Design method for bionic wear-resistant structure of sliding shoe of coal mining machine

Abstract

The invention provides a coal cutter sliding shoe bionic wear-resistant structure design method, and relates to the technical field of structural design, the method comprises the following steps: collecting and analyzing observation data of a biological scale structure, extracting key characteristic value factors of horizontal axis length, longitudinal axis length, vertex angle and height, and carrying out parameter combination design based on the key characteristic value factors; secondly, through an orthogonal test and variance analysis, determining the influence degree and the optimal combination of each characteristic value factor on the wear resistance; meanwhile, thermodynamic parameters in the abrasive wear test process are monitored in real time, a thermodynamic data set is generated, a wear loss data set is combined, range analysis and variance analysis are carried out, and significance of all factors is verified; and finally, synthesizing the thermodynamic influence index and the variance analysis coefficient to construct a comprehensive fine-tuning model, and providing a scientific fine-tuning strategy for optimization of characteristic values.

Description

Technical Field

[0001] The invention relates to the technical field of structural design, in particular to a bionic wear-resistant structural design method for a coal mining machine sliding shoe. Background Art

[0002] The shearer skid is a key component that contacts the ground during the operation of the shearer. Its wear resistance is directly related to the working efficiency and service life of the shearer. With the continuous development of coal mining technology, the material and structural design of the shearer skid are also constantly improving. Traditional shearer skids are mostly made of high-strength alloy steel, and their hardness and wear resistance are improved through surface heat treatment. However, this method is limited by the characteristics of the material itself and it is difficult to maintain good wear resistance in a long-term high-intensity working environment. At the same time, the research on bionics has gradually received attention in recent years. Bionics simulates the structure and function of organisms in nature to design engineering structures with excellent performance. For example, the scale structure of some organisms shows extremely high wear resistance and self-cleaning ability, which provides new ideas for the design of shearer skids.

[0003] Although the existing technology has made some progress in the wear resistance of coal mining machine skids, there are still some shortcomings. First, traditional high-strength alloy steel skids are prone to fatigue cracks under high pressure and friction environments, which in turn leads to skid failure. Secondly, most of the existing wear-resistant designs rely on the hardness improvement of the material itself, and fail to fully utilize structural optimization to improve wear resistance; in addition, in the design of the skid, there is a lack of systematic research on the thermodynamic behavior during the wear process, which makes it impossible to accurately predict the service life and wear of the skid;

[0004] The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method for designing a bionic wear-resistant structure of a coal mining machine sliding shoe to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for designing a bionic wear-resistant structure of a shearer sliding shoe, the specific steps comprising:

[0008] Step S1: observing the overlapping polygonal scale structures to obtain observation data of multiple scales, and extracting the horizontal axis length a, the vertical axis length b, the vertex angle θ and the height h in the scale observation data as characteristic value factors;

[0009] Step S2: receiving eigenvalue factors, performing parameter combination design on the horizontal axis length, the vertical axis length and the height among the multiple eigenvalue factors, and using the parameter combination design result for orthogonal test;

[0010] Step S3: obtaining surface data of several shearer boots, taking a specific range of a rectangular block in the central area of ​​the shearer boot surface as an analysis object sample, then modeling the selected analysis object sample, and the model includes a parameter combination design result and a smooth surface with a surface roughness of CRa micrometers, and obtaining a processed physical sample according to the modeling result;

[0011] Step S4: performing an abrasive wear test on the processed physical samples, and during the test, monitoring the thermodynamic parameters of the sample surface in real time to obtain a thermodynamic data set; after the test, measuring the wear amount by using a weight loss method to obtain a wear amount data set;

[0012] Step S5: taking the thermodynamic data set and the wear data set as output variables of the orthogonal test table in step S2, obtaining the output data in the orthogonal test table and performing range analysis to obtain the range analysis coefficient, which is used to determine the priority and optimal combination of the selected characteristic value factors;

[0013] Step S6: Based on the primary and secondary results determined in step S5, the output data is obtained from the orthogonal test table and variance analysis is performed to obtain the variance analysis coefficient. The variance analysis coefficient is used to verify the significance of the selected characteristic value factors on the wear amount and to draw a significance conclusion of each factor;

[0014] Step S7: Acquire the thermodynamic data set, analyze and process it, generate the thermodynamic influence index, and build a comprehensive fine-tuning model by combining the thermodynamic influence index and the variance analysis coefficient. The comprehensive fine-tuning model is used to provide a fine-tuning strategy for the eigenvalue factors.

[0015] Furthermore, the polygonal scale structure is arranged in a hexagonal shape; and the acquisition of the eigenvalue factors specifically includes:

[0016] The horizontal axis length a is the maximum length of the polygonal scale structure in the horizontal direction;

[0017] The longitudinal axis length b is the maximum length of the polygonal scale structure in the vertical direction;

[0018] The vertex angle θ is the angle between the edges on both sides of the vertex of the polygonal scale structure;

[0019] The height h is the vertical distance from the base to the top of the polygonal scale structure;

[0020] Record the extracted eigenvalue factor set {a, b, θ, h} in the data table;

[0021] According to the observation data of multiple scales, the value ranges of the horizontal axis length a, the vertical axis length b and the height h are set as follows: 7mm≤a≤10.5mm, 35mm≤b≤52.5mm, 1.5mm≤h≤2.25mm.

[0022] Furthermore, the data in the orthogonal test table are obtained and range analysis is performed to obtain range analysis coefficients, which are used to determine the priority and optimal combination of the selected characteristic value factors, specifically including:

[0023] Inputting the thermodynamic data set and the wear volume data set into the orthogonal test table used in step S2;

[0024] When the horizontal axis lengths are defined as a1, a2, and a3, the corresponding average values ​​of wear are

[0025] When the longitudinal axis lengths are defined as b1, b2, and b3, the corresponding average values ​​of wear loss are

[0026] When the heights are defined as h1, h2 and h3, the corresponding average values ​​of wear are

[0027] The range of wear amount corresponding to the horizontal axis length is marked as Ra:

[0028]

[0029] The range of wear amount corresponding to the longitudinal axis length is marked as Ra;

[0030]

[0031] The range of height corresponding to wear amount is marked as Rh;

[0032]

[0033] After Ra, Rh, and Rb are standardized, we can obtain Introduce weight functions wa, wb, wh;

[0034] The range analysis coefficient is marked as Jc, and the range analysis coefficient Jc is calculated by combining the standardized range value and the weight function. The calculation formula is as follows:

[0035]

[0036] in,

[0037] Limit the valid value range of Jc to (0,1):

[0038] When Jc is closer to 0, it means that the selected characteristic value factor has less influence on the wear amount, and the corresponding combination parameter has less influence on the wear amount;

[0039] When Jc is closer to 1, it means that the currently selected characteristic value factors have a more significant impact on the wear amount, and these corresponding combination parameters need to be optimized;

[0040] According to the range values ​​of the selected characteristic value factors, the range analysis coefficients are calculated and ranked; Ra>Rh>Rb is initially set;

[0041] According to the ranking of range analysis coefficients, the primary and secondary relationship of the selected characteristic value factors is determined to be a>h>b;

[0042] According to the results of range analysis, the combination with the minimum wear amount and the best thermodynamic parameters is selected as the optimal combination.

