A method for controlling rust removal and polishing of a double-layer structure

Abstract

This invention belongs to the field of robotic automation technology, specifically a two-layer rust removal and grinding control method, comprising the following steps: S101, upper layer: firstly, establishing an offline database; S102, grinding judgment: acquiring real-time images of the rust surface using machine vision; S103, calculating the rust degree evaluation coefficient c; S104, calculating the rust roughness evaluation coefficient δ; S105, querying the offline database; S201, grinding, lower layer; S202, adaptive adjustment; The offline grinding database is established using machine vision technology. When preparing for grinding, the roughness and rust degree evaluation coefficients of the grinding surface can be obtained. These evaluation coefficients are input into the offline database, and grinding parameters are output. During the grinding process, real-time grinding images can be continuously acquired through vision to judge the grinding effect and adaptively adjust the grinding parameters. This avoids the poor grinding effect and low grinding efficiency that may result from a fixed grinding torque, as well as the inaccuracies and dangers that may occur with manual grinding.

Description

Technical Field

[0001] This invention belongs to the field of robot automation technology, specifically relating to a rust removal and grinding control method with a double-layer structure. Background Technology

[0002] Currently, grinding technology has been widely used in many fields. Wind turbines are usually located outdoors and are frequently exposed to harsh weather conditions such as wind, rain, and salt spray. Therefore, they need to be regularly derusted to extend their service life.

[0003] Due to their massive size and height, the removal of rust from wind turbines currently relies heavily on manual operation and fixed-torque grinders. Workers need to use cranes or other tools to ascend to the towering blades and towers, then use sandpaper, grinders, or certain chemical methods to remove the rust and maintain the turbine's normal operation. However, this method is highly dependent on manpower, inefficient, and the grinding quality is difficult to guarantee, while also posing significant safety risks. Furthermore, in work scenarios where grinders are used, their grinding torque is generally fixed, making it difficult to adapt to complex and varied surfaces. The grinders cannot adjust their parameters in real time according to surface characteristics, making it difficult to guarantee both efficiency and quality.

[0004] Therefore, based on the limitations of the above application scenarios, this invention proposes a double-layer structure rust removal and grinding control method. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a double-layer rust removal and grinding control method, the specific technical solution of which is as follows:

[0006] A rust removal and grinding control method with a double-layer structure includes the following steps:

[0007] S101, Upper layer: First, establish an offline database, which includes: the grayscale value range α for rust. min ~α max Grinding threshold, rust type, grinding pressure F N and the rotational speed v of the grinding head;

[0008] S102. Grinding judgment: Machine vision is used to acquire images of the rust surface in real time, and the images are denoised and grayscaled. The proportion of rust pixels is calculated as the rust area evaluation coefficient b. If the proportion of rust pixels extracted in real time does not exceed the grinding threshold, grinding is not required, and the grinding robot continues to move and continue to calculate the proportion of rust pixels. If the proportion of rust pixels extracted in real time exceeds the grinding threshold, the robot stops and prepares for grinding.

[0009] S103. Calculate the corrosion degree evaluation coefficient c, obtain the rust color evaluation coefficient a and the area evaluation coefficient b, and calculate the value of the corrosion degree evaluation coefficient c.

[0010] S104. Calculate the rust roughness evaluation coefficient δ, and define the rust grayscale value range α. min ~α max The rust region in the image is obtained by separating the pixels within the image, and the high-frequency energy ratio is calculated after performing a Fast Fourier Transform (FFT) on the image, which is used as the rust roughness evaluation coefficient δ.

[0011] S105. Query the offline database. Based on the obtained rust corrosion degree evaluation coefficient c and rust roughness evaluation coefficient δ, query the offline database to obtain the rust type and the corresponding grinding head speed v and grinding pressure F. N ;

[0012] S201. Grinding, lower layer: Based on the parameter data from step S105, common control methods, such as adaptive control, sliding mode control, and robust control, are used to control the vertical drive motor 1 and the grinding execution motor 2 respectively, to achieve the grinding positive pressure F during the grinding process. N Precise control of the grinding head rotation speed v is required before grinding is performed.

[0013] S202. Adaptive adjustment: After grinding for Δt time, return to the upper layer to obtain real-time grinding images visually, judge the grinding effect, and repeat steps S102 to S105 to obtain the grinding head speed v and grinding normal pressure F. N The grinding parameters are adjusted adaptively to form a closed-loop control.

