Method and system for predicting film thickness and spraying parameters for circular arc spraying trajectories

Abstract

The application discloses a kind of circular arc spraying trajectory coating thickness and the prediction method and system of spraying parameter, relevant spraying parameter of spraying system is collected, film thickness data is collected, the average film thickness difference data of adjacent two times spraying is obtained from the difference of Nth film thickness and first film thickness;Regression prediction based on sparrow search algorithm establishes spray gun wear degradation model, to carry out health state detection of spraying system;Based on polar coordinate value, the thickness distribution coordinate grid of preset spraying process parameter is established, the thickness distribution model of static coating is introduced, and the dynamic thickness distribution model of coating based on circular arc trajectory spraying is established;Nonlinear parameter optimization algorithm is used to predict and solve the above-mentioned spraying parameter.Solve the existing spraying method for circular arc spraying trajectory spraying coating thickness difficult to predict and control, the problem of difficult spraying parameter setting.

Description

Technical Field

[0001] This invention relates to the field of automatic spraying technology, and in particular to a method and system for predicting coating thickness and spraying parameters for an arc spraying trajectory. Background Technology

[0002] Air spray guns are widely used in industrial production, including automobiles, ships, and furniture, due to their excellent atomization effect. Currently, spraying parameters can only be set based on human experience to achieve the ideal coating thickness on the workpiece. However, this method of setting spraying parameters heavily relies on human experience and only produces results on simple planes. Setting spraying parameters based solely on human experience is difficult on complex planes. In particular, this invention targets the arc-shaped trajectory spraying of workpieces such as the end face of steel coils. Since the spraying process is arc-shaped, the coating thickness is difficult to guarantee, and setting spraying parameters based on human experience is also difficult. The coating thickness and uniformity cannot be predicted, especially during the automatic arc-shaped trajectory spraying process, where controlling the coating thickness and uniformity presents certain challenges.

[0003] Currently, the method for simulating the thickness and uniformity of sprayed coatings is generally finite element modeling simulation, which usually uses commercial simulation software to simulate the constitutive model of actual spraying. The disadvantages of this method are: (1) It does not take into account the wear and degradation of the spray gun in the actual spraying process, while in the actual spraying experiment, the different wear levels of the spray gun have a significant impact on the spraying quality. (2) The spraying state of the arc spraying object is more complex, and general finite element simulation software cannot realistically simulate the film thickness distribution of the arc trajectory dynamic spraying. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for predicting the coating thickness and spraying parameters of a circular arc spraying trajectory, aiming to solve the problems of difficulty in predicting and controlling the coating thickness of a circular arc spraying trajectory and difficulty in setting spraying parameters in existing spraying methods.

[0005] The technical solution adopted in this invention is: A method for predicting coating thickness and spraying parameters for an arc-shaped spraying trajectory includes the following steps: Collect relevant spraying parameters of the spraying system, including spraying pressure, spraying flow rate, atomization cone angle, spraying distance, and spray gun spraying speed. Collect film thickness data, and obtain the average film thickness difference data between two adjacent sprays from the difference between the Nth film thickness and the first film thickness; A spray gun wear degradation model is established based on the sparrow search algorithm regression prediction to obtain the wear rate W for each spraying process, so as to detect the health status of the spraying system. A thickness distribution coordinate grid based on polar coordinate values ​​is established for the preset spraying process parameters. A thickness distribution model of the static coating is introduced to obtain the average difference in sprayed film thickness. Thus, a dynamic thickness distribution model of the coating based on arc trajectory spraying is established. The above-mentioned spraying parameters are predicted and solved using a nonlinear parameter optimization algorithm.

[0006] In the above technical solution, the spray gun wear and degradation model includes the state results of the inside of the spray gun after being eroded by the spraying solution.

[0007] In the above technical solution, a spray gun wear model is established based on a fan-shaped nozzle, wherein the fan-shaped nozzle includes forming air holes, atomizing air holes, gas-liquid mixing air holes, and auxiliary air pressure holes.

[0008] In the above technical solution, the wear degradation model is as follows: ; Where W is the wear rate in units of time, P is the spraying pressure in kPa, T is time, e is the error, and N is the coating pressure in kPa. l P l S l and C l These represent the nozzle atomization cone angle, spraying distance, spray gun spraying speed, and the difference in film thickness between the two spraying operations, respectively; b0, b1, b2, b3, b4, b5, and b6 are the output regression coefficients.

