Robotic spraying quality analysis method based on improved double-β paint film distribution model

Abstract

The present invention relates to a robot spraying quality analysis method based on an improved double-β paint film distribution model. A double-β paint film distribution model is created based on a single-β paint film distribution model. The spraying speed is introduced into the double-β paint film distribution model to obtain a dynamic film thickness distribution model. The spraying height and ambient humidity are introduced. The dynamic film thickness distribution model under the introduction of the spraying height and ambient humidity is introduced. The spray width pressure, atomization pressure, and injection pressure are introduced to obtain a dynamic film thickness distribution model under multiple process parameters. The least squares method is used to fit the undetermined parameters in the final dynamic film thickness distribution model under multiple process parameters. The accuracy and generalization performance of the final dynamic film thickness distribution model under multiple process parameters meet the preset requirements and output a film thickness distribution equation. Otherwise, the fitting algorithm parameter values are adjusted and the corresponding parameters are re-derived. The dynamic film thickness distribution model under multiple process parameters is used to achieve quality analysis and prediction of the average film thickness and the standard deviation of the paint film thickness of the robot spraying process.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot spraying quality analysis, and in particular relates to a robot spraying quality analysis method based on an improved double-β paint film distribution model. Background Art

[0002] Gas turbines are highly efficient thermal engines. Their advantages include high operating power, low pollution, fast startup, high energy efficiency, and excellent reliability. They are widely used in a variety of military and civilian applications, including electrical production and aerospace. Turbine blades are one of the most critical core components in gas turbine design and manufacturing. Their surface quality directly impacts the blade's aerodynamic shape and conversion efficiency, and thus the overall gas turbine's operating efficiency and service life. Due to the harsh operating environment of high temperature, high pressure, and high speed, gas turbine blades are susceptible to oxidation, thermal corrosion, and thermal shock. Therefore, spray coating is necessary to improve the quality and performance of gas turbine blades. Spray coating gas turbine blades not only significantly reduces environmental impacts on blade quality and extends blade service life, but also reduces surface friction and airflow resistance through optimized coating design, improving aerodynamic shape and increasing conversion efficiency. However, the working environment and intensity of gas turbine blade spraying are harsh, and some coatings contain toxic and hazardous substances such as chromium trioxide. Traditional manual spraying not only results in significant labor costs and inconsistent product quality, but also poses significant risks to the operator's occupational health. Therefore, it is necessary to upgrade the gas turbine blade spraying process, using spray robots to replace manual spraying operations and build an automated gas turbine blade spraying production line. Compared with manual spraying, robotic spraying has the advantages of increasing production efficiency, reducing production costs, improving coating quality, saving spraying fuel, and reducing health risks.

[0003] The core task of using spray robots to build an automated production line is to refer to production experience, analyze and summarize the spraying process, summarize the influencing rules of the paint film quality of the robot's gas turbine blade spraying, and then combine the robot's motion planning to achieve high-quality processing of gas turbine blades. How to construct a gas turbine blade quality analysis and evaluation model by analyzing and modeling the coupling effects of factors such as spraying process parameters, spraying device parameters, and environmental parameters on the paint film quality of gas turbine blades is one of the pain points in the development of industry technology. At present, the main research direction of gas turbine blade quality analysis is to analyze the impact of different process parameters on the quality of gas turbine blades, construct a coating thickness distribution model for gas turbine blades under different process parameters, and then construct a mapping model through statistical or machine learning methods to achieve predictive analysis of paint film thickness.

[0004] In the existing technology, some researchers have developed an offline programming system that can complete film thickness prediction. It can determine the spraying strategy, path, etc. by giving corresponding parameters. Through actual spraying experiments, the model predicts a small deviation between the thickness and the actual thickness

[11] . In order to study the film thickness distribution, some researchers have conducted multiple sets of plane spraying experiments for spray gun speed and spray gun distance, and used the β model to simulate the film thickness distribution under different conditions. Some researchers have conducted experimental analysis on the influence of different spray angles on coating distribution and the influence of spray gun speed on temperature and residual stress under the condition that the process parameters such as spray flow rate and spray gun distance remain unchanged. The experimental results show that adjusting the appropriate spray angle and variable speed spraying can improve the spraying quality. Some researchers have used Fluent software to build a spray simulation model, and based on the model, orthogonal simulation of different process parameters was performed. The obtained experimental data were fitted with the elliptical double β model and genetic algorithm to obtain the final film thickness distribution formula. There is also a salt-baked company that uses Ansys software to simulate the spray flow based on the DPM and TAB models, and analyzes the influence of molding air pressure on the air flow field and coating thickness distribution. Others have proposed a coating thickness distribution model based on electrostatic rotary cup uniform spraying, and analyzed the influence of characteristic points as various coating process parameters change through a multi-level, multi-factor orthogonal experimental group.

[0005] However, the above research on spraying process parameters and spray paint film thickness still has some research breakpoints. First, the current analysis of spraying process parameters is not comprehensive enough. Most of the analysis focuses on the four input parameters of spraying speed, spraying height, atomization pressure and injection pressure, while ignoring the influence of injection pressure based on paint characteristics and environmental humidity and other factors on paint film quality. Second, current research basically stays at the research of single-pass spraying, while actual blade spraying operations often adopt multi-pass spraying. Therefore, the overlap distance between the spraying tracks also affects the overall paint film quality on the blade surface. Third, the research on paint film quality only stays at the prediction of paint film thickness, and has not formed a complete and standardized paint film quality analysis method, nor has it proposed a clear paint film quality assessment standard.