[0014] Preferably, the offline database establishment in step S101 includes the following:

[0015] The steps to establish the S1 polishing threshold are as follows:

[0016] First, acquire enough images that meet the polishing precision requirements, and then perform noise reduction and grayscale processing on them;

[0017] Then, obtain the percentage of rust pixel area in each image;

[0018] Finally, the maximum percentage of these rust pixels is taken as the polishing threshold;

[0019] S2 rust grayness value range α min ~α max The steps to establish it are as follows:

[0020] First, acquire enough images of rust and then denoise and convert them to grayscale.

[0021] Then, obtain the grayscale value of the rust in each image;

[0022] Finally, the normal distribution curve of the rust gray value was statistically analyzed, and -3σ to 3σ was selected as the range α of the rust gray value. min ~α max ;

[0023] The steps to create the S3 rust type are as follows:

[0024] First, acquire enough images of rust and then denoise and convert them to grayscale.

[0025] Secondly, obtain the corrosion degree evaluation coefficient c and rust roughness evaluation coefficient δ for each image;

[0026] Subsequently, c and δ were sorted out separately, and the corrosion degree evaluation coefficient c was divided into low rust a, medium rust b, high rust c, and ultra-high rust d according to the quartile method, and the roughness evaluation coefficient δ was divided into low roughness A, medium roughness B, high roughness C, and ultra-high roughness D.

[0027] Finally, the classification of corrosion degree evaluation coefficients and roughness evaluation coefficients are combined to obtain 16 categories: aA, aB, aC, aD, bA, bB, bC, bD, cA, cB, cC, cD, dA, dB, dC, and dD.

[0028] S4 Grinding Positive Pressure F N The steps to establish the rotational speed v of the grinding head are as follows:

[0029] First, photographs of the rust were taken and polishing experiments were conducted to obtain the corresponding polishing force F for each rust image. N and the value of the grinding head rotation speed v;

[0030] Then, the rust was classified into 16 types according to the S3 method;

[0031] Finally, calculate the grinding force F for each type of rust. N The average value of the grinding head rotation speed v is used to establish the process.

[0032] Preferably, the rust area evaluation coefficient b in step S102 is calculated by the following formula:

[0033]

[0034] Where β is the total number of pixels in the image;

[0035] γ is the rust gray value α set in advance. min ~α max The number of pixels within the range.

[0036] Preferably, the step in step S103 to obtain the rust color evaluation coefficient a and the area evaluation coefficient b to obtain the rust degree evaluation coefficient c is as follows:

[0037] S301. Calculate the rust color evaluation coefficient 'a', as shown in the following formula:

[0038]

[0039] In the formula, α represents the range of grayscale values ​​of rust extracted from the image by machine vision. min ~α max The average grayscale value of all pixels within the range, α min and α max These are the upper and lower boundary values ​​of the grayscale range for rust in the image;

[0040] S302. Calculate the trade-off factor between rust color and rust area, as follows:

[0041]

[0042] In the formula, σ a and σ b The mean squared error of a sufficiently large number of samples a and b;

[0043] S302. Calculate the corrosion degree evaluation coefficient c, which combines the evaluation coefficients for rust color and area to more accurately determine the degree of corrosion. The corrosion degree evaluation coefficient c is calculated as follows:

[0044] c = d·|a| + e·b

[0045] In the formula, d and e are the weighting coefficients for rust color and rust area, and b is the rust area evaluation coefficient value in step S102.

[0046] Preferably, the specific steps for obtaining the rust roughness evaluation coefficient δ in step S104 are as follows:

[0047] S401, Set the rust grayscale value range α min ~α max The pixels within the image are separated to obtain the rust region.

[0048] S402. Perform Fast Fourier Transform (FFT) on the rust region of the image to obtain the frequency spectrum of the image;

[0049] S403. Calculate the proportion of high-frequency energy in the image frequency spectrum, and use it as the rust roughness evaluation coefficient δ.

[0050] Preferably, a general control method is used in step S201. In this example, the first vertical drive motor adopts constrained following control (CFC), and the second grinding execution motor adopts PID control. The specific implementation steps are as follows:

[0051] S501. The dynamic model is established as follows:

[0052]

[0053] In the formula, K t Let N be the motor torque constant, N be the transmission ratio of the belt drive system, η be the transmission efficiency, L be the lead of the lead screw, m be the mass of the slider, and F be the torque constant of the motor. f Let K be the frictional force acting on the slider, a be the acceleration of the slider, and K be the acceleration of the slider. p K is the proportionality constant. v The vibration coefficient is denoted by v, which represents the magnitude of the vibration.