[0009] In the above technical solution, the coating thickness model of static coordinates includes three regions: the spraying area of ​​the arc trajectory distributed according to the major axis of the ellipse, the division area of ​​the spraying coordinate grid represented by the annular area between the two large circles, and the static deposition model failure area represented by the area where the major axis does not coincide.

[0010] In the above technical solution, the polar coordinates of any point P in the elliptical spraying area on the coating thickness model of static coordinates are substituted into the coating thickness model of static coordinates to obtain the static thickness value of point P. By combining the change in thickness at point P caused by the arc spraying trajectory, and performing quaternion spherical linear interpolation on the obtained static thickness value at point P, the change in thickness at point P after one spraying during the dynamic spraying process can be calculated. .

[0011] In the above technical solution, after obtaining the thickness difference of point P in one spraying, the average film thickness of point P in N spraying processes is then obtained: .

[0012] In the above technical solution, the average coating thickness of the spraying system is calculated based on the dynamic thickness difference of point P N times and the thickness change of point P caused by the arc spraying trajectory.

[0013] In the above technical solution, the index coordinates of the four points adjacent to the index coordinates of point P are vectorized respectively, and interpolation is performed using an interpolation formula. ; Among them, weight This represents the position of the end of the interpolation vector on the sprayed arc and ; Construct a 4×4 matrix M. The unit eigenvector corresponding to the largest eigenvalue of M is the thickness change difference at point P that we are looking for. .

[0014] Optionally, a thickness dataset of the coating is collected, and the spraying parameters are predicted and adjusted based on the coating thickness dataset.

[0015] Optionally, a static coating thickness distribution model is introduced, which can predict the elliptical coating thickness distribution under initial spraying parameters in a static state.

[0016] Optionally, based on the thickness distribution model of the static coating and the arc spraying trajectory, a thickness distribution coordinate grid of preset spraying process parameters is established using polar coordinate values.

[0017] Optionally, for the coating thickness at a point P within the elliptical spraying area, based on the coating thickness distribution model and other factors influencing coating thickness, the coating thickness variation caused by all spraying influencing factors is calculated. .

[0018] Regarding the coating thickness variation value Calculate the average value to obtain the final coating thickness at point P.

[0019] Optionally, a nonlinear parameter optimization algorithm can be used to solve for specific parameters.

[0020] Optionally, the predicted film thickness T for the next spraying process can be calculated using a prediction model.

[0021] Optionally, a computer system is provided, comprising: a memory and a processor, wherein the memory can store readable instructions on a computer, and the processor can execute the computer program to implement the above method.

[0022] Optionally, under preset spraying process parameters, the relevant spraying parameters can be recorded and modified through the computer system.

[0023] The beneficial effects that this application can achieve are as follows: The method for predicting the coating thickness and spraying parameters of the arc spraying trajectory proposed in this application can accurately predict the coating thickness distribution of the arc trajectory under the influence of different spraying parameters. It can predict the size of the coating thickness on the sprayed surface in advance, and then make the coating on the workpiece surface more uniform and reliable by changing the spraying parameters.

[0024] Meanwhile, this invention combines static and dynamic thickness, continuously collecting the average film thickness difference data between two adjacent sprays from the difference between the Nth film thickness and the first film thickness during the spraying process; the prediction results are more accurate.

[0025] This invention incorporates spray gun parameters, making it more suitable for data from the production site. Attached Figure Description

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a schematic diagram illustrating the prediction process of coating thickness and spraying parameters for the arc spraying trajectory of the present invention.

[0027] Figure 2 This is a flowchart of the regression prediction process based on the sparrow search algorithm of this invention.

[0028] Figure 3 This is a flowchart of the nonlinear parameter optimization algorithm of the present invention.

[0029] Figure 4 This is a partial effect diagram of the spray deposition model of the present invention.

[0030] Figure 5 This is a nozzle shape diagram of the spray gun wear model of the present invention.

[0031] Figure 6 This is a schematic diagram of the arc spray coating thickness mesh model of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0033] like Figure 1As shown, as an optional technical solution, this embodiment provides a method for predicting the coating thickness of complex surfaces. First, relevant spraying parameters of the spraying system are collected, including spraying pressure, spraying flow rate, atomization cone angle, spraying distance, and spray gun speed. Then, the average film thickness difference between two adjacent sprays is obtained from the difference between the Nth and the first film thickness. Based on the sparrow search algorithm, a spray gun wear degradation model is established through regression prediction. Then, a thickness distribution coordinate grid of preset spraying process parameters is established based on polar coordinate values. A static coating thickness distribution model is introduced, and a dynamic thickness distribution model is obtained from the arc trajectory. Finally, the average film thickness value at point P is obtained. A nonlinear parameter optimization algorithm is used to predict and solve the above spraying parameters.