[0043] Furthermore, the output data is obtained from the orthogonal test table and variance analysis is performed to obtain the variance analysis coefficient. The variance analysis coefficient is used to verify the significance of the selected characteristic value factors on the wear amount and to draw the significance conclusions of each factor, including:

[0044] The variance analysis coefficient of the horizontal axis length is defined as Fa, and the calculation formula is as follows:

[0045]

[0046] Among them, MSa is the mean square value of the horizontal axis length, and MSe is the mean square value of the error term;

[0047] The variance analysis coefficient of the vertical axis length is defined as Fb, and the calculation formula is as follows:

[0048]

[0049] Among them, MSb is the mean square value of the vertical axis length, and MSe is the mean square value of the error term;

[0050] The variance analysis coefficient of height is defined as Fh, and the calculation formula is as follows:

[0051]

[0052] Among them, MSh is the mean square value of height, and MSe is the mean square value of error term;

[0053] According to the above calculated Fa, Fb, Fh, combined with the F distribution table in the prior art, the critical value Fcritical under a given significance level is determined by consulting the F distribution table;

[0054] Determine the critical value of Fa as Fcritical,a, the critical value of Fb as Fcritical,b, and the critical value of Fh as Fcritical,h:

[0055] like The corresponding factor is considered significant;

[0056] like The corresponding factors are considered insignificant.

[0057] Furthermore, the thermodynamic influence index is defined as Ithermal, and the calculation formula is as follows:

[0058]

[0059] Among them, Tavg, norm and are the normalized average temperature and average temperature gradient, μ1 is a positive constant, 0.01≤μ1≤0.12, which is used to avoid the situation where the numerator is 0, w T and is the weight coefficient,

[0060] Combine the thermodynamic influence index Ithermal with the variance analysis coefficients Fa, Fb and Fh to generate the comprehensive influence coefficient Icombined:

[0061]

[0062] Among them, ra, rb, rh and wthermal are weight coefficients, e is the natural base, μ2 is a positive constant, 0.02≤μ2≤0.56,

[0063] Define the valid value range of Icombined as (0, 1).

[0064] The valid value range of Icombined (0, 1) is divided into the high interval (0.7, 1), the medium interval (0.4, 7] and the low interval (0, 0.4]; at the same time, it is necessary to ensure that the horizontal axis length a, the vertical axis length b and the height h are within the following corresponding value ranges;

[0065] 7mm≤a≤10.5mm, 35mm≤b≤52.5mm, 1.5mm≤h≤2.25mm.

[0066] Compared with the prior art, the beneficial effects of the present invention are as follows: by collecting and analyzing the observation data of the biological scale structure, the key eigenvalue factors of the horizontal axis length, the vertical axis length, the top angle and the height are extracted, and parameter combination design is carried out on this basis; secondly, through orthogonal experiments and variance analysis, the influence degree and the optimal combination of the selected eigenvalue factors on the wear resistance are determined; at the same time, by real-time monitoring of the thermodynamic parameters during the abrasive wear test, a thermodynamic data set is generated, and combined with the wear amount data set, range analysis and variance analysis are carried out to verify the significance of each factor; finally, the thermodynamic influence index and variance analysis coefficient are combined to construct a comprehensive fine-tuning model, which provides a scientific fine-tuning strategy for the optimization of the eigenvalue; compared with the prior art, our invention not only focuses on the improvement of material hardness, but also pays more attention to the research on structural optimization and thermodynamic behavior, thereby improving the wear resistance of the coal mining machine sliding shoe in multiple dimensions, which has significant practical value and innovative significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0068] Figure 2 This is a schematic diagram of the overall structure of the sliding shoe with a polygonal scale structure of the present invention;

[0069] Figure 3 for Figure 2 main view. DETAILED DESCRIPTION

[0070] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0071] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0072] Embodiment 1:

[0073] See also Figure 1 , Figure 2 and Figure 3 , the present invention provides a technical solution:

[0074] A method for designing a bionic wear-resistant structure of a shearer sliding shoe, the specific steps comprising:

[0075] Step S1: observing the overlapping polygonal scale structures to obtain observation data of multiple scales, and extracting the horizontal axis length a, the vertical axis length b, the vertex angle θ and the height h in the scale observation data as characteristic value factors;

[0076] Step S2: receiving eigenvalue factors, performing parameter combination design on the horizontal axis length, the vertical axis length and the height among the multiple eigenvalue factors, and using the parameter combination design result for orthogonal test;

[0077] Step S3: obtaining surface data of several shearer boots, taking a specific range of a rectangular block in the central area of ​​the shearer boot surface as an analysis object sample, then modeling the selected analysis object sample, and the model includes a parameter combination design result and a smooth surface with a surface roughness of CRa micrometers, and obtaining a processed physical sample according to the modeling result;

[0078] Step S4: performing an abrasive wear test on the processed physical samples, and during the test, monitoring the thermodynamic parameters of the sample surface in real time to obtain a thermodynamic data set; after the test, measuring the wear amount by using a weight loss method to obtain a wear amount data set;

[0079] Step S5: taking the thermodynamic data set and the wear data set as output variables of the orthogonal test table in step S2, obtaining the output data in the orthogonal test table and performing range analysis to obtain the range analysis coefficient, which is used to determine the priority and optimal combination of the selected characteristic value factors;

[0080] Step S6: Based on the primary and secondary results determined in step S5, the output data is obtained from the orthogonal test table and variance analysis is performed to obtain the variance analysis coefficient. The variance analysis coefficient is used to verify the significance of the selected characteristic value factors on the wear amount and to draw a significance conclusion of each factor;

[0081] Step S7: Acquire the thermodynamic data set, analyze and process it, generate the thermodynamic influence index, and build a comprehensive fine-tuning model by combining the thermodynamic influence index and the variance analysis coefficient. The comprehensive fine-tuning model is used to provide a fine-tuning strategy for the eigenvalue factors.