[0054] S502. The first vertical drive motor is controlled by Constraint Follower Control (CFC). The controller formula is as follows:

[0055]

[0056] In the formula, F N It is the actual pressure, F ref It is reference pressure, i ref This is the reference value for the current, K. c is the control gain, e is the error signal, and the others are parameters in the dynamic model;

[0057] Then, design a constraint condition as follows:

[0058] |e|≤e max

[0059] Where e max This is the maximum allowable error;

[0060] Finally, based on the constraints, a suitable control gain K is selected. c This allows the error signal e to converge to zero quickly when the constraints are met, thus completing the establishment of the constraint-following controller.

[0061] S503. A PID controller is used to achieve precise tracking control of the desired rotational speed v during the grinding process. The specific steps are as follows:

[0062] PID control is as follows:

[0063]

[0064] By adjusting the proportional coefficient K p Differential coefficient K i and integral coefficient K d To achieve constant rotational speed control, these three parameters are selected based on the actual situation, ultimately enabling precise tracking control of the desired rotational speed v during the grinding process.

[0065] Preferably, the grinding time Δt in step S202 is obtained through the following steps, since the grinding parameters are the same for the same type of rust:

[0066] First, a large amount of rust is polished. During the polishing process, the polishing time is constantly adjusted so that the type of rust changes when returning to the upper layer. The polishing time for each step is recorded.

[0067] Then, the average of all polishing times is calculated as Δt;

[0068] When an image is input, the program can denoise and convert it to grayscale, then obtain c, δ, rust type, and grinding parameters. As shown in the lower layer of the example, CFC and PID distributed control maintain the grinding positive pressure and grinding head speed at a constant value for a grinding period Δt, and can judge the grinding effect. Steps S102 to S105 are repeated to obtain the grinding head speed v and grinding positive pressure F. N The grinding parameters are adjusted adaptively to form a closed-loop control.

[0069] Preferably, the grinding device includes a first motor, the output shaft of the first motor is driven by a belt and a pulley, a screw guide rail is fixed on the pulley, a nut is screwed onto the screw guide rail, a slider is installed at the bottom of the nut, a second motor is installed on the nut, a grinding head is fixed at the end of the output shaft of the second motor, and the output shaft of the second motor is rotatably connected to the nut through a bearing.

[0070] Compared with existing technologies, the advantages of this invention are: This invention can utilize machine vision technology to establish an offline grinding database. When preparing for grinding, it can obtain evaluation coefficients for the surface roughness and corrosion degree, input these coefficients into the offline database, and output grinding parameters. During the grinding process, it can continuously acquire real-time grinding images through vision, judge the grinding effect, and adaptively adjust the grinding parameters. This avoids the poor grinding effect and low grinding efficiency that may result from fixed grinding torque, as well as the inaccuracies and dangers that may occur with manual grinding. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating the working process of the grinding robot of the present invention.

[0072] Figure 2 This is a schematic diagram of the grinding device in the present invention;

[0073] Figure 3 This is a flowchart illustrating the upper-level process of an example in this invention;

[0074] Figure 4 This is a lower-level flowchart of an example in this invention.

[0075] Reference numerals in the attached diagram: 1. First motor; 2. Belt; 3. Pulley; 4. Nut; 5. Slider; 6. Screw guide rail; 7. Grinding head; 8. Bearing; 9. Second motor. Detailed Implementation

[0076] The technical solutions in the embodiments of the present invention are described below.

[0077] Please see Figure 1-4 This invention provides a technical solution: a double-layer structure rust removal and grinding control method, comprising the following steps:

[0078] S101, Upper layer: First, establish an offline database, which includes: the grayscale value range α for rust. min ~α max Grinding threshold, rust type, grinding pressure F N and the rotational speed v of the grinding head;

[0079] S102. Grinding judgment: Machine vision is used to acquire images of the rust surface in real time, and the images are denoised and grayscaled. The proportion of rust pixels is calculated as the rust area evaluation coefficient b. If the proportion of rust pixels extracted in real time does not exceed the grinding threshold, grinding is not required, and the grinding robot continues to move and continue to calculate the proportion of rust pixels. If the proportion of rust pixels extracted in real time exceeds the grinding threshold, the robot stops and prepares for grinding.