[0006] Therefore, the present invention aims at the problem that there are still some research breakpoints in the research on spraying process parameters and spray paint film thickness. A single-pass paint film distribution model for gas turbine blades and a prediction method for the average film thickness and film thickness difference of gas turbine blades based on the model are proposed, forming a clear quality assessment standard and a standardized paint film quality analysis method for gas turbine blades to solve the above-mentioned technical problems that the analysis of spraying process parameters is not comprehensive enough, the research basically stays in the research of single-pass spraying, and does not involve the multi-pass spraying method often used in blade spraying operations, and there is no complete and standardized paint film quality analysis method. Summary of the Invention

[0007] The purpose of the present invention is to provide a robotic spraying quality analysis method based on an improved double-β paint film distribution model, and a method for predicting the average film thickness and film thickness difference of gas turbine blades based on the model, so as to form a clear quality assessment standard and a standardized paint film quality analysis method for gas turbine blades, so as to solve the above-mentioned technical problems that the analysis of spraying process parameters is not comprehensive enough, the research basically stays in the research of single-pass spraying, and does not involve the multi-pass spraying method often used in blade spraying operations, and the lack of a complete and standardized paint film quality analysis method.

[0008] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0009] The robot spraying quality analysis method based on the improved double-β paint film distribution model includes the following steps:

[0010] S1: Create a single β paint film distribution model;

[0011] S2: Create a double-β paint film distribution model based on the single-β paint film distribution model;

[0012] S3: introducing the spraying speed into the double-β paint film distribution model to obtain a dynamic film thickness distribution model;

[0013] S4: introducing spraying height and ambient humidity into the film thickness distribution model under the dynamic state;

[0014] S5: Introducing the dynamic film thickness distribution model under spraying height and ambient humidity, introducing spray width pressure, atomization pressure, and injection pressure to obtain the final dynamic film thickness distribution model under multiple process parameters;

[0015] S6: using the least squares method to fit the undetermined parameters in the final dynamic film thickness distribution model under multiple process parameters, and judging whether the accuracy and generalization performance of the final dynamic film thickness distribution model under multiple process parameters meet the preset requirements. If so, output the film thickness distribution equation based on the fitting parameters at this time and end; if not, execute step S7;

[0016] S7: Adjust the fitting algorithm parameter values, recalculate the corresponding parameters, and again judge whether the accuracy and generalization performance of the dynamic film thickness distribution model under the final multi-process parameters meet the preset requirements, until the accuracy and generalization performance of the dynamic film thickness distribution model under the final multi-process parameters meet the preset requirements.

[0017] Preferably, the expression of the single β paint film distribution model in step S1 is:

[0018]

[0019] Among them, β represents the shaping parameter, which determines the distribution shape of the model film thickness, q maxrepresents the maximum film thickness at the center point, q(x) represents the film thickness at a distance x from the center point, and r represents the spray radius of the spray gun.

[0020] Preferably, the specific process of creating the double-β paint film distribution model based on the single-β paint film distribution model in step S2 is:

[0021] S21: Based on the expression of the single β paint film distribution model, the film thickness distribution formula at the specified point A(x,y) is obtained:

[0022]

[0023] Among them, q max is the maximum film thickness growth rate value at the center point O of the paint elliptical distribution area image, and β1 is the shaping parameter value of the cross section in this direction;

[0024] S22: Calculate the film thickness growth rate at the x and y positions in the y direction. The calculation formula is as follows:

[0025]

[0026] This formula is the double-β paint film distribution model formula;

[0027] Among them, a and b represent the major axis and minor axis values of the paint elliptical distribution area image, respectively, q(x,y) represents the film thickness growth rate value at the x and y positions, and it is assumed that the x and y directions have the same film thickness distribution morphology, that is, the β values of each section with the same direction are the same, β1 and β2 are the film thickness shaping parameter values in different directions, and their sizes are determined by the shaping air pressure process parameter value.

[0028] Preferably, in step S3, the specific process of introducing the spraying speed into the double-β paint film distribution model to obtain the dynamic film thickness distribution model is as follows:

[0029] S31: Calculate the distance swept by the spray gun. The specific formula is:

[0030]

[0031] S32: Calculate the time the spray gun sweeps through. The specific formula is as follows:

[0032]

[0033] S33: Calculate the cumulative coating thickness at any point O (x0, y0), that is, the integral of the time that point O is swept by the spray gun:

[0034]

[0035] S34: Change the integration limit to obtain:

[0036]

[0037] S35: Let β1 = β2 = 2, and substitute Afterwards, bring in The coating profile thickness distribution is obtained when the spray gun sprays at a uniform speed along the workpiece surface.

