A prediction modeling method for film thickness of spray coating based on cylindrical surface pose change

Abstract

The application discloses a kind of based on cylindrical surface variable pose spray coating thickness prediction modeling method, it is related to signal processing technical field.Step one: establish three-dimensional model of spray gun cap and control domain based on cylindrical surface static spraying, carry out numerical simulation experiment and collect numerical simulation experimental data;Step two: data analysis, analyze the coating film thickness law under the variable pose spraying of different cylindrical diameter, obtain the adjustable range of variable pose spray gun;Step three: establish spray gun mathematical model, combined with the adjustable range of spray gun pose, carry out spraying experiment;Step four: collect experimental coating data, according to variable pose spray gun mathematical model, using Trust-Region optimization algorithm is fitted to experimental data, obtains cylindrical surface variable pose model to be identified parameter, carries out multiple linear regression analysis, obtains cylindrical surface variable pose spraying coating film thickness prediction model.

Description

Technical Field

[0001] This invention relates to the field of CNC spraying technology, specifically to a predictive modeling method for coating thickness in cylindrical surface variable pose spraying. Background Technology

[0002] Currently, with the rapid development of automation and information technology, robotic spraying has gradually replaced manual spraying and is widely used in coating manufacturing in the automotive, aviation, aerospace and shipbuilding industries. Clarifying the film formation law of spraying and constructing a coating thickness distribution prediction model are the foundation for realizing CNC spraying trajectory planning of spraying robots based on offline programming.

[0003] With the development of computational fluid dynamics (CFD), the use of CFD combined with experimental data to calculate coating thickness is theoretically applicable to the simulation of spray coating processes under various conditions. Conner Zhao et al. established a mathematical model for the coating thickness distribution of electrostatic air spraying by analyzing the influence of the moving speed of an electrostatic rotary cup on the coating distribution in a plane. However, the established model did not consider the influence of the spray gun posture on the coating distribution and was only applicable to electrostatic rotary cup spray guns, not air spray guns. Once the spray gun is changed, the model will no longer be applicable. Since the air field, fog field, and target field in air spraying are important factors affecting coating transfer and film quality, scholars have attempted to reveal the film formation mechanism and characteristics of air spraying under the action of these three fields. For example, Fogliati considered the influence of the air field and fog field and used the Lagrangian method to simulate the coating transfer and film formation process on a plane. Barry used a realizable k-ε turbulence model to simulate the influence of parameters such as airflow and spray height on coating deposition during the jet impact process in air spraying. The process of a jet impacting a target plane was simulated. Garbero et al. found that the higher the viscosity of the coating, the less the rebound or splashing phenomenon of the atomized droplets impacting the solid surface. Based on this conclusion, Tafuri et al. obtained the coating thickness distribution map by recording the position and mass of the droplets impacting the surface. However, these studies were all conducted on planar spraying and have not yet studied the influence of the target surface on the film formation law. Therefore, the research is not comprehensive enough. Chen Yan et al. used the Euler-Euler method and the Euler-Lagrange method to study the film formation mechanism and characteristics of spraying on circular arc surfaces and spherical surfaces of different sizes. However, the above research results were only obtained when the spray gun was perpendicular to the surface and the position was fixed. The influence of the spray gun posture parameters on the film formation law has not been considered. The research results are far from meeting the needs of complex curved surface variable spraying.

[0004] Therefore, it is urgent to clarify the influence of spray gun posture on the film formation law of complex curved surface spraying. To address the above problem, a predictive modeling method for coating thickness based on cylindrical surface variable posture spraying is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a predictive modeling method for coating thickness based on cylindrical surface variable pose spraying, so as to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a predictive modeling method for coating thickness based on variable pose spraying on a cylindrical surface, comprising the following steps:

[0007] Step 1: Establish a 3D model of the spray gun cap and a control domain based on static spraying on a cylindrical surface, conduct numerical simulation experiments and collect numerical simulation experimental data; Step 2: Data analysis. Analyze the coating thickness pattern under different cylinder diameters and varying postures to determine the adjustable range of the varying posture spray gun. Step 3: Establish a mathematical model of the spray gun, and conduct spraying experiments based on the adjustable range of the spray gun's position. Step 4: Collect experimental coating data. Based on the mathematical model of the variable pose spray gun, use the Trust-Region optimization algorithm to fit the experimental data to obtain the parameters to be identified for the cylindrical variable pose model. Perform multiple linear regression analysis to obtain the prediction model for the coating thickness of the cylindrical variable pose spraying.

