Precise hardware surface fine polishing treatment method and device

Abstract

The invention discloses a precise hardware surface fine polishing treatment method and device, and relates to the related technical field of hardware surface treatment.The method comprises the steps that the polishing state of target hardware is dynamically monitored to obtain target polishing information; introducing a damage evaluation strategy to analyze and obtain a target introduction damage rate of the target hardware; obtaining a target polishing state index; when the target polishing state index is not in the preset state index threshold, a polishing adjustment instruction is sent out; optimizing and adjusting the target process information of the target hardware; and fine polishing treatment is conducted according to the optimal polishing process. The technical problems that in the prior art, the polishing state is not monitored in real time, the material removal amount is difficult to control, damage balance is introduced, the hardware surface polishing quality is not stable, and the surface quality and the polishing efficiency are not balanced are solved, and the technical effects that polishing treatment is accurately controlled, and the hardware surface quality is improved are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to hardware surface treatment, and specifically to a method and device for fine polishing treatment of the surface of precision hardware parts. Background Art

[0002] In modern industrial manufacturing, precision hardware is widely used in high-end manufacturing fields such as aerospace, automobile manufacturing, medical equipment, precision instruments, and electronic and electrical equipment. The requirements for its surface quality are increasing day by day. As a key component, the surface quality of hardware not only directly affects the performance and service life of the overall product, but also affects the assembly accuracy, wear resistance and aesthetics. Especially in scenarios with high requirements for high precision and high finish, the polishing of hardware has become an important part of the manufacturing process. However, traditional polishing methods are difficult to effectively meet the increasingly stringent surface treatment requirements, and there are uncontrollable risks of material removal and damage during the polishing process, which limits the polishing accuracy and process stability. The existing polishing process usually manually sets the polishing parameters and gradually adjusts them during the process. Although technicians can observe the polishing effect to judge the degree of polishing, there are obvious subjective errors, and due to the differences in the physical properties of each batch of workpieces, it is difficult to achieve standardized polishing quality. At the same time, manual polishing is also prone to over-polishing of the surface or uneven polishing of local areas, which is difficult to meet the needs of automated and intelligent production.

[0003] Therefore, in the current technology related to the surface polishing of hardware, there are technical problems such as lack of real-time monitoring of the polishing status, difficulty in controlling the balance between material removal and introduced damage, which in turn leads to unstable quality of hardware surface polishing and imbalance between surface quality and polishing efficiency. Summary of the invention

[0004] The present application provides a method and device for fine-grained polishing of the surface of precision hardware, thereby solving the technical problems in the prior art of lack of real-time monitoring of the polishing status, difficulty in controlling the balance between material removal and introduction of damage, which leads to unstable surface polishing quality of hardware and imbalance between surface quality and polishing efficiency, thereby achieving the technical effect of accurately controlling polishing processing and improving the surface quality of hardware.

[0005] The present application provides a method for fine-finishing the surface of precision hardware, the method comprising: dynamically monitoring the polishing state of a target hardware to obtain target polishing information, wherein the target polishing information comprises target material removal information and target introduced damage information; introducing a damage assessment strategy to analyze the target introduced damage information to obtain a target introduced damage rate of the target hardware; weighted calculation of the target introduced damage rate and the target material removal rate obtained by analyzing the target material removal information to obtain a target polishing state index; issuing a polishing adjustment instruction when the target polishing state index is not within a predetermined state index threshold; a polishing control component optimizing and adjusting the target process information of the target hardware based on the polishing adjustment instruction to obtain an optimal polishing process; and fine-finishing the target hardware according to the optimal polishing process.

[0006] In a possible implementation, the polishing state of the target hardware is dynamically monitored to obtain target polishing information, and the following processing is also performed: a predetermined Langmuir probe array is activated, and the predetermined Langmuir probe array includes a plurality of probes with three-dimensional coordinate identifiers; plasma characteristics of a target neighborhood of the target hardware are monitored by the predetermined Langmuir probe array to obtain neighborhood plasma characteristic information, and the neighborhood plasma characteristic information includes a plurality of groups of characteristic information corresponding to the plurality of probes with three-dimensional coordinate identifiers; first characteristic information from the plurality of groups of characteristic information is extracted, and the first characteristic information corresponds to a first three-dimensional coordinate; a target plasma characteristic visual model of the target hardware is generated based on a first corresponding relationship between the first characteristic information and the first three-dimensional coordinate; and the target introduced damage information is obtained according to the target plasma characteristic visual model.

[0007] In a possible implementation, the target introduced damage information is obtained according to the target plasma characteristic visual model, and the following processing is also performed: a first surface point of the target hardware is randomly acquired, and the first surface point corresponds to a first surface three-dimensional coordinate; a first neighborhood of the first surface three-dimensional coordinate is determined in the target plasma characteristic visual model, and the first neighborhood includes N groups of correspondences, and N is an integer greater than or equal to 3; the plasma characteristics of the first surface three-dimensional coordinate are predicted according to the N groups of correspondences to obtain first predicted plasma characteristic information; the first predicted plasma characteristic information is used as the first surface characteristic information of the first surface three-dimensional coordinate; and the first surface characteristic information is analyzed to obtain the target introduced damage information.

[0008] In a possible implementation, the plasma characteristics of the first surface three-dimensional coordinates are predicted based on the N groups of correspondences, and the following processing is also performed: extracting any corresponding relationship from the N groups of correspondences, wherein the arbitrary corresponding relationship refers to the corresponding relationship between arbitrary characteristic information and arbitrary three-dimensional coordinates; calculating any spatial distance between the arbitrary three-dimensional coordinates and the first surface three-dimensional coordinates, and determining any coefficient based on the arbitrary spatial distance; combining the arbitrary coefficient with the arbitrary characteristic information to calculate the first predicted plasma characteristic information; wherein the arbitrary characteristic information includes multiple parameter data corresponding to predetermined plasma indicators, and the predetermined plasma indicators include at least electron temperature, ion temperature, electron density and ion density.

