Main shaft friction testing device and testing method

Abstract

The invention relates to a spindle friction testing device and method, and the method comprises the following steps: carrying out the positioning of a friction sensitive region of a target spindle based on a stress distribution result, and obtaining the friction sensitive region of the target spindle; performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area; performing texture feature analysis on the friction sensitive area microstructure image to obtain a microstructure texture feature set; carrying out wear mode identification based on the microstructure texture feature set to obtain a wear mode of the target main shaft under the friction effect; and carrying out surface topography evolution analysis on the target main shaft based on the wear mode to obtain a surface wear evolution rule, and formulating a maintenance strategy of the target main shaft based on the surface wear evolution rule, thereby solving the technical problem that there is no effective means to position and analyze a friction sensitive area in the main shaft.

Description

Technical Field

[0001] The invention relates to the technical field of spindles, and in particular to a spindle friction testing device and a testing method. Background Art

[0002] As an indispensable core component of mechanical equipment, the performance of the spindle is directly related to the stability and efficiency of the entire system. In the fields of industrial manufacturing, precision machining, etc., the operating status of the spindle has a decisive influence on product quality. As modern manufacturing develops towards high precision, high speed and high reliability, the workload borne by the spindle is increasing, and the friction and wear problems are becoming more complex and severe. However, traditionally, research on spindle friction has mostly focused on the macro level, and the status of the spindle is evaluated through overall performance testing. This method is difficult to accurately reflect the specific conditions of the internal friction-sensitive areas and cannot provide sufficient basis for targeted maintenance.

[0003] A major problem currently exists is the lack of effective means to locate and analyze friction-sensitive areas inside the spindle, which limits our understanding of the spindle wear mechanism. Although a variety of non-destructive testing techniques and surface analysis methods have been applied to the health monitoring of mechanical components, they often cannot deeply reveal the connection between microstructural changes and macroscopic wear phenomena. This lack of information makes it difficult for engineers to predict the wear trend of the spindle under different working conditions, and it is impossible to develop a scientific and reasonable preventive maintenance plan. Therefore, it is particularly urgent to develop a method that can accurately capture the friction characteristics of the spindle.

[0004] In order to meet the above challenges, a new spindle friction test method is proposed. This method not only focuses on the overall stress condition of the spindle under external torque, but also goes further into the calculation of stress distribution and the identification of friction-sensitive areas. By performing microstructural imaging and texture feature analysis on these areas, we can understand the change patterns of the material surface in more detail and identify specific wear patterns accordingly. Finally, with the help of surface morphology evolution analysis, we can not only understand the wear dynamics of the spindle during long-term use, but also establish a complete maintenance strategy based on this, thereby extending the service life of the spindle and improving the safety and economy of equipment operation. Summary of the invention

[0005] The main purpose of the present invention is to provide a spindle friction testing device and a testing method, which solves the technical problem of lack of effective means to locate and analyze the friction sensitive area inside the spindle.

[0006] To achieve the above object, the present invention provides a spindle friction testing device, comprising: An applying module, used for applying a predetermined torque to a target spindle to obtain a force state of the target spindle; a calculation module, configured to calculate the stress distribution of the target spindle based on the stress state of the target spindle to obtain the stress distribution result of the target spindle, and locate the friction sensitive area of ​​the target spindle based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle; An imaging module, used for performing microstructure imaging of the friction sensitive area to obtain a microstructure image of the friction sensitive area; An analysis module, used for performing texture feature analysis on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set; An identification module, used for performing wear pattern identification based on the microstructure texture feature set to obtain the wear pattern of the target spindle under the action of friction; The evolution module is used to perform surface morphology evolution analysis on the target spindle based on the wear mode, obtain the surface wear evolution law, and formulate a maintenance strategy for the target spindle based on the surface wear evolution law.

[0007] The present invention also provides a spindle friction testing method, comprising the following steps: Applying a predetermined torque to the target spindle to obtain a force state of the target spindle; Performing stress distribution calculation on the target spindle based on the stress state of the target spindle to obtain a stress distribution result of the target spindle, and locating a friction sensitive area of ​​the target spindle based on the stress distribution result to obtain a friction sensitive area of ​​the target spindle; Performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area; Performing texture feature analysis on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set; Wear pattern recognition is performed based on the microstructure texture feature set to obtain the wear pattern of the target spindle under friction; Based on the wear pattern, a surface morphology evolution analysis is performed on the target spindle to obtain a surface wear evolution law, and a maintenance strategy for the target spindle is formulated based on the surface wear evolution law.

[0008] Further, applying a predetermined torque to the target spindle to obtain a stress state of the target spindle includes: Applying a predetermined torque to the target spindle, and measuring the force of the target spindle through a preset force sensor to obtain force data of the target spindle at different positions; Performing vector decomposition on the force data of the target main axis at different positions to obtain force components of the target main axis in different directions; Performing a torque balance analysis on the target spindle based on the force components to obtain a torque balance equation of the target spindle; The torque balance equation is solved to obtain the target spindle force state under the predetermined torque.

[0009] Further, the stress distribution calculation of the target spindle is performed based on the stress state of the target spindle to obtain the stress distribution result of the target spindle, and the friction sensitive area of ​​the target spindle is located based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle, including: Performing equivalent stress calculation on the target principal axis stress state by yield criterion to obtain target principal axis equivalent stress distribution, and performing maximum shear stress analysis on the target principal axis equivalent stress distribution to obtain target principal axis shear stress field distribution; wherein the target principal axis shear stress field distribution includes maximum principal stress, intermediate principal stress and minimum principal stress; Performing strength criterion analysis based on the target spindle shear stress field distribution to obtain a target spindle strength parameter set, and performing failure criterion calculation on the target spindle strength parameter set to obtain a target spindle stress distribution result; wherein the target spindle stress distribution result includes cohesion coefficient, internal friction angle and shear strength value; Perform strain analysis and calculation based on the target spindle stress distribution result to obtain the target spindle strain energy distribution; Performing eigenvalue decomposition on the target spindle strain energy distribution to obtain a target spindle internal stress feature set, and performing friction mechanics analysis based on the target spindle internal stress feature set to obtain a target spindle friction field distribution; wherein the target spindle friction field distribution includes a friction evolution rate, a friction critical value, and a friction accumulation amount; Based on the friction field distribution of the target spindle, a fatigue extension law is calculated for the target spindle to obtain a friction sensitive point of the target spindle; Performing wear equation analysis on the friction sensitive points of the target spindle to obtain the wear rate distribution of the target spindle; Based on the target spindle friction field distribution, the target spindle fatigue sensitive point and the target spindle wear rate distribution, the target spindle friction field distribution is subjected to stress statistical analysis to obtain the friction sensitive area of ​​the target spindle.

[0010] Furthermore, the performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area includes: Scanning the surface morphology of the friction sensitive area by a scanning electron microscope to obtain a regional secondary electron signal, and performing backscattered electron diffraction analysis and microstructure imaging on the regional secondary electron signal to obtain a surface micromorphology feature map; wherein the surface micromorphology feature map includes a grain boundary distribution map, a dislocation density value, and a microcrack direction; Performing atomic force microscopic analysis based on the surface microscopic morphology feature map to obtain surface three-dimensional topographic data, and reconstructing the surface three-dimensional topographic data with nanometer-scale resolution to obtain a microscopic surface contour map; Performing Raman spectroscopy analysis on the microscopic surface profile to obtain material molecular vibration characteristics, and performing Fourier transform infrared spectroscopy processing on the material molecular vibration characteristics to obtain a surface chemical component distribution map; wherein the surface chemical component distribution map includes oxide layer thickness, element content ratio and lattice defect degree; Performing element mapping on the surface chemical component distribution map through X-ray energy spectrum to obtain a component distribution spectrum, and performing electron backscatter diffraction indexing on the component distribution spectrum to obtain a crystal orientation distribution; wherein the crystal orientation distribution includes pole figure data, texture coefficient and orientation density; Based on the crystal orientation distribution, transmission electron microscopy imaging is performed to obtain a microstructure image of the friction sensitive area; wherein the microstructure image of the friction sensitive area includes internal microstructure features, interlayer structure distribution and atomic scale structure features.

[0011] Furthermore, the texture feature analysis is performed on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set, including: Performing Hough-Gabor transformation on the microstructure image of the friction sensitive area to obtain a grain boundary texture feature map; Performing multi-scale decomposition on the grain boundary texture feature map by Morlet wavelet to obtain microcrack morphology feature data, wherein the microcrack morphology feature data includes crack propagation path, dislocation density in stress concentration area and grain boundary stress distribution data; Performing Ridgelet transformation on the microcrack morphology characteristic data to obtain an oxide layer morphology distribution map; Performing Hilbert-Huang decomposition on the oxide layer morphology distribution diagram to obtain an element distribution characteristic spectrum; Performing texture analysis on the element distribution characteristic spectrum by Walsh-Hadamard transformation to obtain a crystal structure characteristic set; The crystal structure feature set is subjected to Zernike moment analysis to obtain a microstructure texture feature set, wherein the microstructure texture feature set includes surface micromorphology evolution characteristics, internal defect distribution laws and material organization structure characteristic quantities.