[0082] Further explanation, the polygonal scale structure is arranged in a hexagonal shape, and the polygonal scale structure of this embodiment is selected from the belly of the skin of the Elaphe snake;

[0083] Acquisition of scale observation data, including:

[0084] The samples were selected from the abdominal scales of Elaphe snake skin with hexagonal arrangement; the selection criteria were clear scale structure and overlapping polygonal scale characteristics;

[0085] Sample processing: Clean and dry the selected scales to ensure the accuracy of the observed data;

[0086] Use a high-resolution microscope and digital image acquisition system to photograph the scales; ensure image clarity and resolution for subsequent data extraction;

[0087] Image acquisition: Place the scales under a microscope, adjust the focus, and obtain a high-resolution image of the scales; ensure that the hexagonal structure of the scales is clearly visible in the image;

[0088] Use image processing software to process the collected scale images, including denoising, binarization, and edge detection, to ensure clear boundaries of the scale structure;

[0089] The acquisition of characteristic values ​​includes:

[0090] For the horizontal axis length a: In the processed image, calculate the maximum length of each polygonal flake structure in the horizontal direction. Specifically, mark the horizontal boundary points of the polygonal flake structure in the image as (x1, y) and (x2, y), respectively. The calculation formula for the horizontal axis length a is:

[0091] a=|x2-x1|

[0092] For the vertical axis length b: calculate the maximum length of each polygonal flake structure in the vertical direction. Specifically, mark the vertical boundary points of the polygonal flake structure in the image as (x, y1) and (x, y2), and the calculation formula for the vertical axis length b is:

[0093] b=|y2-y1|

[0094] For the vertex angle θ: measure the angle between the edges on both sides of the vertex of the polygonal scale structure to describe the shape of the scale; this angle can be obtained through the angle measurement tool of the image processing software; mark the two edge lengths of the polygonal scale structure at the corresponding vertex in the image as l1 and l2 respectively, and the angle between the two edges is θ, then the calculation formula of the vertex angle θ is:

[0095]

[0096] Where d is the distance between the starting point and the end point of the vertex edge;

[0097] For height h: measure the vertical distance from the base to the top of the polygonal scale structure; equivalent to the thickness or three-dimensional height of the scale, obtained through three-dimensional reconstruction technology or the Z-axis adjustment function of the microscope;

[0098] Record the extracted eigenvalues ​​{a, b, θ, h} in the data table; ensure that the eigenvalues ​​of each sample are complete and accurate;

[0099] Data collation: collate and classify recorded data to facilitate subsequent analysis and use; ensure data integrity and consistency;

[0100] The orthogonal test selected the L9 (3^4) orthogonal experimental table. During the abrasive wear test, according to the observation data of multiple scales, the value ranges of the horizontal axis length a, the vertical axis length b and the height h were set as follows: a is 7mm to 10.5mm, b is 35mm to 52.5mm, and h is 1.5mm to 2.25mm.

[0101] Further explanation, the initial reference value of the vertex angle is set to θ = 90°;

[0102] After selecting the horizontal axis length, vertical axis length and height from the eigenvalue factors, parameter combination design is performed, and the parameter combination design results are used in orthogonal experiments, including:

[0103] The horizontal axis length a, the vertical axis length b and the height h are selected from the received characteristic values ​​as parameters for combined design; the reason for selecting the characteristic values ​​is that these parameters can directly affect the geometric shape of the structure, thereby affecting its wear resistance;

[0104] The value range of the horizontal axis length a is set to a1, a2, and a3 respectively;

[0105] The value range of the vertical axis length b is set to b1, b2, and b3 in sequence;

[0106] The value range of the set height h is h1, h2, and h3 respectively;

[0107] Select a suitable L9(3^4) orthogonal table for experimental design;

[0108] According to the orthogonal table, the design test table is as follows Table 1:

[0109] Table 1

[0110] Test No. Horizontal axis length a Longitudinal axis length b Height Wear loss (W) 1 <![CDATA[a1]]> <![CDATA[b1]]> <![CDATA[h1]]> <![CDATA[W1]]> 2 <![CDATA[a1]]> <![CDATA[b2]]> <![CDATA[h2]]> <![CDATA[W2]]> 3 <![CDATA[a1]]> <![CDATA[b3]]> <![CDATA[h3]]> <![CDATA[W3]]> 4 <![CDATA[a2]]> <![CDATA[b1]]> <![CDATA[h2]]> <![CDATA[W4]]> 5 <![CDATA[a2]]> <![CDATA[b2]]> <![CDATA[h3]]> <![CDATA[W5]]> 6 <![CDATA[a2]]> <![CDATA[b3]]> <![CDATA[h1]]> <![CDATA[W6]]> 7 <![CDATA[a3]]> <![CDATA[b1]]> <![CDATA[h3]]> <![CDATA[W7]]> 8 <![CDATA[a3]]> <![CDATA[b2]]> <![CDATA[h1]]> <![CDATA[W8]]> 9 <![CDATA[a3]]> <![CDATA[b3]]> <![CDATA[h2]]> <![CDATA[W9]]>

[0111] The output variable of the orthogonal test is set to be the wear amount (W); the wear amount is obtained through experimental measurement to evaluate the wear resistance of the structure under various parameter combinations;

[0112] A smooth surface with a surface roughness of CRa = 0.8 μm was selected as the reference group to compare the wear resistance of different parameter combination designs;

[0113] Nine tests were conducted according to the orthogonal table, and each test was performed according to the set parameter combination and processing of the analysis object sample.

[0114] Further explanation, the specific range is: at the center of the surface of the shearer shoe, a cuboid with a size determined according to actual needs is selected as the analysis object sample; in this embodiment, a cuboid within the range of 55mm×100mm×5mm is selected as the analysis object sample;

[0115] Modeling method: 9 sets of surface data were modeled by SolidWorks software, and the modeling content included a smooth surface with a surface roughness of CRa = 0.8 microns. The sample material was 42CrMn, and a metal 3D printer was used for printing to obtain a solid sample.

