Hub bearing inner and outer flange ferrule channel superfinishing machine preparation process

Abstract

The invention discloses a hub bearing inner and outer flange ferrule channel superfinishing machine manufacturing technology, and relates to the technical field of machine manufacturing, and the hub bearing inner and outer flange ferrule channel superfinishing machine manufacturing technology comprises the steps that a high-precision bearing steel billet is selected, and pre-finish turning is conducted; the channel is polished; carrying out superfinishing on the surface of the channel; performing nanoscale finishing treatment on the surface of the channel; a white light interferometer and a self-adaptive texture algorithm are adopted to carry out micro texture analysis on the surface of the channel; and a process evaluation model is constructed, and machining parameters are monitored in real time through a closed-loop control system. Advanced technologies such as numerical control end face grinding wheel accurate grinding, magneto-rheological polishing and low-temperature plasma finishing are combined, the roughness, residual stress and texture distribution of the surface of the channel are accurately controlled, the surface quality and machining precision are remarkably improved, machining parameters are adjusted in real time through a closed-loop control system, the stability and consistency of the machining process are ensured, and the machining precision is improved. And personal errors are reduced, the wear resistance and fatigue resistance of the product are improved, and the service life of the product is prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical manufacturing, in particular to a preparation process of a hub bearing inner and outer flange sleeve ring channel superfinishing machine. BACKGROUND

[0002] In the manufacturing of high-precision mechanical parts, hub bearings, as one of the key components, have extremely high requirements for machining precision and surface quality. The channel part of the hub bearing inner and outer flange sleeve ring usually needs to withstand high-strength mechanical load and long-term working friction, so the requirement for surface quality is more stringent. Traditional manufacturing processes often rely on manual operation or conventional machine tool machining, making it difficult to achieve ultra-high precision surface finish and fine control of micro-morphology. In order to improve the wear resistance, life and reliability of the hub bearing, it is of great significance to develop a superfinishing machine preparation process suitable for the hub bearing inner and outer flange sleeve ring channel.

[0003] At present, although there are various superfinishing technologies in the prior art, such as numerical control lathe, precision grinding and polishing process, etc., most of the processes have problems such as limited machining precision, difficult temperature control in the machining process, and unable to effectively reduce surface roughness. In addition, the traditional finishing process mostly ignores the release of residual stress in the processing process, which is easy to cause fatigue cracks or deformation of the product in long-term use, affecting its long-term performance. Therefore, we propose a hub bearing inner and outer flange sleeve ring channel superfinishing machine preparation process. SUMMARY

[0004] The purpose of the present application is to provide a hub bearing inner and outer flange sleeve ring channel superfinishing machine preparation process to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides the following technical solution: a hub bearing inner and outer flange sleeve ring channel superfinishing machine preparation process, comprising the following steps:

[0006] Step 1, select high-precision bearing steel billet, and perform pre-finishing machining;

[0007] Step 2, use numerical control end face grinding wheel precision grinding process to polish the channel;

[0008] Step 3, perform channel surface superfinishing by magnetorheological polishing technology;

[0009] Step 4, use low-temperature plasma finishing technology to perform nanoscale finishing treatment on the channel surface to release residual stress;

[0010] Step 5, use white light interferometer and adaptive texture algorithm to analyze the micro-texture of the channel surface to ensure the uniformity and directionality of the texture distribution;

[0011] Step 6, build a process evaluation model, real-time monitoring of processing parameters through closed-loop control system to ensure the processing quality meets the target standard.

[0012] Preferably, the specific steps of step 1 include:

[0013] The bearing steel blank with alloy composition GCr15SiMn is selected, and the carbon content is 0.95%-1.05%;

[0014] Pre-precision turning is carried out by numerical control lathe, so that the roughness of the channel base surface is ≤0.8 microns, and the feed amount is 0.02-0.05 mm / r;

[0015] Induction heating and controlled cooling process are adopted for quenching and tempering treatment to ensure that the microstructure of the steel blank is fine pearlite and ferrite.