[0012] Furthermore, the wear pattern recognition is performed based on the microstructure texture feature set to obtain the wear pattern of the target spindle under the friction, including: Performing spectral clustering analysis on the microstructure texture feature set to obtain a surface texture damage area distribution map; The surface texture damage area distribution map is dynamically evolved and calculated by a preset cellular automaton evolution to obtain the surface wear and peeling degree parameter of the target spindle; Performing wavelet packet energy entropy analysis on the surface wear and spalling degree parameters to obtain a wear state feature vector; Performing Lyapunov exponent analysis based on the wear state characteristic vector to obtain wear dynamic evolution characteristics; Based on the dynamic evolution characteristics of wear, the wear mode of the target spindle under friction is obtained; wherein the wear mode includes adhesive wear, abrasive wear and fatigue wear.

[0013] Furthermore, the surface morphology evolution analysis of the target spindle is performed based on the wear mode to obtain the surface wear evolution law, and a corresponding spindle maintenance strategy is formulated based on the surface wear evolution law, including: Based on the wear pattern, the fractal dimension of the surface of the target spindle is calculated to obtain the fractal characteristic value of the spindle surface, and the fractal characteristic value of the spindle surface is analyzed by wavelet packet transform to obtain a surface morphology feature sequence; Calculating the surface wear particle distribution of the target spindle based on the surface morphology feature sequence to obtain a wear particle distribution density map, and performing morphology entropy value analysis on the wear particle distribution density map to obtain surface damage evolution characteristics; The contact stress field of the surface damage evolution characteristics is calculated by Hertz contact theory to obtain local stress distribution data, and the strain energy density of the local stress distribution data is analyzed to obtain surface microscopic deformation characteristics; Performing friction power consumption analysis on the surface micro-deformation characteristics to obtain interface energy distribution data, and calculating the wear rate based on the interface energy distribution data to obtain the surface wear evolution law, wherein the surface wear evolution law includes material removal rate, surface hardening degree and micro-crack growth rate; Based on the surface wear evolution law, life prediction analysis is performed to obtain the remaining life prediction result of the spindle; The maintenance cycle is optimized and calculated for the remaining life prediction result of the spindle to obtain an optimal maintenance time sequence, and a preventive maintenance plan is formulated based on the optimal maintenance time sequence to obtain a spindle maintenance strategy, wherein the spindle maintenance strategy includes an inspection cycle, a lubrication replacement time, and a parts replacement sequence.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The present invention provides a spindle friction test method, comprising the following steps: applying a predetermined torque to a target spindle to obtain a stress state of the target spindle; performing stress distribution calculation on the target spindle based on the stress state of the target spindle to obtain a stress distribution result of the target spindle, and locating a friction sensitive area of ​​the target spindle based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle; performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area; performing texture feature analysis on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set; performing wear mode identification based on the microstructure texture feature set to obtain a wear mode of the target spindle under friction; performing surface morphology evolution analysis on the target spindle based on the wear mode to obtain a surface wear evolution law, and formulating a maintenance strategy for the target spindle based on the surface wear evolution law. Through the above-mentioned technical means, the technical problem of lacking effective means to locate and analyze the friction sensitive area inside the spindle is solved, and it is achieved that different wear modes can be effectively identified through the study of the microstructure texture feature set. This step is crucial for predicting future wear trends of equipment because it helps engineers understand in advance what factors will cause a specific type of wear and thus the beneficial effects of preventive measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a schematic diagram of the steps of a spindle friction testing method in one embodiment of the present invention; Figure 2 is a structural block diagram of a spindle friction testing device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a spindle friction testing method in one embodiment of the present invention; In one embodiment of the present invention, a spindle friction testing method is provided, comprising the following steps: Step S1, applying a predetermined torque to a target spindle to obtain a force state of the target spindle.

[0021] Specifically, in the step of applying a predetermined torque to the target spindle and obtaining the stress state of the target spindle, it is first necessary to clarify that in order to ensure the effectiveness and accuracy of the test, the applied torque should be pre-set based on the maximum or typical load that the spindle may encounter under actual working conditions. This means that before conducting this test, engineers must have a full understanding of the working environment of the spindle, including but not limited to its rotation speed, load-bearing capacity, and the role it plays in the mechanical equipment. For example, in a CNC machine tool, the spindle is responsible for driving the tool to rotate at high speed to complete the cutting task of the workpiece. Therefore, in this application scenario, we can determine a reasonable torque value as the test condition by analyzing historical operating data or referring to the technical parameters provided by the manufacturer. Once the predetermined torque value is determined, the next step is how to stably apply this torque to the target spindle. This usually requires the use of a specially designed loading device, such as a torque loader driven by a servo motor, which can accurately control the output torque and keep it constant to simulate a real operating scenario. During the application process, high-precision sensors are also required to monitor the actual force of the spindle in real time to ensure that the applied torque is consistent with the preset value. These sensors can be installed at different positions of the spindle to fully capture the response changes of the spindle during the entire force process, so as to obtain the accurate force state of the target spindle. After the torque is applied, the next step is to analyze the behavioral characteristics of the target spindle under this specific force state based on the collected data. The "force state" mentioned here not only refers to the simple torque value, but also includes a series of physical phenomena caused by it, such as deformation, vibration characteristics, etc. Through the study of these phenomena, we can have a deeper understanding of the working mechanism of the spindle under different loads, and provide an important basis for the subsequent stress distribution calculation. For example, in the application of CNC machine tools, if a part is found to have abnormally large deformation or vibration when the predetermined torque is applied, it indicates that the area may be a potential friction sensitive point, which is of guiding significance for the subsequent positioning of the friction sensitive area. The whole process is closely connected, forming a complete closed loop from applying the predetermined torque to obtaining the force state of the target spindle, laying a solid foundation for the subsequent stress distribution calculation and wear pattern recognition.

[0022] Step S2, calculating the stress distribution of the target spindle based on the stress state of the target spindle to obtain the stress distribution result of the target spindle, and locating the friction sensitive area of ​​the target spindle based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle.

[0023] Specifically, based on the stress state of the target spindle, the stress distribution of the target spindle is calculated to obtain the stress distribution result of the target spindle, and based on the stress distribution result, the friction sensitive area of ​​the target spindle is located to obtain the friction sensitive area of ​​the target spindle. This process is a key link in the entire spindle friction test method. It not only relies on the stress state information obtained by applying a predetermined torque, but also involves complex data analysis and modeling technology. In the application scenario of CNC machine tools, when the spindle is subjected to a predetermined torque, engineers will use advanced numerical simulation tools such as finite element analysis (FEA) to build a virtual model that accurately reflects the internal structure and material properties of the spindle based on the actual measured force data. This model can simulate the stress response of the spindle at different positions and directions, thereby calculating the detailed stress distribution. For example, in CNC machine tools, since the spindle needs to withstand repeated impacts from the cutting force of the tool, certain specific parts such as near the bearing may have higher stress concentration. Through the stress distribution calculation, we can intuitively see these high stress areas, which provides important clues for the subsequent positioning of friction sensitive areas. Once the stress distribution results of the target spindle are obtained, the next task is to identify which areas are most likely to become friction-sensitive points. The "friction-sensitive areas" mentioned here refer to those places where friction and wear are most likely to occur during operation, and are usually the parts with high stress levels or more drastic stress changes. In order to accurately locate these areas, researchers will combine the stress distribution map with the actual working environment of the spindle, and comprehensively consider the influence of factors such as temperature and lubrication conditions. Taking CNC machine tools as an example, if it is found that a certain bearing area of ​​the spindle shows an abnormally high stress value in the stress distribution calculation, and the area is at high temperature and insufficient lubrication, it can be preliminarily judged that this is an important friction-sensitive area of ​​the spindle. This positioning method not only improves the pertinence of maintenance work, but also effectively prevents the occurrence of potential faults and ensures the safe and stable operation of the equipment. In summary, by performing detailed stress distribution calculations on the stress state of the target spindle after applying a predetermined torque, we can accurately find the friction-sensitive areas inside the spindle. This series of operations not only deepens the understanding of the working mechanism of the spindle, but also lays a solid foundation for subsequent microstructure imaging and wear pattern analysis, and ultimately helps to formulate scientific and reasonable maintenance strategies to ensure the efficient operation of mechanical equipment.

[0024] Step S3, performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area.

[0025] Specifically, the step of performing microstructural imaging of the friction-sensitive area and obtaining the microstructural image of the friction-sensitive area is an important part of the spindle friction test method, which follows the calculation of stress distribution and the positioning of the friction-sensitive area. To achieve this, researchers need to use high-resolution imaging technology to capture the detailed features of these specific areas in order to further analyze their surface conditions and material properties. In the application scenario of CNC machine tools, once the friction-sensitive area on the spindle is determined, for example, an area near a bearing identified by the previous steps shows abnormally high stress values, and the area is under high temperature and insufficient lubrication, the next step is to use advanced microscopy technology, such as scanning electron microscopy (SEM) or atomic force microscopy (AFM), to perform detailed microstructural imaging of these selected friction-sensitive areas. These microscopes can provide nanometer-level resolution, allowing us to not only see the morphological features of the material surface, but also observe the crystal structure, crack propagation path, and distribution of wear particles at the microscopic level. For example, in a specific CNC machine tool case, by performing SEM imaging on a friction-sensitive area, surface scratches and peeling caused by long-term operation can be clearly seen, which provides valuable visual data for subsequent texture feature analysis. In addition, in order to ensure the accuracy and repeatability of the imaging process, operators need to follow strict standard procedures. First, the target spindle is carefully removed from the equipment, and appropriate protective measures are taken to prevent additional surface damage and friction. Then, the sample is fixed on a special sample stage and the various parameters of the microscope are adjusted to obtain the best imaging effect. In this process, some auxiliary tools may also be used, such as coating devices to increase conductivity and thus improve image quality. At the same time, considering that different positions may have different microscopic properties, researchers usually select multiple representative points in each friction-sensitive area for imaging to ensure the comprehensiveness and reliability of the data. By performing such detailed microstructural imaging of the friction-sensitive area, we can obtain first-hand information about the changes on the material surface. These images are not only the basis for understanding the wear mechanism, but also provide an important basis for subsequent texture feature analysis and wear pattern recognition. Ultimately, with the help of these microstructure images, engineers can more deeply explore the wear behavior of the spindle under actual working conditions, and then formulate more scientific and reasonable maintenance strategies to ensure the safe and stable operation of mechanical equipment such as CNC machine tools.