[0116] It is further explained that the abrasive wear test uses a JMM abrasive wear testing machine;

[0117] Fix the sensor on the surface of the physical specimen or at the relevant position of the testing machine to ensure that the sensor can accurately capture the thermodynamic parameters during the wear process;

[0118] Calibrate the sensor to ensure the accuracy and consistency of its measurement data;

[0119] The wear velocity of the abrasive wear tester was set to v = 0.5 m / s; the load was set to 100 N;

[0120] Start the abrasive wear testing machine and perform wear tests on the physical specimens according to the set wear speed and load;

[0121] Use thermocouples or infrared thermal imaging equipment to monitor the thermodynamic parameters of the sample surface in real time;

[0122] Thermodynamic parameters include average temperature and average temperature gradient parameters. The monitored thermal imaging images and corresponding thermodynamic parameters are transmitted to the data acquisition system in real time to form a thermodynamic data set.

[0123] After reaching the preset wear time or wear amount, stop the testing machine and remove the sample;

[0124] Process and analyze the collected thermodynamic data, use data analysis software to process infrared thermal imaging images, and extract key thermodynamic features, including:

[0125] 1.1) Import thermal imaging images;

[0126] Use data analysis software (such as MATLAB, Python's OpenCV library, etc.) to open the thermal imaging image file and load it into the software;

[0127] 1.2) Perform image filtering;

[0128] Select Gaussian filtering or mean filtering to remove noise from the image;

[0129] The specific operation is to apply filters to smooth the image and reduce high-frequency noise;

[0130] 1.3) Adjust image contrast;

[0131] Adjust the contrast of the image to highlight the temperature distribution. The method in this embodiment is histogram equalization.

[0132] After contrast adjustment, the temperature changes in the image are more obvious, which is convenient for subsequent analysis;

[0133] 1.4) Perform image correction;

[0134] Perform geometric correction to ensure image accuracy, either through affine transformation or perspective transformation;

[0135] Make sure all parts of the image are correctly mapped to their respective locations;

[0136] 1.5) Extract temperature data;

[0137] 1.51) Image Segmentation:

[0138] Divide the preprocessed image into several grids;

[0139] 1.52) Determine the sampling method:

[0140] Uniform division method: the image is evenly divided into fixed grids, and the center point of each grid is used as a sampling point to obtain its temperature value;

[0141] 1.53) Set the sampling point grid:

[0142] In the data analysis software, load the pre-processed thermal imaging image;

[0143] Use the grid tool in the software to divide the entire image area into a uniform grid, which in this embodiment is a 10x10 grid;

[0144] Record the temperature value Ti of each sampling point;

[0145] 1.54) Record and save sampling data:

[0146] The temperature data of all sampling points are recorded and saved in the data acquisition system to form a complete thermodynamic data set;

[0147] Ensure that the recorded data includes the exact location and corresponding temperature value of each sampling point;

[0148] 1.55) Extract temperature data:

[0149] Extract temperature data from each sampling point and generate a corresponding temperature matrix;

[0150] The average temperature is labeled as Tavg and the calculation formula is as follows;

[0151]

[0152] Where N is the number of sampling points, Ti is the temperature of the i-th sampling point;

[0153] Traverse all sampling points and find adjacent pairs of sampling points. In this embodiment, it is assumed that each sampling point has several adjacent points, specifically four adjacent points in the upper, lower, left and right directions of the grid.

[0154] Calculate the temperature gradient of each pair of adjacent sampling points using the following formula:

[0155]

[0156] in, is the temperature gradient between adjacent sampling points i and u, Ti and Tu are the temperature values ​​of adjacent sampling points i and u, respectively, and di,u is the distance between sampling points i and u;

[0157] Add the temperature gradients of all adjacent sampling points to obtain the total temperature gradient;

[0158] Dividing the total temperature gradient by the number of adjacent sampling point pairs yields the following average temperature gradient;

[0159]

[0160] is the average temperature gradient; M is the total number of adjacent sampling point pairs;

[0161] The weight loss method was used to measure the wear volume, and the wear volume data set was obtained, specifically:

[0162] Use anhydrous ethanol to ultrasonically clean several worn solid samples; there are 10 solid samples, and the ultrasonic cleaning time is 10 minutes to ensure that there is no residue on the surface of the sample that affects the weight measurement;

[0163] After drying, the solid sample is weighed using a balance to obtain the weight of the solid sample after wear;

[0164] By recording and comparing the weight before and after wear, the wear volume is calculated to form a wear volume data set.

[0165] The specific operation process is as follows:

[0166] The abrasive used in the abrasive wear test was a mixture of 96.5wt.% 20-40 mesh quartz sand and 3.5wt.% 200 mesh bentonite. The moisture content of the abrasive was controlled within the range of 3-5wt.% by a flow meter. During the test, the indoor temperature was 16-18°C and the air humidity was 40%-45%.

[0167] The core components of the JMM turntable abrasive wear tester include a sample mounting frame (II), a deep loosening shovel (III), a pressure roller (IV), a scraper plate (V), and a turntable (VI); the sample mounting frame has four mounting positions, the interval between adjacent mounting positions is 90°, the distance r between the physical sample mounting position and the turntable main axis is 400mm, the embedding depth D of the physical sample in the abrasive after installation is 80mm, the impact angle between the wear surface and the abrasive is 35°, and the four physical samples are automatically transposed in turn for testing;

[0168] The turntable is driven by a three-phase asynchronous motor with a power of 12.5kW and a rated speed of 1450r / min. The total speed ratio of the secondary reducer is 1 / 36. The turntable speed is controlled by a Siemens MICROMASTEREco inverter. During the test, the turntable drives the abrasive to rotate while the physical sample remains stationary. Therefore, the linear speed of the turntable at r = 400mm is regarded as the relative sliding speed between the abrasive and the wear surface of the physical sample. Based on the previous trial test and the limitations of the JMM abrasive wear tester, a total of 2.35m / s was set, and the corresponding turntable speeds were 56r / min;

[0169] The grinding distance of the physical specimen after one replacement is 803.4m, and one round of grinding is when each specimen is replaced 8 times. The rated grinding distance of each specimen is 4 rounds, that is, the total distance is 25708.8m. During the test, 30% of the abrasive is replaced after each rated grinding distance, and all the abrasive is replaced after 4 rated grinding distances.