[0034] This invention establishes a wear and degradation model of the spray gun, collects film thickness data between the Nth and first sprayings, establishes a thickness distribution coordinate grid for preset spraying process parameters, obtains the average difference in sprayed film thickness, and finally establishes a dynamic thickness distribution model of the arc trajectory coating. A nonlinear parameter optimization algorithm is then used to solve for specific spraying parameters (spraying speed, spraying flow rate, spraying distance, and spraying pressure in the dynamic thickness distribution model of the arc trajectory spraying).

[0035] Currently, the methods for simulating the thickness and uniformity of sprayed coatings are generally finite element modeling simulations, which typically use commercial simulation software to simulate the constitutive model of the actual spraying.

[0036] The disadvantage of this method is that (1) it does not take into account the wear and degradation of the spray gun in the actual spraying process. In the actual spraying experiment, the different wear levels of the spray gun have an undeniable impact on the spraying quality.

[0037] (2) The spraying state of the object being sprayed on the arc is quite complex, and general finite element simulation software cannot accurately reflect the film thickness distribution of the dynamic spraying of the arc trajectory.

[0038] As an optional technical solution, the spray gun wear model established in this invention should also include the state results of the inside of the spray gun after being eroded by the spraying solution.

[0039] As an optional technical solution, the establishment of the spray gun wear model is based on Figure 5 The fan-shaped nozzle mainly includes forming air holes 4, atomizing air holes 5, gas-liquid mixing air holes 6, and auxiliary air pressure holes 7.

[0040] As an optional technical solution, this invention establishes a wear and degradation model for a spray gun, such as... Figure 2As shown, firstly, film thickness data from two consecutive spraying operations are collected to obtain the difference in film thickness between the two operations. Then, relevant spraying parameters of the spraying system are collected, including spraying pressure, spraying flow rate, atomization cone angle, spraying distance, and spray gun spraying speed. Finally, a wear and degradation model of the spray gun is established based on regression prediction using the sparrow search algorithm. ; Where W is the wear rate in units of time, P is the spraying pressure in kPa, T is time, e is the error, and N is the wear rate in units of time. l P l S l and C l These represent the nozzle atomization cone angle, spraying distance, spray gun speed, and the difference in film thickness between the two spraying operations, respectively. b0, b1, b2, b3, b4, b5, and b6 are the output regression coefficients.

[0041] As an optional technical solution, based on the established spraying model and the special characteristics of the arc spraying trajectory, a circular thickness distribution grid corresponding to the spraying process parameters and coating thickness model is established and preset. The thickness distribution grid is established by selecting the polar coordinate axis.

[0042] As an optional technical solution, establish such Figure 6 Using the thickness distribution grid 6, the polar coordinates of point P are substituted into the static coordinate coating thickness model to calculate the static thickness value of point P. The static coordinate coating thickness model is as follows: Figure 4 As shown, the arc-shaped spraying trajectory is distributed along the major axis of the ellipse (i.e., the ellipse major axis coincidence region 3). The annular region between the two large circles is the division region 1 of the spraying coordinate grid, and the region where the major axes do not coincide is the static deposition model failure region 2. By combining the thickness change value at point P caused by the arc-shaped spraying trajectory with the obtained static thickness value at point P, the thickness change value at point P during the dynamic spraying process can be obtained by performing quaternion spherical linear interpolation. Specifically: Figure 6 In the diagram, the index coordinates or two-dimensional coordinates of point P are expressed in polar coordinates as: P( , The index coordinates of the four adjacent points are: A( , ), B ( , ), C ( , ), D ( , To make the predicted thickness of P more accurate, quaternion spherical linear interpolation is used to calculate the thickness variation at point P caused by the arc spraying trajectory. Based on the N dynamic thickness differences at point P and the thickness variation at point P caused by the arc spraying trajectory, the average spraying thickness of the spraying system can be calculated. The vectorized points A, B, C, and D are then... The interpolation formula is

[0043] Among them, weight This represents the position of the end of the interpolation vector on the sprayed arc and Construct a 4×4 matrix M. The unit eigenvector corresponding to the largest eigenvalue of M is the thickness change difference at point P that we are looking for.