[0026] Step S4, performing texture feature analysis on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set.

[0027] Specifically, the step of analyzing the texture features of the microstructure image of the friction-sensitive area to obtain the microstructure texture feature set is an indispensable part of the entire spindle friction test method, which is immediately after the microstructure imaging. To achieve this goal, researchers need to use advanced image processing and pattern recognition techniques to parse these high-resolution microscopic images to extract key information that can reflect the surface changes of the material. In the application scenario of CNC machine tools, once the microstructure image of the friction-sensitive area is obtained, such as a detailed image of the area near the bearing captured by a scanning electron microscope (SEM) or an atomic force microscope (AFM), the next step is to use professional image analysis software to perform in-depth texture feature analysis on these images. These software usually have a variety of algorithms, such as gray level co-occurrence matrix (GLCM), local binary pattern (LBP), etc., which can be used to quantify the texture characteristics in the image. For example, in a specific CNC machine tool case, by analyzing the SEM image of a friction-sensitive area, we can calculate the parameters such as the roughness, uniformity, contrast, etc. of the area, which together constitute the so-called "microstructure texture feature set". This set not only reflects the morphological characteristics of the material surface, but also reveals the microscopic mechanism of the wear process. In order to ensure the accuracy and reliability of the analysis results, researchers also need to consider the influence of multiple factors. First, it is crucial to choose the right analysis window size, because it determines the scope of the analysis area and whether it can capture enough details. Secondly, since different locations may have different wear levels and texture features, it is necessary to select multiple sample points in each selected friction-sensitive area for analysis, and combine these data to obtain a comprehensive understanding. In addition, considering that changes in environmental conditions such as temperature and humidity may affect the characteristics of the material surface, the influence of these external variables should be controlled as much as possible during the analysis to ensure the consistency of experimental conditions. By performing such a detailed texture feature analysis on the microstructure image of the friction-sensitive area, we can deeply understand the change law of the material surface from the microscopic level, and these feature sets provide a solid foundation for subsequent wear pattern recognition. For example, in CNC machine tools, if a specific texture feature is found to be associated with severe wear phenomena, special attention can be paid to these features in future design and maintenance processes, and corresponding preventive measures can be taken. Ultimately, with the help of this systematic analysis method, engineers can not only more accurately predict the wear trend of the spindle, but also develop more durable and efficient mechanical components to ensure long-term stable operation of the equipment.

[0028] Step S5, performing wear pattern recognition based on the microstructure texture feature set to obtain the wear pattern of the target spindle under the action of friction.

[0029] Specifically, the wear pattern recognition based on the microstructure texture feature set is performed to obtain the wear pattern of the target spindle under friction. This process is one of the core steps in the spindle friction test method. It is based on the previous stress distribution calculation, friction sensitive area positioning, microstructure imaging and texture feature analysis, and aims to reveal the specific wear form experienced by the spindle in actual operation through scientific methods. In the application scenario of CNC machine tools, after completing the texture feature analysis of the microstructure image of the friction sensitive area, we have obtained rich data, which constitute a detailed microstructure texture feature set. In order to identify specific wear patterns, researchers will use advanced data analysis tools such as machine learning algorithms or artificial neural networks. For example, in a specific case, by training the texture feature set of multiple sample points, we can build a classification model that can automatically determine the corresponding wear type based on the input new data. Common wear patterns include abrasive wear, adhesive wear, fatigue wear, etc., and each pattern has its own unique texture feature performance. By comparing the newly collected data with the known wear pattern database, it is possible to accurately identify which type of wear the target spindle has experienced under friction. In order to ensure the accuracy of wear pattern recognition, researchers not only rely on a single texture feature parameter, but also consider other related factors, such as material properties, working environment conditions (temperature, humidity, lubrication status), etc. For example, in a CNC machine tool, if a bearing area shows abnormally high roughness and uniformity degradation, and the area is at high temperature and insufficient lubrication, it can be preliminarily inferred that more serious abrasive wear has occurred here. Further, by combining these texture features with other physical parameters, we can more accurately confirm the wear pattern and deeply understand the mechanism behind it. This multi-dimensional data fusion method improves the reliability of the recognition results, allowing engineers to take more targeted maintenance measures. In addition, the results of wear pattern recognition are crucial for formulating subsequent surface morphology evolution analysis strategies. Once the wear pattern is identified, the wear trend of the spindle in the future can be predicted, providing a scientific basis for formulating a reasonable maintenance plan. For example, in a CNC machine tool, if a part of the spindle is found to have early signs of adhesive wear, the wear process can be delayed by adjusting the lubrication conditions or optimizing the processing parameters, thereby extending the service life of the equipment and improving production efficiency. In summary, by performing wear pattern recognition on the microstructure texture feature set, we can not only gain a deep understanding of the behavioral characteristics of the spindle under friction, but also provide strong support for preventive maintenance and optimized design.

[0030] Step S6, performing surface morphology evolution analysis on the target spindle based on the wear mode to obtain a surface wear evolution law, and formulating a maintenance strategy for the target spindle based on the surface wear evolution law.

[0031] Specifically, the surface morphology evolution analysis of the target spindle is performed based on the wear pattern to obtain the surface wear evolution law, and the maintenance strategy of the target spindle is formulated based on the surface wear evolution law. This process is the last crucial step in the entire spindle friction test method. It is directly related to how to convert the data obtained in all the previous steps into preventive maintenance measures in practical applications. In the application scenario of CNC machine tools, after the specific wear pattern is identified through texture feature analysis and machine learning algorithms, the next task is to conduct in-depth surface morphology evolution analysis of these wear patterns. This requires combining long-term monitoring data and simulation experiment results to build a model that can predict the change of the spindle surface over time. For example, in a CNC machine tool, if a part of the spindle is found to have experienced severe abrasive wear, the finite element analysis (FEA) software can be used to simulate the stress distribution and material removal of the area under different working conditions, and then derive the law of its surface morphology evolution over time. This evolution law not only describes the change trend of physical parameters such as surface roughness and microstructure, but also reveals the possibility and time point of potential failure. In order to ensure the accuracy of the surface morphology evolution analysis, researchers will comprehensively consider the influence of multiple factors, such as workload, ambient temperature, lubrication conditions, etc. Taking CNC machine tools as an example, if a bearing area shows early signs of adhesive wear under high temperature and insufficient lubrication, the working environment can be improved by adjusting the lubricant type or adding a cooling system, while monitoring the impact of these changes on the evolution of surface morphology. By comparing and analyzing the effects of different interventions, the most optimized maintenance plan can be found. In addition, accelerated life tests can be introduced, that is, extreme working conditions are simulated under laboratory conditions to quickly obtain the long-term behavior characteristics of the spindle under specific wear modes, providing support for the formulation of more accurate maintenance strategies. Finally, based on the surface wear evolution law, engineers can develop a scientific and reasonable maintenance strategy. This strategy is not only to deal with existing problems, but more importantly to prevent possible future failures. For example, in CNC machine tools, if it is known from evolution analysis that a part of the spindle will enter the rapid wear stage after a specific use cycle, regular inspections and necessary maintenance work can be arranged before this time point, such as replacing worn parts or adjusting processing parameters. In this way, not only can the service life of the spindle be extended, but also unplanned downtime can be reduced and production efficiency can be improved. In short, by conducting a detailed surface morphology evolution analysis of the wear mode, we can not only accurately predict the future wear trend of the spindle, but also provide a solid theoretical and technical guarantee for achieving efficient and reliable equipment maintenance.

[0032] In a specific embodiment, applying a predetermined torque to the target spindle to obtain a force state of the target spindle includes: Applying a predetermined torque to the target spindle, and measuring the force of the target spindle through a preset force sensor to obtain force data of the target spindle at different positions; Performing vector decomposition on the force data of the target main axis at different positions to obtain force components of the target main axis in different directions; Performing a torque balance analysis on the target spindle based on the force components to obtain a torque balance equation of the target spindle; The torque balance equation is solved to obtain the target spindle force state under the predetermined torque.