[0080] S103. Calculate the corrosion degree evaluation coefficient c, obtain the rust color evaluation coefficient a and the area evaluation coefficient b, and calculate the value of the corrosion degree evaluation coefficient c.

[0081] S104. Calculate the rust roughness evaluation coefficient δ, and define the rust grayscale value range α. min ~α max The rust region in the image is obtained by separating the pixels within the image, and the high-frequency energy ratio is calculated after performing a Fast Fourier Transform (FFT) on the image, which is used as the rust roughness evaluation coefficient δ.

[0082] S105. Query the offline database. Based on the obtained rust corrosion degree evaluation coefficient c and rust roughness evaluation coefficient δ, query the offline database to obtain the rust type and the corresponding grinding head speed v and grinding pressure F. N ;

[0083] S201. Grinding, lower layer: Based on the parameter data from step S105, common control methods, such as adaptive control, sliding mode control, and robust control, are used to control the vertical drive motor 1 and the grinding execution motor 2 respectively, to achieve the grinding positive pressure F during the grinding process. N Precise control of the grinding head rotation speed v is required before grinding is performed.

[0084] S202. Adaptive adjustment: After grinding for Δt time, return to the upper layer to obtain real-time grinding images visually, judge the grinding effect, and repeat steps S102 to S105 to obtain the grinding head speed v and grinding normal pressure F. N The grinding parameters are adjusted adaptively to form a closed-loop control.

[0085] In this embodiment, the present invention can establish an offline grinding database using machine vision technology. When preparing for grinding, it can obtain evaluation coefficients for the surface roughness and corrosion degree, input these coefficients into the offline database, and output grinding parameters. During the grinding process, it can continuously acquire real-time grinding images through vision, judge the grinding effect, and adaptively adjust the grinding parameters. This avoids the poor grinding effect and low grinding efficiency that may result from a fixed grinding torque, as well as the inaccuracies and dangers that may occur with manual grinding.

[0086] Specifically, the offline database establishment in step S101 includes the following:

[0087] The steps to establish the S1 polishing threshold are as follows:

[0088] First, acquire enough images that meet the polishing precision requirements, and then perform noise reduction and grayscale processing on them;

[0089] Then, obtain the percentage of rust pixel area in each image;

[0090] Finally, the maximum percentage of these rust pixels is taken as the polishing threshold;

[0091] S2 rust grayness value range α min ~α max The steps to establish it are as follows:

[0092] First, acquire enough images of rust and then denoise and convert them to grayscale.

[0093] Then, obtain the grayscale value of the rust in each image;

[0094] Finally, the normal distribution curve of the rust gray value was statistically analyzed, and -3σ to 3σ was selected as the range α of the rust gray value. min ~α max ;

[0095] The steps to create the S3 rust type are as follows:

[0096] First, acquire enough images of rust and then denoise and convert them to grayscale.

[0097] Secondly, obtain the corrosion degree evaluation coefficient c and rust roughness evaluation coefficient δ for each image;

[0098] Subsequently, c and δ were sorted out separately, and the corrosion degree evaluation coefficient c was divided into low rust a, medium rust b, high rust c, and ultra-high rust d according to the quartile method, and the roughness evaluation coefficient δ was divided into low roughness A, medium roughness B, high roughness C, and ultra-high roughness D.

[0099] Finally, the classification of corrosion degree evaluation coefficients and roughness evaluation coefficients are combined to obtain 16 categories: aA, aB, aC, aD, bA, bB, bC, bD, cA, cB, cC, cD, dA, dB, dC, and dD.

[0100] S4 Grinding Positive Pressure F N The steps to establish the rotational speed v of the grinding head are as follows:

[0101] First, photographs of the rust were taken and polishing experiments were conducted to obtain the corresponding polishing force F for each rust image. N and the value of the grinding head rotation speed v;

[0102] Then, the rust was classified into 16 types according to the S3 method;

[0103] Finally, calculate the grinding force F for each type of rust. N The average value of the grinding head rotation speed v is used to establish the process.

[0104] Specifically, the rust area evaluation coefficient b in step S102 is calculated using the following formula:

[0105]

[0106] Where β is the total number of pixels in the image;

[0107] γ is the rust gray value α set in advance. min ~α max The number of pixels within the range.

