Rotating-speed-adjustable brush system and method for cleaning aviation turbine blades

Abstract

The invention relates to the technical field of general cleaning, in particular to a rotating speed adjustable brush system and method for cleaning aviation turbine blades. The method comprises the following steps: acquiring point cloud data of the surface of the aviation turbine blade, fitting, generating a three-dimensional curved surface model, evaluating pollutant cleaning difficulty, and generating a pollutant score; the cleaning intensity is calculated based on the pollutant score, and the brush pressure and the brush rotating speed are dynamically adjusted; based on the pollutant score, the brush pressure and the brush rotating speed, the cleaning path is optimized, and cleaning is conducted along the optimal cleaning path. The method solves the problems that a current cleaning method is difficult to adapt to the complex geometric surface of the aviation turbine blade, so that partial areas are cleaned insufficiently or excessively; the dynamic evaluation of pollutant characteristics is lacked, so that the cleaning efficiency is low, and the surface coating of the aviation turbine blade is easily damaged; the problems that a path planning algorithm is single, the whole area of the aviation turbine blade cannot be effectively covered, and path energy consumption cannot be dynamically optimized, so that resources are wasted, and cleaning is not thorough are solved.

Description

Technical Field

[0001] The invention relates to the general field of cleaning technology, and in particular to a brush system and method with adjustable speed for cleaning aviation turbine blades. Background Art

[0002] Aviation turbine blades are the core components of aircraft engines, and their performance directly affects the overall efficiency and service life of the engine. During the operation of turbine blades, their surfaces are often affected by complex environments, including high temperature, high pressure, airflow impact, and the effects of various particles and chemicals, resulting in the accumulation of dirt, oxides, carbon deposits and other pollutants on the surface of aviation turbine blades. These pollutants will not only increase the surface roughness of aviation turbine blades and reduce aerodynamic performance, but may also affect the heat transfer efficiency, further exacerbating the mechanical stress and fatigue damage of aviation turbine blades. As the operating time increases, the accumulation of pollutants will significantly reduce the fuel efficiency of the engine and shorten the engine maintenance cycle. Therefore, regular cleaning of aviation turbine blades is a key link in maintaining the performance of aircraft engines.

[0003] Since aircraft turbine blades are usually made of complex materials that are resistant to high temperatures and corrosion, and the surface is covered with a special coating that not only protects but also helps to improve the thermal performance of the blades. Therefore, cleaning aircraft turbine blades requires not only thorough removal of contaminants, but also avoids any damage to the surface coating. In addition, the complex geometry of aircraft turbine blades, including curved blade edges, high-curvature surfaces, fine grooves, and hard-to-reach gaps, further increases the difficulty of cleaning. There are also significant spatial differences in the distribution and characteristics of contaminants on the surfaces of aircraft turbine blades. The thickness, adhesion, and roughness of contaminants in different areas may vary greatly, and the cleaning process needs to be able to flexibly adapt to these different contamination characteristics.

[0004] However, the current cleaning methods have the following technical problems: it is difficult to adapt to the complex geometric surfaces of aviation turbine blades, which often leads to insufficient or excessive cleaning of some areas, affecting the performance and service life of aviation turbine blades; there is a lack of dynamic evaluation of pollutant characteristics, and it is impossible to adjust the cleaning intensity according to the thickness, adhesion and surface roughness of the pollutants, resulting in low cleaning efficiency and easy damage to the surface coating of aviation turbine blades; the path planning algorithm is single and mostly uses preset trajectories, which cannot effectively cover the entire area of ​​​​the aviation turbine blades, nor can it dynamically optimize the path energy consumption, resulting in waste of resources and incomplete cleaning. Summary of the invention

[0005] The present invention provides a brush system and method with adjustable speed for cleaning aviation turbine blades, so as to solve the problems that the current cleaning method is difficult to adapt to the complex geometric surface of aviation turbine blades, often resulting in insufficient or excessive cleaning of some areas, affecting the performance and service life of aviation turbine blades; lacks dynamic evaluation of pollutant characteristics, and cannot adjust the cleaning intensity according to the thickness, adhesion and surface roughness of pollutants, resulting in low cleaning efficiency and easy damage to the surface coating of aviation turbine blades; the path planning algorithm is single, mostly using preset trajectories, cannot effectively cover the entire area of ​​​​the aviation turbine blades, and cannot dynamically optimize the path energy consumption, resulting in resource waste and incomplete cleaning.