[0009] In a possible implementation, a damage assessment strategy is introduced to analyze the target introduced damage information to obtain the target introduced damage rate of the target hardware, and the following processing is also performed: based on the significance principle, a visual significance analysis is performed on the target introduced damage information to obtain surface significant points, and the surface significant points correspond to surface significant three-dimensional coordinates; support analysis is performed on the surface significant characteristic information corresponding to the surface significant points according to the surface significant neighborhood of the surface significant three-dimensional coordinates to obtain surface significant support; it is determined whether the surface significant support meets the predetermined support threshold; if not, the surface significant characteristic information is used as the surface polishing evaluation benchmark of the target hardware, and the target introduced damage rate is obtained by analysis.

[0010] In a possible implementation, it is determined whether the surface significant support meets the predetermined support threshold, and the following processing is also performed: if it meets, any surface significant characteristic information corresponding to any surface point in the target introduced damage information is analyzed; the average of the arbitrary surface significant characteristic information and the surface significant characteristic information is taken, and they are combined to form the surface polishing evaluation benchmark of the target hardware; the target introduced damage rate is obtained based on the surface polishing evaluation benchmark.

[0011] In a possible implementation, the target introduced damage rate is obtained based on the surface polishing evaluation benchmark, and the following processing is also performed: a damage prediction model is trained based on big data, the damage prediction model includes a damage density prediction unit and a damage depth prediction unit; the surface significant characteristic information is analyzed by the damage density prediction unit to obtain a predicted target damage density; the surface significant characteristic information is analyzed by the damage depth prediction unit to obtain a predicted target damage depth; the predicted target damage density and the predicted target damage depth are weighted to obtain the target introduced damage rate.

[0012] The present application also provides a device for fine-finishing the surface of precision hardware, including: a target polishing information acquisition module, which is used to dynamically monitor the polishing state of the target hardware to obtain target polishing information, wherein the target polishing information includes target material removal information and target introduced damage information; a target introduced damage rate acquisition module, which is used to introduce a damage assessment strategy to analyze the target introduced damage information to obtain the target introduced damage rate of the target hardware; a target polishing state index acquisition module, which is used to perform weighted calculation on the target introduced damage rate and the target material removal rate obtained by analyzing the target material removal information to obtain a target polishing state index; a polishing adjustment instruction issuance module, which is used to issue a polishing adjustment instruction when the target polishing state index is not within a predetermined state index threshold; an optimal polishing process acquisition module, which is used for the polishing control component to optimize and adjust the target process information of the target hardware based on the polishing adjustment instruction to obtain an optimal polishing process; a fine polishing processing module, which is used to perform fine polishing processing on the target hardware according to the optimal polishing process.

[0013] The method and device for fine polishing of the surface of precision hardware proposed in this application is intended to dynamically monitor the polishing state of the target hardware to obtain the target polishing information; introduce the damage assessment strategy to analyze the target introduced damage rate of the target hardware; obtain the target polishing state index; when the target polishing state index is not within the predetermined state index threshold, issue a polishing adjustment instruction; optimize and adjust the target process information of the target hardware; and perform fine polishing according to the optimal polishing process. The technical problems existing in the prior art, such as the lack of real-time monitoring of the polishing state, the difficulty in controlling the balance between material removal and introduced damage, and the instability of the surface polishing quality of hardware and the imbalance between surface quality and polishing efficiency, are solved, and the technical effect of accurately controlling the polishing process and improving the surface quality of hardware is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of a process flow of a method for fine polishing of a surface of a precision hardware provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the structure of a device for fine-finishing polishing of the surface of precision hardware provided in an embodiment of the present application.

[0016] Description of reference numerals: target polishing information obtaining module 10 , target introduced damage rate obtaining module 20 , target polishing state index obtaining module 30 , polishing adjustment instruction issuing module 40 , optimal polishing process obtaining module 50 , refined polishing processing module 60 . DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0020] The present application provides a method for fine polishing of the surface of precision hardware parts, such as Figure 1 As shown, the method includes: Step S100, dynamically monitoring the polishing state of a target hardware component to obtain target polishing information, wherein the target polishing information includes target material removal information and target introduced damage information.

[0021] Preferably, during the polishing process of the target hardware, sensors or detection equipment (such as high-resolution cameras, laser detection equipment, etc.) are used to collect surface state data such as material removal amount, surface finish, introduced damage, temperature change, etc. of the hardware in real time as target polishing information, wherein the target polishing information includes target material removal information and target introduced damage information. Specifically, the target material removal information refers to the relevant data on the amount of material removed from the surface of the hardware during the polishing process and the removal method, which may include material removal rate, removal uniformity, material removal amount and removal amount accuracy, etc. The removal rate represents the rate at which material is removed from the surface per unit time, which is usually estimated by detecting the polishing depth or surface finish change; removal uniformity represents the consistency of material removal in different areas of the polishing surface to ensure consistent overall surface finish; removal amount refers to the amount of material removed from the surface of the hardware during the polishing process. The surface roughness sensor or laser measuring equipment can be used to detect the change in the surface height of the workpiece in real time to determine the thickness or volume of the material removed during polishing, and then judge the material removal amount, and ensure that it is not excessively removed before the desired finish and shape are achieved. Target introduced damage information refers to the microscopic damage introduced by friction, cutting or high temperature during the polishing process, including potential defects such as cracks, scratches, residual stress, etc., which may include microcracks and scratches, residual stress and thermal damage. Microcracks and scratches refer to the fine cracks or scratches formed on the surface during the polishing process, which will affect the overall performance and appearance of the hardware; residual stress refers to the internal stress introduced on the surface of the hardware during the polishing process, which may cause the hardware to deform or crack during use; thermal damage is the surface material deterioration, hardness reduction or microstructural change caused by friction or local temperature rise during the polishing process. By monitoring and obtaining target material removal information and target introduced damage information, precise control of the polishing process can be achieved to ensure that the amount of material removed meets the set polishing standards, and timely adjust the polishing parameters to reduce the occurrence of surface damage and achieve higher surface finish and consistency.