[0008] Based on the establishment of the three-dimensional model of the spray gun cap and the simulation control domain described in step one above, the specific steps are as follows: Step 1 (1): First, establish the air spray gun nozzle model. The center of the nozzle is the paint hole, and the outer side of the paint hole is the annular central atomizing hole. Two auxiliary atomizing holes are arranged on both sides of the central atomizing hole. Two fan-shaped control holes are arranged on the horn-shaped structure on both sides of the air nozzle. Step 1 (2): Based on the need to change the cylinder diameter and the variable posture spraying, this study uses the variable posture spraying method to conduct numerical simulation for cylinders with different diameters, namely D1, D2 and D3. According to the spray gun process requirements, the adjustable range of the spraying height is H1~H2, and the preset spraying angle adjustment range is α1~α2. For different spraying heights, spraying angles and cylinder diameters, different control domains need to be set for simulation calculation.

[0009] The numerical simulation calculation method for static variable pose spraying based on cylindrical surfaces, as described in step one above, includes the following steps: Step 1 (3): Set two phases for the spray flow field. The first phase is the gas phase, which represents the air in the spray flow field. The second phase is the liquid phase, which represents the paint droplets in the flow field. The paint is a commercial water-based paint. Its density and viscosity are measured by measuring instruments. According to the boundary conditions of numerical simulation and the required parameters, the gas phase mass flow rate, liquid phase mass flow rate, liquid phase initial velocity, liquid phase inlet volume fraction, turbulence intensity, hydraulic diameter, spray gun atomization pressure, gravity, operating pressure, and static spraying time are set respectively. Step 1 (4): Collect numerical simulation experimental data: For the paint droplets sprayed onto the cylindrical wall, establish a coordinate system with the spray gun cone direction as the Z direction, the circumferential direction of the cylindrical surface as the X direction, and the axial direction of the cylindrical surface as the Y direction, and collect numerical simulation data points in the X and Y directions on the cylindrical wall respectively.

[0010] The method for analyzing the coating thickness under different cylinder diameters and varying postures in step two above includes the following steps: Step 2 (1): First, the method of controlling variables is used to analyze the law of coating thickness change of cylinder diameter, spraying height and spraying angle. That is, two of the quantities are kept constant, and the influence of the other quantity on the coating thickness change is studied. Spraying height, spraying angle and cylinder diameter will all affect the coating thickness distribution.

[0011] Step 2 (2): Through static spraying experiments, it was found that if the maximum coating thickness in the coating distribution curve is too low, the leveling will be poor, while if the coating thickness is too high, the workpiece surface will be dripped. Both of these situations will affect the coating quality. Considering the viscosity of the coating used in the experiment, in order to ensure the coating quality, the maximum coating thickness is set in the range of T1~T2. Based on the above numerical simulation results, and specifying the adjustment range of the spraying height as H1~H2, the gray prediction and fitting interpolation method can be used to obtain the adjustable range of the spraying angle within the coating thickness range. Step 2 (3): By analyzing the variation law of the spraying angle obtained by cylinders of different diameters at different spraying heights, the variation relationship of the spray gun posture within the maximum coating thickness range can be obtained. Based on the above step 3, the mathematical model of the spray gun is established as follows: Step 3 (1): Assuming that the spraying process parameters and spraying environment remain constant during the static spraying process, the schematic diagram of cylindrical surface spraying is as follows. Figure 1 As shown, point S is any point within the spraying area, r is the distance from the center point of the spray gun to point S, R is the spraying radius, θ is the angle between the line connecting point S and the spray gun and the center line of the spray gun, φ is the angle of the spray gun, and α is the spray angle of the spray gun.

