Operation method of milling machine based on precise control of particle size of milling material

Abstract

The invention discloses an operation method of a milling machine based on milling material particle size accurate control, the milling machine comprises a power device, a control system, a belt conveying device, a milling material particle size identification device, a support leg lifting device, a milling hub and milling chamber mechanism and a walking device, a milling material particle size recognition device is installed at the discharging end of the belt conveying device, a control system is arranged on the milling machine operation platform and used for collecting and analyzing particle size distribution data input by a sensor of the milling material particle size recognition device, and particle size matching parameters are adjusted in real time according to the milling material particle size distribution data. And the milling speed of the milling machine and the rotating speed of the milling hub are controlled, so that the particle size of the milled material is ensured to be within a set range, the utilization rate of the milled material is greatly improved, and effective utilization of resources is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of road construction milling equipment, and in particular relates to an operation method of a milling machine based on precise control of milling material particle size. Background Art

[0002] Road milling is a common road maintenance and repair method, which involves removing a layer of material from the old road surface in order to re-pave a new road surface. The milling material produced in this process can be recycled and reused to reduce resource waste and environmental impact. However, in the actual milling process, the particle size distribution of the milling material varies greatly due to the influence of the selection and mix design of the original road material, the variability of the road material, the different pavement structure layers during the asphalt pavement milling process, and the inconsistent working environment. At the same time, since the milling material will agglomerate and the density, color, dielectric constant and other physical properties of the agglomerated materials are similar, it has a great impact on the detection of the milling material particle size. Therefore, the milling material is less used in subsequent regeneration.

[0003] At present, the most widely used method is to use screening to screen the milling material sample through a series of sieves with different apertures, and then determine the particle size distribution according to the particle weight or number of particles in different sieve holes. In this case, the particle size distribution of the milling material is not controlled during the milling operation, and only a very small part of the milling material is used in the subsequent recycling, resulting in low utilization of the milling material and further waste of resources. Summary of the invention

[0004] In order to solve the problem that the particle size of milling materials cannot be accurately controlled during the milling process of the milling machine in the current market, the present invention proposes an operation method of the milling machine based on the accurate control of the particle size of the milling materials.

[0005] In order to solve the above-mentioned technical problems, the present invention adopts the following technical scheme, which is an operation method of a milling machine based on precise control of the particle size of the milling material, and is characterized in that: the milling machine includes a power device, a control system, a belt conveyor device, a milling material particle size identification device, a leg lifting device, a milling hub and a milling chamber mechanism and a walking device, wherein the power device is used to provide power for walking, lifting and milling, a milling material particle size identification device is installed at the discharge end of the belt conveyor device, and a control system is provided on the milling machine operating platform, which is used to collect and analyze the particle size distribution data input by the sensor of the milling material particle size identification device, adjust the particle size matching parameters in real time according to the milling material particle size distribution data, and control the milling speed and milling hub rotation speed of the milling machine.

[0006] Since milling materials will agglomerate and the density, color, dielectric constant and other physical properties of the agglomerated materials are similar, the particle size distribution data of the milling materials is used as the input of the control system.

[0007] It is further defined that the milling hub includes a milling hub quick-change structure and a speed adjustment device, the number of milling heads and the spacing between the milling heads in the milling hub have a first direct correspondence with the particle size distribution of the milling material, and before the milling machine performs the milling task, the particle size control distribution range of the milling material is determined, and the milling machine quickly replaces the milling hub corresponding thereto; since the operating speed of the milling machine and the rotational speed of the milling hub have a second direct correspondence, the milling machine adjusts the operating speed of the milling machine and the rotational speed of the milling hub in real time through the control system and the walking device.

[0008] It is further defined that when the milling material agglomeration cannot be effectively controlled by adjusting the operating speed of the milling machine and the rotation speed of the milling hub in real time through the control system and the traveling device, the milling hub and the milling chamber mechanism also include a milling material agglomeration auxiliary crushing device. When the milling material particle size identification device detects a large amount of milling material agglomeration, the milling material agglomeration auxiliary crushing device changes the running path of the milling material in the milling chamber, increases the residence time of the milling material in the milling chamber, reduces the agglomeration phenomenon, and achieves the purpose of controlling the milling particle size.