[0038] Preferably, the specific process of introducing the spraying height and the ambient humidity into the film thickness distribution model under the dynamic state in step S4 is as follows:

[0039] S41: Set the fog cone angle α in the Y-axis direction y Along the direction of movement of the spray gun trajectory, the spray cone angle α in the X-axis direction x Perpendicular to the direction of movement of the spray gun, the major and minor axes of the ellipse and the spray cone angle are obtained. The specific formula is:

[0040]

[0041] S42: Establish the relationship between the spraying distance d and the major and minor axes a and b of the elliptical spray spot:

[0042]

[0043] S43: Establish the relationship between the paint application rate ε and d:

[0044]

[0045] Among them, k ε is the paint rate coefficient, h is the spraying distance;

[0046] S44: Other spraying parameters are constant, and the spraying distance does not affect the total amount of paint sprayed by the spray gun per unit time. The total amount of paint at any time is fixed, and the relationship is obtained:

[0047]

[0048] Among them, S(h1) and S(h2) are the areas of the coating along the longitudinal section obtained by single-pass spraying on the flat plate at spraying distances h1 and h2, respectively. The calculation formula is:

[0049]

[0050] Then we get:

[0051]

[0052] Preferably, the specific process of introducing the spray width pressure, atomization pressure, and injection pressure in step S5 is as follows:

[0053] S51: Establish atomization pressure p w , spray pressure pf , injection pressure p i The relationship between them is as follows:

[0054]

[0055] S52: Establish injection pressure p i and the maximum coating thickness d max The relationship:

[0056]

[0057] S53: Establish a multi-parameter coating growth model to obtain the maximum coating thickness within the elliptical spray spot:

[0058]

[0059] S54: Calculate the coating thickness when the spray gun sprays uniformly along the workpiece surface:

[0060]

[0061] Among them, k x 、k y , a1, b1, a2, b2 are unknown coefficients, K m is the coating growth rate coefficient, and K' is the generalization coefficient, which are obtained by fitting the experimental data.

[0062] Preferably, the method further includes establishing an expression for the paint film thickness of two adjacent spraying operations in a single spraying section under a multi-pass spraying process:

[0063]

[0064] Among them, q1 ( x, y) represents the film thickness distribution expression on the first path, q2(x, v) represents the film thickness distribution expression on the second path. In multi-pass spraying operation, that is, the film thickness distribution in single-pass spraying operation obeys q1(x, v), and the overlapping area obeys q2(x, v).

[0065] Preferably, the method further includes establishing a paint film average thickness prediction model under a single cross section:

[0066]

[0067] Where d is the spray track spacing, n is the number of spray tracks, x is the corresponding point on the spray cross section, and T(x) is the film thickness value at the corresponding point.

[0068] Preferably, the method further includes establishing a standard deviation model of the paint film thickness under a single cross section:

[0069]

[0070] Where N is the number of data points, x i is the cross-sectional point corresponding to N sampling data points, T(x i ) is a single x i The film thickness value at the specified location.

[0071] The beneficial effects of the present invention include:

[0072] The present invention provides a robot spraying quality analysis method based on an improved double-β paint film distribution model. The double-β paint film distribution model is created based on the single-β paint film distribution model. The spraying speed is introduced into the double-β paint film distribution model to obtain a dynamic film thickness distribution model. The spraying height and ambient humidity are introduced. The dynamic film thickness distribution model under the introduction of the spraying height and ambient humidity introduces the spray width pressure, atomization pressure, and injection pressure to obtain a dynamic film thickness distribution model under multiple process parameters. The least squares method is used to fit the undetermined parameters in the final dynamic film thickness distribution model under multiple process parameters. The accuracy and generalization performance of the final dynamic film thickness distribution model under multiple process parameters meet the preset requirements and output the film thickness distribution equation. Otherwise, the fitting algorithm parameter values are adjusted and the corresponding parameters are re-derived. The dynamic film thickness distribution model under multiple process parameters is used to achieve quality analysis and prediction of the average film thickness and the standard deviation of the paint film thickness of the robot spraying process.

[0073] First, a single β paint film distribution model was created, and the applicable film thickness distribution model was obtained by adjusting the β value according to different processes. Since the single β distribution model formula simulates the spray width range for an ideal conical distribution, in actual spraying operations, additional gas channels are set on both sides of the automatic spray gun to adjust the spray shape in order to facilitate adjustment and control of the coating shape, resulting in the final workpiece surface spray width profile being elliptical rather than circular. Therefore, considering the influence of the spray width pressure on the spray cone shape in actual spraying and air spraying, the elliptical double β paint film distribution model is used for modeling and analysis, which is more in line with the elliptical spray plate shape generated during the actual spraying operation and more in line with actual production operations, thus achieving modeling analysis and accurate prediction of the variation law of process parameters.

[0074] Secondly, since the process parameters will affect the quality of paint film formation during the robot's spraying process, combined with the robot spraying operation experiment of gas turbine blades, the spraying speed, spraying height, spray width pressure, atomization pressure, and injection pressure are set as the input parameters of the improved double-β paint film distribution model. Then, the influence of environmental factors is introduced into the spraying height to ensure that the model is more in line with the actual spraying effect.

[0075] Thirdly, since a single-pass spraying operation cannot meet the process requirements of paint film spraying, a multi-pass spraying operation is required to ensure that the paint film quality on the blade surface meets the standards. During single-pass spraying, the paint film thickness distribution on the spraying path presents a normal distribution with thicker in the middle and thinner on both sides. When multiple-pass spraying operations are performed at the same time, there will be overlap in the spraying range, and the paint film thickness in the overlapping area will increase due to repeated spraying. Therefore, the present invention combines the spraying process requirements and analyzes and optimizes the overlap distance during the multi-pass spraying operation to ensure the consistency of the paint film thickness between the single-track spraying area and the overlap spraying operation area, thereby ensuring the film thickness and paint film uniformity of the paint film cross section of the gas turbine blade.