[0006] The invention discloses a brush system and method for cleaning aviation turbine blades with adjustable speed, which specifically includes the following technical solutions:

[0007] A speed-adjustable brush method for cleaning aviation turbine blades comprises the following steps:

[0008] S1. Collect point cloud data on the surface of aviation turbine blades, perform fitting, and generate a three-dimensional surface model; based on the three-dimensional surface model, evaluate the difficulty of pollutant cleaning and generate a pollutant score;

[0009] S2. Calculate the cleaning intensity based on the pollutant score; dynamically adjust the brush pressure and brush speed based on the cleaning intensity; optimize the robot arm motion path based on the pollutant score, brush pressure and brush speed, generate the optimal cleaning path, and perform cleaning along the optimal cleaning path.

[0010] Preferably, the S1 specifically includes:

[0011] The quadratic polynomial form is used in combination with the least squares method to fit the point cloud data and generate a three-dimensional surface model.

[0012] Preferably, the S1 specifically includes:

[0013] Perform partial derivative operations on the three-dimensional surface model to calculate the surface gradient and curvature.

[0014] Preferably, the S1 specifically includes:

[0015] Calculate contaminant thickness, adhesion, and surface roughness based on surface gradient and curvature.

[0016] Preferably, the S1 specifically includes:

[0017] Based on the thickness, adhesion and surface roughness of pollutants, a pollutant scoring formula is designed to calculate the pollutant score; the pollutant score calculation formula is as follows:

[0018]

[0019] Where S is the pollutant score; h max is the maximum value of the contaminant thickness; τ max is the maximum adhesion; σ max is the maximum value of the surface roughness of the aviation turbine blade material; h is the contaminant thickness; τ is the adhesion; σ is the surface roughness.

[0020] Preferably, the S2 specifically includes:

[0021] Based on the pollutant score, combined with the brush pressure, brush speed and the material properties of the aviation turbine blades, the cleaning intensity is calculated. The specific calculation formula is:

[0022]

[0023] Among them, F C is the cleaning intensity; t is the cleaning time; k1 is the weight coefficient of the thickness derivative of the contaminant score; S is the contaminant score; represents the thickness derivative of the contamination score; σ is the surface roughness; P base is the base pressure of the brush; k2 is the weight coefficient of the logarithm of pressure; H is the material hardness of the aviation turbine blade; E m Elastic modulus of aviation turbine blades; ω init is the initial speed of the brush; M is the nonlinear characteristic coefficient of the aviation turbine blade material; k3 is the weight coefficient of the square of the speed.

[0024] Preferably, the S2 specifically includes:

[0025] Based on the cleaning intensity, the sine function and logarithm are introduced to dynamically adjust the brush pressure.

[0026] Preferably, the S2 specifically includes:

[0027] The brush speed is adjusted synchronously by combining the partial derivative of the cleaning intensity with the brush pressure and the partial derivative of the pollutant score with the pollutant thickness with the cosine function.

[0028] Preferably, the S2 specifically includes:

[0029] Based on the pollutant score, brush pressure and brush speed, combined with the path length and cleaning coverage area, the path optimization objective function is constructed. The path optimization objective function is:

[0030]

[0031] Where E is the path optimization objective function value, which represents the path energy consumption; T is the cleaning task completion time; L(t) is the path length at time t; C(t) is the cleaning coverage area at time t; P is the brush pressure; ω is the brush speed; ΔS(t) is the change in pollutant score when the cleaning time changes by Δt; represents the cleaning efficiency; α1 is the weight coefficient of the path length; α2 is the weight coefficient of the pressure and speed ratio; α3 is the weight coefficient of the cleaning efficiency;

[0032] The path optimization objective function is solved by a nonlinear programming algorithm. The cleaning path is optimized with the goal of minimizing the path energy consumption to generate the optimal cleaning path.