[0022] Step S100 further includes step S110, activating a predetermined Langmuir probe array, wherein the predetermined Langmuir probe array includes a plurality of probes with three-dimensional coordinate identifiers; step S120, monitoring the plasma characteristics of a target neighborhood of the target hardware through the predetermined Langmuir probe array to obtain neighborhood plasma characteristic information, wherein the neighborhood plasma characteristic information includes a plurality of groups of characteristic information corresponding to the plurality of probes with three-dimensional coordinate identifiers; step S130, extracting first characteristic information from the plurality of groups of characteristic information, wherein the first characteristic information corresponds to a first three-dimensional coordinate; step S140, generating a target plasma characteristic visual model of the target hardware based on a first corresponding relationship between the first characteristic information and the first three-dimensional coordinate; and step S150, obtaining the target introduced damage information according to the target plasma characteristic visual model.

[0023] Preferably, a predetermined Langmuir probe array is used to monitor the plasma characteristics of the target neighborhood of the hardware to obtain the plasma characteristic information of the area, and finally generate a plasma characteristic visual model to analyze and extract surface damage information. Specifically, the predetermined Langmuir probe array is a group of probe devices configured at a specific position, each probe has a three-dimensional coordinate mark, indicating its exact position in space. The Langmuir probe is a key device for measuring plasma characteristics, and is usually used to monitor parameters such as electron density, ion density, and electron temperature in the plasma. By activating the probe array, the plasma environment around the target hardware is started to be characteristically monitored, and the plasma characteristic information of the target neighborhood is captured, including the plasma characteristics measured by multiple probes at specific three-dimensional coordinate positions, which are used to reflect the distribution and changes of the plasma in the target neighborhood. Each probe collects plasma characteristic information at its three-dimensional coordinate position. For example, a probe may measure electron density and temperature at a specific position, and another probe may measure ion density at a different position, thereby obtaining multiple sets of characteristic information, and each set of information is associated with the three-dimensional coordinate position of the probe, marking the spatial distribution of plasma characteristics in the target neighborhood.

[0024] Preferably, first characteristic information, i.e., plasma characteristic data at this position, is then randomly extracted from multiple sets of characteristic information, and the three-dimensional coordinates corresponding to the data are recorded. The first characteristic information may include key parameters such as electron density and temperature at a specific position. Then, based on the extracted first characteristic information and its corresponding three-dimensional coordinates, a target plasma characteristic visual model is generated to represent the spatial distribution and characteristic changes of plasma in the neighborhood of the hardware. Through the target plasma characteristic visual model, it is possible to intuitively see how the plasma characteristics are distributed in the target neighborhood, and then to analyze the plasma action on the surface of the hardware. Finally, the target plasma characteristic visual model is used to evaluate the plasma damage on the surface of the hardware. For example, if the plasma density or temperature in some areas shown in the model is higher, it may indicate that the area is more susceptible to material damage. By analyzing the plasma characteristic data, the intensity of the plasma action on the material surface in this area is inferred, and then the target introduced damage information, i.e., the characteristics and degree of damage that the hardware surface may suffer, is generated.

[0025] Step S150 further includes step S151, randomly acquiring a first surface point of the target hardware, wherein the first surface point corresponds to a first surface three-dimensional coordinate; step S152, determining a first neighborhood of the first surface three-dimensional coordinate in the target plasma characteristic visual model, wherein the first neighborhood includes N groups of correspondences, where N is an integer greater than or equal to 3; step S153, predicting the plasma characteristics of the first surface three-dimensional coordinate according to the N groups of correspondences to obtain first predicted plasma characteristic information; step S154, using the first predicted plasma characteristic information as the first surface characteristic information of the first surface three-dimensional coordinate; and step S155, analyzing the first surface characteristic information to obtain the target introduced damage information.

[0026] Preferably, a point is randomly selected on the surface of the target hardware as the first surface point, representing a specific position on the surface of the hardware, and its coordinates are recorded as the first surface three-dimensional coordinates. Through the target plasma characteristic visual model, the neighborhood range of the first surface three-dimensional coordinates is found in the three-dimensional space, that is, the first neighborhood, which includes multiple spatial points adjacent to the surface point, each adjacent point has plasma characteristics and three-dimensional coordinate information, and N groups of corresponding relationships are obtained, wherein N is an integer greater than or equal to 3, such as N is 10, so as to determine the spatial relationship and characteristic association of multiple adjacent points with the point in space; then, according to the correspondence between the N groups of plasma characteristics and the three-dimensional coordinate information in the first neighborhood, the first neighborhood is trained by machine learning. The plasma characteristics of the surface three-dimensional coordinate point are predicted, such as predicting the plasma characteristics such as electron density, ion density, point or temperature of the first surface point, and obtaining the first predicted plasma characteristic information. Then the estimated first predicted plasma characteristic information is used as the first surface characteristic information of the surface point, indicating that the predicted characteristic data obtained by integrating the neighborhood information is assigned to the surface point, so that the point has a complete plasma characteristic description. Finally, the first surface characteristic information is used to analyze the plasma action intensity of the surface point and determine the possible damage. For example, if the predicted plasma characteristics show that the electron temperature of the point is high, the surface point is prone to microscopic damage, cracks or surface deformation in the plasma environment. By evaluating the plasma characteristic information, the target introduced damage information is generated, that is, the evaluation of the potential damage risk and damage type of the surface point, which helps to accurately locate the damaged area and optimize the polishing and surface treatment process.