[0009] It is further defined that the control system includes a milling material particle size identification control system and a milling operation control system. The milling material particle size identification control system collects data from a depth sensor and an image sensor, and performs fusion calculations on the collected data to obtain real-time milling material particle size distribution data, and transmits the milling material particle size distribution data to the milling operation control system. The milling operation controller collects parameters such as the milling machine operating speed, milling depth of the milling machine, milling hub speed, and milling material particle size distribution data, and compares the milling material particle size distribution data with the milling material particle size setting data. The milling operation controller controls the milling operation speed and the milling hub speed according to the difference.

[0010] It is further defined that the milling material particle size identification device is installed at the front section of the belt conveyor device, and the milling material particle size identification device includes a milling material feed port, a depth sensor and an image sensor, and the depth sensor and the image sensor are respectively installed around the milling material particle size identification device, and the installation positions and installation quantities of the depth sensor and the image sensor are within the scope of the present claim, the depth sensor collects the depth relationship between the milling material and the depth sensor, and the image sensor collects the image of the milling material particle size.

[0011] It is further defined that the milling material particle size identification device also includes a milling material particle size distribution data storage system, which is used to store the milling material particle size distribution data to the milling material particle size identification controller, and perform data analysis with the milling operation speed and the milling hub speed data to predict the optimal operation parameters, and at the same time predict the causes and performance of the milling material particle size variation based on the data.

[0012] It is further defined that the specific steps of the milling material particle size identification device for identifying the particle size of the milling machine material are:

[0013] Step S1: Use imaging principle (X-ray CT) and digital image processing technology to identify the particle size of milling materials, reduce the influence of false particle size caused by the agglomeration of milling material particle size on the precise control of milling material particle size, use binocular matching calculation, take the upper left corner of image F as the origin, and express the m×n image in a matrix as follows:

[0014]

[0015] Using the energy function formula defined by the Markov random field model, the energy function is related to the difference between the corresponding pixels of the two images and the disparity of two adjacent pixels in the same image to obtain the best matching result:

[0016] E = mathCOST + smoothnessCOST

[0017] mathCOST=∑ x,y |I(x,y)-J(x+d x,y ,y)

[0018] smoothnessCOST=∑ q,q' |d q -d q'

[0019] Where: mathCOST is the difference between the corresponding pixels of the two images; smoothnessCOST is the disparity of adjacent pixels in the same image; I(x, y), J(x, y) are the coordinates of the camera images represented by matrices; d q ,d q' Respectively represent the depth values ​​corresponding to adjacent pixel points q, q';

[0020] The best matching result is obtained by minimizing the energy function, and the matching results of each pixel in the images obtained by the left and right cameras are combined to finally obtain an energy-optimized matching image.

[0021] Step S2: Perform edge detection on the matching image

[0022] ①Convert the RGB image into a grayscale image:

[0023] data_Gray=a R *data_R+a G *data_G+a B *data_B

[0024] Where: data_Gray is the gray value after conversion, a R 、a G 、a B is the channel adaptation parameter, data_R, data_G, data_B are the channel data;

[0025] ② Use Gaussian filter to smooth the image and remove noise points:

[0026]

[0027] Where: A represents the weight matrix; H represents the image matrix;

[0028] ③ Use the Sobel operator to calculate the gradient strength and direction of each pixel in the image, and calculate the gradient and gradient approximation and direction in the x and y directions

[0029]

[0030] Where: G x ,G y , G represent the gradient of the image in the x, y and total directions respectively; S x ,S y Represent the convolution kernels in the x and y directions respectively; A represents the image matrix; θ represents the gradient direction;

[0031] ④ Apply non-maximum suppression to eliminate spurious responses caused by edge detection;

[0032] ⑤ Apply dual threshold detection to determine the real and potential edges and separate the voids, mortar and aggregates in the image;

[0033] ⑥Finally complete edge detection by suppressing isolated weak edges;

[0034] Step S3: For the image after edge detection, various parameters of standard milling material particle size and depth sensor data are introduced according to the random forest algorithm to monitor the milling material particle size recognition of the milling machine in real time, especially the recognition of false particle size.