[0076] Finally, although in the actual spraying process, changes in the state of the spray gun or robot will cause fluctuations in the spraying process parameters during the spraying process, which in turn will cause certain fluctuations in the paint film quality at different positions in the paint film cross-section during a single spraying process, based on the paint film average thickness prediction model and the paint film thickness standard deviation model under a single cross-section, the present invention achieves a reasonable prediction and accurate analysis of the paint film quality under different paint film process parameters during the robot spraying operation of the gas turbine blade. Based on the double-β paint film distribution model, the present invention constructs a complete paint film quality analysis method for the robot spraying operation of the gas turbine blade. Based on this method, it not only provides standard and reliable evaluation indicators and analysis schemes for the process iteration of the gas turbine blade, but also significantly improves the paint film process analysis method and increases the speed of the paint film process iteration. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 The figure is a flow chart of the robot spraying quality analysis method based on the improved double-β paint film distribution model of the present invention.

[0078] Figure 2 This is a schematic diagram showing different film thickness distribution forms presented by different β value distribution models of the present invention.

[0079] Figure 3 It is a schematic diagram of the dynamic spraying process of the spray gun of the present invention.

[0080] Figure 4 Schematic diagram of the relationship between the major axis and minor axis of the elliptical spray spot and the spraying height of the present invention.

[0081] Figure 5 Schematic diagram of the overlapping distance between adjacent tracks of the present invention.

[0082] Figure 6 Schematic diagram of film thickness distribution of overlap distance. DETAILED DESCRIPTION

[0083] The following is combined with Figures 1 to 6 The present invention is described in further detail:

[0084] Example 1

[0085] See attached Figure 1 As shown, the robot spraying quality analysis method based on the improved double-β paint film distribution model includes the following steps:

[0086] S1: Create a single β paint film distribution model;

[0087] S2: Create a double-β paint film distribution model based on the single-β paint film distribution model. By creating a single-β paint film distribution model and adjusting the β value according to different processes, a suitable film thickness distribution model is obtained. Since the simulated spray range of the single-β distribution model formula is an ideal conical distribution, in actual spraying operations, in order to facilitate adjustment and control of the coating shape, additional gas channels are set on both sides of the automatic spray gun to adjust the spray shape, resulting in the final workpiece surface spray profile being elliptical rather than circular. Therefore, taking into account the influence of the spray pressure on the mist cone shape in actual spraying and air spraying, the use of the elliptical double-β paint film distribution model for modeling and analysis is more in line with the elliptical spray plate shape generated during the actual spraying operation, and is more in line with actual production operations, thereby achieving modeling analysis and accurate prediction of the law of process parameter changes.

[0088] S3: introducing the spraying speed into the double-β paint film distribution model to obtain a dynamic film thickness distribution model;

[0089] S4: introducing spraying height and ambient humidity into the film thickness distribution model under the dynamic state;

[0090] S5: Introducing the dynamic film thickness distribution model under spraying height and ambient humidity, the spray width pressure, atomization pressure, and injection pressure are introduced to obtain the final dynamic film thickness distribution model under multiple process parameters; since the process parameters will affect the paint film quality during the robot's spraying process, the spraying speed, spraying height, spray width pressure, atomization pressure, and injection pressure are set as the input parameters of the improved double-β paint film distribution model in combination with the robot spraying operation experiment of the gas turbine blade, and then the influence of environmental factors is introduced in the spraying height to ensure that the model is more in line with the actual spraying effect.

[0091] S6: using the least squares method to fit the undetermined parameters in the final dynamic film thickness distribution model under multiple process parameters, and judging whether the accuracy and generalization performance of the final dynamic film thickness distribution model under multiple process parameters meet the preset requirements. If so, output the film thickness distribution equation based on the fitting parameters at this time and end; if not, execute step S7;

[0092] S7: Adjust the fitting algorithm parameter values, recalculate the corresponding parameters, and again judge whether the accuracy and generalization performance of the dynamic film thickness distribution model under the final multi-process parameters meet the preset requirements, until the accuracy and generalization performance of the dynamic film thickness distribution model under the final multi-process parameters meet the preset requirements.

[0093] In this embodiment, the expression of the single β paint film distribution model in step S1 is:

[0094]

[0095] Among them, β represents the shaping parameter, which determines the distribution shape of the model film thickness, q max Indicates the maximum film thickness at the center point, q(x) indicates the film thickness at a distance x from the center point, and r indicates the spray radius of the spray gun. Figure 2 As shown in the figure, from the outside to the inside, the film thickness distribution morphology is shown for β values of 1.5, 2, 4, and 8. It can be seen that the distribution model presents different film thickness distribution morphologies for different β values. When β is 1.5, the film thickness profile is semi-elliptical. When β is 2, the film thickness profile becomes parabolic. When β is larger, the profile shape becomes Gaussian. It can be seen that the β value affects the sharpness and flatness of the distribution. As the β value increases, this trend also increases. Therefore, in practical applications, the appropriate film thickness distribution model can be obtained by adjusting the β value according to different processes.

