] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0092] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0093] The present invention solves the problem of uneven coating thickness during electrostatic spraying of special-shaped tubes. The method first uses three-dimensional laser scanning technology to construct an accurate three-dimensional geometric model of the special-shaped tube, and imports electrostatic field simulation software to obtain electric field distribution data. Then, an improved genetic algorithm is used to optimize electrostatic spraying parameters such as spray gun voltage, current, spraying distance and moving speed based on the electric field distribution data. During the spraying process, the relevant parameters are monitored in real time by sensors installed on the equipment, and compared with the optimized parameters, and the deviation is automatically adjusted to achieve real-time and precise control. At the same time, the various steps of the improved genetic algorithm, the construction of the physical model of the spraying process, and the construction of the coating deposition model are elaborated in detail. After verification in actual application, this method significantly improves the uniformity and quality of the surface coating of the special-shaped tube, effectively improves production efficiency and reduces costs, provides a new direction for the development of special-shaped tube surface treatment technology, and has broad application prospects.

[0094] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for automated electrostatic spraying parameters for surface treatment of special-shaped tubes, characterized in that: The following steps are involved: Step 1: Constructing the geometric model of the special-shaped tube: First, use 3D laser scanning technology to scan the special-shaped tube to obtain its accurate 3D point cloud data. Then, use the point cloud processing algorithm to convert the point cloud data into an accurate 3D geometric model. Step 2: Electrostatic field simulation analysis: Import the constructed 3D geometric model of the special-shaped tube into the electrostatic field simulation software. Combined with the physical principles of electrostatic spraying, the electrostatic spraying parameters of the spray gun, i.e., the initial parameters of the position, number, voltage, and current of the spray gun, are set. The electrostatic field distribution on the surface of the special-shaped tube under the initial parameters is simulated. Through software simulation analysis, the electric field intensity and electric field direction distribution data of different parts of the special-shaped tube are obtained. Step 3: Parameter optimization based on improved genetic algorithm: The electrostatic spraying parameters are optimized using the improved genetic algorithm. Specifically, based on the electric field distribution data obtained from the electrostatic field simulation, the optimized parameters are used as the initial values. The improved genetic algorithm is continuously iterated to find the combination of spray gun voltage, current, spray distance, and spray gun movement speed that achieves the best uniformity of coating thickness on the special-shaped tube surface. Step 4. Real-time parameter control: Install sensors on the electrostatic spraying equipment to monitor in real time the distance between the spray gun and the special-shaped tube, the angle of the spray gun, and the current and voltage parameters during the spraying process; compare and analyze the data collected by the sensor with the parameters obtained by optimizing the improved genetic algorithm; when there is a deviation between the actual monitored parameters and the optimized parameters, the control system automatically adjusts the position, angle, movement speed, spray voltage and current parameters of the spray gun through the motor drive actuator according to the deviation value, so as to achieve real-time and precise control of the electrostatic spraying process, ensuring that all parts of the special-shaped tube surface are sprayed under the optimal parameters during the entire spraying process to obtain a uniform coating.

2. The method for intelligently controlling parameters of automated electrostatic spraying for surface treatment of special-shaped pipes according to claim 1, characterized in that: The specific implementation steps for the improved genetic algorithm in electrostatic spraying parameter optimization are as follows: Step A, encoding and initialization population: Using real number encoding, the electrostatic spraying parameters voltage V, current I, spraying distance D and spray gun moving speed S parameters of step 2 are combined into a parameter vector As individuals of the genetic algorithm; then randomly generate N initial individuals to form the initial population P(0), where the parameter value of each initial individual is randomly generated within the corresponding preset value range; Step B, fitness function calculation: Construct a fitness function based on the evaluation index of the thickness uniformity of the coating on the surface of the special-shaped tube Specifically, according to the electric field strength E(x,y,z) and electric field direction obtained by the electrostatic field simulation in step 2 The distribution data of the electric field force is obtained from the electrostatic field simulation software. gravity and airflow force Generate a physical model of the spraying process that affects the trajectory of paint particles. Using the physical model of the spraying process, calculate the coating thickness of each discrete point under different parameter combinations, and then obtain the distribution of coating thickness, that is, calculate the different parameter combinations Coating thickness at various locations on the surface of the lower profile tube Where (x, y, z) is the coordinate of a point on the surface of the special-shaped tube, q is the charge of the coating particles, m is the mass of the coating particles, and g is the acceleration of gravity. It is determined by combining the fluid mechanics model with the actual spraying environment; Set the uniformity evaluation standard as the coating thickness standard deviation To minimize, the fitness function is defined as: in is the average thickness of the coating on the surface of the special-shaped tube under the parameter combination X, where n is the number of discrete points on the surface of the special-shaped tube selected when calculating the coating thickness. For each initial individual Calculate its fitness value Repeat this step to complete the calculation of the fitness values of all individuals in the population P(l), where l is the current iteration number; Step C, use tournament selection to perform selection operation: After completing the calculation of the fitness values of all individuals in the population P(l), start the selection operation, that is, randomly select k individuals from the population P(l) each time to form a tournament group, and then pass the selected k individuals through the fitness function Calculate and select the individual with the highest fitness value to enter the next generation population P(l+1); Step D, crossover operation: After completing the selection operation to obtain the new generation population P(l+1), perform the crossover operation; first set the crossover probability P c , for individuals in the population P(l+1), randomly select two individuals and According to the crossover probability P c Perform arithmetic crossover operation, set the crossover factor to α∈(0,1), and generate new individuals after crossover and The calculation is as follows: According to the above crossover operation, the individuals in the population P(l+1) are crossovered one by one to generate new individuals to form the crossover population; Step E, mutation operation: first set the mutation probability P m , for the individuals in the population after crossover If a parameter V i According to the mutation probability P m If it is selected for mutation, it will undergo a random change of ±5% within its value range, that is, Likewise, for I i ,D i ,S i The parameters are also mutated in the same way as above; Step F, iterative termination condition judgment: set a maximum number of iterations T, when the number of iterations t reaches T, the iteration is stopped, at this time the parameter combination corresponding to the individual with the highest fitness value in the population P(l) That is, the combination of spray gun voltage, current, spray distance and spray gun moving speed parameters to achieve the best uniformity of coating thickness on the surface of special-shaped tube.