[0033] A speed-adjustable brush system for cleaning aviation turbine blades comprises the following parts:

[0034] Data acquisition and analysis module, pollutant scoring module, path planning module, dynamic cleaning intensity control module, mechanical control and execution module;

[0035] The data acquisition and analysis module collects point cloud data of the surface of aviation turbine blades, performs fitting, and generates a three-dimensional surface model; combines the surface gradient and curvature of the three-dimensional surface model to calculate the thickness, adhesion, and surface roughness of the pollutants; and transmits the thickness, adhesion, and surface roughness of the pollutants to the pollutant scoring module;

[0036] A pollutant scoring module calculates the pollutant score through a pollutant scoring formula based on the pollutant thickness, adhesion and surface roughness; and transmits the pollutant score to the path planning module and the dynamic cleaning intensity control module;

[0037] The path planning module constructs a path optimization objective function based on the pollutant score, brush pressure and brush speed, combined with the path length and cleaning coverage area; solves the path optimization objective function through a nonlinear programming algorithm, optimizes the cleaning path with the goal of minimizing the path energy consumption, and generates the optimal cleaning path; transmits the optimal cleaning path to the mechanical control and execution module;

[0038] A dynamic cleaning intensity control module calculates the cleaning intensity based on the pollutant score and dynamically adjusts the brush pressure and brush speed; transmits the dynamically adjusted brush pressure and brush speed to the path planning module and the mechanical control and execution module;

[0039] The mechanical control and execution module controls the synchronous movement of the robot arm and the brush based on the brush pressure, brush speed and optimal cleaning path, and performs dynamic cleaning operations along the optimal cleaning path.

[0040] The beneficial effects of the technical solution of the present invention are:

[0041] 1. The present invention uses a laser sensor to scan the surface of aviation turbine blades to generate a three-dimensional surface model, accurately capturing the geometric characteristics of the surface of aviation turbine blades, including curvature and surface gradient; the pollutant scoring method based on the three-dimensional surface model comprehensively considers the thickness, adhesion and surface roughness of the pollutants, reflects the difficulty of cleaning the pollutants, and provides basic data for subsequent optimization of cleaning parameters.

[0042] 2. Through the combination of pollutant scoring and cleaning intensity function, the speed-adjustable brush system can dynamically adjust the brush pressure and brush speed according to the surface complexity and pollutant characteristics of the aviation turbine blades, making the cleaning process more accurate for different areas. The surface gradient and curvature of the three-dimensional surface model provide a geometric basis for the pollutant scoring, and the cleaning intensity function further refines the role of the pollutant scoring, making the cleaning parameters more targeted and avoiding incomplete cleaning or coating damage caused by excessive or insufficient cleaning intensity.

[0043] 3. Generate the optimal motion trajectory of the robot arm through the path optimization objective function to achieve full coverage cleaning of the surface of aviation turbine blades; combined with the nonlinear programming algorithm, it can minimize the path length, reduce the cleaning coverage area and maximize the cleaning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a structural diagram of a brush system with adjustable speed for cleaning aviation turbine blades according to the present invention;

[0045] Figure 2 The present invention is a flow chart of a method for using a brush with adjustable speed to clean aviation turbine blades. DETAILED DESCRIPTION

[0046] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0048] The specific scheme of a speed-adjustable brush system and method for cleaning aviation turbine blades provided by the present invention is described in detail below with reference to the accompanying drawings.

[0049] Refer to the attached Figure 1, which shows a structural diagram of a speed-adjustable brush system for cleaning aviation turbine blades provided by an embodiment of the present invention, the system includes the following parts:

[0050] Data acquisition and analysis module, pollutant scoring module, path planning module, dynamic cleaning intensity control module, mechanical control and execution module;

[0051] The data acquisition and analysis module uses a laser sensor to scan the surface of the aviation turbine blade, obtains point cloud data, and performs fitting to generate a three-dimensional surface model; combines the surface gradient and curvature of the three-dimensional surface model to calculate parameters such as pollutant thickness, adhesion, and surface roughness; and transmits pollutant thickness, adhesion, and surface roughness to the pollutant scoring module;