[0170] Further explanation: obtain the data in the orthogonal test table and perform range analysis to obtain the range analysis coefficient. The range analysis coefficient is used to determine the priority and optimal combination of the selected characteristic value factors, including:

[0171] According to the L9 (3^4) orthogonal table used in step S2, the thermodynamic data set and the wear data set are input into the orthogonal test table to form the following Table 2:

[0172] Table 2

[0173]

[0174]

[0175] When the horizontal axis lengths are defined as a1, a2, and a3, the corresponding average values ​​of wear are The calculation formula is as follows:

[0176]

[0177] When the longitudinal axis lengths are defined as b1, b2, and b3, the corresponding average values ​​of wear loss are The calculation formula is as follows:

[0178]

[0179] When the heights are defined as h1, h2 and h3, the corresponding average values ​​of wear are The calculation formula is as follows:

[0180]

[0181] The range of wear amount corresponding to the horizontal axis length is marked as Ra:

[0182]

[0183] The range of wear amount corresponding to the longitudinal axis length is marked as Ra;

[0184]

[0185] The range of height corresponding to wear amount is marked as Rh;

[0186]

[0187] After Ra, Rh, and Rb are standardized to eliminate the influence of dimension, we can obtain The calculation formula for standardization is as follows:

[0188]

[0189] The weight functions wa, wb, and wh are introduced; the weights of wa, wb, and wh are determined by expert scoring or historical data, and the value ranges of Ra, Rh, and Rb are all normal numbers:

[0190] The range analysis coefficient is marked as Jc, and the range analysis coefficient Jc is calculated by combining the standardized range value and the weight function. The calculation formula is as follows:

[0191]

[0192] in,

[0193] The valid value range of Jc is (0,1), and other values ​​are not included in the following judgment strategy:

[0194] When Jc is closer to 0, it means that the selected characteristic value factor has less influence on the wear amount, which means that the current setting factors are balanced and the combined parameters have less influence on the wear amount;

[0195] When Jc is closer to 1, it means that the currently selected characteristic value factors have a more significant impact on the wear amount, and these parameters need to be optimized;

[0196] According to the range values ​​of the selected characteristic value factors, the range analysis coefficients are calculated and ranked; Ra>Rh>Rb is initially set;

[0197] According to the ranking of range analysis coefficients, the primary and secondary relationship of the selected characteristic value factors is determined to be a>h>b;

[0198] According to the results of range analysis, the combination with the minimum wear amount and the best thermodynamic parameters is selected as the optimal combination.

[0199] Further explanation: the output data is obtained from the orthogonal test table and variance analysis is performed to obtain the variance analysis coefficient. The variance analysis coefficient is used to verify the significance of the selected characteristic value factors on the wear amount and to draw the significance conclusions of each factor, including:

[0200] Let L be the number of levels of the horizontal axis length a; let M be the number of levels of the vertical axis length b; let N′ be the number of levels of the height h;

[0201] Calculate the sum of the squares of the lengths of the horizontal axis:

[0202]

[0203] in, represents the average wear amount of factor a at level i′, represents the overall average wear amount, It represents the number of trials at the i′th level for the horizontal axis length a;

[0204] Calculate the sum of the squares of the lengths of the vertical axis:

[0205]

[0206] in, represents the number of trials of the vertical axis length b at the jth level, represents the average wear amount of factor b at level j;

[0207] Calculate the sum of the squares of the heights h:

[0208]

[0209] in, represents the number of trials at the kth level of height h, represents the average wear amount of factor h at level k;

[0210] Calculate the sum of squared errors:

[0211]

[0212] Among them, Wi′jk represents the ′ , bj, hk combination, It represents the average wear amount under this combination;

[0213] Calculate the degrees of freedom for the horizontal axis length a:

[0214] DFa=L-1

[0215] Where DFa is the degree of freedom of the horizontal axis length a;

[0216] Calculate the degrees of freedom for the length of the vertical axis b:

[0217] DFb=M-1

[0218] Where DFb is the degree of freedom of the longitudinal axis length b;

[0219] Calculate the degrees of freedom for height h:

[0220] DFh=N′-1

[0221] Where DFh is the degree of freedom of height h;

[0222] The degrees of freedom of the error term is defined as DFe, and the calculation formula is as follows:

[0223] DFe=L×M×N′-LMN′+2

[0224] The mean square value of the horizontal axis length is defined as MSa, and the calculation formula is as follows:

[0225]

[0226] The mean square value of the vertical axis length is defined as MSb, and the calculation formula is as follows:

[0227]

[0228] The mean square value of the height is defined as MSh, and the calculation formula is as follows:

[0229]

[0230] The mean square value of the error term is defined as MSe, and the calculation formula is as follows:

[0231]

[0232] The variance analysis coefficient of the horizontal axis length is defined as Fa, and the calculation formula is as follows:

[0233]

[0234] Among them, MSa is the mean square value of the horizontal axis length, and MSe is the mean square value of the error term;

[0235] The variance analysis coefficient of the vertical axis length is defined as Fb, and the calculation formula is as follows:

[0236]

[0237] Among them, MSb is the mean square value of the vertical axis length, and MSe is the mean square value of the error term;

[0238] The variance analysis coefficient of height is defined as Fh, and the calculation formula is as follows:

[0239]

[0240] Among them, MSh is the mean square value of height, and MSe is the mean square value of error term;

[0241] By consulting the existing F distribution table, determine the critical value Fcritical at a given significance level;

[0242] Determine Fa, Fb, and Fh as the calculated F values, F∈{Fa, Fb, Fh}, and determine the critical value of Fa as Fcritical,a, the critical value of Fb as Fcritical,b, and the critical value of Fh as Fcritical,h:

[0243] like The corresponding factor is considered significant;

[0244] like The corresponding factors are considered insignificant.

[0245] Further explanation, the thermodynamic influence index is defined as Ithermal, and the calculation formula is as follows:

[0246]

[0247] Among them, Tavg, norm and are the normalized average temperature and average temperature gradient, μ1 is a positive constant, 0.01≤μ1≤0.12, which is used to avoid the situation where the numerator is 0, w T and is the weight coefficient, w T and Determined by the expert group through experimental data;

[0248] Combine the thermodynamic influence index Ithermal with the variance analysis coefficients Fa, Fb and Fh to generate the comprehensive influence coefficient Icombined:

[0249]

[0250] Among them, ra, rb, rh and wthermal are weight coefficients, e is the natural base, μ2 is a positive constant, 0.02≤μ2≤0.56, ra, rb, rh and wthermal are determined by the expert group through experimental data;

[0251] The valid value range of Icombined is defined as (0, 1). When the value of Icombined is outside the range of (0, 1), the following fine-tuning strategy is not performed; the fine-tuning strategy is as follows:

[0252] The valid value range of Icombined (0, 1) is divided into the high interval (0.7, 1), the medium interval (0.4, 7] and the low interval (0, 0.4]; at the same time, it is necessary to ensure that the horizontal axis length a, the vertical axis length b and the height h are within the following corresponding value ranges;

[0253] 7mm≤a≤10.5mm, 35mm≤b≤52.5mm, 1.5mm≤h≤2.25mm; the values ​​of a, b, and h can be adaptively adjusted according to the experimental data and application scenarios of the expert group, and will not be elaborated on;

[0254] If the value of Icombined is in the high range (0.7, 1), it means that the current parameter combination has a significant effect on the wear resistance. The eigenvalue factor is fine-tuned locally to achieve the optimization purpose by increasing or decreasing the adjustment amount within 3% of the current eigenvalue factor.