[0033] Specifically, in the process of applying a predetermined torque to the target spindle and analyzing its stress state, the entire process closely revolves around precise force measurement, vector decomposition, torque balance analysis, and final equation solution. This series of steps is designed to ensure that the mechanical behavior of the spindle under working conditions can be accurately evaluated and understood, thereby providing a scientific basis for optimizing design, improving processes, or fault diagnosis. First, the application of a predetermined torque to the target spindle is completed by a specific loading device. In this process, in order to obtain detailed force information, high-precision force sensors need to be pre-installed to monitor the force conditions at different positions in real time. Taking the spindle in a CNC machine tool as an example, when engineers want to understand the specific performance of the spindle when subjected to external loads, they use hydraulic or electric loading devices to apply a torque of known magnitude and direction to the spindle. At the same time, multiple force sensors are installed at key parts of the spindle, which can accurately capture the stress changes caused by external forces and transmit these data to the control system for recording and processing. For example, on a CNC machine tool, if you want to test the load-bearing capacity of the area near the spindle bearing, then arrange several force sensors around the area to ensure that every position that may be affected is covered. This not only obtains comprehensive data support, but also provides a solid foundation for subsequent mechanical analysis. Next, in order to further analyze these force data, it is necessary to vector decompose them. This is because in practical applications, the force is often multi-directional, and the data directly read from the sensor only reflects the resultant force. Through the method of vector decomposition, the complex three-dimensional force value can be decomposed into components along the directions of each coordinate axis, that is, the force components of the target spindle in different directions are obtained. Continuing with the above-mentioned CNC machine tool as an example, assuming that the spindle is affected by the torque in the radial and axial directions during the processing, then by vector decomposing the total force data collected, we can calculate the specific force conditions in these two directions respectively. This approach helps to more clearly understand the real load distribution of each part of the spindle and prepare for the next step of moment balance analysis. Subsequently, based on the force components that have been obtained, the researchers will use the principles of mechanics to perform a moment balance analysis on the target spindle. The core of this step is to establish a mathematical model that can describe the interaction between all internal and external forces - the torque balance equation. For CNC machine tools, this means considering a variety of factors including but not limited to motor drive torque, cutting torque, friction torque and other factors. By comprehensively considering the influence of these factors, a set of equations containing all relevant variables is constructed. For example, when analyzing the torque balance of the spindle system of a CNC machine tool, it is necessary to consider not only the reaction torque from the workpiece, but also the resistance torque provided by the lubrication system and the additional torque brought by other auxiliary components. In this way, the torque balance equation is not just a simple formula expression, but covers a complex network of mechanical interactions within the entire system.Finally, once the torque balance equation is established, it is necessary to solve it to determine the exact stress state of the target spindle under the predetermined torque. The solution method can select appropriate mathematical tools according to the characteristics of the equation, such as linear algebra, numerical analysis, etc. Taking the spindle of a CNC machine tool as an example, if a set of torque balance equations for the force components of the spindle at different positions and directions has been obtained through the previous steps, then the best solution that satisfies these equations can be found by matrix operations or iterative algorithms. This solution will give the stress distribution, deformation degree and other key mechanical parameters of the spindle under the predetermined torque. Such results are of great significance for predicting the working performance of the spindle, evaluating its safety and guiding maintenance. For example, if the solution results show that a certain section of the spindle may be at risk of excessive wear or fatigue damage after long-term operation, preventive measures can be taken in daily maintenance, such as adjusting working parameters, replacing wearing parts or strengthening lubrication, etc., to avoid potential problems from turning into actual failures, thereby ensuring the stability of the production process and product quality. In summary, the process of applying a predetermined torque to the target spindle and analyzing its stress state is actually a practical way to combine theoretical mechanics with modern sensing technology. It not only helps us to deeply understand the mechanical mechanism inside the mechanical system, but also provides effective solutions to problems encountered in engineering practice. Through the above steps, whether it is for CNC machine tools or other similar application scenarios, a complete mechanical analysis framework can be established, which provides strong support for improving equipment performance, extending service life and improving work efficiency.

[0034] In a specific embodiment, the step of calculating the stress distribution of the target spindle based on the stress state of the target spindle to obtain the stress distribution result of the target spindle, and locating the friction sensitive area of ​​the target spindle based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle includes: Performing equivalent stress calculation on the target principal axis stress state by yield criterion to obtain target principal axis equivalent stress distribution, and performing maximum shear stress analysis on the target principal axis equivalent stress distribution to obtain target principal axis shear stress field distribution; wherein the target principal axis shear stress field distribution includes maximum principal stress, intermediate principal stress and minimum principal stress; Performing strength criterion analysis based on the target spindle shear stress field distribution to obtain a target spindle strength parameter set, and performing failure criterion calculation on the target spindle strength parameter set to obtain a target spindle stress distribution result; wherein the target spindle stress distribution result includes cohesion coefficient, internal friction angle and shear strength value; Perform strain analysis and calculation based on the target spindle stress distribution result to obtain the target spindle strain energy distribution; Performing eigenvalue decomposition on the target spindle strain energy distribution to obtain a target spindle internal stress feature set, and performing friction mechanics analysis based on the target spindle internal stress feature set to obtain a target spindle friction field distribution; wherein the target spindle friction field distribution includes a friction evolution rate, a friction critical value, and a friction accumulation amount; Based on the friction field distribution of the target spindle, a fatigue extension law is calculated for the target spindle to obtain a friction sensitive point of the target spindle; Performing wear equation analysis on the friction sensitive points of the target spindle to obtain the wear rate distribution of the target spindle; Based on the target spindle friction field distribution, the target spindle fatigue sensitive point and the target spindle wear rate distribution, the target spindle friction field distribution is subjected to stress statistical analysis to obtain the friction sensitive area of ​​the target spindle.

[0035] Specifically, the process of calculating the stress distribution of the target spindle based on the stress state of the target spindle, obtaining the stress distribution result of the target spindle, and locating the friction sensitive area of ​​the target spindle based on the stress distribution result is one of the core steps of the entire spindle friction test method. It not only relies on the stress state information of the target spindle obtained after applying a predetermined torque in the early stage, but also needs to reveal the response characteristics of the internal structure of the spindle under different load conditions through a series of complex mechanical analysis and numerical simulation. First, in order to achieve a comprehensive understanding of the stress state of the target spindle, the researchers used the yield criterion to calculate the equivalent stress of the target spindle stress state to obtain the equivalent stress distribution of the target spindle. The yield criterion here refers to the physical law followed when the material begins to undergo plastic deformation, such as the Von Mises yield criterion or the Tresca yield criterion. By applying these criteria, the complex and changeable three-dimensional stress state can be simplified to a scalar value-equivalent stress, which helps to more intuitively evaluate the actual pressure on each part of the spindle. Next, in order to further understand the specific stress conditions inside the spindle, especially those key parts that may cause material failure, it is necessary to perform a maximum shear stress analysis on the obtained equivalent stress distribution to obtain the target spindle shear stress field distribution. This shear stress field distribution covers information in three dimensions: maximum principal stress, intermediate principal stress, and minimum principal stress, which together describe the stress levels of the spindle in all directions under stress. For example, in a CNC machine tool, if an abnormally high maximum principal stress value is found in the vicinity of a bearing, this may indicate that there is a high risk of wear here and special attention is required. Subsequently, a strength criterion analysis is performed based on the above target spindle shear stress field distribution to obtain the target spindle strength parameter set. The strength criterion here refers to a series of criteria used to determine whether a material will fail under given stress conditions, such as the Rankine criterion, the Mohr-Coulomb criterion, etc. By applying these criteria, key parameters such as the cohesion coefficient, the internal friction angle, and the shear strength value can be quantified, which constitute part of the target spindle stress distribution results. These parameters are crucial for evaluating the bearing capacity of the spindle under extreme conditions, and can also help identify which areas are most likely to become potential failure points. Continuing with the example of CNC machine tools, if a spindle component exhibits a low cohesion coefficient under high-intensity processing tasks, it means that the material bonding in this area is weak and prone to crack propagation or other forms of damage, which has important guiding significance for the formulation of subsequent maintenance strategies. Next, strain analysis and calculation are performed based on the target spindle stress distribution results to obtain the target spindle strain energy distribution. Strain refers to the degree of deformation of a material under the action of an external force, and strain energy is the energy stored in this process.By analyzing the strain conditions and corresponding energy accumulation at various locations inside the spindle, we can gain a deep understanding of the behavior characteristics of the material at the microscopic level, especially those areas under high stress. This analysis is of great significance for predicting the long-term performance changes of materials because it is directly related to the aging and fatigue of materials. For example, in a CNC machine tool, if a large amount of strain energy is detected to be accumulated in a certain spindle area under specific working conditions, it indicates that the area may experience faster wear or fracture in the future, and measures need to be taken in advance to prevent it. Further, the strain energy distribution of the target spindle is subjected to eigenvalue decomposition to obtain the target spindle internal stress feature set, and friction mechanics analysis is performed based on the target spindle internal stress feature set to obtain the target spindle friction field distribution. Eigenvalue decomposition is a mathematical tool that can extract the most representative components, namely eigenvectors and eigenvalues, from complex matrix data. When applied to spindle analysis, it can help us identify the directions or positions that contribute the most to the strain energy, thereby forming an internal stress feature set. Then, through friction mechanics analysis, the friction evolution rate, friction critical value and friction accumulation of these feature points in actual use can be evaluated. The friction field distribution provides us with a panoramic view of the friction behavior of the material surface, allowing engineers to more accurately predict the health of the spindle in long-term operation. For example, in a CNC machine tool, if it is found that the accumulated friction of a part of the spindle shows an accelerating upward trend with the increase of working time, then this means that the material in this area is rapidly deteriorating and must be repaired or replaced as soon as possible. Finally, the fatigue extension law of the target spindle is calculated based on the friction field distribution of the target spindle to obtain the friction sensitive points of the target spindle. Fatigue refers to the phenomenon that the material gradually produces tiny cracks and eventually breaks under repeated alternating loads. Through the study of the friction field distribution, it is possible to determine which parts are most susceptible to fatigue, namely the so-called friction sensitive points. Then, using the Paris formula or other similar fatigue extension law models, the speed at which cracks grow over time at these sensitive points can be quantitatively described, that is, the wear rate distribution. The wear rate distribution reflects the process of the spindle surface material being gradually removed under the action of friction, and is one of the important indicators for measuring its service life. For example, in a CNC machine tool, if a friction-sensitive point near a spindle bearing is found to show a high wear rate, this may be due to the fact that the area is often subjected to large alternating stresses, suggesting that we need to optimize lubrication conditions or adjust processing parameters to slow down this wear. In summary, the entire process is a progressive analysis chain from macroscopic stress state to microscopic structural characteristics, and each step is closely linked, aiming to reveal the internal stress distribution law and its relationship with the external working environment through a comprehensive and detailed study of the target spindle.Finally, by performing stress statistical analysis on the target spindle friction field distribution, the target spindle friction sensitive points and the target spindle wear rate distribution, we can lock in those areas where stress concentration is most significant, that is, the friction sensitive areas of the target spindle. These areas are often where spindle problems are most likely to occur. Therefore, in this way, engineers can obtain the valuable information needed to formulate scientific and reasonable maintenance strategies to ensure the safe and stable operation of mechanical equipment such as CNC machine tools, extend equipment life and improve production efficiency. For example, in a CNC machine tool, suppose that after the above series of analyses, it is found that the area near a certain bearing is a friction sensitive area, and the area shows a high friction accumulation and wear rate. This means that this area may become a source of failure in the future, so it can be given priority in the maintenance plan to conduct regular inspections of this area, increase the frequency of lubrication or replace related parts. In this way, not only can potential problems be prevented, but also unplanned downtime can be effectively reduced to ensure production continuity and product quality.