[0052] A pollutant scoring module calculates the pollutant score through a pollutant scoring formula based on the pollutant thickness, adhesion and surface roughness; and transmits the pollutant score to the path planning module and the dynamic cleaning intensity control module;

[0053] The path planning module constructs a path optimization objective function based on the pollutant score, brush pressure and brush speed, combined with the path length and cleaning coverage area; solves the path optimization objective function through a nonlinear programming algorithm, optimizes the cleaning path with the goal of minimizing the path energy consumption, and generates the optimal cleaning path; transmits the optimal cleaning path to the mechanical control and execution module;

[0054] A dynamic cleaning intensity control module calculates the cleaning intensity based on the pollutant score and dynamically adjusts the brush pressure and brush speed; transmits the dynamically adjusted brush pressure and brush speed to the path planning module and the mechanical control and execution module;

[0055] The mechanical control and execution module controls the synchronous movement of the robot arm and the brush based on the brush pressure, brush speed and optimal cleaning path, and realizes dynamic cleaning operation along the optimal cleaning path.

[0056] Refer to the attached Figure 2 , which shows a flow chart of a method for cleaning aviation turbine blades with an adjustable speed brush provided by an embodiment of the present invention, the method comprising the following steps:

[0057] S1. Collect point cloud data on the surface of aviation turbine blades, perform fitting, and generate a three-dimensional surface model; based on the three-dimensional surface model, evaluate the difficulty of pollutant cleaning and generate a pollutant score;

[0058] In order to plan the cleaning path for the complex geometric shapes of the surface of aviation turbine blades, it is first necessary to use a laser sensor to obtain the point cloud data of the surface of aviation turbine blades. The point cloud data contains a large number of spatial points distributed on the surface of aviation turbine blades. The amount of spatial point data is huge and unevenly distributed.

[0059] Then, the point cloud data is fitted into a continuous three-dimensional surface model, which can not only reflect the overall geometric shape of the surface of the aviation turbine blade, but also quantify the local curvature characteristics of the surface of the aviation turbine blade, which is convenient for subsequent cleaning path planning and cleaning intensity adjustment. The three-dimensional surface model adopts the form of a quadratic polynomial, which can capture the main curvature changes of the surface of the aviation turbine blade without introducing too much computational complexity; the fitting process is carried out by the least squares method, and the goal is to minimize the deviation from the point cloud data to the surface, so as to ensure that the three-dimensional surface model can fully describe the characteristics of the actual blade surface of the aviation turbine, while meeting the requirements of cleaning path planning for geometric accuracy;

[0060] The three-dimensional surface model expression is:

[0061] f(x,y,z)=ax 2 +by 2 +cz 2 +dxy+eyz+fxz+g

[0062] Among them, f(x,y,z) is a three-dimensional surface model, which represents the height of the blade surface and has the dimension of length. x,y,z are the three-dimensional coordinates of the point cloud data and have the dimension of length. a, b, and c are quadratic coefficients, which represent the curvature of the three-dimensional surface in the x,y, and z directions and are obtained by least squares fitting and are dimensionless. d, e, and f are mixed coefficients, which represent the coupling relationship between x,y, and z and are obtained by matrix decomposition fitting of point cloud data and are dimensionless. g is a constant term, which represents the height offset of the entire three-dimensional surface and is obtained by experiment and has the dimension of length. The three-dimensional surface model is used to describe the geometric shape of the surface of aviation turbine blades. Its curvature and surface gradient will affect the cleaning path and pressure of the subsequent brush.

[0063] The pollutant score is the core indicator for measuring the difficulty of pollutant cleaning in the speed-adjustable brush system. It needs to comprehensively reflect factors such as pollutant thickness h, adhesion τ, and surface roughness σ. The three-dimensional surface model f(x, y, z) describes the geometric shape of the surface of the aviation turbine blade. Its surface gradient and curvature can reflect the local characteristics of the surface of the aviation turbine blade. The distribution of pollutant thickness h, adhesion τ, and surface roughness σ will be affected by the geometric characteristics of the surface of the aviation turbine blade. Therefore, the pollutant score needs to be calculated in combination with the geometric parameters of the three-dimensional surface model, especially the curvature and surface gradient.