[0108] Specifically, the steps in step S103 to obtain the rust color evaluation coefficient a and the area evaluation coefficient b to get the rust degree evaluation coefficient c value are as follows:

[0109] S301. Calculate the rust color evaluation coefficient 'a', as shown in the following formula:

[0110]

[0111] In the formula, α represents the range of grayscale values ​​of rust extracted from the image by machine vision. min ~α max The average grayscale value of all pixels within the range, α min and α maxThese are the upper and lower boundary values ​​of the grayscale range for rust in the image;

[0112] S302. Calculate the trade-off factor between rust color and rust area, as follows:

[0113]

[0114] In the formula, σ a and σ b The mean squared error of a sufficiently large number of samples a and b;

[0115] S302. Calculate the corrosion degree evaluation coefficient c, which combines the evaluation coefficients for rust color and area to more accurately determine the degree of corrosion. The corrosion degree evaluation coefficient c is calculated as follows:

[0116] c = d·|a| + e·b

[0117] In the formula, d and e are the weighting coefficients for rust color and rust area, and b is the rust area evaluation coefficient value in step S102.

[0118] Specifically, the steps for obtaining the rust roughness evaluation coefficient δ in step S104 are as follows:

[0119] S401. Separate the pixels within the grayscale range of rust to obtain the rust area in the image;

[0120] S402. Perform Fast Fourier Transform (FFT) on the rust region of the image to obtain the frequency spectrum of the image;

[0121] S403. Calculate the proportion of high-frequency energy in the image frequency spectrum, and use it as the rust roughness evaluation coefficient δ.

[0122] Specifically, in step S201, a general control method is used. In this example, the first vertical drive motor adopts constrained following control (CFC), and the second grinding execution motor adopts PID control. The specific implementation steps are as follows:

[0123] S501. The dynamic model is established as follows:

[0124]

[0125] In the formula, K t Let N be the motor torque constant, N be the transmission ratio of the belt drive system, η be the transmission efficiency, L be the lead of the lead screw, m be the mass of the slider, and F be the torque constant of the motor. f Let K be the frictional force acting on the slider, a be the acceleration of the slider, and K be the acceleration of the slider. p K is the proportionality constant. v The vibration coefficient is denoted by v, which represents the magnitude of the vibration.

[0126] S502. The first vertical drive motor is controlled by Constraint Follower Control (CFC). The controller formula is as follows:

[0127]

[0128] In the formula, F N It is the actual pressure, F ref It is reference pressure, i ref This is the reference value for the current, K. c is the control gain, e is the error signal, and the others are parameters in the dynamic model;

[0129] Then, design a constraint condition as follows:

[0130] |e|≤e max

[0131] Where e max This is the maximum allowable error;

[0132] Finally, based on the constraints, a suitable control gain K is selected. c This allows the error signal e to converge to zero quickly when the constraints are met, thus completing the establishment of the constraint-following controller.

[0133] S503. A PID controller is used to precisely control the desired rotational speed v during the grinding process. The specific steps are as follows:

[0134] PID control is as follows:

[0135]

[0136] By adjusting the proportional coefficient K p Differential coefficient K i and integral coefficient K d To achieve constant speed control, these three parameters are selected according to the actual situation, and ultimately, precise tracking control of the desired speed v can be achieved.

[0137] Specifically, the grinding time Δt in step S202 is obtained through the following steps, since the grinding parameters are the same for the same type of rust:

[0138] First, a large amount of rust is polished. During the polishing process, the polishing time is constantly adjusted so that the type of rust changes when returning to the upper layer. The polishing time for each step is recorded.

[0139] Then, the average of all polishing times is calculated as Δt;

[0140] When an image is input, the program can denoise and convert it to grayscale, then obtain c, δ, rust type, and grinding parameters. As shown in the lower layer of the example, CFC and PID distributed control maintain the grinding positive pressure and grinding head speed at a constant value for a grinding period Δt, and can judge the grinding effect. Steps S102 to S105 are repeated to obtain the grinding head speed v and grinding positive pressure F. N The grinding parameters are adjusted adaptively to form a closed-loop control.