[0255] After fine-tuning, the comprehensive influence coefficient needs to be maintained in the current range; ensuring that the wear resistance continues to be significantly improved;

[0256] Set the judgment threshold T1 of this interval to 0.85;

[0257] When Icombined>T1, the following local fine-tuning is performed on the eigenvalue factors:

[0258] Increase the horizontal axis length a by 2% of the current value;

[0259] The length b of the longitudinal axis remains unchanged;

[0260] Reduce the vertex angle θ by 1% of its current value;

[0261] When Icombined≤T1, the following local fine-tuning is performed on the eigenvalue factors:

[0262] Increase the horizontal axis length a by 1% of the current value;

[0263] The length b of the longitudinal axis remains unchanged;

[0264] Reduce the vertex angle θ by 0.5% of its current value;

[0265] If the value of Icombined is in the medium range (0.4, 7], it means that the current parameter combination has a medium influence on the wear resistance. A small adjustment is made to the characteristic value factor. The wear resistance is adjusted by increasing or decreasing the adjustment amount within 5% of the current characteristic value factor. After adjustment, the comprehensive influence coefficient needs to be gradually increased to the high range to improve the wear resistance.

[0266] Set the judgment threshold T2 of this interval to 0.55;

[0267] When Icombined>T2, the following minor adjustments are made to the eigenvalue factors:

[0268] Increase the horizontal axis length a by 5% of the current value;

[0269] The length b of the longitudinal axis remains unchanged;

[0270] Reduce the vertex angle θ by 3% of its current value;

[0271] When Icombined≤T2, the following minor adjustments are made to the eigenvalue factors:

[0272] Increase the horizontal axis length a by 3% of the current value;

[0273] The length b of the longitudinal axis remains unchanged;

[0274] Reduce the vertex angle θ by 1.5% of its current value;

[0275] If the value of Icombined is in the low range (0, 0.4], it means that the current parameter combination has a low influence on the wear resistance. The characteristic value factor should be adjusted significantly. The wear resistance can be greatly optimized by increasing or decreasing the current characteristic value factor by more than 5%. After adjustment, the comprehensive influence coefficient needs to be gradually increased from the low range to the medium or high range.

[0276] Set the judgment threshold T3 of this interval to 0.2;

[0277] When Icombined>T3, the following major adjustments are made to the eigenvalue factors:

[0278] Increase the horizontal axis length a by 10% of the current value;

[0279] The length b of the longitudinal axis remains unchanged;

[0280] Reduce the vertex angle θ by 5% of its current value;

[0281] When Icombined≤T3, the following major adjustments are made to the eigenvalue factors:

[0282] Increase the horizontal axis length a by 7% of the current value;

[0283] The length b of the longitudinal axis remains unchanged;

[0284] Reduce the vertex angle θ to 3.5% of its current value.

[0285] Embodiment 2:

[0286] Further explanation is given on the basis of Example 1. In this example, the test object is the wear resistance of a composite material, which is used for industrial equipment in a high temperature environment. The purpose of the test is to collect and analyze the observation data of scales with overlapping polygonal scale structures, extract the horizontal axis length, vertical axis length, vertex angle and height as eigenvalue factors, and conduct a series of tests and analyses. Finally, through the analysis and processing of the thermodynamic data set, a thermodynamic influence index is generated, and combined with the variance analysis coefficient, a comprehensive fine-tuning model is constructed to provide a fine-tuning strategy for the eigenvalue factors to optimize the wear resistance of the composite material.

[0287] According to the value of Icombined, the fine-tuning strategy is as follows:

[0288] High range (0.7,1): local fine-tuning is performed within 3%;

[0289] Medium range (0.4, 0.7]: small adjustments within 5%;

[0290] Low range (0,0.4]: make a large adjustment, the adjustment amount is more than 5%;

[0291] The following Table 1 is the specific test data, all data are experimental simulation data within a reasonable range;

[0292] Table 1

[0293]

[0294]

[0295] According to the data in the table, we can see the differences in thermodynamic influence index and comprehensive influence coefficient of different composite materials. Through the above-mentioned fine-tuning strategy, the wear resistance of the composite materials can be further optimized.

[0296] It can be seen from the table that the values ​​of the comprehensive influence coefficient Icombined in different materials are distributed between 0.528 and 0.668, all of which are in the medium range (0.4, 0.7]; this indicates that the influence of the current parameter combination on the wear resistance is moderate, and the wear resistance can be further optimized through appropriate fine-tuning strategies;

[0297] The combined influence coefficient Icombined of material D is 0.633, which is in the medium range. According to the fine-tuning strategy, its wear resistance can be gradually improved by increasing the horizontal axis length a by 5% of the current value (i.e., from 9.5 mm to 9.975 mm) and reducing the vertex angle θ by 3% of the current value (i.e., from 105 degrees to 101.85 degrees).

[0298] Low range (0,0.4]): No material is currently in this range; this indicates that the initial parameter combinations of all current materials already have a certain wear resistance and do not need to be significantly adjusted;

[0299] Through the above-mentioned fine-tuning strategy, it can be expected that the combined influence coefficient Icombined of all materials will gradually increase from the medium range to the high range; the specific technical effects of the fine-tuning strategy are as follows:

[0300] Increasing the horizontal axis length a can improve the wear resistance of the material. For every 5% increase in the horizontal axis length, the comprehensive influence coefficient Icombined increases by an average of 0.02 (±0.005);

[0301] Reducing the vertex angle θ can reduce the amount of material wear. For every 3% reduction in the vertex angle, the comprehensive influence coefficient Icombined increases by an average of 0.03 (±0.01);

[0302] By analyzing the numerical changes of the formula parameters, we can see the interactive changes of the numerical values ​​of the parameters, as follows:

[0303] Tavg, norm and The normalized values ​​of the average temperature and the average temperature gradient have a direct impact on the thermodynamic influence index Ithermal; as Tavg,norm increases, Ithermal also increases; similarly, as increases, Ithermal will also increase; the weight coefficient w of the two T and Determines the weight of its impact;

[0304] Ithermal and Icombined: The thermodynamic influence index Ithermal has a direct impact on the comprehensive influence coefficient Icombined; as Ithermal increases, Icombined will also increase; the weight coefficient wthermal determines the magnitude of its influence;

[0305] Variance analysis coefficients Fa, Fb and Fh: These coefficients are used to verify the significance of the eigenvalue factors on the wear volume; for every 0.05 increase in the variance analysis coefficient, the combined influence coefficient Icombined increases by an average of 0.02 (±0.005).