[0027] Step S153 further includes step S1531, extracting any corresponding relationship from the N groups of corresponding relationships, wherein the arbitrary corresponding relationship refers to the corresponding relationship between arbitrary characteristic information and arbitrary three-dimensional coordinates; step S1532, calculating any spatial distance between the arbitrary three-dimensional coordinates and the first surface three-dimensional coordinates, and determining any coefficient based on the arbitrary spatial distance; step S1533, combining the arbitrary coefficient with the arbitrary characteristic information to calculate and obtain the first predicted plasma characteristic information; step S1534, wherein the arbitrary characteristic information includes multiple parameter data corresponding to predetermined plasma indicators, and the predetermined plasma indicators include at least electron temperature, ion temperature, electron density and ion density.

[0028] Preferably, in the N groups of corresponding relationships within the neighborhood, the relationship between the characteristic information of any neighboring point and its three-dimensional coordinates is randomly extracted, that is, a spatial point and its corresponding plasma characteristic information are selected from the neighborhood point set, and the relative position relationship between the target point (first surface three-dimensional coordinate) and the extracted neighboring point (arbitrary three-dimensional coordinate) is calculated according to the spatial distance between the two points, which is called the arbitrary spatial distance, and an arbitrary coefficient is determined based on this distance to represent the weight of the arbitrary spatial distance, that is, the point with a closer distance has a greater impact on the target point, and the point with a farther distance has a smaller impact; then the determined arbitrary coefficient is combined with the plasma characteristic information (arbitrary characteristic information) of the neighboring point for weighted calculation, that is, the weighted characteristic information of multiple neighboring points is weighted averaged to obtain The first predicted plasma characteristic information is obtained, that is, the estimated plasma characteristic of the target point. For example, the combined influence of the electron temperature data and ion density data of the neighboring points under specific weights will form the electron temperature and ion density prediction values ​​of the target point, wherein any characteristic information refers to the plasma characteristic data collected from each neighboring point, including parameter data of multiple plasma indicators. The electron temperature represents the average energy of electrons in the plasma, reflecting the thermodynamic state of electron motion; the ion temperature represents the average energy of ions in the plasma, which is related to the kinetic energy and temperature of the ions; the electron density represents the number of electrons per unit volume, reflecting the concentration and charge distribution of the plasma; the ion density represents the number of ions per unit volume, which is used to describe the number density of ions in the plasma.

[0029] Step S200: introducing a damage assessment strategy to analyze the target introduced damage information to obtain a target introduced damage rate of the target hardware.

[0030] Preferably, the damage assessment strategy is used to analyze the damage information introduced into the target, which means that the surface damage of the hardware during the polishing process is accurately evaluated through real-time monitoring and dynamic analysis, so as to control the damage within an acceptable range while achieving the expected smoothness. The damage assessment strategy collects surface data through a variety of sensors (such as visual sensors, acoustic emission sensors, laser detection equipment, etc.), and uses image processing or signal processing algorithms to identify scratches, microcracks, pits and other damages generated during the polishing process. For example, the location, type and amount of damage can be distinguished from the data by using methods such as machine vision, edge detection or spectrum analysis. Specifically, damage features are extracted from the damage data collected in real time during the polishing process, including different types of damage such as scratches, microcracks, pits, damage sizes such as length, depth, width, and damage density, morphology, and distribution. Then, a classification standard is established based on the damage features to classify the damage into different levels (such as mild, moderate, and severe damage). For example, the depth of the scratch or the length of the crack is classified into different severity levels. Then, a damage accumulation model is established to analyze the growth of damage during the polishing process, that is, a prediction model is constructed based on a machine learning algorithm, and polishing time, pressure, temperature and other factors are used as input to predict the accumulation rate of damage under different process parameters. This is used to understand the damage accumulation trend under the current polishing conditions, and then predict future damage changes. Finally, the target introduced damage rate is calculated through the damage accumulation model, that is, the rate of increase of damage per unit time or unit area. Among them, the introduced damage rate is the core indicator for judging whether the polishing is excessive, and it can quantify the rate of damage generation during the polishing process. A higher damage rate often means that the polishing conditions are too intense, and it may be necessary to reduce the pressure or the polishing speed. Through the implementation of the damage assessment strategy, the surface damage of the target hardware polishing process can be more accurately controlled, while ensuring processing efficiency and quality consistency.

[0031] Step S200 further includes step S210, performing a visual significance analysis on the target introduced damage information based on the significance principle to obtain surface significant points, and the surface significant points correspond to surface significant three-dimensional coordinates; step S220, performing support analysis on the surface significant characteristic information corresponding to the surface significant points according to the surface significant neighborhood of the surface significant three-dimensional coordinates to obtain surface significant support; step S230, judging whether the surface significant support meets a predetermined support threshold; step S240, if not, using the surface significant characteristic information as a surface polishing evaluation benchmark for the target hardware, and analyzing to obtain the target introduced damage rate.