[0064] By performing partial derivative operations on the three-dimensional surface model f(x, y, z), the surface gradient and curvature are calculated. The surface gradient represents the slope of the surface of the aviation turbine blade, while the curvature describes the degree of curvature of the surface of the aviation turbine blade. The calculation formulas for the surface gradient and curvature are:

[0065]

[0066] in, is the surface gradient, with the dimension of 1 / length; K is the curvature, with the dimension of 1 / length. The surface gradient and curvature are prior conditions for the distribution of pollutants.

[0067] The thickness of the pollutant is related to the size of the surface gradient. If the surface gradient is large (the slope of the surface of the aviation turbine blade is steep), the attachment of the pollutant may be reduced due to gravity, so the thickness of the pollutant will decrease as the surface gradient increases. At the same time, the thickness variation range of the entire surface of the aviation turbine blade is used as the upper limit constraint. The thickness of the pollutant is affected by the surface gradient. The larger the gradient, the smaller the thickness of the pollutant. The specific formula for calculating the pollutant thickness t is as follows:

[0068]

[0069] Among them, t max is the maximum value of the contaminant thickness, which is determined by the material properties and operating conditions of the aviation turbine blades and has the dimension of length; k is the gradient influence coefficient, with the dimension of 1 / length, ranging from 0.1 to 5, and is calibrated through experiments.

[0070] Adhesion is the adhesion force between the contaminant and the surface of the aviation turbine blade, which is usually affected by the curvature of the aviation turbine blade surface. In the concave and convex areas with higher curvature, the contact area of ​​the contaminant is greater, resulting in stronger adhesion. In addition, the influence of the surface gradient also needs to be considered, because the surface gradient will affect the stability of the contaminant adhesion. As the curvature increases, the adhesion increases, and as the gradient increases, the adhesion weakens. Therefore, the calculation formula for adhesion τ is:

[0071]

[0072] Among them, τ base It is the basic value of adhesion, which is determined by the characteristics of aviation turbine blade materials and pollutants, and its dimension is force; λ is the curvature gain coefficient, which reflects the influence of curvature on adhesion enhancement. It is obtained through experiments, its dimension is length, and its value range is 0.5-10; β is the gradient influence coefficient, which is used to control the negative impact of surface gradient on adhesion. It is obtained through experiments, its dimension is length, and its value range is 0.1-3.

[0073] Surface roughness is related to the micro-geometry of aircraft turbine blades. The curvature and surface gradient of aircraft turbine blades will affect the surface roughness distribution. When the curvature is high, the surface of aircraft turbine blades is more likely to accumulate micro-protrusions, resulting in increased surface roughness. When the surface gradient is large, the surface roughness will be reduced due to surface smoothing. There are surface micro-protrusions in high curvature areas, which leads to increased roughness. Therefore, 1+μ·K is used to represent the contribution of curvature to roughness. Where the gradient is larger, the surface is more easily smoothed by airflow and friction, resulting in a decrease in roughness. Therefore, exponential decay is used. To describe; therefore, the surface roughness σ is calculated as follows:

[0074]

[0075] Among them, σ base It is the basic surface roughness, which is determined by the material properties of aviation turbine blades and has the dimension of length; μ is the curvature influence coefficient, which reflects the influence of curvature on the enhancement of surface roughness. It is obtained through experiments, has the dimension of length, and has a value range of 0.1 to 3; γ is the gradient influence coefficient, which indicates the degree of reduction of surface roughness by surface gradient. It is obtained through experiments, has the dimension of length, and has a value range of 0.2 to 5.