[0012] When the spray gun sprays vertically on a plane, a Gaussian model is established, and its expression formula is as follows:

[0013] (1)

[0014] In the formula, ω i r i , σ i Let i be the parameters to be identified, i = 1, 2, …, N. As N approaches infinity, this function can approximate any distribution, but as N increases, the complexity of the model also increases dramatically. In this case, we use the sum of three Gaussian kernel functions for modeling.

[0015] (2)

[0016] As shown in Figure 2(1), select any point S within the spraying range. Plane P1 is the reference plane, and plane P2, which is parallel to P1 and passes through point S, is the working plane. The spray gun sprays out circular areas C1 and C2 perpendicular to the spraying direction, which intersect at P1 and P2. H and H S Let P1 and P2 be the heights perpendicular to the spraying plane, respectively. According to the differential geometric area magnification theorem, we can obtain: (3)

[0017] Since the total amount of paint remains constant, the relationship between the coating thicknesses on C1 and C2 can be determined as follows:

[0018] (4)

[0019] Assume there is a circular region C3 in the spraying plane, intersecting at point S and forming an angle α with C2, as shown in Figure 2(2). Then the coating thickness of region C3 is:

[0020] (5)

[0021] In summary, the variable-pose spraying model for cylindrical surfaces is as follows:

[0022] (6)

[0023] In the formula

[0024] (7)

[0025] (8) Step 3 (2): Conduct a robot spraying experiment based on the adjustable range of the spray gun pose obtained from the numerical simulation.

[0026] The method for predicting the coating thickness of cylindrical surface variable pose spraying described in step four above includes the following steps: Step four (1): Through numerical simulation experiments of variable pose spraying of different cylinder diameters at different heights and tilt angles, coating thickness data points are collected. The Trust-Region algorithm in the MATLAB fitting tool is used to fit and solve the parameters to be identified, and the values ​​of the parameters to be solved in the corresponding coating thickness distribution model when different diameter cylinders are sprayed in variable pose are obtained; Step four (2): By comparing the coefficients obtained from the fitting, it can be found that some data have obvious differences, while others are quite similar. Here, we need to analyze the patterns. For the parameters to be identified with obvious patterns, multiple linear regression analysis is performed with spraying height (H), spraying angle (α), and cylinder diameter (D) as independent variables and the parameter to be identified ω as the dependent variable. The prediction function shown in equation (9) is obtained, so that the value of the parameter to be identified can be predicted. Combined with equation (6), the coating thickness distribution prediction model when different diameter cylinders are sprayed in variable pose is obtained:

[0027] (9)

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] 1. This predictive modeling method for coating thickness based on cylindrical surface variable pose spraying uses a combination of CFD numerical simulation and analytical functions to study the spraying film formation law and establish a coating thickness prediction method. This method can not only save spraying costs, but also allow for a more intuitive observation of the coating thickness variation law.

[0030] 2. The predictive modeling method for coating thickness based on variable posture spraying on cylindrical surfaces can more quickly control the quality of the sprayed coating by adopting different spraying postures when spraying workpieces with different curvatures. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the cylindrical surface spraying of the present invention;

[0032] Figures 2(1) and 2(2) are schematic diagrams of the mathematical model for cylindrical surface spraying of the present invention;

[0033] Figure 3 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] A predictive modeling method for coating thickness based on variable pose spraying on a cylindrical surface includes the following steps:

[0036] Step 1: Establish a 3D model of the spray gun cap and a control domain based on static spraying on a cylindrical surface, conduct numerical simulation experiments and collect numerical simulation experimental data; Step 2: Data analysis. Analyze the coating thickness pattern under different cylinder diameters and varying postures to determine the adjustable range of the varying posture spray gun. Step 3: Establish a mathematical model of the spray gun, and conduct spraying experiments based on the adjustable range of the spray gun's position. Step 4: Collect experimental coating data. Based on the mathematical model of the variable pose spray gun, use the Trust-Region optimization algorithm to fit the experimental data to obtain the parameters to be identified for the cylindrical variable pose model. Perform multiple linear regression analysis to obtain the prediction model for the coating thickness of the cylindrical variable pose spraying.