[0306] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0307] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0308] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0309] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for designing a bionic wear-resistant structure of a shearer sliding shoe, characterized in that: The specific steps include: Step S1: observing a polygonal scale structure having overlaps with each other to obtain observation data of a plurality of scales, and extracting the horizontal axis length a, vertical axis length b, vertex angle θ, and height h from the scale observation data as characteristic value factors; Step S2: receiving eigenvalue factors, performing parameter combination design on the horizontal axis length, vertical axis length and height of the multiple eigenvalue factors, and using the parameter combination design results in an orthogonal test; Step S3: Surface data of several shearer shoes are obtained, and a rectangular parallelepiped in a specific range in the center area of ​​the shearer shoe surface is used as an analysis object sample. Subsequently, a model is created for the selected analysis object sample, and the model includes the parameter combination design results and a smooth surface with a surface roughness of CRa microns. A machined physical sample is obtained based on the modeling results; Step S4: performing abrasive wear tests on the processed physical samples, and during the test, monitoring the thermodynamic parameters of the sample surface in real time to obtain a thermodynamic data set; after the test, measuring the wear volume using the weight loss method to obtain a wear volume data set; Step S5: Using the thermodynamic data set and the wear data set as output variables of the orthogonal test table in step S2, obtaining the output data in the orthogonal test table and performing range analysis to obtain the range analysis coefficient. The range analysis coefficient is used to determine the priority and optimal combination of the selected eigenvalue factors; Step S6: Based on the primary and secondary results determined in step S5, the output data is obtained from the orthogonal test table and variance analysis is performed to obtain the variance analysis coefficient. The variance analysis coefficient is used to verify the significance of the selected characteristic value factors on the wear amount and draw a significance conclusion for each factor; Step S7: Obtain a thermodynamic data set, analyze and process it, generate a thermodynamic influence index, and construct a comprehensive fine-tuning model by combining the thermodynamic influence index and the variance analysis coefficient. The comprehensive fine-tuning model is used to provide a fine-tuning strategy for the eigenvalue factors.

2. The method for designing a bionic wear-resistant structure of a shearer sliding shoe according to claim 1, characterized in that: The polygonal scale structures are arranged in a hexagonal shape; The horizontal axis length a is the maximum length of the polygonal scale structure in the horizontal direction; The longitudinal axis length b is the maximum length of the polygonal scale structure in the vertical direction; The vertex angle θ is the angle between the edges on both sides of the vertex of the polygonal scale structure; The height h is the vertical distance from the base to the top of the polygonal scale structure; Record the extracted eigenvalue factor set {a, b, θ, h} in the data table; According to the observation data of multiple scales, the value ranges of the horizontal axis length a, the vertical axis length b and the height h are set as follows: 7mm≤a≤10.5mm, 35mm≤b≤52.5mm, 1.5mm≤h≤2.25mm.

3. The method for designing a bionic wear-resistant structure of a shearer sliding shoe according to claim 2, characterized in that: Set the initial reference value of the vertex angle to θ = 90°; After selecting the horizontal axis length, vertical axis length and height from the eigenvalue factors, parameter combination design is performed, and the parameter combination design result is used in the orthogonal experiment. The output variable of the orthogonal experiment is set as the wear amount, and the wear amount is marked as W.

4. The method for designing a bionic wear-resistant structure of a shearer sliding shoe according to claim 3, characterized in that: The specific range is defined as follows: At the center of the shearer shoe surface, a rectangular parallelepiped with dimensions determined according to actual needs is selected as the analysis object sample. The selection range of the rectangular parallelepiped is limited to 55mm×100mm×5mm. The modeling content includes a smooth surface with a surface roughness of CRa = 0.8 microns.

5. The method for designing a bionic wear-resistant structure of a shearer sliding shoe according to claim 4, characterized in that: The thermodynamic parameters include average temperature and average temperature gradient parameters, and the monitored thermal imaging images and corresponding thermodynamic parameters are transmitted to the data acquisition system in real time to form a thermodynamic data set; Preprocessing thermal imaging images; Divide the preprocessed image into several grids; The image is evenly divided into fixed grids, and the center point of each grid is used as a sampling point. The number of divided sampling points is N, and the temperature value of the sampling point is obtained; Record the temperature value of the sampling point as Ti, where i is the index of the sampling point; The temperature data of all sampling points are recorded and saved in the data acquisition system to form a complete thermodynamic data set; The average temperature is marked as Tavg, and the calculation formula is as follows; Where N is the number of sampling points, Ti is the temperature of the i-th sampling point; Traverse all sampling points, find adjacent sampling point pairs, and calculate their temperature gradients. Set the total number of adjacent sampling point pairs to M, and set the sampling point adjacent to sampling point i to u, and u∈[1,N],u≠i; The calculation formula used to calculate the temperature gradient between adjacent sampling points is as follows: in, is the temperature gradient between adjacent sampling points i and u, Ti and Tu are the temperature values ​​of adjacent sampling points i and u respectively, and di,u is the distance between adjacent sampling points i and u; The temperature gradients of all adjacent sampling points are added together to obtain the total temperature gradient, and then the total temperature gradient is divided by the number of adjacent sampling point pairs to obtain the average temperature gradient. The wear volume was measured using the weight loss method, and the wear volume data set was obtained, specifically: Several worn solid specimens were ultrasonically cleaned using anhydrous ethanol; After drying, the physical sample is weighed using a balance to obtain the weight of the physical sample after wear; By recording and comparing the weight before and after wear, the wear volume is calculated to form a wear volume data set.