[0032] Preferably, the significance principle is used to perform a visual significance analysis on the damage information on the surface of the hardware to find out the areas on the surface that may have large damage or significant damage. These significant damage areas are regarded as surface significant points, representing the points with the most prominent damage characteristics, and their positions are represented by three-dimensional coordinates, that is, the surface significant points correspond to the surface significant three-dimensional coordinates. For each surface significant point, an area containing the adjacent points around the point is determined, which is called the surface significant neighborhood. The significance principle refers to the process of data analysis and processing, through feature extraction and weight calculation, to identify the parts with significant differences or importance in the data, so that these parts are more prominent in the analysis visualization; in this neighborhood, the damage characteristic information (such as damage density, depth, electron temperature, etc.) of multiple adjacent points is analyzed, and these characteristic data are weighted and calculated to obtain the surface significant support of the surface significant point, so as to obtain the surface significant support of the surface significant point. Verify whether the characteristics of the significant point are consistent within the neighborhood, that is, whether the point truly reflects the significant damage on the surface; then for each significant point on the surface, determine whether its support reaches the preset support threshold, where the threshold is set to screen out points in the significant damage area that do have a higher risk of damage. If the surface significant support does not meet the threshold, it indicates that the significant point lacks sufficient support in the neighborhood, that is, the significance of the damage information may not be stable or strong enough. The characteristic information of the point (that is, the surface significant characteristic information) is used as the evaluation benchmark for the surface polishing of hardware parts, and is used to define polishing parameters so that the polishing process can focus on these areas where subtle damage may exist. Based on the significant characteristic information that does not meet the standard, the damage data of the point is analyzed, and the corresponding damage rate (that is, the introduced damage rate) is calculated, which reflects the material removal amount and the rate of damage accumulation during the polishing process.

[0033] Step S230 further includes step S231, if it is in compliance, analyzing any surface significant characteristic information corresponding to any surface point in the target introduced damage information; step S232, taking the average of the arbitrary surface significant characteristic information and the surface significant characteristic information, and forming the surface polishing evaluation benchmark of the target hardware; step S233, obtaining the target introduced damage rate based on the surface polishing evaluation benchmark.

[0034] Preferably, if the significant support meets the predetermined threshold, select any surface point in the target introduced damage information, obtain the significant characteristic information of the point, so as to evaluate the surface damage of the target hardware, and average the significant characteristic information of any surface and the significant characteristic information of the whole surface of the region (that is, the significant characteristic data obtained in the area around the significant point) so that the characteristic data of any point and the characteristic data of the whole significant area are comprehensively balanced, and the random deviation of a single point is smoothed by the mean to generate a more representative surface polishing evaluation benchmark, and the averaged characteristic information is combined to form a surface polishing evaluation benchmark for the target hardware, which includes the mean data of representative significant characteristics and is used to evaluate the damage characteristics and polishing requirements of the entire surface; finally, the significant characteristic information in the surface polishing evaluation benchmark is used to calculate the target introduced damage rate, that is, by analyzing the relationship between the material removal rate and the growth rate of surface damage during the polishing process, the target introduced damage rate is obtained, which reflects the increment of surface damage during the polishing process, and helps to control the surface damage within an ideal range while meeting the surface finish requirements.

[0035] Step S233 further includes step S2331, training a damage prediction model based on big data, wherein the damage prediction model includes a damage density prediction unit and a damage depth prediction unit; step S2332, analyzing the surface significant characteristic information through the damage density prediction unit to obtain a predicted target damage density; step S2333, analyzing the surface significant characteristic information through the damage depth prediction unit to obtain a predicted target damage depth; step S2334, weighting the predicted target damage density and the predicted target damage depth to obtain the target introduced damage rate.

[0036] Preferably, a large amount of historical damage data is obtained based on big data, including relevant information such as different materials, surface treatment conditions, polishing parameters, plasma effects, etc., and then these historical damage data are used to train a damage prediction model based on machine learning (such as decision trees, neural networks, regression analysis, etc.). The damage indicators corresponding to different process parameters can be predicted according to the input damage characteristic information, including a damage density prediction unit and a damage depth prediction unit. Among them, the damage density prediction unit is responsible for predicting the damage density of the target surface area. The damage density generally refers to the number or distribution of damage per unit area, which measures the concentration of damage characteristics on the surface and reflects the damage accumulation of the surface under specific processing conditions. Specifically, the surface significant characteristic information (such as electron density, ion temperature, electron temperature, etc.) is input into the damage density prediction unit, and the correlation between the surface significant characteristic information and the damage characteristics is identified. The damage density of the surface points is the predicted target damage density. For example, higher electron temperature and ion density may lead to higher damage density. The damage depth prediction unit is responsible for predicting the depth of surface damage. The damage depth refers to the degree of penetration of the damage characteristics below the surface. The greater the depth, the more significant the impact of surface damage on the material structure. The surface significant characteristic information is also input into the damage depth prediction unit to analyze the relationship between the plasma characteristic information and the damage depth to obtain the predicted target damage depth. For example, too high an ion temperature may lead to deeper damage, especially in brittle materials. Finally, the predicted damage density and damage depth are weighted. For example, considering the impact of different damage indicators on surface quality and material properties, the weighted result is introduced as the target to the damage rate to reflect the overall risk of surface damage. The higher the damage rate, the more serious the damage to the surface area.

[0037] Step S300, performing weighted calculation on the target introduced damage rate and the target material removal rate obtained by analyzing the target material removal information to obtain a target polishing state index.