[0044]

[0045] After obtaining the thickness difference of point P in one spraying process, the average film thickness of point P in N spraying processes can be obtained: .

[0046] As an optional technical solution, the following can be adopted: Figure 3 The nonlinear parameter optimization algorithm solves for specific parameters such as spraying speed, spraying flow rate, spraying distance, and spraying pressure.

[0047] As an optional technical solution, a computer device is provided, comprising: a memory and a processor, wherein the memory can store readable instructions on a computer, and the processor can execute the computer program to implement the above method.

[0048] The provided computer device is connected to an industrial camera to calculate the surface roughness of the workpiece based on the unevenness of the workpiece surface being sprayed.

[0049] The provided computer device outputs the optimized spraying parameters and obtains the final predicted spraying film thickness based on the dynamic spraying model.

[0050] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for predicting coating thickness and spraying parameters for an arc-shaped spraying trajectory. Includes the following steps: S1, collect relevant spraying parameters of the spraying system, including spraying pressure, spraying flow rate, atomization cone angle, spraying distance, and spray gun spraying speed; S2, collect film thickness data, and obtain the average film thickness difference data between two adjacent sprays from the difference between the Nth film thickness and the first film thickness. S3. A spray gun wear degradation model is established based on the sparrow search algorithm regression prediction to obtain the wear rate W for each spraying process, so as to detect the health status of the spraying system; the wear degradation model is as follows: ； in; W is the wear rate measured in time; P is the spraying pressure in kPa; T is time; e is the error; N l P l S l and C l These represent the nozzle atomization cone angle, spraying distance, spray gun spraying speed, and the difference in film thickness between the two spraying operations, respectively; b0, b1, b2, b3, b4, b5, and b6 are the output regression coefficients. S4. Based on the polar coordinate values, establish a thickness distribution coordinate grid for the preset spraying process parameters, introduce the thickness distribution model of the static coating, and then obtain the average difference in sprayed film thickness. Thus, establish a dynamic thickness distribution model of the coating based on arc trajectory spraying. Substitute the polar coordinate values ​​of any point P in the elliptical spraying area on the static coordinate coating thickness model into the static coordinate coating thickness model to obtain the static thickness value of point P. By combining the change in thickness at point P caused by the arc spraying trajectory, and performing quaternion spherical linear interpolation on the obtained static thickness value at point P, the change in thickness at point P after one spraying during the dynamic spraying process can be calculated. Vectorize the index coordinates of the four points adjacent to point P, and then perform interpolation using the interpolation formula: ； Among them, weight This represents the position of the end of the interpolation vector on the sprayed arc and ; Construct a 4×4 matrix M. The unit eigenvector corresponding to the largest eigenvalue of M is the thickness change difference at point P that we are looking for. ; After obtaining the thickness difference of point P in one spraying process, the average film thickness of point P in N spraying processes is then obtained: ; S5. A nonlinear parameter optimization algorithm is used to predict and solve the above-mentioned spraying parameters.

2. The method for predicting coating thickness and spraying parameters for arc spraying trajectory according to claim 1, characterized in that, The spray gun wear and degradation model includes the state of the spray gun's interior after being eroded by the spraying solution.

3. The method for predicting coating thickness and spraying parameters for arc spraying trajectory according to claim 1, characterized in that, A spray gun wear model is established based on a fan-shaped nozzle, which includes forming air holes, atomizing air holes, gas-liquid mixing air holes, and auxiliary air pressure holes.

4. The method for predicting coating thickness and spraying parameters for arc spraying trajectory according to claim 1, characterized in that, The static coordinate coating thickness model includes three regions: the spraying area with arc trajectory distributed along the major axis of the ellipse, the division area of ​​the spraying coordinate grid represented by the annular area between the two large circles, and the static deposition model failure area represented by the area where the major axis does not coincide.

5. The method for predicting coating thickness and spraying parameters for arc spraying trajectory according to claim 1, characterized in that, Under the preset spraying process parameters, the relevant spraying parameters are recorded and modified through a computer system.

6. A computer system, comprising: A memory and a processor, wherein the memory may store computer-readable instructions, and the processor may execute the computer program to implement the method described in any one of claims 1-4.

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