[0076] When designing the pollutant scoring formula, the nonlinear superposition effect of pollutant thickness and adhesion on the difficulty of pollutant cleaning was taken into account, so the square root form was adopted, and the negative contribution of surface roughness to the pollutant score was weakened by the normalized form of surface roughness. Based on the principles of surface cleaning and tribology, the pollutant score S is calculated as follows:

[0077]

[0078] Where S is the pollutant score, which is used to evaluate the difficulty of pollutant cleaning and is dimensionless; τ max is the maximum adhesion, which is determined by the material properties of the aviation turbine blade; σ max is the maximum value of the surface roughness of the aviation turbine blade material, which is the experimental calibration value; Reflects the nonlinear effect of contaminant thickness and adhesion on contaminant scores; The purpose of pollutant scoring is to reflect the difficulty of cleaning pollutants and provide basic data for the subsequent adjustment of brush pressure and brush speed.

[0079] S2. Calculate the cleaning intensity based on the pollutant score; dynamically adjust the brush pressure and brush speed based on the cleaning intensity; optimize the robot arm motion path based on the pollutant score, brush pressure and brush speed, generate the optimal cleaning path, and perform cleaning along the optimal cleaning path.

[0080] The pollutant score is one of the inputs of the cleaning intensity function, and together with the brush pressure, brush speed, and the material properties of the aviation turbine blades, it constitutes the core variable for calculating the cleaning intensity; the cleaning intensity function is used to dynamically adjust the brush pressure and brush speed of the adjustable speed brush system so that it can adapt to the cleaning needs of different areas. The cleaning intensity depends on the combined effects of the pollutant score, pressure, and speed. A nonlinear relationship is used to ensure that the cleaning intensity can dynamically adapt to different areas. Referring to the nonlinear cleaning force model in tribology, fluid dynamics, and industrial cleaning engineering, the specific calculation formula of the cleaning intensity function is:

[0081]

[0082] Among them, F C is the cleaning intensity, which is used to dynamically adjust the brush pressure and brush speed, and its dimension is force; t is the cleaning time; the first term The linear effect of the thickness derivative of the pollutant score on the cleaning intensity is expressed by combining the cosine function of the surface roughness to capture the regulatory effect of the surface texture of the aviation turbine blade on the brush pressure distribution. k1 is the weight coefficient of the thickness derivative of the pollutant score, which is calibrated experimentally and ranges from 0.2 to 0.6. The second term k2·ln(1+P base ) is the logarithmic scaling of the brush pressure, reflecting the nonlinear effect of brush pressure on cleaning intensity and avoiding excessive linear increase, P base is the basic pressure of the brush, which is determined by the initial characteristics of the aviation turbine blade material. k2 is the weight coefficient of the logarithm of the pressure, which is calibrated by experiments and has a value range of 0.1 to 0.4. The third item It represents the contribution of the square of the brush speed to the cleaning intensity. The cleaning intensity is related to the material hardness H of the aviation turbine blade and the elastic modulus E of the aviation turbine blade. m The inverse relationship between the speed of the brush and the cleaning intensity is used to control the effect of the brush speed on the cleaning intensity, ω init is the initial speed of the brush, which is set by empirical data; M is the nonlinear characteristic coefficient of the aviation turbine blade material, which is measured by the properties of the aviation turbine blade material; k3 is the weight coefficient of the square of the speed, which is calibrated by experiments and has a value range of 0.2 to 0.5; k1+k2+k3=1.

[0083] Based on the cleaning intensity, the brush pressure is dynamically adjusted to adapt to the surface characteristics and contaminant distribution of the aviation turbine blades. During the cleaning process, the brush pressure must change dynamically to avoid over-cleaning or under-cleaning. The nonlinear relationship between the contaminant score and the pressure is described by a logarithmic function. The cleaning intensity is used as a feedback control signal and is smoothly adjusted using a sine function. The base pressure provides a reference value to ensure that the pressure will not be zero. Therefore, the formula for brush pressure adjustment is:

[0084] P=P base ·(1+kP ·sin(F C )·ln(S+1))

[0085] Where P is the brush pressure, dimension is [M][L] -1 [T] -2 ; The dynamic effect of cleaning intensity on brush pressure is mapped by a sine function, and the distinction between different pollutant scores is enhanced by logarithmic scaling; k P It represents the sensitivity of brush pressure adjustment, with a value range of 0.1 to 2, dimensionless, and determined through experiments to ensure that the brush pressure does not exceed the threshold allowed by the aviation turbine blade material during the adjustment process.