[0035] The specific steps of the operation method of the milling machine based on the precise control of the particle size of the milling material described in the present invention are:

[0036] Step S1: Investigate the structural parameters and original particle size distribution of the milling operation section, determine the milling operation depth and set the milling material particle size control range, calculate the number of milling heads and milling head spacing parameters of the milling hub, and replace the milling hub corresponding to the operation parameters;

[0037] Step S2: inputting milling hub parameters, original milling material size distribution, milling material depth, milling material thickness and milling material size control range into the milling operation controller and performing the milling operation, the milling material size identification device and the milling material size identification controller identifying the particle size distribution of the milling material passing through the belt conveyor, and transmitting the milling material size distribution data to the milling operation controller;

[0038] Step S3: The milling operation controller controls the milling operation depth through machine learning and the leg lifting device; the milling operation controller performs difference calculation on the particle size distribution data of the planed material and the set milling material particle size control range data, and performs machine learning and nonlinear regression according to the calculation result to determine whether to adjust the milling hub speed, adjust the milling operation speed, or adjust the milling hub speed, the milling operation speed and the milling material agglomeration auxiliary crushing device at the same time, so as to achieve the purpose of setting the operation parameters and controlling the milling material particle size;

[0039] Step S4: Save and display the adjustment process and adjustment result data of the operating parameters to accumulate data for the subsequent generation of particle size distribution curves and statistical data.

[0040] Compared with the prior art, the present invention has the following advantages and beneficial effects: the present invention effectively solves the problem that the particle size of the milling material cannot be accurately controlled during the milling process of the milling machine on the current market, and proposes a technology for online detection of the particle size distribution of the milling material and a control method that corresponds this technology to the operating speed of the milling machine and the rotation speed of the milling hub, ensuring that the particle size of the milling material is within the set range, greatly improving the utilization rate of the milling material, and realizing the effective utilization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a structural schematic diagram of a milling machine based on precise control of the particle size of milling material in the present invention;

[0042] Figure 2 It is a structural schematic diagram of the milling hub and the milling chamber mechanism of the present invention;

[0043] Figure 3 It is a structural schematic diagram of the device for identifying the particle size of milling material in the present invention;

[0044] Figure 4 It is a block diagram of the working principle of the milling machine based on the precise control of the particle size of the milling material in the present invention;

[0045] Figure 5 It is a schematic diagram of the operation method of the milling machine based on the precise control of the particle size of the milling material in the present invention.

[0046] In the figure: 1-power device; 2-control system; 3-belt conveyor device; 4-milling material particle size identification device; 5-leg lifting device; 6-milling hub and milling chamber mechanism; 7-traveling device; 6.1-milling material agglomeration auxiliary crushing device. DETAILED DESCRIPTION

[0047] like Figures 1-2As shown, an operating method of a milling machine based on precise control of the particle size of milling materials, the milling machine includes a power device 1, a control system 2, a belt conveyor device 3, a milling material particle size identification device 4, a leg lifting device 5, a milling hub and milling chamber mechanism 6 and a walking device 7, wherein the power device provides power for walking, lifting and milling, a milling material particle size identification device is installed at the discharge end of the belt conveyor device, a control system is set on the milling machine operating platform, the particle size distribution data input by the sensor of the milling material particle size identification device is collected and analyzed, the particle size matching parameters are adjusted in real time according to the particle size distribution of the milling material, and the milling speed and the milling hub speed of the milling machine are controlled.

[0048] Since milling materials will agglomerate and the density, color, dielectric constant and other physical properties of the agglomerated materials are similar, the particle size distribution data of the milling materials is used as the input of the control system.

[0049] The milling hub described in the present invention includes a milling hub quick-change structure and a speed adjustment device. The number of milling cutter heads and the spacing between the milling cutter heads in the milling hub have a first direct corresponding relationship with the particle size distribution of the milling material. Before the milling machine performs the milling task, the particle size control distribution range of the milling material is determined, and the milling machine quickly replaces the milling hub corresponding thereto. Similarly, since the operating speed of the milling machine and the rotational speed of the milling hub have a second direct corresponding relationship, the milling machine adjusts the operating speed of the milling machine and the rotational speed of the milling hub in real time through the control system and the walking device.

[0050] In the present invention, when the milling material agglomeration cannot be effectively controlled by adjusting the operating speed of the milling machine and the rotation speed of the milling hub in real time through the control system and the traveling device, the milling hub and the milling chamber mechanism include a milling material agglomeration auxiliary crushing device 6.1. When the milling material particle size identification device detects a large amount of milling material agglomeration, the milling material agglomeration auxiliary crushing device changes the running path of the milling material in the milling chamber, increases the residence time of the milling material in the milling chamber, reduces the agglomeration phenomenon, and achieves the purpose of controlling the milling particle size.