[0141] Specifically, the grinding device includes a first motor 1, the output shaft of the first motor 1 is driven by a belt 2 and a pulley 3, a screw guide rail 6 is fixed on the pulley 3, a nut 4 is screwed onto the screw guide rail 6, a slider 5 is installed at the bottom of the nut 4, a second motor 9 is installed on the nut 4, a grinding head 7 is fixed at the end of the output shaft of the second motor 9, and the output shaft of the second motor 9 is rotatably connected to the nut 4 through a bearing 8.

[0142] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rust removal and grinding control method with a double-layer structure, characterized in that, Includes the following steps: S101, Upper layer: First, establish an offline database, which includes: the grayscale value range of rust. Grinding threshold, rust type, grinding pressure and the rotational speed v of the grinding head; S102. Grinding judgment: Machine vision is used to acquire images of the rust surface in real time, and the images are denoised and grayscaled. The proportion of rust pixels is calculated as the rust area evaluation coefficient b. If the proportion of rust pixels extracted in real time does not exceed the grinding threshold, grinding is not required, and the grinding robot continues to move and continue to calculate the proportion of rust pixels. If the proportion of rust pixels extracted in real time exceeds the grinding threshold, the robot stops and prepares for grinding. S103. Calculate the corrosion degree evaluation coefficient c, obtain the rust color evaluation coefficient a and the area evaluation coefficient b, and calculate the value of the corrosion degree evaluation coefficient c. S104. Calculate the rust roughness evaluation coefficient δ, and define the rust grayscale value range. The rust region in the image is obtained by separating the pixels within the image, and the high-frequency energy ratio is calculated after performing a Fast Fourier Transform (FFT) on the image, which is used as the rust roughness evaluation coefficient δ. S105. Query the offline database. Based on the obtained rust corrosion degree evaluation coefficient c and rust roughness evaluation coefficient δ, query the offline database to obtain the rust type and the corresponding grinding head speed v and grinding pressure. ; S201, Grinding, lower layer: Based on the parameter data from step S105, a common control method, such as adaptive control, sliding mode control, and robust control, is used to control the vertical drive motor (1) and the grinding execution motor (2) respectively, so as to realize the grinding positive pressure during the grinding process. Precise control of the grinding head rotation speed v is required before grinding is performed. S202, Adaptive adjustment: After grinding for Δt time, return to the upper layer to obtain real-time grinding images through vision, judge the grinding effect, repeat steps S102 to S105, obtain the grinding parameters of the grinding head, and adaptively adjust the grinding parameters to form a closed-loop control. The offline database establishment in step S101 includes the following: The steps to establish the S1 polishing threshold are as follows: First, images that meet the grinding precision requirements are acquired, and they are then denoised and converted to grayscale. Then, obtain the percentage of rust pixel area in each image; Finally, the maximum percentage of these rust pixels is taken as the polishing threshold; S2 Rust Gray Value Range The steps to establish it are as follows: First, images of rust are acquired and then denoised and converted to grayscale. Then, obtain the grayscale value of the rust in each image; Finally, the normal distribution curve of the rust gray value was calculated, and selected... Rust gray value range ; The steps to create the S3 rust type are as follows: First, images of rust are acquired and then denoised and converted to grayscale. Secondly, obtain the corrosion degree evaluation coefficient c and rust roughness evaluation coefficient δ for each image; Subsequently, c and δ were sorted out separately, and the corrosion degree evaluation coefficient c was divided into low rust a, medium rust b, high rust c, and ultra-high rust d according to the quartile method, and the roughness evaluation coefficient δ was divided into low roughness A, medium roughness B, high roughness C, and ultra-high roughness D. Finally, the classification of corrosion degree evaluation coefficients and roughness evaluation coefficients are combined to obtain 16 categories: aA, aB, aC, aD, bA, bB, bC, bD, cA, cB, cC, cD, dA, dB, dC, and dD. S4 grinding positive pressure The steps to establish the rotational speed v of the grinding head are as follows: First, photographs of the rust were taken and polishing experiments were conducted to determine the corresponding polishing pressure for each rust image. and the value of the grinding head rotation speed v; Then, the rust was classified into 16 types according to the S3 method; Finally, calculate the grinding force for each type of rust. The average value of the grinding head rotation speed v is used to establish the process.

2. The rust removal and grinding control method for a double-layer structure according to claim 1, characterized in that, In step S102, the rust area evaluation coefficient b is calculated using the following formula: Where β is the total number of pixels in the image; γ is the preset rust gray value The number of pixels within the range.