[0037] Based on the establishment of the three-dimensional model of the spray gun cap and the simulation control domain in step one above, the specific steps are as follows: Step 1 (1): First, establish the air spray gun nozzle model. The center of the nozzle is the paint hole, and the outer side of the paint hole is the annular central atomizing hole. Two auxiliary atomizing holes are arranged on both sides of the central atomizing hole. Two fan-shaped control holes are arranged on the horn-shaped structure on both sides of the air nozzle. Step 1 (2): Based on the need to change the cylinder diameter and the variable posture spraying, this study uses the variable posture spraying method to conduct numerical simulation for cylinders with different diameters, namely D1, D2 and D3. According to the spray gun process requirements, the adjustable range of the spraying height is H1~H2, and the preset spraying angle adjustment range is α1~α2. For different spraying heights, spraying angles and cylinder diameters, different control domains need to be set for simulation calculation.

[0038] The numerical simulation calculation method for static variable pose spraying based on cylindrical surfaces, as described in step one above, includes the following steps: Step 1 (3): Set two phases for the spray flow field. The first phase is the gas phase, which represents the air in the spray flow field. The second phase is the liquid phase, which represents the paint droplets in the flow field. The paint is a commercial water-based paint. Its density and viscosity are measured by measuring instruments. According to the boundary conditions of numerical simulation and the required parameters, the gas phase mass flow rate, liquid phase mass flow rate, liquid phase initial velocity, liquid phase inlet volume fraction, turbulence intensity, hydraulic diameter, spray gun atomization pressure, gravity, operating pressure, and static spraying time are set respectively. Step 1 (4): Collect numerical simulation experimental data: For the paint droplets sprayed onto the cylindrical wall, establish a coordinate system with the spray gun cone direction as the Z direction, the circumferential direction of the cylindrical surface as the X direction, and the axial direction of the cylindrical surface as the Y direction, and collect numerical simulation data points in the X and Y directions on the cylindrical wall respectively.

[0039] The method for analyzing the coating thickness under different cylinder diameters and varying spraying postures in step two above includes the following steps: Step 2 (1): First, the method of controlling variables is used to analyze the law of coating thickness change of cylinder diameter, spraying height and spraying angle. That is, two of the quantities are kept constant, and the influence of the other quantity on the coating thickness change is studied. Spraying height, spraying angle and cylinder diameter will all affect the coating thickness distribution.

[0040] Step 2 (2): Through static spraying experiments, it was found that if the maximum coating thickness in the coating distribution curve is too low, the leveling will be poor, while if the coating thickness is too high, the workpiece surface will be dripped. Both of these situations will affect the coating quality. Considering the viscosity of the coating used in the experiment, in order to ensure the coating quality, the maximum coating thickness is set in the range of T1~T2. Based on the above numerical simulation results, and specifying the adjustment range of the spraying height as H1~H2, the gray prediction and fitting interpolation method can be used to obtain the adjustable range of the spraying angle within the coating thickness range. Step 2 (3): By analyzing the variation law of the spraying angle obtained by cylinders of different diameters at different spraying heights, the variation relationship of the spray gun posture within the maximum coating thickness range can be obtained. Based on the above Step 3, the mathematical model of the spray gun is established as follows: Step 3 (1): Assuming that the spraying process parameters and spraying environment remain constant during the static spraying process, the schematic diagram of cylindrical surface spraying is as follows. Figure 1 As shown, point S is any point within the spraying area, r is the distance from the center point of the spray gun to point S, R is the spraying radius, θ is the angle between the line connecting point S and the spray gun and the center line of the spray gun, φ is the angle of the spray gun, and α is the spray angle of the spray gun.