[0096] Since gas turbine blade spray guns generally use air spraying for spraying operations, in actual air spraying production, in order to form a more uniform paint film on the workpiece surface, compressed air needs to be drawn out on both sides of the spray gun nozzle to flatten the paint mist cone, forming a fan-shaped body with a certain thickness, forming an elliptical paint film area on the plane perpendicular to the axis of the spray gun. This is quite different from the circular paint film area formed by the right cone approximation of the β model. In addition, considering the influence of the spray pressure on the spray cone shape in air spraying, the elliptical double β curve model, namely the double β paint film distribution model, is used for modeling and analysis to better fit the elliptical spray plate shape produced during the actual spraying operation.

[0097] The specific process of creating a double-β paint film distribution model based on the single-β paint film distribution model in step S2 is:

[0098] S21: Based on the expression of the single β paint film distribution model, the film thickness distribution formula at the specified point A(x,y) is obtained:

[0099]

[0100] Among them, q max is the maximum film thickness growth rate value at the center point O of the paint elliptical distribution area image, and β1 is the shaping parameter value of the cross section in this direction;

[0101] S22: Calculate the film thickness growth rate at the x and y positions in the y direction. The calculation formula is as follows:

[0102]

[0103] This formula is the double-β paint film distribution model formula.

[0104] Wherein, a and b represent the major axis and minor axis values of the paint elliptical distribution area image respectively, q(x,y) represents the film thickness growth rate value at the x and y positions, and it is assumed that the x direction and the y direction have the same film thickness distribution morphology, that is, the β values of each section with the same direction are the same, β1 and β2 are the film thickness shaping parameter values in different directions, and their sizes are determined by the shaping air pressure process parameter value. It can be seen that the single β distribution model formula simulates the spray width range as an ideal conical distribution. However, in actual spraying operations, in order to facilitate adjustment and control of the coating shape, additional gas channels are set on both sides of the automatic spray gun to adjust the spray shape, resulting in the final workpiece surface spray width profile being elliptical rather than circular. Therefore, the double β distribution model formula is more in line with actual production operations. The present invention derives the film thickness distribution formula for spraying operations based on the double β paint film distribution model.

[0105] Example 2

[0106] When using the traditional double-beta distribution model to predict paint film thickness distribution, the model lacks process parameter terms and therefore cannot reflect the actual impact of process parameter changes on paint film thickness distribution. Instead, it only demonstrates high prediction accuracy under specific process parameters. Consequently, when facing complex process conditions, the model's application scope and prediction accuracy cannot meet requirements. Therefore, to improve the model's applicability and generalization capabilities, it is necessary to introduce process parameters into the traditional double-beta curve model and construct an improved double-beta curve distribution prediction model based on process parameters. This allows for modeling, analysis, and accurate prediction of process parameter variations.

[0107] Therefore, in the actual spraying process, the spray gun does not need to be stationary, but needs to be moved in conjunction with the spraying process. And the best paint film effect is achieved when the spray gun moves at a constant speed with the short axis direction of the spray spot as the spraying direction. For the spraying process, please refer to Figure 3 shown.

[0108] On the basis of Example 1, in step S3, the spraying speed is introduced into the double-β paint film distribution model, and the specific process of obtaining the dynamic film thickness distribution model is as follows:

[0109] S31: Calculate the distance swept by the spray gun. The specific formula is:

[0110]

[0111] S32: Calculate the time the spray gun sweeps through. The specific formula is as follows:

[0112]

[0113] S33: Calculate the cumulative coating thickness at any point O (x0, y0), that is, the integral of the time that point O is swept by the spray gun:

[0114]

[0115] S34: Change the integration limit to obtain:

[0116]

[0117] S35: Let β1 = β2 = 2, and substitute Afterwards, bring in The coating profile thickness distribution is obtained when the spray gun sprays at a uniform speed along the workpiece surface.

[0118] During the spraying process, the paint is atomized by the spray gun and sprayed out in a cone-shaped mist, forming an elliptical spray spot on the surface of the object. Figure 4 As shown in the figure, since the major axis and minor axis of the elliptical spray spot are directly related to the spraying height, the concept of spray cone angle is set to describe the changing relationship between the major axis and minor axis and the spraying height.

[0119] The specific process of introducing the spraying height and the ambient humidity into the film thickness distribution model under the dynamic state in step S4 is as follows:

[0120] S41: Set the fog cone angle α in the Y-axis direction y Along the direction of movement of the spray gun trajectory, the spray cone angle α in the X-axis direction x Perpendicular to the direction of movement of the spray gun, the major and minor axes of the ellipse and the spray cone angle are obtained. The specific formula is:

[0121]

[0122] S42: The spray distance d is a parameter directly controlled by the motion trajectory of the robot end and is independent of the spray gun settings. When the spray cone angle is constant, the major axis and minor axis a, b of the elliptical spray spot become larger and larger with the increase of the spray distance. The relationship between the spray distance d and the major axis and minor axis a, b of the elliptical spray spot is established:

[0123]

[0124] S43: The spraying distance and the humidity of the spraying environment will affect the paint application rate ε on the surface. The longer the spraying distance, the less paint will be applied to the workpiece surface, which means the paint application rate will be lower. Within a certain spraying distance range, the relationship between the paint application rate ε and d is established as:

[0125]

[0126] Among them, k ε is the paint rate coefficient, h is the spraying distance;

[0127] S44: Other spraying parameters are constant, and the spraying distance does not affect the total amount of paint sprayed by the spray gun per unit time. The total amount of paint at any time is fixed, and the relationship is obtained:

[0128]