[0038] Preferably, a target introduced damage rate and a target material removal rate are weightedly calculated to obtain a target polishing state index, which is used to reflect the overall quality status of the polishing process in real time. The target polishing state index is combined by the weights of the damage rate and the removal rate to dynamically determine whether the current polishing process is within the ideal range. Specifically, the target introduced damage rate is usually expressed as the damage change per unit time or unit area, for example, the number of scratches generated per minute or the microcrack growth rate per square millimeter. The higher the damage rate, the more serious the surface damage. The target material removal rate indicates the amount of material removed from the surface of the hardware per unit time during the polishing process, which is usually measured by the change in material thickness or volume. The higher the removal rate, the faster the polishing speed, but too high a removal rate may result in insufficient surface finish or surface damage. Reasonable control of the target material removal rate ensures the efficiency and surface quality of polishing. In the weighted calculation process, it is necessary to assign corresponding weight coefficients to the damage rate and removal rate according to the priority and quality requirements of the polishing process. For example, when the surface finish and precision requirements are high, the weight of the damage rate may be set higher to control the accumulation of damage; in the scenario of pursuing polishing efficiency, the weight of the material removal rate may be set higher to speed up the polishing speed; the weight coefficient is generally adjusted based on the purpose, quality requirements and process requirements of the workpiece to ensure that the comprehensive index can adapt to different polishing goals. Finally, through the set weight coefficient, the target is introduced into the damage rate and target material removal rate for weighted calculation to obtain the target polishing state index. The closer to the preset ideal range, the closer the current polishing process is to the optimal state.

[0039] Step S400: When the target polishing state index is not within a predetermined state index threshold, a polishing adjustment instruction is issued.

[0040] Preferably, if the target polishing state index (combined with the comprehensive evaluation value of the damage rate and the removal rate) determines that the current process parameters are not within the predetermined state index threshold, that is, they have not reached the ideal range, a polishing adjustment instruction is automatically generated to optimize the polishing process. Specifically, the content and execution steps of the polishing adjustment instruction may include reducing the polishing speed (if the state index indicates that the damage rate is high, that is, too many scratches or microcracks appear on the surface, an instruction is issued to reduce the polishing speed, thereby reducing the wear pressure), adjusting the polishing pressure (according to the feedback of the polishing state index, if the damage rate is too high, it is instructed to reduce the contact pressure of the polishing tool, thereby reducing the surface stress introduced during the material removal process and reducing the probability of damage), resetting the polishing cycle (according to the polishing state index, the polishing time cycle is adjusted, for example, if the removal rate is insufficient or the damage rate is lower than the target level, an instruction is issued to extend the polishing time so as to achieve a higher surface finish while maintaining low damage; on the contrary, if the removal rate is high and the damage is large, the polishing cycle is shortened to avoid over-polishing), etc. The polishing adjustment instruction ensures high-precision and low-damage processing during the polishing process, making the polishing operation more automated and intelligent, thereby improving the polishing efficiency while ensuring the surface quality.

[0041] Step S500: The polishing control component optimizes and adjusts the target process information of the target hardware based on the polishing adjustment instruction to obtain an optimal polishing process.

[0042] After receiving the polishing adjustment instruction, the polishing control component automatically adjusts the polishing parameters according to the instruction to optimize the polishing process of the target hardware in real time, obtain the optimal polishing process, and then ensure the balance between surface quality and polishing efficiency. Specifically, the polishing control component can adjust the speed and feed rate of the polishing tool according to the adjustment instruction. For example, if the surface damage is detected to be increased, the polishing speed is reduced or the feed rate is slowed down to reduce the surface damage; the control component can adjust the contact strength between the polishing tool and the workpiece surface by automatically controlling the pressure of the polishing head. When the damage rate is detected to be too high, the polishing pressure can be reduced to avoid surface scratches or microcracks. When the material needs to be removed faster, the pressure can be appropriately increased to improve the removal efficiency; after adjusting various parameters, the polishing control component continues to dynamically monitor the state parameters in the polishing process, obtains the latest polishing state index (including damage rate and removal rate) in real time, verifies the adjustment effect through monitoring and feedback, and optimizes according to the new feedback loop to ensure that the polishing parameters reach the optimal setting, realize the continuously optimized polishing process, and finally obtain the optimal polishing process, so that the polishing process is always in the best state, ensuring the stability and consistency of the polishing quality, thereby improving the surface finish and processing efficiency of the hardware.

[0043] Step S600: performing fine polishing on the target hardware according to the optimal polishing process.

[0044] Preferably, the target hardware is finely polished according to the optimal polishing process, that is, after determining the optimal polishing parameter combination that meets the current hardware surface requirements, the target hardware is precisely and evenly polished to achieve high finish and minimize damage. Specifically, the optimal polishing parameters are optimized according to the polishing control component, for example, according to the balance point between material removal rate and damage control, the optimal polishing speed is set to ensure uniform material removal and smooth surface; ensure that the contact pressure between the polishing tool and the workpiece reaches the ideal value, and a balance is achieved between polishing efficiency and surface damage control; in the process of executing the optimal polishing process, real-time monitoring is still maintained, and the polishing parameters are fine-tuned through the feedback loop to cope with possible changes in the workpiece surface or uncertain factors. For example, if the removal rate is found to be slightly low, the pressure can be appropriately increased; if the temperature is too high, the cooling medium flow rate is increased. After the polishing process is completed, the workpiece surface is precision inspected to check the finish, surface roughness and whether there is any residual damage, to ensure that the hardware surface reaches the ideal finish and consistency, avoid surface damage or unevenness, and make the polishing quality stable and meet high-precision requirements.

[0045] In the above, refer to Figure 1 The present invention describes in detail the method for fine polishing of the surface of precision hardware according to an embodiment of the present invention. Figure 2 A device for finely polishing the surface of a precision hardware part according to an embodiment of the present invention is described.