[0051] The control system described in the present invention includes a milling material particle size identification control system and a milling operation control system. The milling material particle size identification control system collects data from a depth sensor and an image sensor, and performs fusion calculation on the data to obtain milling material particle size distribution data in real time, and transmits the milling material particle size distribution data to the milling operation control system. The milling operation controller collects parameters such as the milling machine operation speed, milling depth of the milling machine, milling hub speed, and milling material particle size distribution data, and compares the milling material particle size distribution data with the milling material particle size setting data. The milling operation controller controls the milling operation speed and the milling hub speed according to the difference.

[0052] The milling material particle size identification device described in the present invention is installed at the front section of the belt conveyor device. The milling material particle size identification device includes a milling material feed port, a depth sensor and an image sensor. The depth sensor and the image sensor are respectively installed around the milling material particle size identification device. The installation positions and installation quantities of the depth sensor and the image sensor are within the scope of the present claim. The depth sensor collects the depth relationship between the milling material and the depth sensor, and the image sensor collects the particle size image of the milling material.

[0053] The milling material particle size identification device of the present invention also includes a milling material particle size distribution data storage system, which is used to store the milling material particle size distribution data to the milling material particle size identification controller, and perform data analysis with the milling operation speed and milling hub speed data to predict the optimal operation parameters, and at the same time predict the causes and performance of the milling material particle size variation based on the data.

[0054] The specific steps of the milling material particle size identification device of the present invention for identifying the particle size of the milling machine material are as follows:

[0055] Step S1: Use imaging principle (X-ray CT) and digital image processing technology to identify the particle size of milling materials, and reduce the influence of false particle size caused by the agglomeration of milling material particle size on the precise control of milling material particle size. Using binocular matching calculation, take the upper left corner of image F as the origin, and express the m×n image in a matrix as follows:

[0056]

[0057] Using the energy function formula defined by the Markov random field model, the energy function is related to the difference between the corresponding pixels of the two images and the disparity of two adjacent pixels in the same image to obtain the best matching result:

[0058] E = mathCOST + smoothnessCOST

[0059] mathCOST=∑ x,y I(x,y)-J(x+d x,y ,y)

[0060] smoothnessCOST=∑ q,q' |d q -d q' |

[0061] Where: mathCOST is the difference between the corresponding pixels of the two images; smoothnessCOST is the disparity of adjacent pixels in the same image; I(x, y), J(x, y) are the coordinates of the camera images represented by matrices; d q ,d q' Respectively represent the depth values ​​corresponding to adjacent pixel points q, q';

[0062] By minimizing and optimizing the energy function, an optimal matching result is obtained, and the matching results of each pixel in the images obtained by the left and right cameras are combined to finally obtain an energy-optimized matching image;

[0063] Step S2: Perform edge detection on the matching image

[0064] ①Convert the RGB image into a grayscale image:

[0065] data_Gray=a R *data_R+a G *data_G+a B *data_B

[0066] Where: data_Gray is the gray value after conversion, a R 、a G 、a B is the channel adaptation parameter, data_R, data_G, data_B are the channel data;

[0067] ② Use Gaussian filter to smooth the image and remove noise points:

[0068]

[0069] Where: A represents the weight matrix; H represents the image matrix;

[0070] ③ Use the Sobel operator to calculate the gradient strength and direction of each pixel in the image, and calculate the gradient and gradient approximation and direction in the x and y directions

[0071]

[0072] Where: G x ,G y , G represent the gradient of the image in the x, y and total directions respectively; S x ,S y Represent the convolution kernels in the x and y directions respectively; A represents the image matrix; θ represents the gradient direction;

[0073] ④ Apply non-maximum suppression to eliminate spurious responses caused by edge detection;

[0074] ⑤ Apply dual threshold detection to determine the real and potential edges and separate the voids, mortar and aggregates in the image;

[0075] ⑥Finally complete edge detection by suppressing isolated weak edges;

[0076] Step S3: For the image after edge detection, various parameters of standard milling material particle size and depth sensor data are introduced according to the random forest algorithm to monitor the milling material particle size recognition of the milling machine in real time, especially the recognition of false particle size.