[0129] Among them, S(h1) and S(h2) are the areas of the coating along the longitudinal section obtained by single-pass spraying on the flat plate at spraying distances h1 and h2, respectively. The calculation formula is:

[0130]

[0131] Then we get:

[0132]

[0133] In the actual spraying process, by adjusting the atomization pressure p w and spray pressure p f Change the fan shape and atomization effect of the paint. The X-axis fog cone angle changes with the atomization pressure p w and spray pressure p f The fog cone angle in the Y-axis direction increases with the increase of the atomization pressure p w The spray width pressure p increases with the increase of f The injection pressure has almost no effect on the spray cone angle. The injection pressure only determines the content of droplets in the spray cone. When the injection pressure is p i When α increases, x and α y The specific process of introducing the spray width pressure, atomization pressure, and injection pressure in step S5 is as follows:

[0134] S51: Establish atomization pressure p w , spray pressure p f , injection pressure p i The relationship between them is as follows:

[0135]

[0136] S52: Although the injection pressure p i It does not affect the spray cone angle, but the injection pressure p i Directly affects the amount of paint deposited on the surface of the object, p iAs the value increases, the coating deposition thickness increases. Assuming that the coating cross section still obeys the same β distribution, the injection pressure p is established by fitting the experimental data. i and the maximum coating thickness d max The relationship:

[0137]

[0138] S53: Establish a multi-parameter coating growth model to obtain the maximum coating thickness within the elliptical spray spot:

[0139]

[0140] S54: Calculate the coating thickness when the spray gun sprays uniformly along the workpiece surface:

[0141]

[0142] Among them, k x 、k y , a1, b1, a2, b2 are unknown coefficients, K m is the coating growth rate coefficient, and K' is the generalization coefficient, which are obtained by fitting the experimental data.

[0143] Based on the five process parameters of spraying speed, spraying height, spray width pressure, atomization pressure, injection pressure and ambient humidity, a multi-process parameter coating thickness accumulation model of a single-pass spraying trajectory for the needs of robotic spraying operations of gas turbine blades has been established, and the fitting and calculation of the unknown coefficients of the formula are combined with experiments.

[0144] Example 3

[0145] Due to the paint film quality requirements and spray gun structure limitations of the robot spraying operation of the gas turbine blade, a single-pass spraying operation cannot meet the process requirements of the paint film spraying, so a multi-pass spraying operation is required to ensure that the paint film quality on the blade surface meets the standards. During single-pass spraying, the paint film thickness distribution on the spraying path shows a normal distribution with thicker in the middle and thinner on both sides. When multiple-pass spraying operations are performed at the same time, there will be overlap in the spraying range. The paint film thickness in the overlapping area increases due to repeated spraying. Therefore, it is necessary to analyze and optimize the overlap distance during the multi-pass spraying operation in combination with the spraying process requirements to ensure the consistency of the paint film thickness of the single-track spraying area and the overlapped spraying operation area, thereby ensuring the film thickness and uniformity of the paint film cross section of the gas turbine blade. During multi-pass spraying operations, all single-pass spraying paint film thickness distribution models are set to be the same, and the paint film overlap distance is the same. Therefore, the paint film thickness analysis under the entire cross section can be simplified to the film thickness analysis of two adjacent paint film tracks. Therefore, taking two adjacent spraying operations as an example, the spraying overlap is analyzed. The overlap distance of adjacent tracks can be found in Figure 5 As shown, the overlap distance and film thickness distribution refer to Figure 6 shown.

[0146] On the basis of Example 1 or Example 2, based on the requirements of the spraying operation, the present invention sets parameters for the overlap problem of the robot spraying process of the gas turbine blade. First, the moving center Y axis of the elliptical spraying trajectory of the first spraying operation is set to coincide with the upper edge of the paint film cross section, that is, the origin of the single-pass paint film thickness distribution model is located at the upper edge of the paint film cross section. Second, the area with a middle distance d within the elliptical spraying range is set as the overlap area. The gas turbine blade is sprayed once in the single-pass spraying area and is repeatedly sprayed in the overlap area. The paint film thickness of the two adjacent spraying operations under a single spraying cross section under the multi-pass spraying process is expressed as:

[0147]

[0148] Here, q1(x, v) represents the film thickness distribution expression along the first path, and q2(x, v) represents the film thickness distribution expression along the second path. In multi-pass spraying operations, the film thickness distribution in both single-pass spraying and overlapping areas follows q1(x, v), while that in overlapping areas follows q2(x, v). The film thickness distribution on a single cross-section exhibits a segmented, cyclical distribution. By varying the track spacing d, the film thickness within the single-pass spraying area and the overlapping area, as well as the uniformity of the film thickness across different areas, can be optimized.

[0149] In this embodiment, a prediction model for the average thickness of the paint film under a single cross section is also established:

[0150]

[0151] Where d is the spray track spacing, n is the number of spray tracks, x is the corresponding point on the spray cross section, and T(x) is the film thickness value at the corresponding point.

[0152] For the robot spraying operation of gas turbine blades, the core of the paint film quality analysis is whether the paint film thickness meets the standard and whether the paint film uniformity meets the requirements. And due to the coupling effect of the spraying process parameter setting and the overlap of the spraying trajectory, the paint film distribution of the paint film cross section during the spraying operation will have a great impact on the paint film quality. Therefore, it is necessary to set a paint film sampling section in the processing direction of the gas turbine blade, and ensure the paint film quality on the blade surface by ensuring the paint film thickness and paint film uniformity on the cross section. Based on the single-pass paint film thickness distribution model and the paint film thickness expression of two adjacent spraying operations under a single spraying section, the present invention constructs a paint film average film thickness prediction model and a paint film thickness standard deviation model under a single cross section of the gas turbine blade with a limited number of samplings, thereby realizing approximate analysis and prediction of the gas turbine blade cross section.