[0041] When the spray gun sprays vertically on a plane, a Gaussian model is established, and its expression formula is as follows:

[0042] (1)

[0043] In the formula, ω i r i , σ i Let i be the parameters to be identified, i = 1, 2, …, N. As N approaches infinity, this function can approximate any distribution, but as N increases, the complexity of the model also increases dramatically. In this case, we use the sum of three Gaussian kernel functions for modeling.

[0044] (2)

[0045] As shown in Figure 2(1), select any point S within the spraying range. Plane P1 is the reference plane, and plane P2, which is parallel to P1 and passes through point S, is the working plane. The spray gun sprays out circular areas C1 and C2 perpendicular to the spraying direction, which intersect at P1 and P2. H and H S Let P1 and P2 be the heights perpendicular to the spraying plane, respectively. According to the differential geometric area magnification theorem, we can obtain: (3)

[0046] Since the total amount of paint remains constant, the relationship between the coating thicknesses on C1 and C2 can be determined as follows:

[0047] (4)

[0048] Assume there is a circular region C3 in the spraying plane, intersecting at point S and forming an angle α with C2, as shown in Figure 2(2). Then the coating thickness of region C3 is:

[0049] (5)

[0050] In summary, the variable-pose spraying model for cylindrical surfaces is as follows:

[0051] (6)

[0052] In the formula

[0053] (7)

[0054] (8) Step 3 (2): Conduct a robot spraying experiment based on the adjustable range of the spray gun pose obtained from the numerical simulation.

[0055] The method for predicting the coating thickness of cylindrical surface variable pose spraying in step four above includes the following steps: Step 4 (1): Through numerical simulation experiments of different cylinder diameters sprayed at different heights and tilt angles, collect coating thickness data points, use the Trust-Region algorithm in MATLAB fitting tool to fit and solve the parameters to be identified, and obtain the parameter values ​​to be solved in the corresponding coating thickness distribution model when different diameter cylinders are sprayed in different poses. Step 4 (2): Comparing the coefficients obtained from the fitting, we can find that some data have obvious differences, while others are quite similar. Here, we need to analyze the patterns. For the parameters to be identified that have obvious patterns, we use the spraying height (H), spraying angle (α), and cylinder diameter (D) as independent variables and the parameter to be identified ω as the dependent variable to perform multiple linear regression analysis, and obtain the prediction function shown in equation (9), so that the value of the parameter to be identified can be predicted. Combining equation (6), we can obtain the coating thickness distribution prediction model when spraying cylinders of different diameters with varying poses:

[0056] (9).

[0057] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A predictive modeling method for coating thickness based on variable pose spraying on a cylindrical surface, characterized in that: Includes the following steps: Step 1: Establish a 3D model of the spray gun cap and a control domain based on static spraying on a cylindrical surface, conduct numerical simulation experiments and collect numerical simulation experimental data; Step 2: Data analysis. Analyze the coating thickness pattern under different cylinder diameters and varying postures to determine the adjustable range of the varying posture spray gun. Step 3: Establish a mathematical model of the spray gun, and conduct spraying experiments based on the adjustable range of the spray gun's position. Step 4: Collect experimental coating data, and use the Trust-Region optimization algorithm to fit the experimental data according to the mathematical model of the variable pose spray gun to obtain the parameters to be identified in the cylindrical variable pose model. Perform multiple linear regression analysis to obtain the cylindrical variable pose spray coating thickness prediction model. The method for predicting the coating thickness of cylindrical surface variable pose spraying as described in step four above includes the following steps: Step 4 (1): Through numerical simulation experiments of different cylinder diameters sprayed at different heights and tilt angles, collect coating thickness data points, use the Trust-Region algorithm in MATLAB fitting tool to fit and solve the parameters to be identified, and obtain the parameter values ​​to be solved in the corresponding coating thickness distribution model when different diameter cylinders are sprayed in different poses. Step 4 (2): Using the spraying height H, spraying angle α and cylinder diameter D as independent variables and the parameter to be identified ω as the dependent variable, a multiple linear regression analysis is performed to predict the value of the parameter to be identified, and thus obtain the coating thickness distribution prediction model when spraying cylinders of different diameters in different poses.