[0077] The specific steps of the operation method of the milling machine based on the precise control of the particle size of the milling material described in the present invention are:

[0078] Step S1: Investigate the structural parameters and original particle size distribution of the milling operation section, determine the milling operation depth and set the milling material particle size control range, calculate the number of milling heads and milling head spacing parameters of the milling hub, and replace the milling hub corresponding to the operation parameters;

[0079] Step S2: inputting milling hub parameters, original milling material size distribution, milling material depth, milling material thickness and milling material size control range into the milling operation controller and performing the milling operation, the milling material size identification device and the milling material size identification controller identifying the particle size distribution of the milling material passing through the belt conveyor, and transmitting the milling material size distribution data to the milling operation controller;

[0080] Step S3: The milling operation controller controls the milling operation depth through machine learning and the leg lifting device; the milling operation controller performs difference calculation on the particle size distribution data of the planed material and the set milling material particle size control range data, and performs machine learning and nonlinear regression according to the calculation result to determine whether to adjust the milling hub speed, adjust the milling operation speed, or adjust the milling hub speed, the milling operation speed and the milling material agglomeration auxiliary crushing device at the same time, so as to achieve the purpose of setting the operation parameters and controlling the milling material particle size;

[0081] Step S4: Save and display the adjustment process and adjustment result data of the operating parameters to accumulate data for the subsequent generation of particle size distribution curves and statistical data.

Claims

1. A method for operating a milling machine based on precise control of the particle size of the milled material, characterized in that: The milling machine includes a power unit, a control system, a belt conveyor, a milling material particle size identification device, a leg lifting device, a milling hub and a milling chamber mechanism and a walking device, wherein the power unit is used to provide power for walking, lifting and milling, the milling material particle size identification device is installed at the discharge end of the belt conveyor, and a control system is provided on the milling machine operating platform, which is used to collect and analyze the particle size distribution data input by the sensor of the milling material particle size identification device, adjust the particle size matching parameters in real time according to the milling material particle size distribution data, and control the milling speed and milling hub rotation speed of the milling machine.

2. The method for operating a milling machine based on precise control of the particle size of milled material according to claim 1, characterized in that: The milling hub includes a milling hub quick-change structure and a speed adjustment device. The number of milling cutter heads and the spacing between the milling cutter heads in the milling hub have a first direct corresponding relationship with the particle size distribution of the milling material. Before the milling machine performs the milling task, the particle size control distribution range of the milling material is determined, and the milling machine quickly replaces the corresponding milling hub; since the operating speed of the milling machine and the rotational speed of the milling hub have a second direct corresponding relationship, the milling machine adjusts the operating speed of the milling machine and the rotational speed of the milling hub in real time through the control system and the traveling device.

3. The method for operating a milling machine based on precise control of the particle size of milled material according to claim 1, characterized in that: The milling hub and milling chamber mechanism also includes a milling material agglomeration auxiliary crushing device. When the milling material particle size identification device detects a large amount of milling material agglomeration, the milling material agglomeration auxiliary crushing device changes the running path of the milling material in the milling chamber, increases the residence time of the milling material in the milling chamber, reduces the agglomeration phenomenon, and achieves the purpose of controlling the milling particle size.

4. The method for operating a milling machine based on precise control of the particle size of milled material according to claim 1, characterized in that: The control system includes a milling material particle size identification control system and a milling operation control system. The milling material particle size identification control system collects data from a depth sensor and an image sensor, and performs fusion calculation on the collected data to obtain real-time milling material particle size distribution data, and transmits the milling material particle size distribution data to the milling operation control system. The milling operation controller collects parameters such as the milling machine operation speed, milling depth of the milling machine, milling hub speed, and milling material particle size distribution data, and compares the milling material particle size distribution data with the milling material particle size setting data. The milling operation controller controls the milling operation speed and the milling hub speed according to the difference.

5. The method for operating a milling machine based on precise control of the particle size of milled material according to claim 1, characterized in that: The milling material particle size identification device is installed at the front end of the belt conveyor device. The milling material particle size identification device includes a milling material feed port, a depth sensor and an image sensor. The depth sensor and the image sensor are respectively installed around the milling material particle size identification device. The installation position and installation quantity of the depth sensor and the image sensor are both within the scope of the present claim. The depth sensor collects the depth relationship between the milling material and the depth sensor, and the image sensor collects the milling material particle size image.