[0016] Preferably, the specific steps of step 2 include:

[0017] CNC end face grinding wheel is adopted, the linear speed of the grinding wheel is controlled at 30-35 m / s, and the feed amount is 0.02-0.05 mm / r;

[0018] The temperature of the grinding fluid is constantly controlled at 25±1℃ to ensure that the temperature fluctuation during the processing process does not exceed ±1℃.

[0019] Preferably, the specific steps of step 3 include:

[0020] Double diamond roller and magnetic rheological polishing technology are adopted, the particle size is controlled at 1 micron, and the surface roughness is ensured to be ≤0.03 microns;

[0021] The magnetic induction intensity of the magnetic rheological fluid is controlled at 0.3-0.6 T to make the channel surface achieve uniform finishing effect.

[0022] Preferably, the specific steps of step 4 include:

[0023] Low-temperature plasma finishing technology is adopted, the frequency range is 40-60 Hz, and the processing time is 30-60 s to ensure the smoothness of the channel surface and the residual stress control;

[0024] The residual stress of the channel surface is monitored, and the residual stress should be ≤80 MPa, and the calculation formula of the residual stress is:

[0025] ;

[0026] Wherein, E is the elastic modulus of the material, △L is the deformation of the channel, and L0 is the original length of the channel.

[0027] Preferably, the specific steps of step 5 include:

[0028] The micro-texture of the groove surface is detected by a white light interferometer, and the surface power spectral density is measured;

[0029] The anisotropy index beta of the roughness of the groove surface is analyzed by using an adaptive texture distribution algorithm, and the calculation formula is:

[0030] ;

[0031] Wherein, Ra1 represents the roughness of the groove surface in the horizontal direction, Ra2 represents the roughness of the groove in the vertical direction, beta is less than 1, which represents that the roughness in the vertical direction is large, beta is greater than 1, which represents that the roughness in the horizontal direction is large, and if beta is greater than 1.2, it is judged that there is a directional scratch on the surface.

[0032] Preferably, the specific steps of step 6 comprise:

[0033] According to the roughness Ra, the residual stress , and the curvature p, a comprehensive evaluation function Q is constructed:

[0034] ;

[0035] Wherein, The weight coefficients of each index are respectively, p0 is the target curvature, and Q is the comprehensive evaluation value of the processing quality;

[0036] When Q exceeds the set threshold Q t , the grinding pressure and the finishing time are adjusted, and the closed-loop control system is corrected.

[0037] Preferably, the set threshold Q t represents the maximum error limit allowed in the processing process, and Q t is set according to the roughness Ra t , the residual stress sigma{r,t} and the curvature error p t factors are dynamically adjusted.

[0038] Preferably, the superfinishing machine is equipped with a multi-channel monitoring module, including a surface temperature sensor, a vibration accelerometer and an optical roughness sensor, which realizes real-time state monitoring through a data fusion algorithm, and performs abnormal early warning in the processing process.

[0039] Preferably, the closed-loop optimization system adopts a self-learning parameter adjustment module based on a genetic algorithm, which updates the grinding parameter set {v, f, B, t} in real time to minimize the target function Q to ensure the stability of the superfinishing process.

[0040] The technical effects and advantages of the present application are:

[0041] The application combines advanced technologies such as numerical control end face grinding wheel fine grinding, magnetorheological polishing, low-temperature plasma finishing, precisely controls the roughness, residual stress and texture distribution of the channel surface, significantly improves the surface quality and machining precision, adjusts the machining parameters in real time through a closed-loop control system, ensures the stability and consistency of the machining process, reduces human errors, improves the wear resistance, fatigue resistance and service life of the product, and is especially suitable for high-precision and high-load working environments. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 It is a process flow diagram of the application. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0044] The application provides a wheel hub bearing inner and outer flange sleeve ring channel superfinishing machine preparation process as shown in the following formula (I): Figure 1 The process comprises the following steps:

[0045] Step 1, high-precision bearing steel blank is selected for pre-finishing machining;

[0046] Step 2, numerical control end face grinding wheel fine grinding process is used for polishing the channel;

[0047] Step 3, magnetorheological polishing technology is used for channel surface superfinishing;

[0048] Step 4, low-temperature plasma finishing technology is used for nanoscale finishing treatment of the channel surface to release residual stress;

[0049] Step 5, white light interferometer and adaptive texture algorithm are used for micro-texture analysis of the channel surface to ensure the uniformity and directionality of the texture distribution;

[0050] Step 6, a process evaluation model is constructed, and a closed-loop control system is used to monitor the machining parameters in real time to ensure that the machining quality meets the target standard.