2. The predictive modeling method for coating thickness based on cylindrical surface variable pose spraying according to claim 1, characterized in that: Based on the establishment of the three-dimensional model of the spray gun cap and the simulation control domain described in step one above, the specific steps are as follows: Step 1 (1): First, establish the air spray gun nozzle model. The center of the nozzle is the paint hole, and the outer side of the paint hole is the annular central atomizing hole. Two auxiliary atomizing holes are arranged on both sides of the central atomizing hole. Two fan-shaped control holes are arranged on the horn-shaped structure on both sides of the air nozzle. Step 1 (2): According to the need to change the cylinder diameter and the positional spraying, numerical simulation is carried out for cylinders with different diameters using the positional spraying method. The cylinder diameters are D1, D2 and D3 respectively. According to the spray gun process requirements, the adjustable range of the spraying height is H1~H2, and the preset spraying angle adjustment range is α1~α2. For different spraying heights, spraying angles and cylinder diameters, different control domains need to be set for simulation calculation.

3. The predictive modeling method for coating thickness based on cylindrical surface variable pose spraying according to claim 2, characterized in that: The numerical simulation calculation method based on the above-mentioned variable pose spraying includes the following steps: Step 1 (3): Set two phases for the spray flow field. The first phase is the gas phase, which represents the air in the spray flow field. The second phase is the liquid phase, which represents the paint droplets in the flow field. The paint is water-based paint. Its density and viscosity are measured by measuring instruments. According to the boundary conditions of numerical simulation and the required parameters, the gas phase mass flow rate, liquid phase mass flow rate, liquid phase initial velocity, liquid phase inlet volume fraction, turbulence intensity, hydraulic diameter, spray gun atomization pressure, gravity, operating pressure, and static spraying time are set respectively. Step 1 (4): Collect numerical simulation experimental data: For the paint droplets sprayed onto the cylindrical wall, establish a coordinate system with the spray gun cone direction as the Z direction, the circumferential direction of the cylindrical surface as the X direction, and the axial direction of the cylindrical surface as the Y direction, and collect numerical simulation data points in the X and Y directions on the cylindrical wall respectively.

4. The predictive modeling method for coating thickness based on cylindrical surface variable pose spraying according to claim 1, characterized in that: The method for analyzing the coating thickness under different cylinder diameters and varying postures in step two above includes the following steps: Step 2 (1): First, the method of controlling variables is used to analyze the law of coating thickness change of cylinder diameter, spraying height and spraying angle. That is, two of the quantities are kept constant and the influence of the other quantity on the coating thickness change is studied. Spraying height, spraying angle and cylinder diameter will all affect the coating thickness distribution. Step 2 (2): Through static spraying experiments, it was found that if the maximum coating thickness in the coating distribution curve is too low, it will result in poor leveling, while if the coating thickness is too high, it will result in dripping on the workpiece surface. Both situations will affect the coating quality. Considering the viscosity of the coating used in the experiment, in order to ensure the coating quality, the maximum coating thickness is set in the range of T1~T2. Based on the above numerical simulation results, and specifying the adjustment range of the spraying height as H1~H2, the gray prediction and fitting interpolation method can be used to obtain the adjustable range of the spraying angle within the coating thickness range. Step 2 (3): By analyzing the variation law of the spraying angle obtained by cylinders of different diameters at different spraying heights, the relationship between the maximum coating thickness and the spray gun posture can be obtained.

5. The predictive modeling method for coating thickness based on cylindrical surface variable pose spraying according to claim 1, characterized in that: The steps for establishing the mathematical model of the spray gun based on step three above are as follows: Step 3 (1): Assume that the spraying process parameters and spraying environment remain constant during the static spraying process. Point S is any point in the spraying area, r is the distance from the center point of the spray gun to point S, R is the spraying radius, θ is the angle between the line connecting point S and the spray gun and the center line of the spray gun, φ is the opening angle of the spray gun, and α is the spray tilt angle of the spray gun. Step 3 (2): Conduct a robot spraying experiment based on the adjustable range of the spray gun pose obtained from the numerical simulation.

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