6. The method for operating a milling machine based on precise control of the particle size of milled material according to claim 1, characterized in that: The milling material particle size identification device also includes a milling material particle size distribution data storage system, which is used to store the milling material particle size distribution data in the milling material particle size identification controller, and perform data analysis with the milling operation speed and the milling hub speed data to predict the optimal operation parameters, and at the same time predict the causes and performance of the milling material particle size variation based on the data.

7. The method for operating a milling machine based on precise control of the particle size of milled material according to claim 1, characterized in that: The specific steps of the milling material particle size identification device for identifying the particle size of the milling machine material are as follows: Step S1: Use imaging principle (X-ray CT) and digital image processing technology to identify the particle size of milling materials, reduce the influence of false particle size caused by the agglomeration of milling material particle size on the precise control of milling material particle size, use binocular matching calculation, take the upper left corner of image F as the origin, and express the m×n image in a matrix as follows: Using the energy function formula defined by the Markov random field model, the energy function is related to the difference between the corresponding pixels of the two images and the disparity of two adjacent pixels in the same image to obtain the best matching result: E = mathCOST + smoothnessCOST mathCOST=∑ x,y I(x,y)-J(x+d x,y ,y) smoothnessCOST=∑ q,q' |d q -d q' | Where: mathCOST is the difference between the corresponding pixels of the two images; smoothnessCOST is the disparity of adjacent pixels in the same image; I(x,y), J(x,y) are the coordinates corresponding to the camera images represented by matrices; d q ,d q' Respectively represent the depth values ​​corresponding to adjacent pixel points q, q'; The best matching result is obtained by minimizing the energy function, and the matching results of each pixel in the images obtained by the left and right cameras are combined to finally obtain an energy-optimized matching image. Step S2: Perform edge detection on the matching image ①Convert the RGB image into a grayscale image: data_Gray=a R *data_R+a G *data_G+a B *data_B Where: data_Gray is the gray value after conversion, a R 、a G 、a B is the channel adaptation parameter, data_R, data_G, data_B are the channel data; ② Use Gaussian filter to smooth the image and remove noise points: Where: A represents the weight matrix; H represents the image matrix; ③ Use the Sobel operator to calculate the gradient strength and direction of each pixel in the image, and calculate the gradient and gradient approximation and direction in the x and y directions Where: G x ,G y , G represent the gradient of the image in the x, y and total directions respectively; S x ,S y Represent the convolution kernels in the x and y directions respectively; A represents the image matrix; θ represents the gradient direction; ④ Apply non-maximum suppression to eliminate spurious responses caused by edge detection; ⑤ Apply dual threshold detection to determine the real and potential edges and separate the voids, mortar and aggregates in the image; ⑥Finally complete edge detection by suppressing isolated weak edges; Step S3: For the image after edge detection, various parameters of standard milling material particle size and depth sensor data are introduced according to the random forest algorithm to monitor the milling material particle size recognition of the milling machine in real time, especially the recognition of false particle size.

8. The method for operating a milling machine based on precise control of the particle size of the milled material according to claim 1, characterized in that The specific steps are: Step S1: Investigate the structural parameters and original particle size distribution of the milling operation section, determine the milling operation depth and set the milling material particle size control range, calculate the number of milling heads and milling head spacing parameters of the milling hub, and replace the milling hub corresponding to the operation parameters; Step S2: inputting milling hub parameters, original milling material size distribution, milling material depth, milling material thickness and milling material size control range into the milling operation controller and performing the milling operation, the milling material size identification device and the milling material size identification controller identifying the particle size distribution of the milling material passing through the belt conveyor, and transmitting the milling material size distribution data to the milling operation controller; Step S3: The milling operation controller controls the milling operation depth through machine learning and the leg lifting device; the milling operation controller performs difference calculation on the particle size distribution data of the planed material and the set milling material particle size control range data, and performs machine learning and nonlinear regression according to the calculation result to determine whether to adjust the milling hub speed, adjust the milling operation speed, or adjust the milling hub speed, the milling operation speed and the milling material agglomeration auxiliary crushing device at the same time, so as to achieve the purpose of setting the operation parameters and controlling the milling material particle size; Step S4: Save and display the adjustment process and adjustment result data of the operating parameters to accumulate data for the subsequent generation of particle size distribution curves and statistical data.

Images