[0051] Step 1, high-precision bearing steel blank is selected to ensure good hardness and wear resistance, and numerical control lathe is used for pre-finishing machining to ensure that the preliminary size and geometric shape of the workpiece meet the subsequent machining requirements, provide a smooth preliminary surface for subsequent steps (such as fine grinding and polishing), reduce the material removal amount during subsequent machining, and thus improve the overall process efficiency and machining precision.

[0052] The fine grinding process in Step 2 can significantly improve the accuracy of the workpiece surface, further optimize the geometry of the channel, and effectively reduce surface roughness by precisely controlling grinding parameters, ensuring that the surface quality meets the accuracy requirements and laying the foundation for subsequent superfinishing and surface treatment.

[0053] Step 3 uses magnetic fluid polishing to achieve extremely high accuracy and smoothness of the channel surface, effectively removing tiny burrs and processing marks, providing a high-quality foundation for subsequent nanoscale finishing, further improving surface quality and ensuring that the channel surface meets ultra-precision requirements.

[0054] In Step 4, low-temperature plasma finishing can effectively improve surface smoothness and release internal stress, preventing subsequent deformation caused by residual stress. By precisely controlling the action time and intensity of low-temperature plasma, the uniformity of surface finishing is ensured, surface roughness is reduced, and fatigue life is improved.

[0055] In Step 5, precise micro-texture analysis can effectively detect the directionality of surface texture, thereby determining whether there are directional grinding marks. By adjusting process parameters through adaptive algorithms, surface texture distribution is further optimized, surface quality and processing accuracy are improved, and surface without obvious directional grinding marks is ensured, meeting precision machining standards.

[0056] In Step 6, real-time feedback adjustment through a closed-loop control system ensures accurate control of each processing step, avoiding processing quality problems caused by process parameter deviations, making the entire processing process more automated and intelligent, significantly improving production efficiency, reducing human intervention errors, and improving product consistency and reliability.

[0057] Specific steps of Step 1 include:

[0058] The bearing steel blank with alloy composition GCr15SiMn is selected, with a carbon content of 0.95%-1.05%. GCr15SiMn steel material has good processing performance and wear resistance, can operate for a long time under high load, and has good heat treatment performance, which helps to achieve the required surface accuracy and durability in subsequent processes. By selecting such materials, the finished product has a longer service life and higher reliability during use;

[0059] The pre-finishing is performed by a numerical control lathe to make the roughness of the groove base surface less than or equal to 0.8 microns, and the feed rate is 0.02-0.05 mm / r; the pre-finishing is performed by a numerical control lathe to accurately control the cutting speed, feed rate and cutting depth, through this technology, the accuracy of the groove base surface is ensured, and the predetermined roughness requirement is met, by accurately controlling the feed rate and turning depth, the roughness of the groove surface can be significantly reduced to meet the processing requirements of subsequent fine grinding and polishing, the pre-finishing greatly reduces the cutting amount and surface defects of subsequent processes, improves the efficiency and quality of subsequent fine grinding and finishing process, and avoids unnecessary material waste.

[0060] The induction heating and controlled cooling process is adopted for quenching and tempering treatment to ensure that the billet organization is fine pearlite and ferrite, the induction heating technology is adopted to uniformly heat the billet, so that it reaches the required heating temperature range (about 850-900℃) in a short time. This process can realize rapid heating of local area, avoid the temperature unevenness problem that may occur in traditional heating methods, through induction heating, the surface temperature of the steel can be accurately controlled, and the overheating or local overcooling phenomenon is avoided, to ensure that the steel obtains good mechanical properties, the heated billet is cooled through the controlled cooling process, and oil quenching or gas cooling is adopted to ensure the stability of the cooling process and make the material change into fine pearlite and ferrite structure. In the cooling process, the cooling rate is accurately controlled to make the steel obtain the required organizational structure (fine pearlite and ferrite) to improve the toughness and hardness of the steel and optimize its processing performance.