[0036] In a specific embodiment, the performing microstructure imaging on the friction sensitive area to obtain the microstructure image of the friction sensitive area includes: Scanning the surface morphology of the friction sensitive area by a scanning electron microscope to obtain a regional secondary electron signal, and performing backscattered electron diffraction analysis and microstructure imaging on the regional secondary electron signal to obtain a surface micromorphology feature map; wherein the surface micromorphology feature map includes a grain boundary distribution map, a dislocation density value, and a microcrack direction; Performing atomic force microscopic analysis based on the surface microscopic morphology feature map to obtain surface three-dimensional topographic data, and reconstructing the surface three-dimensional topographic data with nanometer-scale resolution to obtain a microscopic surface contour map; Performing Raman spectroscopy analysis on the microscopic surface profile to obtain material molecular vibration characteristics, and performing Fourier transform infrared spectroscopy processing on the material molecular vibration characteristics to obtain a surface chemical component distribution map; wherein the surface chemical component distribution map includes oxide layer thickness, element content ratio and lattice defect degree; Performing element mapping on the surface chemical component distribution map through X-ray energy spectrum to obtain a component distribution spectrum, and performing electron backscatter diffraction indexing on the component distribution spectrum to obtain a crystal orientation distribution; wherein the crystal orientation distribution includes pole figure data, texture coefficient and orientation density; Based on the crystal orientation distribution, transmission electron microscopy imaging is performed to obtain a microstructure image of the friction sensitive area; wherein the microstructure image of the friction sensitive area includes internal microstructure features, interlayer structure distribution and atomic scale structure features.

[0037] Specifically, the process of performing microstructural imaging on the friction-sensitive area and obtaining the microstructural image of the friction-sensitive area is an indispensable part of the spindle friction test method. It aims to reveal the subtle structural changes inside these specific areas through a series of high-resolution microscopic imaging techniques and in-depth analysis methods. This process not only relies on advanced microscopy technology, but also requires the combination of multiple physical and chemical analysis methods to obtain comprehensive and accurate data. First, when performing microstructural imaging on the friction-sensitive area, researchers use a scanning electron microscope (SEM) to scan the surface morphology. SEM can provide nanometer-level resolution, allowing us to observe the detailed features of the material surface at extremely high magnifications. When scanning a friction-sensitive area in a CNC machine tool, the SEM bombards the sample surface by emitting a focused electron beam to excite secondary electron signals. After these secondary electron signals are captured by the detector, not only a detailed surface microscopic morphological feature map can be generated, but also backscattered electron diffraction analysis and microstructural imaging can be further performed. For example, in a CNC machine tool, if an area near a bearing showing signs of abnormal wear is selected as the research object, then by performing SEM scanning and backscattered electron diffraction analysis on the area, we can clearly see the distribution of grain boundaries, dislocation density values, and the specific direction of microcracks. These microstructural features are crucial to understanding the behavior of materials under actual working conditions, as they are directly related to the mechanical properties and wear resistance of the materials. Next, based on the obtained surface micromorphological feature map, atomic force microscopy (AFM) is further used for deeper analysis. AFM is a technology that can measure the three-dimensional topographic data of the sample surface. The tip of the probe is in contact with the sample surface or slightly vibrates, thereby recording the height change information. By reconstructing these height data with nanoscale resolution, an accurate microscopic surface profile can be obtained. Continuing with the example of CNC machine tools, assuming that some special micromorphological features are found in a friction-sensitive area, such as a high dislocation density or a complex microcrack network, then using AFM to map them in detail can provide us with an intuitive view of the three-dimensional morphology of these features. This microscopic surface profile not only shows the surface undulations, but also reveals subtle differences that are imperceptible to the naked eye, laying the foundation for subsequent molecular vibration feature analysis. Then, in order to gain a deeper understanding of the molecular-level properties of the material, the resulting microscopic surface profile needs to be subjected to Raman spectroscopy analysis. Raman spectroscopy is a non-destructive optical analysis method that can reflect the vibration modes of molecules inside the material by detecting changes in frequency in scattered light. By performing Raman spectroscopy scanning on the friction-sensitive area, the chemical bonding state between different components and their relative proportions can be identified. Furthermore, these molecular vibration feature data are processed by Fourier transform infrared spectroscopy (FTIR) to obtain a more detailed distribution map of surface chemical components.This distribution map includes important parameters such as oxide layer thickness, element content ratio and lattice defect. For example, in CNC machine tools, if a thick oxide layer is found on a part of the spindle surface, it may indicate that the area has been in a high temperature environment for a long time, causing oxidation reaction of the material. Through FTIR analysis, we can accurately measure the specific thickness of the oxide layer and evaluate its impact on material performance. In addition, in order to have a more comprehensive understanding of the chemical composition of the friction-sensitive area, the surface chemical component distribution map is also elementally mapped by X-ray energy spectroscopy (EDS). EDS is a method to determine the type and concentration of elements in a sample based on the energy difference of X-rays. By performing EDS scanning on the friction-sensitive area, a component distribution spectrum can be generated, which not only identifies the position distribution of various elements, but also reflects the relative content relationship between them. On this basis, the component distribution spectrum is indexed by electron backscatter diffraction (EBSD) to obtain crystal orientation distribution information. The crystal orientation distribution mentioned here includes pole figure data, texture coefficient and orientation density, which together describe the directionality and arrangement law of the crystal structure inside the material. For example, in CNC machine tools, if it is found that a part of the spindle material shows obvious texture effect, that is, some crystal orientations are particularly concentrated, this may be due to the external force applied during the processing, which affects the mechanical properties and fatigue resistance of the material. Finally, based on the above crystal orientation distribution information, transmission electron microscopy (TEM) is used for the final imaging step to obtain a complete microstructure image of the friction-sensitive area. TEM has a higher resolution and can analyze the internal microstructure characteristics, interlayer structure distribution and atomic scale structure characteristics of the material at the sub-nanometer level. For example, in CNC machine tools, through TEM imaging, we can observe the grain boundaries, phase interfaces and any defects or impurity particles inside the material, which are key factors in determining the performance of the material. With the help of high-resolution images provided by TEM, engineers can gain a deep understanding of the behavior of friction-sensitive areas at the microscopic level, providing a scientific basis for further optimizing the design and improving the manufacturing process. In summary, the entire microstructure imaging process is a systematic process from surface morphology scanning to deep chemical analysis to fine structure analysis. Each step is closely linked and interlocking to ensure the integrity and accuracy of the data. In this way, we can not only obtain the most detailed information about friction-sensitive areas, but also provide a solid foundation for subsequent texture feature analysis, wear pattern recognition and other steps, which will ultimately help to formulate scientific and reasonable maintenance strategies, ensure the safe and stable operation of mechanical equipment such as CNC machine tools, extend equipment life and improve production efficiency. For example, in a CNC machine tool, suppose that after the above series of analyses, it is found that the microstructure of the area near a certain bearing has more microcracks and a higher dislocation density, and a certain thickness of oxide layer is formed on the surface.These microstructural features suggest that this area may have experienced severe wear and high temperature. Therefore, in the maintenance plan, regular inspections of this area, increased lubrication frequency, or replacement of related parts can be given priority to prevent potential problems from turning into actual failures, thereby ensuring production continuity and product quality.

[0038] In a specific embodiment, the texture feature analysis of the microstructure image of the friction sensitive area to obtain a microstructure texture feature set includes: Performing Hough-Gabor transformation on the microstructure image of the friction sensitive area to obtain a grain boundary texture feature map; Performing multi-scale decomposition on the grain boundary texture feature map by Morlet wavelet to obtain microcrack morphology feature data, wherein the microcrack morphology feature data includes crack propagation path, dislocation density in stress concentration area and grain boundary stress distribution data; Performing Ridgelet transformation on the microcrack morphology characteristic data to obtain an oxide layer morphology distribution map; Performing Hilbert-Huang decomposition on the oxide layer morphology distribution diagram to obtain an element distribution characteristic spectrum; Performing texture analysis on the element distribution characteristic spectrum by Walsh-Hadamard transformation to obtain a crystal structure characteristic set; The crystal structure feature set is subjected to Zernike moment analysis to obtain a microstructure texture feature set, wherein the microstructure texture feature set includes surface micromorphology evolution characteristics, internal defect distribution laws and material organization structure characteristic quantities.