[0086] After the brush pressure is adjusted, the brush speed also needs to be adjusted synchronously to coordinate the changes in cleaning intensity and brush pressure. The brush speed optimization formula is as follows:

[0087]

[0088] Where ω is the brush speed, dimension is [T] -1 ; Through the partial derivative of cleaning intensity with respect to brush pressure and the partial derivative of the contaminant score with respect to the contaminant thickness The product of k is used to express the nonlinear contribution of the interaction between the two to the optimization of the brush speed. The cosine function is used to smoothly adjust the change amplitude of the brush speed. ω It is the speed sensitivity adjustment parameter, which is used to balance the influence of brush speed change on cleaning effect. It is calibrated through experiments, with a value range of 0.1 to 2 and is dimensionless.

[0089] During the cleaning process, the path optimization objective function is constructed based on the pollutant score, brush pressure and brush speed, combined with the path length and cleaning coverage area; the brush is moved along the optimal path to achieve the best cleaning effect with minimum energy consumption; the specific formula of the path optimization objective function is:

[0090]

[0091] Where E is the path optimization objective function value, i.e., the path energy consumption; T is the cleaning task completion time; L(t) is the path length at time t; C(t) is the cleaning coverage area at time t, i.e., the area of ​​the aviation turbine blade surface contacted by the brush; ΔS(t) is the change in the pollutant score when the cleaning time changes by Δt; It represents the cleaning efficiency; α1 is the weight coefficient of the path length, which is adjusted through experiments and has a value range of 0.1 to 1; α2 is the weight coefficient of the pressure-speed ratio, which is obtained through the design of the speed-adjustable brush system and the requirements for the surface cleaning of aviation turbine blades, and has a value range of 0.01 to 0.5; α3 is the weight coefficient of the cleaning efficiency, which is adjusted through experiments to achieve the best overall optimization effect, and has a value range of 0.2 to 2.

[0092] The path optimization process is solved using a nonlinear programming algorithm (such as a particle swarm optimization algorithm) with the goal of minimizing the path energy consumption E and outputting the optimal cleaning path to ensure that the cleaning process is efficient and there is no over-cleaning or under-cleaning. The optimal cleaning path is the optimal motion trajectory of the robot arm, which represents the motion sequence of the brush in three-dimensional space within time T.

[0093] In summary, a speed-adjustable brush system and method for cleaning aviation turbine blades are completed.

[0094] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0096] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A speed-adjustable brush method for cleaning aviation turbine blades, characterized in that: The following steps are involved: S1. Collect point cloud data on the surface of aviation turbine blades, perform fitting, and generate a three-dimensional surface model; based on the three-dimensional surface model, evaluate the difficulty of pollutant cleaning and generate a pollutant score; S2, calculating the cleaning intensity based on the pollutant score; Dynamically adjust brush pressure and brush speed based on cleaning intensity; Based on the pollutant score, brush pressure and brush speed, the robot arm motion path is optimized to generate the optimal cleaning path, and cleaning is performed along the optimal cleaning path.

2. A method for cleaning aviation turbine blades with an adjustable speed brush according to claim 1, characterized in that: The S1 specifically includes: The quadratic polynomial form is used in combination with the least squares method to fit the point cloud data and generate a three-dimensional surface model.

3. A speed-adjustable brush method for cleaning aviation turbine blades according to claim 2, characterized in that: The S1 specifically includes: Perform partial derivative operations on the three-dimensional surface model to calculate the surface gradient and curvature.

4. A speed-adjustable brush method for cleaning aviation turbine blades according to claim 3, characterized in that: The S1 specifically includes: Calculate contaminant thickness, adhesion, and surface roughness based on surface gradient and curvature.

5. A speed-adjustable brush method for cleaning aviation turbine blades according to claim 4, characterized in that: The S1 specifically includes: Based on the thickness, adhesion and surface roughness of pollutants, a pollutant scoring formula is designed to calculate the pollutant score; the pollutant score calculation formula is as follows: Where S is the pollutant score; h max is the maximum value of the contaminant thickness; τ max is the maximum adhesion; σ max is the maximum value of the surface roughness of the aviation turbine blade material; h is the contaminant thickness; τ is the adhesion; σ is the surface roughness.