[0153] Establish a standard deviation model of paint film thickness under a single cross section:

[0154]

[0155] Where N is the number of data points, x i is the cross-sectional point corresponding to N sampling data points, T(x i ) is a single x i The film thickness value at the position. Due to the complex formula of the single-layer paint film thickness distribution model, this integral is not easy to solve directly. However, in theory, the average film thickness can be calculated through numerical integration. By discretizing the coating thickness range and then multiplying the thickness of each discrete point by the corresponding probability density value and summing them, the average film thickness can be approximately calculated.

[0156] Although in the actual spraying operation, the changes in the state of the spray gun or robot will cause the spraying process parameters to fluctuate during the spraying operation, which will in turn cause certain fluctuations in the paint film quality at different positions of the paint film cross-section during a single spraying process, but based on the paint film average film thickness prediction model and the paint film thickness standard deviation model under a single cross-section, the present invention can achieve reasonable prediction and accurate analysis of the paint film quality under different paint film process parameters during the robot spraying operation of the gas turbine blade. Based on all the above mathematical models, the present invention constructs a complete paint film quality analysis method for the robot spraying operation of the gas turbine blade. Based on this method, it not only provides standard and reliable evaluation indicators and analysis schemes for the iteration of the gas turbine blade process, but also significantly improves the paint film process analysis method and increases the speed of the paint film process iteration.

[0157] In summary, the present invention provides a robot spraying quality analysis method based on an improved double-β paint film distribution model. By creating a single-β paint film distribution model and adjusting the β value according to different processes, a suitable film thickness distribution model is obtained. Since the single-β distribution model formula simulates a spray width range of an ideal conical distribution, in actual spraying operations, in order to facilitate adjustment and control of the coating shape, additional gas channels are set on both sides of the automatic spray gun to adjust the spray shape, resulting in the final workpiece surface spray width profile being elliptical rather than circular. Therefore, taking into account the influence of the spray width pressure on the mist cone shape in actual spraying and air spraying, the use of the elliptical double-β paint film distribution model for modeling and analysis is more in line with the elliptical spray plate shape generated during the actual spraying operation, and is more in line with actual production operations, thereby achieving modeling analysis and accurate prediction of the law of process parameter changes. Since the process parameters will affect the quality of paint film formation during the robot spraying process, combined with the robot spraying operation experiment of gas turbine blades, the spraying speed, spraying height, spray width pressure, atomization pressure, and injection pressure are set as the input parameters of the improved double-β paint film distribution model. Then, the influence of environmental factors is introduced into the spraying height to ensure that the model is more in line with the actual spraying effect.

[0158] Since a single-pass spraying operation cannot meet the process requirements of paint film spraying, a multi-pass spraying operation is required to ensure that the paint film quality on the blade surface meets the standards. During single-pass spraying, the paint film thickness distribution on the spraying path presents a normal distribution with thicker in the middle and thinner on both sides. When multiple-pass spraying operations are performed at the same time, there will be overlap in the spraying range. The paint film thickness in the overlapping area increases due to repeated spraying. Therefore, the present invention combines the spraying process requirements and analyzes and optimizes the overlap distance during the multi-pass spraying operation to ensure the consistency of the paint film thickness of the single-track spraying area and the overlap spraying operation area, thereby ensuring the film thickness and paint film uniformity of the paint film cross section of the gas turbine blade. Although in the actual spraying process, changes in the state of the spray gun or robot will cause fluctuations in the spraying process parameters during the spraying process, which in turn will cause certain fluctuations in the paint film quality at different positions in the paint film cross-section during a single spraying process, based on the paint film average film thickness prediction model and the paint film thickness standard deviation model under a single cross-section, the present invention achieves a reasonable prediction and accurate analysis of the paint film quality under different paint film process parameters during the robot spraying operation of the gas turbine blade. Based on the double-β paint film distribution model, the present invention constructs a complete paint film quality analysis method for the robot spraying operation of the gas turbine blade. Based on this method, it not only provides standard and reliable evaluation indicators and analysis schemes for the process iteration of the gas turbine blade, but also significantly improves the paint film process analysis method and increases the speed of the paint film process iteration.