[0061] The specific steps of step 2 include:

[0062] The CNC face grinding wheel fine grinding is adopted, the linear speed of the grinding wheel is controlled at 30-35 m / s, and the feed rate is 0.02-0.05 mm / r; the computer numerical control (CNC) technology is adopted for face grinding wheel fine grinding of the groove to ensure the high precision and consistency of the grinding process. The feed, speed and grinding depth of the grinding wheel are accurately controlled through the CNC system to ensure that each grinding is within the preset range;

[0063] The temperature of the grinding fluid is constantly controlled at 25±1℃ to ensure that the temperature fluctuation during the processing does not exceed ±1℃, avoiding the negative impact of unstable temperature on the processing precision and surface quality. The grinding fluid can effectively reduce the heat accumulation during grinding under constant temperature conditions, ensuring that the temperature during grinding remains stable, thereby reducing the surface deformation and wear of the workpiece caused by thermal stress. At the same time, temperature control also helps to improve the lubrication effect of the grinding fluid, reduce friction in the grinding area, prolong the service life of the grinding wheel, and effectively control the surface roughness.

[0064] The specific steps of step 3 include:

[0065] Adopting double diamond roller and magnetic fluid polishing technology, the granularity control is 1 microns, ensuring that the surface roughness is less than or equal to 0.03 microns; not only can remove small surface defects, but also can improve the surface smoothness, reduce the friction coefficient, and provide better performance for subsequent use;

[0066] The magnetic induction intensity of the magnetic fluid is controlled at 0.3-0.6 T to make the channel surface achieve uniform finishing effect; by accurately adjusting the magnetic induction intensity, the rheological properties of the polishing liquid can be controlled to make it form a uniform finishing effect on the channel surface, and the adjustment of the magnetic induction intensity can optimize the fluidity of the magnetic fluid, so that the liquid is uniformly distributed on the surface, avoiding uneven finishing effect or local over-grinding on the surface.

[0067] The specific steps of step 4 include:

[0068] Adopting low-temperature plasma finishing technology, the frequency range is 40-60 Hz, and the processing time is 30-60 s to ensure the smoothness of the channel surface and the control of residual stress; the low-temperature plasma finishing technology can make the channel surface achieve extremely high smoothness by accurately controlling the frequency and processing time, significantly reducing the surface roughness and providing nanoscale surface finishing effect; this technology can effectively remove micro burrs and machining marks, improve the surface flatness and smoothness, and is especially suitable for application environments requiring extremely high precision;

[0069] The residual stress on the channel surface is monitored, and the residual stress should be ≤80 MPa, and the calculation formula of the residual stress is:

[0070] ;

[0071] Wherein, E is the elastic modulus of the material, △L is the deformation of the channel, and L0 is the original length of the channel; through low-temperature plasma treatment, the residual stress on the channel surface is effectively released and controlled below 80 MPa, which can reduce the risk of subsequent deformation, crack propagation or fatigue failure caused by stress concentration, and stress control can help improve the overall structural stability of the component and ensure its durability in long-term use, especially in high-load and high-temperature change working environment.

[0072] The specific steps of step 5 include:

[0073] The white light interferometer is used to detect the micro-texture of the channel surface and measure the surface power spectral density. The white light interferometer provides high-resolution surface texture measurement, which can accurately evaluate the roughness and texture characteristics of the surface at the microscale. This allows for targeted adjustment of process parameters in subsequent processing to ensure that the surface quality meets the highest requirements. The power spectral density measurement provides a comprehensive surface evaluation that can identify small surface defects or microscopic irregularities, thereby avoiding the production of substandard parts.