[0039] Specifically, the process of performing texture feature analysis on the microstructure image of the friction-sensitive area to obtain a microstructure texture feature set is a key step based on the high-resolution images obtained in the early stage through a series of microscopic imaging technologies. This process not only involves a variety of advanced algorithms in the fields of mathematical transformation and pattern recognition, but also combines professional knowledge in materials science such as crystal structure and defect analysis, aiming to extract quantitative information that can reflect the changes in the physical and chemical properties of the material from complex microstructure images. First, when performing texture feature analysis, researchers will use Hough-Gabor transform to process the microstructure image of the friction-sensitive area. Hough-Gabor transform is a method that combines Hough transform for detecting straight line or curve features with Gabor filter for capturing texture characteristics. By applying this transform to the microstructure image of the friction-sensitive area, the distribution of grain boundaries can be effectively highlighted and a grain boundary texture feature map can be generated. For example, in the friction-sensitive area of ​​the spindle of a CNC machine tool, if the grain boundaries in certain specific directions are found to be abnormally active, it may imply that the area has experienced non-uniform stress, resulting in a change in grain orientation. At this point, the grain boundary texture feature map obtained by Hough-Gabor transform can clearly reveal the orientation of these grain boundaries and their density distribution, providing an important reference for the subsequent microcrack morphological feature data analysis. Next, in order to further explore the development of microcracks near the grain boundaries, it is necessary to perform multi-scale decomposition on the grain boundary texture feature map. The Morlet wavelet transform is used here, which is a wavelet type that is very suitable for time-frequency domain analysis. The Morlet wavelet can not only accurately locate the position of microcracks, but also simultaneously describe their morphological characteristics at different scales. Specifically, after processing the grain boundary texture feature map by Morlet wavelet, a set of microcrack morphological feature data including crack propagation path, dislocation density in stress concentration area, and grain boundary stress distribution data can be obtained. Continuing with the above-mentioned CNC machine tool spindle as an example, assuming that the formation and development of microcracks are observed in a friction-sensitive area, the use of Morlet wavelet transform can help us accurately measure the length, width and curvature radius of each microcrack, and evaluate whether there is a high dislocation density or abnormal stress distribution around them, which are important factors affecting crack propagation behavior. Then, based on the obtained microcrack morphological feature data, Ridgelet transform is performed to generate an oxide layer morphology distribution map. Ridgelet transform is mainly used to enhance linear structural features in the image, such as crack edges or grain boundary lines. When applied to microcrack morphological feature data, it can more clearly show the distribution of the oxide layer along the crack path or other weak points. For example, the oxide layer formed on the surface of the CNC machine tool spindle often has a sudden change in thickness at the crack tip, which may be due to the accelerated oxidation reaction rate in the local high temperature environment.The oxide layer morphology distribution map obtained by Ridgelet transform can not only intuitively show the specific distribution form of the oxide layer, but also help us understand how the oxidation process affects the crack propagation path and speed. In addition, in order to deeply analyze the distribution characteristics of the elements inside the oxide layer, it is also necessary to perform Hilbert-Huang decomposition on the oxide layer morphology distribution map. This is a method suitable for non-stationary signal processing. It can decompose complex image data into several intrinsic mode functions (IMFs), each of which represents an oscillating component in the original data. By performing Hilbert-Huang decomposition on the oxide layer morphology distribution map, a series of element distribution characteristic spectra can be obtained. These characteristic spectra reflect the concentration change trend and spatial distribution law of different elements in the oxide layer. For example, in the oxide layer on the surface of the spindle of a CNC machine tool, the content of elements such as oxygen and iron may show periodic fluctuations with the change of depth. This fluctuation pattern is of great significance for studying the growth mechanism of the oxide layer. Finally, in order to fully understand the crystal structure characteristics in the friction-sensitive area, it is also necessary to perform texture analysis on the element distribution characteristic spectrum through Walsh-Hadamard transform. Walsh-Hadamard transform is a discrete orthogonal transform that can transform image data into a new coordinate system to better capture the periodic and symmetric features. By applying Walsh-Hadamard transform to the element distribution characteristic spectrum, a set of crystal structure feature sets can be obtained, which contain information about crystal orientation, arrangement rules, etc. On this basis, the crystal structure feature set is analyzed by Zernike moment, and finally the microstructure texture feature set is obtained. Zernike moment is a mathematical tool widely used in image shape description, which can provide quantitative indicators about object contour, internal structure, etc. The microstructure texture feature set obtained by Zernike moment analysis includes the evolution characteristics of surface micromorphology, the distribution law of internal defects, and the characteristic quantity of material organization structure. For example, in the spindle of a CNC machine tool, if a part of the material shows unique crystal structure characteristics, such as a higher texture coefficient or a special atomic arrangement, then these characteristics will be reflected in the final microstructure texture feature set, providing us with valuable clues to understand the changes in material properties. In summary, the entire texture feature analysis process is a systematic process from grain boundary texture feature extraction to microcrack morphology feature analysis, to oxide layer morphology distribution description, and finally to the construction of crystal structure feature set. Each link is closely linked and complements each other to ensure the integrity and accuracy of the analysis results. In this way, we can have a deeper understanding of the behavior of friction-sensitive areas at the microscopic level, providing a solid theoretical basis and technical support for predicting material failure modes, optimizing design parameters, and formulating reasonable maintenance strategies.

[0040] In a specific embodiment, the wear pattern recognition based on the microstructure texture feature set is performed to obtain the wear pattern of the target spindle under friction, including: Performing spectral clustering analysis on the microstructure texture feature set to obtain a surface texture damage area distribution map; The surface texture damage area distribution map is dynamically evolved and calculated by a preset cellular automaton evolution to obtain the surface wear and peeling degree parameter of the target spindle; Performing wavelet packet energy entropy analysis on the surface wear and spalling degree parameters to obtain a wear state feature vector; Performing Lyapunov exponent analysis based on the wear state characteristic vector to obtain wear dynamic evolution characteristics; Based on the dynamic evolution characteristics of wear, the wear mode of the target spindle under friction is obtained; wherein the wear mode includes adhesive wear, abrasive wear and fatigue wear.

[0041] Specifically, the process of wear pattern recognition based on the microstructure texture feature set aims to quantitatively analyze the microstructure changes in the friction-sensitive area through a series of complex mathematical operations and machine learning algorithms to determine the specific wear type that the target spindle may encounter during actual operation. This process starts with the spectral clustering analysis of the microstructure texture feature set and extends to the final wear pattern, including different types such as adhesive wear, abrasive wear and fatigue wear. First, in order to understand how the target spindle surface is damaged under friction, researchers need to perform spectral clustering analysis on the microstructure texture feature set that has been obtained. Spectral clustering is a data mining technology that uses spectral methods in graph theory to solve clustering problems. Specifically, here, by mapping the microstructure texture feature set into a set of points in a high-dimensional space and constructing a similarity matrix between these points, and then calculating the eigenvalues ​​and eigenvectors of the matrix, the originally complex and difficult to intuitively understand microstructure information can be converted into several relatively clear and identifiable clusters or groups. These clusters actually represent different types of surface texture damage areas. For example, in the spindle of a CNC machine tool, some areas may be more susceptible to damage due to the crystal structure defects of the material itself, while other areas may be caused by external factors such as insufficient lubrication or overload. By performing spectral clustering analysis on these areas, a map that details the distribution of each damage area can be obtained, namely the surface texture damage area distribution map. This map not only reveals which locations are more severely damaged, but also provides important input data for subsequent steps. Then, in order to further simulate and predict the development trend of these damage areas, it is necessary to use the preset cellular automaton evolution to perform dynamic evolution calculations. Cellular automata are discrete models that are controlled by a series of rules to control the changes in the state of each "cell" over time. In this case, cellular automata are used to simulate the changes that may occur in the surface texture damage area over time, especially for those areas that are most prone to spalling. By setting reasonable initial conditions and evolution rules, the cellular automaton can gradually calculate the wear spalling degree parameters of the target spindle surface according to the surface texture damage area distribution map. For example, in the working environment of a CNC machine tool spindle, if a specific area shows a higher grain boundary stress distribution density or has more microcracks, then according to the preset evolution rules, the area may undergo a spalling process faster, resulting in an increase in its surface roughness. The result of the cellular automaton evolution calculation, namely the surface wear spalling parameter, directly reflects the degree of material loss caused by friction on each part of the spindle surface. Subsequently, in order to more accurately describe this wear state, it is also necessary to perform wavelet packet energy entropy analysis on the above-mentioned surface wear spalling parameters. Wavelet packet transform is a multi-resolution analysis tool that can decompose the signal into different frequency bands and calculate the energy distribution in each frequency band.When applied to the surface wear spalling parameters, the wavelet packet transform can help us identify which frequency components dominate, thus reflecting the main characteristics of the wear process. On this basis, the concept of energy entropy is introduced to measure whether the energy distribution of these frequency components is uniform. If the energy is concentrated in a few frequency bands, it indicates that the wear process has a certain regularity and periodicity; on the contrary, if the energy is dispersed in multiple frequency bands, it means that the wear behavior is relatively random. Therefore, through the wavelet packet energy entropy analysis, a set of wear state eigenvectors can be extracted, which contains important statistical information about the wear process, such as wear speed, wear form, etc. Finally, in order to evaluate the long-term development trend and stability of the wear process of the target spindle, it is also necessary to perform Lyapunov index analysis based on the wear state eigenvector. The Lyapunov index is a key concept in the field of chaotic dynamics. It is used to quantify the speed at which the distance between the system tracks increases over time. In a nonlinear wear system, a positive Lyapunov index means that the future state of the system is extremely sensitive to the initial conditions, and even very slight differences can lead to completely different results. Such a system is usually considered chaotic. On the contrary, a negative Lyapunov index indicates that the system tends to be stable. By performing Lyapunov exponent analysis on the wear state eigenvector, the dynamic evolution characteristics of wear can be obtained. These characteristics help to determine whether the wear process is within the controllable range and whether there are potential risks. For example, in the application scenario of CNC machine tool spindles, if the Lyapunov exponent is found to be positive, it implies that the wear process may be developing in an unpredictable direction, which poses a threat to the safe operation of the equipment; on the contrary, if the exponent is negative, it indicates that the wear process is relatively stable and the service life of the spindle can be extended through appropriate maintenance measures. Finally, based on the dynamic evolution characteristics of wear, we can clearly point out the wear mode of the target spindle under friction, including but not limited to adhesive wear, abrasive wear and fatigue wear. This series of analysis work not only deepens our understanding of the wear mechanism, but also provides a scientific basis for formulating effective prevention and repair strategies.