Claims

1. A robot spraying quality analysis method based on an improved double-β paint film distribution model is characterized by: The following steps are involved: S1: Create a single β paint film distribution model; S2: Create a double-β paint film distribution model based on the single-β paint film distribution model; S3: introducing the spraying speed into the double-β paint film distribution model to obtain a dynamic film thickness distribution model; S4: introducing spraying height and ambient humidity into the film thickness distribution model under the dynamic state; S5: Introducing the dynamic film thickness distribution model under spraying height and ambient humidity, introducing spray width pressure, atomization pressure, and injection pressure to obtain the final dynamic film thickness distribution model under multiple process parameters; S6: using the least squares method to fit and obtain the undetermined parameters in the final dynamic film thickness distribution model under multiple process parameters, and judging whether the accuracy and generalization performance of the final dynamic film thickness distribution model under multiple process parameters meet the preset requirements; if so, outputting the film thickness distribution equation based on the fitting parameters at this time, and ending; If not, proceed to step S7; S7: adjusting the fitting algorithm parameter values, re-calculating the corresponding parameters, and again judging whether the accuracy and generalization performance of the final dynamic film thickness distribution model under the multiple process parameters meet the preset requirements, until the accuracy and generalization performance of the final dynamic film thickness distribution model under the multiple process parameters meet the preset requirements; The specific process of introducing the spraying height and the ambient humidity into the film thickness distribution model under the dynamic state in step S4 is as follows: S41: Set the fog cone angle in the Y-axis direction Along the direction of movement of the spray gun trajectory, the spray cone angle in the X-axis direction Perpendicular to the direction of movement of the spray gun, the major and minor axes of the ellipse and the spray cone angle are obtained. The specific formula is: ； S42: Establishing spray distance d The relationship between the major axis and minor axis a, b of the elliptical spray spot is: ； S43: Establishing the paint rate and d The relationship is: ； in, is the paint rate coefficient, h is the spraying distance; S44: Other spraying parameters are constant, and the spraying distance does not affect the total amount of paint sprayed by the spray gun per unit time. The total amount of paint at any time is fixed, and the relationship is obtained: ； in, S ( h 1) and S ( h 2) Spraying distance and The area of the coating along the longitudinal section obtained by single-pass spraying on a flat plate is calculated as follows: ； Then we get: ； The specific process of introducing the spray width pressure, atomization pressure, and injection pressure in step S5 is as follows: S51: Establish atomization pressure p w , spray pressure p f , injection pressure p i The relationship between them is as follows: ； S52: Build up injection pressure p i Maximum coating thickness d max The relationship: ； S53: Establish a multi-parameter coating growth model to obtain the maximum coating thickness within the elliptical spray spot: ； S54: Calculate the coating thickness when the spray gun sprays uniformly along the workpiece surface: ； ； in, k x 、 k y 、 a 1. b 1. a 2. b 2 is the unknown coefficient, K m is the coating growth rate coefficient, K ' is the generalization coefficient, which is obtained by fitting the experimental data.

2. The robot spraying quality analysis method based on the improved double-β paint film distribution model according to claim 1 is characterized in that: The expression of the single β paint film distribution model in step S1 is: ； in, β Represents the shaping parameter, which determines the distribution shape of the model film thickness. Indicates the maximum film thickness at the center point, q ( x ) indicates the distance from the center point x The film thickness value at r Indicates the spray radius of the spray gun.

3. The robot spraying quality analysis method based on the improved double-β paint film distribution model according to claim 1 is characterized in that: The specific process of creating a double-β paint film distribution model based on the single-β paint film distribution model in step S2 is: S21: Based on the expression of the single β paint film distribution model, the specified point A ( x , y ) film thickness distribution formula: ； in, q max is the maximum film thickness growth rate value at the center point O of the paint elliptical distribution area image, β 1 is the shaping parameter value of the cross section in this direction; S22: Yes y Direction values are x 、 y The film thickness growth rate value at the position is calculated using the following formula: ； ； This formula is the double-β paint film distribution model formula; Among them, a 、 b Respectively represent the major axis and minor axis values of the paint elliptical distribution area image, q ( x , y )express x 、 y The film thickness growth rate value at the position and assuming x Direction and y The film thickness distribution in the same direction is the same. β The value is the same, β 1. β 2 is the film thickness shaping parameter value in different directions, and its size is determined by the shaping air pressure process parameter value.

4. The robot spraying quality analysis method based on the improved double-β paint film distribution model according to claim 1 is characterized in that: In step S3, the specific process of introducing the spraying speed into the double-β paint film distribution model to obtain the dynamic film thickness distribution model is as follows: S31: Calculate the distance swept by the spray gun. The specific formula is: ； S32: Calculate the time the spray gun sweeps through. The specific formula is as follows: ； S33: Calculate the cumulative coating thickness at any point O (x0, y0), that is, the integral of the time that point O is swept by the spray gun: ； S34: Change the integration limit to obtain: ； S35: Order , bring in Afterwards, bring in , and obtain the coating profile thickness distribution when the spray gun sprays at a uniform speed along the workpiece surface.

5. The robot spraying quality analysis method based on the improved double-β paint film distribution model according to claim 1 is characterized in that: It also includes the establishment of the expression for the film thickness of two adjacent spraying operations in a single spraying section under a multi-pass spraying process: ； in, The film thickness distribution expression on the first path is represented by: The film thickness distribution expression on the second path is expressed. In multi-pass spraying operation, that is, the film thickness distribution in single-pass spraying operation obeys , the overlap areas are subject to .

6. The robot spraying quality analysis method based on the improved double-β paint film distribution model according to claim 1 is characterized in that: It also includes the establishment of a paint film average thickness prediction model under a single cross section: ； in, d is the spray track spacing, n is the number of spraying tracks, x The corresponding points of the spraying section are: T ( x ) is the film thickness value at the corresponding point.

7. The robot spraying quality analysis method based on the improved double-β paint film distribution model according to claim 1 is characterized in that: It also includes the establishment of a standard deviation model of the paint film thickness under a single cross section: ； Where N is the number of data points, x i is the cross-section point corresponding to N sampling data points, T ( x i ) is a single x i The film thickness value at the specified location.

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