3. The rust removal and grinding control method for a double-layer structure according to claim 1, characterized in that, The steps in step S103 to obtain the rust color evaluation coefficient a and area evaluation coefficient b to get the rust degree evaluation coefficient c are as follows: S301. Calculate the rust color evaluation coefficient 'a', as shown in the following formula: In the formula, α represents the range of grayscale values ​​of rust extracted from the image by machine vision. The average grayscale value of all pixels within the range. and These are the upper and lower boundary values ​​of the grayscale range for rust in the image; S302. Calculate the trade-off factor between rust color and rust area, as follows: In the formula, where and Let be the mean squared error of samples a and b; S302. Calculate the corrosion degree evaluation coefficient c, which combines the evaluation coefficients for rust color and area to more accurately determine the degree of corrosion. The corrosion degree evaluation coefficient c is calculated as follows: In the formula, d and e are the weighting coefficients for rust color and rust area, and b is the rust area evaluation coefficient value in step S102.

4. The rust removal and grinding control method for a double-layer structure according to claim 1, characterized in that, The specific steps for obtaining the rust roughness evaluation coefficient δ in step S104 are as follows: S401, Adjust the range of rust grayscale values. The pixels within the image are separated to obtain the rust region. S402. Perform Fast Fourier Transform (FFT) on the rust region of the image to obtain the frequency spectrum of the image; S403. Calculate the proportion of high-frequency energy in the image frequency spectrum, and use it as the rust roughness evaluation coefficient δ.

5. The rust removal and grinding control method for a double-layer structure according to claim 1, characterized in that, In step S201, a general control method is used. In this example, the first vertical drive motor adopts constrained following control (CFC), and the second grinding execution motor adopts PID control. The specific implementation steps are as follows: S501. The dynamic model is established as follows: In the formula, where Let be the motor torque constant, N be the transmission ratio of the belt drive system, η be the transmission efficiency, L be the lead of the lead screw, and m be the mass of the slider. Let be the frictional force acting on the slider, and 'a' be the acceleration of the slider. A proportionality constant The vibration coefficient is denoted by v, which represents the magnitude of the vibration. S502. The first vertical drive motor is controlled by Constraint Follower Control (CFC). The controller formula is as follows: In the formula, where It's real pressure. It is reference pressure. This is a reference value for the current. is the control gain, e is the error signal, and the others are parameters in the dynamic model; Then, design a constraint condition as follows: in This is the maximum allowable error; Finally, select a suitable control gain based on the constraints. This allows the error signal e to converge to zero quickly when the constraints are met, thus completing the establishment of the constraint-following controller. S503. A PID controller is used to achieve precise tracking control of the desired rotational speed v during the grinding process. The specific steps are as follows: PID control is as follows: By adjusting the scaling factor Differential coefficients and integral coefficient To achieve precise tracking and control of the rotational speed v, these three parameters are selected according to the actual situation, and ultimately, precise tracking and control of the desired rotational speed v can be achieved during the grinding process.

6. The rust removal and grinding control method for a double-layer structure according to claim 1, characterized in that, In step S202, the grinding time Δt is obtained through the following steps, since the grinding parameters are the same for the same type of rust: First, perform a rust removal operation, continuously adjusting the removal time during the process so that the rust type changes upon returning to the upper layer, and record the removal time for each step; Then, the average of all polishing times is calculated as Δt; When an image is input, the program can denoise and convert it to grayscale, and then obtain c, δ, rust type and grinding parameters; as shown in the lower layer of the example, CFC and PID distributed control keep the grinding positive pressure and grinding head speed constant and grind for a period of time Δt, and can judge the grinding effect and adaptively adjust the grinding parameters to form a closed loop control.

7. A rust removal and grinding device with a double-layer structure, characterized in that: The grinding device according to any one of claims 1-6 is applied to a double-layer structure rust removal and grinding control method, the grinding device includes a first motor (1), the output shaft of the first motor (1) is driven by a belt (2) and a pulley (3), a screw guide rail (6) is fixed on the pulley (3), a nut (4) is screwed onto the screw guide rail (6), a slider (5) is installed at the bottom of the nut (4), a second motor (9) is installed on the nut (4), a grinding head (7) is fixed at the end of the output shaft of the second motor (9), and the output shaft of the second motor (9) is rotatably connected to the nut (4) through a bearing (8).

Images