3. The method for intelligently controlling parameters of automated electrostatic spraying for surface treatment of special-shaped pipes according to claim 2, characterized in that: The specific steps for generating a physical model of the spraying process that affects the trajectory of paint particles are as follows: Three-dimensional discretization of the special-shaped tube and the spraying space: Use professional mesh generation software to perform three-dimensional meshing of the special-shaped tube surface and the entire spraying space, specifically using tetrahedral mesh units. After the meshing is completed, each mesh unit and node is assigned a unique number. At the same time, the three-dimensional coordinates (x, y, z) of each node are accurately recorded, and the area of the mesh unit is set to A. Particle motion trajectory model establishment: The Lagrangian method is used to establish the motion equation of the paint particles. According to Newton's second law, the movement of particles in the spraying space is affected by the electric field force. gravity and airflow force The equation of motion is in is the acceleration of the particle; Numerical solution: The fourth-order Runge-Kutta method is used to perform numerical integration calculations on the equation of motion. Specifically, the time of the equation of motion is discretized, and an appropriate time step Δt is set. Through iterative calculations, the position and velocity information of the particle at each time step is updated; that is, at each time step t, the current position of the known particle is and speed The position and velocity at the next time t+Δt are calculated by the following steps: Calculate four intermediate values: Update speed and position: By repeating the above iterative calculation process, a motion trajectory model of the paint particles in the entire spraying process is obtained; Coating deposition model construction: After completing particle motion trajectory tracking, the coating deposition model is constructed by setting deposition judgment conditions, calculating deposition efficiency, and calculating coating thickness based on deposition mass. The coating thickness distribution is calculated using the coating deposition model to achieve quantitative analysis of the coating deposition process. Construction of the physical model of the spraying process: After completing the particle motion trajectory tracking and deposition judgment, the coating thickness distribution of all grid cells on the surface of the special-shaped tube is calculated to complete the construction of the physical model of the spraying process.

4. The method for intelligently controlling parameters of automated electrostatic spraying for surface treatment of special-shaped pipes according to claim 3, characterized in that: The specific implementation steps of the coating deposition model construction are as follows: Particle deposition judgment condition: Determine the judgment condition: When the particle impact speed v n If the incident angle θ is greater than 1m / s and greater than 60°, the particles will bounce back; otherwise, the particles will be deposited on the surface of the special-shaped tube, where v n is the velocity component perpendicular to the surface of the special-shaped tube, θ is the angle between the particle motion direction and the normal line of the special-shaped tube surface; Deposition efficiency calculation: For particles that meet the deposition conditions, the deposition efficiency model is used to calculate the actual deposition ratio of particles, that is, the deposition efficiency where v c is the critical deposition rate; according to η d , the relationship between particle impact velocity and critical deposition velocity is used to calculate the deposition probability of particles at different impact velocities. Assuming that there are γ particles impacting a grid cell, the actual number of deposited particles is calculated as γ×η based on the deposition efficiency. d ; Coating thickness calculation: Based on the known coating density ρ and the area A of the grid unit, the formula Calculate the coating thickness T, that is, the coating thickness distribution of the mesh unit on the surface of the special-shaped tube, where m dep is the total mass of particles deposited on the grid cells, where m dep =γ×η d ×m.

5. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor: The processor calls the computer program stored in the memory to execute the method for intelligently controlling the automated electrostatic spraying parameters for surface treatment of special-shaped pipes according to any one of claims 1 to 4.

6. A computer program product stored on a computer-readable medium, characterized in that: The invention comprises a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the automatic electrostatic spraying parameter intelligent control method for the surface treatment of a special-shaped pipe as claimed in any one of claims 1 to 4.

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