[0042] In a specific embodiment, the surface morphology evolution analysis of the target spindle is performed based on the wear mode to obtain the surface wear evolution law, and a corresponding spindle maintenance strategy is formulated based on the surface wear evolution law, including: Based on the wear pattern, the fractal dimension of the surface of the target spindle is calculated to obtain the fractal characteristic value of the spindle surface, and the fractal characteristic value of the spindle surface is analyzed by wavelet packet transform to obtain a surface morphology feature sequence; Calculating the surface wear particle distribution of the target spindle based on the surface morphology feature sequence to obtain a wear particle distribution density map, and performing morphology entropy value analysis on the wear particle distribution density map to obtain surface damage evolution characteristics; The contact stress field of the surface damage evolution characteristics is calculated by Hertz contact theory to obtain local stress distribution data, and the strain energy density of the local stress distribution data is analyzed to obtain surface microscopic deformation characteristics; Performing friction power consumption analysis on the surface micro-deformation characteristics to obtain interface energy distribution data, and calculating the wear rate based on the interface energy distribution data to obtain the surface wear evolution law, wherein the surface wear evolution law includes material removal rate, surface hardening degree and micro-crack growth rate; Based on the surface wear evolution law, life prediction analysis is performed to obtain the remaining life prediction result of the spindle; The maintenance cycle is optimized and calculated for the remaining life prediction result of the spindle to obtain an optimal maintenance time sequence, and a preventive maintenance plan is formulated based on the optimal maintenance time sequence to obtain a spindle maintenance strategy, wherein the spindle maintenance strategy includes an inspection cycle, a lubrication replacement time, and a parts replacement sequence.

[0043] Specifically, the process of analyzing the surface morphology evolution of the target spindle based on the wear mode, obtaining the surface wear evolution law, and formulating the corresponding spindle maintenance strategy based on the surface wear evolution law is one of the key links in the entire spindle friction test method. It not only relies on the previous wear pattern recognition results, but also needs to combine a series of mathematical models and physical analysis methods to reveal how the surface morphology of the spindle changes during long-term operation, and formulate a scientific and reasonable maintenance plan based on this. First, in order to quantitatively describe the microstructural characteristics of the target spindle surface under different wear modes, the researchers will calculate the fractal dimension of the target spindle surface based on the wear mode. Fractal dimension is a mathematical tool used to measure the complexity and irregularity of an object. It can provide information about surface roughness, texture distribution, etc. By calculating the fractal dimension of the spindle surface, a value reflecting its geometric characteristics can be obtained, that is, the fractal eigenvalue of the spindle surface. For example, in a CNC machine tool, if a part of the spindle is found to have undergone severe abrasive wear, the fractal eigenvalue of the area may show a higher value, indicating that the surface has more unevenness and complex texture structure. Next, in order to further analyze the information contained in these eigenvalues, it is necessary to perform wavelet packet transform analysis on the obtained spindle surface fractal eigenvalues. Wavelet packet transform can decompose the signal into different frequency bands and capture the detailed changes in each frequency band, thereby generating a set of surface morphology feature sequences. This set of sequences not only contains morphological information at different scales, but also provides basic data for subsequent calculation of wear particle distribution. Then, based on the above surface morphology feature sequence, the researchers will calculate the surface wear particle distribution of the target spindle to obtain the wear particle distribution density map. Abrasive particle distribution refers to the spatial distribution of tiny particles that fall off the surface of the material or are embedded in foreign matter during the friction process. By constructing an appropriate mathematical model, the location where the abrasive particles may gather and their density distribution can be predicted based on the surface morphology feature sequence. For example, in the spindle of a CNC machine tool, some specific areas are more likely to accumulate abrasive particles due to stress concentration or insufficient lubrication, resulting in aggravated surface damage. By analyzing the morphological entropy value of the wear particle distribution density map, the development trend of these damaged areas can be evaluated. The morphological entropy value reflects the randomness and disorder of the surface morphology. A high entropy value means that the surface is more complex and difficult to predict, while a low entropy value indicates that the surface is more regular and smooth. This analysis helps to identify which areas are most likely to become the focus of future wear. Furthermore, in order to understand the impact of surface damage on the overall performance of the spindle, it is also necessary to calculate the contact stress field of the surface damage evolution characteristics through Hertz contact theory. Hertz contact theory is a classical mechanics theory that studies the interaction force between two elastic bodies when they are in contact. It can help us determine the pressure distribution at the contact point and the resulting deformation.Specifically, in the application scenario of CNC machine tool spindles, when damage occurs on the surface, the local stress distribution will change significantly, which may cause additional strain energy to be generated inside the material. Therefore, by performing strain energy density analysis on the local stress distribution data, the surface micro-deformation characteristics can be revealed. Strain energy density is a physical quantity that measures the amount of energy stored in a unit volume, which is directly related to the response characteristics of the material under stress conditions. For example, if the strain energy density in a certain area increases abnormally, it indicates that plastic deformation or even crack propagation may occur there, which poses a potential threat to the safe operation of the spindle. Next, in order to evaluate the energy consumption of the wear process, it is necessary to perform friction power consumption analysis on the surface micro-deformation characteristics. Friction power consumption refers to the energy loss caused by relative motion during the friction process, which is usually converted into heat energy or other forms of loss. By performing a detailed analysis of the interface energy distribution data, important parameters such as material removal rate, surface hardening degree and micro-crack propagation rate can be calculated, and finally a complete surface wear evolution law is formed. For example, in the spindle of a CNC machine tool, if a part of the material is found to show high friction power consumption and fast material removal rate, it means that the area is in a severe wear stage and needs special attention and corresponding protective measures. In addition, the degree of surface hardening reflects the change in hardness of the material after wear, which is crucial for evaluating the wear resistance and service life of the material; while the micro crack propagation rate is directly related to the risk of material fracture and must be closely monitored to prevent unexpected failures. Finally, based on the surface wear evolution law, the researchers will conduct life prediction analysis to obtain the prediction results of the remaining life of the spindle. Life prediction is an estimation of the spindle state in the future based on the wear evolution law, which takes into account various factors such as material properties, working environment and frequency of use. By statistically modeling and trend extrapolating the data of the wear evolution law, the possible failure time point of the spindle in the future can be predicted, thereby providing a basis for the formulation of maintenance strategies. Continuing with the spindle of a CNC machine tool as an example, assuming that it is known through analysis that a key component will enter a rapid wear period after a specific cycle, then a maintenance plan can be arranged in advance to avoid production interruptions due to sudden failures. In order to optimize the maintenance cycle, it is also necessary to optimize the maintenance cycle calculation of the remaining life prediction results of the spindle to obtain the optimal maintenance time series. This time series not only takes into account the actual operating conditions of the equipment, but also takes into account factors such as economic benefits and social impact, aiming to find an optimal balance point that can both ensure safety and reduce costs. Based on the optimal maintenance time series, a preventive maintenance plan can be formulated to obtain a spindle maintenance strategy, which includes specific maintenance cycles, lubrication replacement time, and component replacement sequence.For example, in a CNC machine tool, if a part of the spindle is found to be prone to wear under certain conditions, it can be carefully inspected at each regular inspection, and the lubricant type can be adjusted or damaged parts can be replaced in time according to the actual situation to ensure that the equipment is always in the best working condition. In summary, the entire surface morphology evolution analysis process is a systematic process from fractal dimension calculation to wear rate calculation, and then to life prediction and maintenance strategy formulation. Each step is closely linked and interlocking, ensuring the accuracy and reliability of the analysis results. In this way, we can have a deeper understanding of the wear behavior of the spindle in actual operation, providing solid theoretical and technical support for efficient and reliable equipment maintenance.

[0044] The spindle friction test method in the embodiment of the present invention is described above. The spindle friction test device in the embodiment of the present invention is described below. Figure 2 , an embodiment of the spindle friction testing device in the embodiment of the present invention includes: An applying module 21 is used to apply a predetermined torque to the target spindle to obtain a force state of the target spindle; A calculation module 22, configured to calculate the stress distribution of the target spindle based on the stress state of the target spindle to obtain the stress distribution result of the target spindle, and locate the friction sensitive area of ​​the target spindle based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle; An imaging module 23, used for performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area; An analysis module 24 is used to perform texture feature analysis on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set; An identification module 25, used to perform wear pattern identification based on the microstructure texture feature set to obtain the wear pattern of the target spindle under friction; The evolution module 26 is used to perform surface morphology evolution analysis on the target spindle based on the wear mode, obtain the surface wear evolution law, and formulate a maintenance strategy for the target spindle based on the surface wear evolution law.