6. A speed-adjustable brush method for cleaning aviation turbine blades according to claim 5, characterized in that: The S2 specifically includes: Based on the pollutant score, combined with the brush pressure, brush speed and the material properties of the aviation turbine blades, the cleaning intensity is calculated. The specific calculation formula is: Among them, F C is the cleaning intensity; t is the cleaning time; k1 is the weight coefficient of the thickness derivative of the contaminant score; S is the contaminant score; represents the thickness derivative of the contamination score; σ is the surface roughness; P base is the base pressure of the brush; k2 is the weight coefficient of the logarithm of pressure; H is the material hardness of the aviation turbine blade; E m Elastic modulus of aviation turbine blades; ω init is the initial speed of the brush; M is the nonlinear characteristic coefficient of the aviation turbine blade material; k3 is the weight coefficient of the square of the speed.

7. A speed-adjustable brush method for cleaning aviation turbine blades according to claim 6, characterized in that: The S2 specifically includes: Based on the cleaning intensity, the sine function and logarithm are introduced to dynamically adjust the brush pressure.

8. A speed-adjustable brush method for cleaning aviation turbine blades according to claim 7, characterized in that: The S2 specifically includes: The brush speed is adjusted synchronously by combining the partial derivative of the cleaning intensity with the brush pressure and the partial derivative of the pollutant score with the pollutant thickness with the cosine function.

9. A speed-adjustable brush method for cleaning aviation turbine blades according to claim 8, characterized in that: The S2 specifically includes: Based on the pollutant score, brush pressure and brush speed, combined with the path length and cleaning coverage area, the path optimization objective function is constructed. The path optimization objective function is: Among them, e is the path optimization objective function value, which represents the path energy consumption; T is the cleaning task completion time; L(t) is the path length at time t; C(t) is the cleaning coverage area at time t; P is the brush pressure; ω is the brush speed; ΔS(t) is the change in pollutant score when the cleaning time changes by Δt; represents the cleaning efficiency; α1 is the weight coefficient of the path length; α2 is the weight coefficient of the pressure and speed ratio; α3 is the weight coefficient of the cleaning efficiency; The path optimization objective function is solved by a nonlinear programming algorithm. The cleaning path is optimized with the goal of minimizing the path energy consumption to generate the optimal cleaning path.

10. A speed-adjustable brush system for cleaning aircraft turbine blades, applied to the speed-adjustable brush method for cleaning aircraft turbine blades as claimed in claim 1, characterized in that: Includes the following parts: Data acquisition and analysis module, pollutant scoring module, path planning module, dynamic cleaning intensity control module, mechanical control and execution module; The data acquisition and analysis module collects point cloud data of the surface of aviation turbine blades, performs fitting, and generates a three-dimensional surface model; combines the surface gradient and curvature of the three-dimensional surface model to calculate the thickness, adhesion, and surface roughness of the pollutants; and transmits the thickness, adhesion, and surface roughness of the pollutants to the pollutant scoring module; The pollutant scoring module calculates the pollutant score through the pollutant scoring formula based on the pollutant thickness, adhesion and surface roughness; transmitting the pollutant score to the path planning module and the dynamic cleaning intensity control module; The path planning module constructs the path optimization objective function based on the pollutant score, brush pressure and brush speed, combined with the path length and cleaning coverage area; The path optimization objective function is solved by a nonlinear programming algorithm, and the cleaning path is optimized to generate the optimal cleaning path with the goal of minimizing the path energy consumption; the optimal cleaning path is transmitted to the mechanical control and execution module; A dynamic cleaning intensity control module calculates the cleaning intensity based on the pollutant score and dynamically adjusts the brush pressure and brush speed; transmits the dynamically adjusted brush pressure and brush speed to the path planning module and the mechanical control and execution module; The mechanical control and execution module controls the synchronous movement of the robot arm and the brush based on the brush pressure, brush speed and optimal cleaning path, and performs dynamic cleaning operations along the optimal cleaning path.

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