6. The method for designing a bionic wear-resistant structure of a shearer sliding shoe according to claim 5, characterized in that: The data in the orthogonal test table are obtained and range analysis is performed to obtain the range analysis coefficient, which is used to determine the priority and optimal combination of the selected characteristic value factors. Specifically, it includes: Input the thermodynamic data set and the wear volume data set into the orthogonal test table used in step S2; According to the parameter combination design in the orthogonal test table, when the horizontal axis lengths are defined as a1, a2 and a3 respectively, the corresponding average values ​​of wear amount are According to the parameter combination design in the orthogonal test table, when the longitudinal axis lengths are defined as b1, b2 and b3, the corresponding average values ​​of wear are According to the parameter combination design in the orthogonal test table, when the heights are defined as h1, h2 and h3, the corresponding average values ​​of wear are The range of wear amount corresponding to the horizontal axis length is marked as Ra: The range of wear amount corresponding to the longitudinal axis length is marked as Ra; The range of height corresponding to wear amount is marked as Rh; After normalizing Ra, Rh, and Rb, we can obtain Introduce weight functions wa, wb, wh; The range analysis coefficient is marked as Jc. Combining the standardized range value and the weight function, the range analysis coefficient Jc is calculated. The calculation formula is as follows: in, Limit the valid value range of Jc to (0,1): When Jc is closer to 0, it means that the selected characteristic value factors have less influence on the wear amount, and the corresponding combination parameters have less influence on the wear amount; When Jc approaches 1, it means that the currently selected characteristic value factors have a more significant impact on the wear amount, and these corresponding combination parameters need to be optimized; According to the range values ​​of the selected characteristic value factors, the range analysis coefficients are calculated and ranked; Ra>Rh>Rb are initially set; According to the ranking of range analysis coefficients, the primary and secondary relationship of the selected characteristic value factors is determined to be a>h>b; According to the results of range analysis, the combination with the minimum wear amount and the best thermodynamic parameters was selected as the optimal combination.

7. The method for designing a bionic wear-resistant structure of a shearer sliding shoe according to claim 6, characterized in that: The output data is obtained from the orthogonal test table and variance analysis is performed to obtain the variance analysis coefficient. The variance analysis coefficient is used to verify the significance of the selected characteristic value factors on the wear amount and to draw conclusions on the significance of each factor, including: The mean square value of the error term is defined as MSe, and the calculation formula is as follows: DFe is the degrees of freedom of the error term, SSe is the sum of squared errors; The variance analysis coefficient of the horizontal axis length is defined as Fa, and the calculation formula is as follows: Among them, MSa is the mean square value of the horizontal axis length, and MSe is the mean square value of the error term; The variance analysis coefficient of the vertical axis length is defined as Fb, and the calculation formula is as follows: Among them, MSb is the mean square value of the vertical axis length, and MSe is the mean square value of the error term; The variance analysis coefficient of height is defined as Fh, and the calculation formula is as follows: Among them, MSh is the mean square value of height, MSe is the mean square value of error term; According to the orthogonal test data, the critical value of Fa is determined to be Fcritical,a, the critical value of Fb is determined to be Fcritical,b, and the critical value of Fh is determined to be Fcritical,h: like The corresponding factors are considered significant; like The corresponding factors are considered insignificant.

8. The method for designing a bionic wear-resistant structure of a shearer sliding shoe according to claim 7, characterized in that: The thermodynamic influence index is defined as Ithermal, and the calculation formula is as follows: Among them, Tavg, norm and are the normalized average temperature and average temperature gradient, μ1 is a positive constant, 0.01≤μ1≤0.12, used to avoid the numerator being 0, w T and is the weight coefficient, Combine the thermodynamic influence index Ithermal with the variance analysis coefficients Fa, Fb, and Fh to generate the comprehensive influence coefficient Icombined: Among them, ra, rb, rh and wthermal are weight coefficients, e is the natural base, μ2 is a positive constant, 0.02≤μ2≤0.56, Define the valid value range of Icombined as (0, 1), Divide the valid value range of Icombined (0, 1) into the high interval (0.7, 1), the medium interval (0.4, 7], and the low interval (0, 0.4]; at the same time, ensure that the horizontal axis length a, the vertical axis length b, and the height h are within the following corresponding value ranges; 7mm≤a≤10.5mm, 35mm≤b≤52.5mm, 1.5mm≤h≤2.25mm; If the value of Icombined is in the high range (0.7, 1), it means that the current parameter combination has a significant impact on the wear resistance. Local fine-tuning of the eigenvalue factors is performed, specifically by increasing or decreasing the adjustment amount within 3% of the current eigenvalue factors to achieve the optimization purpose. After fine-tuning, the comprehensive influence coefficient needs to be maintained in the current range. Set the judgment threshold T1 of this interval to 0.85; When Icombined>T1, the following local fine-tuning is performed on the eigenvalue factors: Increase the horizontal axis length a by 2% of its current value; the vertical axis length b remains unchanged; decrease the vertex angle θ by 1% of its current value; When Icombined≤T1, the following local fine-tuning is performed on the eigenvalue factors: Increase the horizontal axis length a by 1% of its current value; keep the vertical axis length b unchanged; and decrease the vertex angle θ by 0.5% of its current value. If the value of Icombined is in the medium range (0.4, 7], it means that the current parameter combination has a medium influence on the wear resistance. A small adjustment is made to the characteristic value factors. Specifically, the wear resistance is adjusted by increasing or decreasing the adjustment amount within 5% of the current characteristic value factors. After adjustment, the comprehensive influence coefficient needs to be gradually increased to the high range to improve the wear resistance. Set the judgment threshold T2 of this interval to 0.55; When Icombined>T2, the following minor adjustments are made to the eigenvalue factors: Increase the horizontal axis length a by 5% of its current value; the vertical axis length b remains unchanged; decrease the top angle θ by 3% of its current value; When Icombined≤T2, the following minor adjustments are made to the eigenvalue factors: Increase the horizontal axis length a by 3% of its current value; the vertical axis length b remains unchanged; decrease the top angle θ by 1.5% of its current value; If the value of Icombined is in the low range (0, 0.4], it means that the current parameter combination has a low impact on the wear resistance. The eigenvalue factors should be significantly adjusted. Specifically, the wear resistance can be optimized by increasing or decreasing the current eigenvalue factors by more than 5%. After adjustment, the comprehensive influence coefficient needs to be gradually increased from the low range to the medium or high range. Set the judgment threshold T3 of this interval to 0.2; When Icombined>T3, the following major adjustments are made to the eigenvalue factors: Increase the horizontal axis length a by 10% of its current value; the vertical axis length b remains unchanged; decrease the top angle θ by 5% of its current value; When Icombined≤T3, the following major adjustments are made to the eigenvalue factors: Increase the horizontal axis length a by 7% of the current value; keep the vertical axis length b unchanged; and reduce the vertex angle θ by 3.5% of the current value.

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