[0045] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0046] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0047] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0048] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0049] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0050] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0051] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A spindle friction testing device, characterized in that: include: An applying module, used for applying a predetermined torque to a target spindle to obtain a force state of the target spindle; a calculation module, configured to calculate the stress distribution of the target spindle based on the stress state of the target spindle to obtain the stress distribution result of the target spindle, and locate the friction sensitive area of ​​the target spindle based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle; An imaging module, used for performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area; An analysis module, used for performing texture feature analysis on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set; An identification module, used for performing wear pattern identification based on the microstructure texture feature set to obtain the wear pattern of the target spindle under the action of friction; The evolution module is used to perform surface morphology evolution analysis on the target spindle based on the wear mode, obtain the surface wear evolution law, and formulate a maintenance strategy for the target spindle based on the surface wear evolution law.

2. A spindle friction testing method, characterized in that: The following steps are involved: Applying a predetermined torque to the target spindle to obtain a force state of the target spindle; Performing stress distribution calculation on the target spindle based on the stress state of the target spindle to obtain a stress distribution result of the target spindle, and locating a friction sensitive area of ​​the target spindle based on the stress distribution result to obtain a friction sensitive area of ​​the target spindle; Performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area; Performing texture feature analysis on the microstructure image of the friction sensitive area to obtain a microstructure texture feature set; Wear pattern recognition is performed based on the microstructure texture feature set to obtain the wear pattern of the target spindle under friction; Based on the wear pattern, a surface morphology evolution analysis is performed on the target spindle to obtain a surface wear evolution law, and a maintenance strategy for the target spindle is formulated based on the surface wear evolution law.

3. The spindle friction testing method according to claim 2, characterized in that: The step of applying a predetermined torque to the target spindle to obtain a stress state of the target spindle includes: Applying a predetermined torque to the target spindle, and measuring the force of the target spindle through a preset force sensor to obtain force data of the target spindle at different positions; Performing vector decomposition on the force data of the target main axis at different positions to obtain force components of the target main axis in different directions; Performing a torque balance analysis on the target spindle based on the force components to obtain a torque balance equation of the target spindle; The torque balance equation is solved to obtain the target spindle force state under the predetermined torque.

4. The spindle friction testing method according to claim 2, characterized in that: The step of calculating the stress distribution of the target spindle based on the stress state of the target spindle to obtain the stress distribution result of the target spindle, and locating the friction sensitive area of ​​the target spindle based on the stress distribution result to obtain the friction sensitive area of ​​the target spindle includes: Performing equivalent stress calculation on the target principal axis stress state by yield criterion to obtain target principal axis equivalent stress distribution, and performing maximum shear stress analysis on the target principal axis equivalent stress distribution to obtain target principal axis shear stress field distribution; wherein the target principal axis shear stress field distribution includes maximum principal stress, intermediate principal stress and minimum principal stress; Performing strength criterion analysis based on the target spindle shear stress field distribution to obtain a target spindle strength parameter set, and performing failure criterion calculation on the target spindle strength parameter set to obtain a target spindle stress distribution result; wherein the target spindle stress distribution result includes cohesion coefficient, internal friction angle and shear strength value; Perform strain analysis and calculation based on the target spindle stress distribution result to obtain the target spindle strain energy distribution; Performing eigenvalue decomposition on the target spindle strain energy distribution to obtain a target spindle internal stress feature set, and performing friction mechanics analysis based on the target spindle internal stress feature set to obtain a target spindle friction field distribution; wherein the target spindle friction field distribution includes a friction evolution rate, a friction critical value, and a friction accumulation amount; Based on the friction field distribution of the target spindle, a fatigue extension law is calculated for the target spindle to obtain a friction sensitive point of the target spindle; Performing wear equation analysis on the friction sensitive points of the target spindle to obtain the wear rate distribution of the target spindle; Based on the target spindle friction field distribution, the target spindle fatigue sensitive point and the target spindle wear rate distribution, the target spindle friction field distribution is subjected to stress statistical analysis to obtain the friction sensitive area of ​​the target spindle.

5. The spindle friction testing method according to claim 2, characterized in that: The performing microstructure imaging on the friction sensitive area to obtain a microstructure image of the friction sensitive area includes: Scanning the surface morphology of the friction sensitive area by a scanning electron microscope to obtain a regional secondary electron signal, and performing backscattered electron diffraction analysis and microstructure imaging on the regional secondary electron signal to obtain a surface micromorphology feature map; wherein the surface micromorphology feature map includes a grain boundary distribution map, a dislocation density value, and a microcrack direction; Performing atomic force microscopic analysis based on the surface microscopic morphology feature map to obtain surface three-dimensional topographic data, and reconstructing the surface three-dimensional topographic data with nanometer-scale resolution to obtain a microscopic surface contour map; Performing Raman spectroscopy analysis on the microscopic surface profile to obtain material molecular vibration characteristics, and performing Fourier transform infrared spectroscopy processing on the material molecular vibration characteristics to obtain a surface chemical component distribution map; wherein the surface chemical component distribution map includes oxide layer thickness, element content ratio and lattice defect degree; Performing element mapping on the surface chemical component distribution map through X-ray energy spectrum to obtain a component distribution spectrum, and performing electron backscatter diffraction indexing on the component distribution spectrum to obtain a crystal orientation distribution; wherein the crystal orientation distribution includes pole figure data, texture coefficient and orientation density; Based on the crystal orientation distribution, transmission electron microscopy imaging is performed to obtain a microstructure image of the friction sensitive area; wherein the microstructure image of the friction sensitive area includes internal microstructure features, interlayer structure distribution and atomic scale structure features.

6. The spindle friction testing method according to claim 2, characterized in that: The texture feature analysis of the microstructure image of the friction sensitive area is performed to obtain a microstructure texture feature set, including: Performing Hough-Gabor transformation on the microstructure image of the friction sensitive area to obtain a grain boundary texture feature map; Performing multi-scale decomposition on the grain boundary texture feature map by Morlet wavelet to obtain microcrack morphology feature data, wherein the microcrack morphology feature data includes crack propagation path, dislocation density in stress concentration area and grain boundary stress distribution data; Performing Ridgelet transformation on the microcrack morphology characteristic data to obtain an oxide layer morphology distribution map; Performing Hilbert-Huang decomposition on the oxide layer morphology distribution diagram to obtain an element distribution characteristic spectrum; Performing texture analysis on the element distribution characteristic spectrum by Walsh-Hadamard transformation to obtain a crystal structure characteristic set; The crystal structure feature set is subjected to Zernike moment analysis to obtain a microstructure texture feature set, wherein the microstructure texture feature set includes surface micromorphology evolution characteristics, internal defect distribution laws and material organization structure characteristic quantities.

7. The spindle friction testing method according to claim 2, characterized in that: The wear pattern recognition based on the microstructure texture feature set is performed to obtain the wear pattern of the target spindle under the friction, including: Performing spectral clustering analysis on the microstructure texture feature set to obtain a surface texture damage area distribution map; The surface texture damage area distribution map is dynamically evolved and calculated by a preset cellular automaton evolution to obtain the surface wear and peeling degree parameter of the target spindle; Performing wavelet packet energy entropy analysis on the surface wear and spalling degree parameters to obtain a wear state feature vector; Performing Lyapunov exponent analysis based on the wear state characteristic vector to obtain wear dynamic evolution characteristics; Based on the dynamic evolution characteristics of wear, the wear mode of the target spindle under friction is obtained; wherein the wear mode includes adhesive wear, abrasive wear and fatigue wear.

8. The spindle friction testing method according to claim 2, characterized in that: The surface morphology evolution analysis of the target spindle is performed based on the wear mode to obtain the surface wear evolution law, and a corresponding spindle maintenance strategy is formulated based on the surface wear evolution law, including: Based on the wear pattern, the fractal dimension of the surface of the target spindle is calculated to obtain the fractal characteristic value of the spindle surface, and the fractal characteristic value of the spindle surface is analyzed by wavelet packet transform to obtain a surface morphology feature sequence; Calculating the surface wear particle distribution of the target spindle based on the surface morphology feature sequence to obtain a wear particle distribution density map, and performing morphology entropy value analysis on the wear particle distribution density map to obtain surface damage evolution characteristics; The contact stress field of the surface damage evolution characteristics is calculated by Hertz contact theory to obtain local stress distribution data, and the strain energy density analysis is performed on the local stress distribution data to obtain surface micro deformation characteristics; Performing friction power consumption analysis on the surface micro deformation characteristics to obtain interface energy distribution data, and calculating the wear rate based on the interface energy distribution data to obtain the surface wear evolution law, wherein the surface wear evolution law includes material removal rate, surface hardening degree and micro crack growth rate; Based on the surface wear evolution law, life prediction analysis is performed to obtain the remaining life prediction result of the spindle; The maintenance cycle is optimized and calculated for the remaining life prediction result of the spindle to obtain an optimal maintenance time sequence, and a preventive maintenance plan is formulated based on the optimal maintenance time sequence to obtain a spindle maintenance strategy, wherein the spindle maintenance strategy includes an inspection cycle, a lubrication replacement time, and a parts replacement sequence.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 2 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 2 to 8 are implemented.

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