[0046] The device for fine polishing of the surface of precision hardware according to the embodiment of the present invention is used to solve the technical problems existing in the prior art, such as lack of real-time monitoring of the polishing state, difficulty in controlling the balance between material removal and introduced damage, which leads to unstable surface polishing quality of hardware and imbalance between surface quality and polishing efficiency, and achieves the technical effect of accurately controlling the polishing process and improving the surface quality of hardware. The device for fine polishing of the surface of precision hardware includes: a target polishing information acquisition module 10, a target introduced damage rate acquisition module 20, a target polishing state index acquisition module 30, a polishing adjustment instruction issuing module 40, an optimal polishing process acquisition module 50, and a fine polishing processing module 60.

[0047] A target polishing information acquisition module 10 is used to dynamically monitor the polishing state of the target hardware to obtain target polishing information, wherein the target polishing information includes target material removal information and target introduced damage information; a target introduced damage rate acquisition module 20 is used to introduce a damage assessment strategy to analyze the target introduced damage information to obtain the target introduced damage rate of the target hardware; a target polishing state index acquisition module 30 is used to perform weighted calculation on the target introduced damage rate and the target material removal rate obtained by analyzing the target material removal information to obtain a target polishing state index; a polishing adjustment instruction issuance module 40 is used to issue a polishing adjustment instruction when the target polishing state index is not within a predetermined state index threshold; an optimal polishing process acquisition module 50 is used for the polishing control component to optimize and adjust the target process information of the target hardware based on the polishing adjustment instruction to obtain an optimal polishing process; a refined polishing processing module 60 is used to perform refined polishing processing on the target hardware according to the optimal polishing process.

[0048] The specific configuration of the target polishing information acquisition module 10 will be described in detail below. The target polishing information acquisition module 10 may further include: activating a predetermined Langmuir probe array, the predetermined Langmuir probe array includes a plurality of probes with three-dimensional coordinate identifiers; monitoring the plasma characteristics of the target neighborhood of the target hardware through the predetermined Langmuir probe array to obtain neighborhood plasma characteristic information, the neighborhood plasma characteristic information includes a plurality of groups of characteristic information corresponding to the plurality of probes with three-dimensional coordinate identifiers; extracting the first characteristic information from the plurality of groups of characteristic information, and the first characteristic information corresponds to a first three-dimensional coordinate; generating a target plasma characteristic visual model of the target hardware based on a first corresponding relationship between the first characteristic information and the first three-dimensional coordinate; and obtaining the target introduced damage information according to the target plasma characteristic visual model.

[0049] The specific configuration of the target polishing information acquisition module 10 will be described in detail below. The target polishing information acquisition module 10 may further include: randomly acquiring a first surface point of the target hardware, the first surface point corresponding to a first surface three-dimensional coordinate; determining a first neighborhood of the first surface three-dimensional coordinate in the target plasma characteristic visual model, and the first neighborhood includes N groups of correspondences, N is an integer greater than or equal to 3; predicting the plasma characteristics of the first surface three-dimensional coordinate according to the N groups of correspondences to obtain first predicted plasma characteristic information; using the first predicted plasma characteristic information as the first surface characteristic information of the first surface three-dimensional coordinate; analyzing the first surface characteristic information to obtain the target introduced damage information.

[0050] The specific configuration of the target polishing information acquisition module 10 will be described in detail below. The target polishing information acquisition module 10 may further include: extracting any corresponding relationship from the N groups of corresponding relationships, wherein the arbitrary corresponding relationship refers to the corresponding relationship between any characteristic information and any three-dimensional coordinates; calculating any spatial distance between the arbitrary three-dimensional coordinates and the first surface three-dimensional coordinates, and determining any coefficient based on the arbitrary spatial distance; combining the arbitrary coefficient with the arbitrary characteristic information to calculate and obtain the first predicted plasma characteristic information; wherein the arbitrary characteristic information includes a plurality of parameter data corresponding to predetermined plasma indicators, wherein the predetermined plasma indicators include at least electron temperature, ion temperature, electron density, and ion density.

[0051] The specific configuration of the target introduced damage rate acquisition module 20 will be described in detail below. The target introduced damage rate acquisition module 20 may further include: performing a visual significance analysis on the target introduced damage information based on the significance principle to obtain surface significant points, and the surface significant points correspond to surface significant three-dimensional coordinates; performing support analysis on the surface significant characteristic information corresponding to the surface significant points according to the surface significant neighborhood of the surface significant three-dimensional coordinates to obtain surface significant support; judging whether the surface significant support meets the predetermined support threshold; if not, using the surface significant characteristic information as the surface polishing evaluation benchmark of the target hardware, and analyzing to obtain the target introduced damage rate.

[0052] The specific configuration of the target introduced damage rate acquisition module 20 will be described in detail below. The target introduced damage rate acquisition module 20 may further include: if it meets the requirements, analyzing any surface significant characteristic information corresponding to any surface point in the target introduced damage information; taking the average of the any surface significant characteristic information and the surface significant characteristic information, and forming the surface polishing evaluation benchmark of the target hardware; obtaining the target introduced damage rate based on the surface polishing evaluation benchmark.

[0053] The specific configuration of the target introduced damage rate acquisition module 20 will be described in detail below. The target introduced damage rate acquisition module 20 may further include: training a damage prediction model based on big data, the damage prediction model including a damage density prediction unit and a damage depth prediction unit; analyzing the surface significant characteristic information through the damage density prediction unit to obtain a predicted target damage density; analyzing the surface significant characteristic information through the damage depth prediction unit to obtain a predicted target damage depth; and weighting the predicted target damage density and the predicted target damage depth to obtain the target introduced damage rate.

[0054] The device for fine-finishing the surface of precision hardware provided in the embodiment of the present invention can execute the method for fine-finishing the surface of precision hardware provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0056] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for fine polishing of the surface of precision hardware, characterized in that: include: Dynamically monitoring the polishing state of the target hardware to obtain target polishing information, wherein the target polishing information includes target material removal information and target introduced damage information; Introducing a damage assessment strategy to analyze the target introduced damage information to obtain a target introduced damage rate of the target hardware; Performing weighted calculation on the target introduced damage rate and the target material removal rate obtained by analyzing the target material removal information to obtain a target polishing state index; When the target polishing state index is not within a predetermined state index threshold, issuing a polishing adjustment instruction; The polishing control component optimizes and adjusts the target process information of the target hardware based on the polishing adjustment instruction to obtain the optimal polishing process; The target hardware is finely polished according to the optimal polishing process.

2. The method for fine polishing of the surface of precision hardware according to claim 1, characterized in that: include: activating a predetermined Langmuir probe array, wherein the predetermined Langmuir probe array includes a plurality of probes with three-dimensional coordinate identifiers; Performing plasma characteristic monitoring on a target neighborhood of the target hardware by means of the predetermined Langmuir probe array to obtain neighborhood plasma characteristic information, wherein the neighborhood plasma characteristic information includes multiple groups of characteristic information corresponding to the multiple probes with three-dimensional coordinate identifiers; Extracting first characteristic information from the multiple sets of characteristic information, where the first characteristic information corresponds to a first three-dimensional coordinate; Generate a target plasma characteristic visual model of the target hardware based on a first corresponding relationship between the first characteristic information and the first three-dimensional coordinates; The target introduced damage information is obtained according to the target plasma characteristic visual model.

3. The method for fine polishing of the surface of precision hardware according to claim 2, characterized in that: include: Randomly acquiring a first surface point of the target hardware component, wherein the first surface point corresponds to a first surface three-dimensional coordinate; Determining a first neighborhood of the three-dimensional coordinates of the first surface in the target plasma characteristic visual model, wherein the first neighborhood includes N groups of corresponding relationships, where N is an integer greater than or equal to 3; Predicting the plasma characteristics of the first surface three-dimensional coordinates according to the N groups of corresponding relationships to obtain first predicted plasma characteristic information; Using the first predicted plasma characteristic information as first surface characteristic information of the first surface three-dimensional coordinates; The first surface characteristic information is analyzed to obtain the target introduced damage information.

4. The method for fine polishing of the surface of precision hardware according to claim 3 is characterized in that: include: Extracting any corresponding relationship from the N groups of corresponding relationships, where the arbitrary corresponding relationship refers to a corresponding relationship between any characteristic information and any three-dimensional coordinates; Calculating an arbitrary spatial distance between the arbitrary three-dimensional coordinate and the first surface three-dimensional coordinate, and determining an arbitrary coefficient based on the arbitrary spatial distance; Combining the arbitrary coefficient with the arbitrary characteristic information, calculating and obtaining the first predicted plasma characteristic information; The arbitrary characteristic information includes a plurality of parameter data corresponding to predetermined plasma indicators, and the predetermined plasma indicators at least include electron temperature, ion temperature, electron density and ion density.

5. The method for fine polishing of the surface of precision hardware according to claim 1, characterized in that: include: Based on the saliency principle, a visual saliency analysis is performed on the damage information introduced to the target to obtain surface saliency points, and the surface saliency points correspond to surface saliency three-dimensional coordinates; Performing support analysis on the surface significant characteristic information corresponding to the surface significant points according to the surface significant neighborhood of the surface significant three-dimensional coordinates to obtain the surface significant support; Determining whether the surface significant support meets a predetermined support threshold; If it does not meet the requirements, the surface significant characteristic information is used as a surface polishing evaluation benchmark for the target hardware component, and the target introduced damage rate is obtained through analysis.

6. The method for fine polishing of the surface of precision hardware according to claim 5, characterized in that: Also includes: If it is in accordance with the above, analyzing any significant characteristic information of the surface corresponding to any surface point in the damage information introduced by the target; Taking the mean of the arbitrary surface significant characteristic information and the surface significant characteristic information, and forming the surface polishing evaluation benchmark of the target hardware; The target introduced damage rate is obtained based on the surface polishing evaluation benchmark.

7. The method for fine polishing of the surface of precision hardware according to claim 6, characterized in that: include: Training a damage prediction model based on big data, wherein the damage prediction model includes a damage density prediction unit and a damage depth prediction unit; The damage density prediction unit analyzes the surface significant characteristic information to obtain a predicted target damage density; The damage depth prediction unit analyzes the surface significant characteristic information to obtain a predicted target damage depth; The target introduced damage rate is obtained by weighting the predicted target damage density and the predicted target damage depth.

8. Precision hardware surface fine polishing device, characterized in that: The device is used to implement the method for fine polishing of the surface of precision hardware according to any one of claims 1 to 7, and the device comprises: A target polishing information acquisition module is used to dynamically monitor the polishing state of the target hardware to obtain target polishing information, wherein the target polishing information includes target material removal information and target introduced damage information; A target introduced damage rate acquisition module is used to introduce a damage assessment strategy to analyze the target introduced damage information to obtain a target introduced damage rate of the target hardware; A target polishing state index obtaining module is used to perform weighted calculation on the target introduced damage rate and the target material removal rate obtained by analyzing the target material removal information to obtain a target polishing state index; A polishing adjustment instruction issuing module, used for issuing a polishing adjustment instruction when the target polishing state index is not within a predetermined state index threshold; An optimal polishing process acquisition module is used for the polishing control component to optimize and adjust the target process information of the target hardware based on the polishing adjustment instruction to obtain the optimal polishing process; A fine polishing processing module is used to perform fine polishing processing on the target hardware according to the optimal polishing process.

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