[0074] The adaptive texture distribution algorithm is used to analyze the anisotropy index β of the roughness of the channel surface, and the calculation formula is:

[0075] ;

[0076] wherein, Ra1 represents the roughness of the channel surface in the horizontal direction, Ra2 represents the roughness of the channel in the vertical direction, and β less than 1 indicates that the roughness in the vertical direction is large, and β greater than 1 indicates that the roughness in the horizontal direction is large. If β is greater than 1.2, it is judged that there are directional scratches on the surface. By ensuring the uniformity of the surface texture, the friction performance of the component in actual application can be significantly improved, wear can be reduced, and the service life can be prolonged. In addition, controlling and optimizing the surface texture can effectively reduce the occurrence of vibration, noise and mechanical failure, and improve the overall performance and reliability of the product.

[0077] The specific steps of step 6 include:

[0078] According to the roughness Ra, the residual stress and the curvature ρ, a comprehensive evaluation function Q is constructed:

[0079] ;

[0080] wherein, are the weight coefficients of each index, ρ0 is the target curvature, and Q is the comprehensive evaluation value of the processing quality; by constructing the comprehensive evaluation function Q, the influence of the surface roughness, the residual stress and the curvature can be quantified comprehensively, and the processing quality can be accurately evaluated. This evaluation function can comprehensively consider the changes of various process parameters, thereby providing a more accurate basis for the control of processing quality and ensuring that each link meets the target standard.

[0081] When Q exceeds the set threshold Q t , adjust the grinding pressure and finishing time, and correct it through the closed-loop control system; when the processing quality does not meet the requirements, the closed-loop control system can automatically adjust the grinding pressure and finishing time according to the real-time feedback of Q, avoid errors caused by manual intervention, and dynamically adjust the process to correct the problem of excessive grinding or insufficient grinding in time. Avoid surface defects caused by improper operation or inaccurate parameter setting to ensure that the final workpiece meets the requirements of precision machining.

[0082] wherein the threshold value Q is set t represents the maximum error limit allowed during processing, and Q t is dynamically adjusted according to the surface roughness Ra t , residual stress σ{r,t} and curvature error ρ t factors, Q t Dynamic adjustment of Q makes the system flexible to respond to quality changes under different processing conditions. For example, in different materials, different temperatures or different processing stages, the system can adaptively adjust the processing parameters to ensure the stability of the processing quality.

[0083] wherein the superfinishing machine is equipped with a multi-channel monitoring module, including surface temperature sensors, vibration accelerometers and optical roughness sensors, which realizes real-time state monitoring through data fusion algorithm and performs processing process abnormal early warning; through multi-channel real-time monitoring, it can track all factors that may affect the quality (such as temperature, vibration and roughness) in the processing process. The system ensures the stability and precision of the processing process, so that the surface quality of each workpiece is more uniform and consistent, and timely monitoring and control of key parameters such as temperature, vibration and roughness can effectively reduce processing defects caused by improper process parameters or equipment problems.

[0084] wherein the closed-loop optimization system adopts a self-learning parameter adjustment module based on genetic algorithm, which updates the grinding parameter set {v, f, B, t} in real time to minimize the objective function Q to ensure the stability of the superfinishing process. The self-learning adjustment module based on genetic algorithm can automatically optimize various parameters in the grinding process. The system reduces the errors caused by human intervention by continuously adjusting the grinding speed, feed rate, magnetorheological fluid intensity and finishing time, ensuring that the processing process is more stable, accurate and efficient. The core objective of the genetic algorithm is to minimize the objective function Q. By continuously evaluating the effect of the current grinding parameters, the genetic algorithm can dynamically adjust and optimize the process so that roughness, residual stress and curvature and other processing quality indicators are as close to the ideal value as possible.

[0085] Finally, it should be noted that the above-described only for the preferred embodiments of the present application, and not for the limitation of the present application, although the foregoing detailed description of the present application is made with reference to the foregoing embodiments, for those skilled in the art, it still can be modified to the technical solutions recorded in the foregoing embodiments, or equivalent replacement of some of the technical features, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A high-precision machining process for the inner and outer flange raceways of a wheel hub bearing, characterized in that, Includes the following steps: Step 1: Select high-precision bearing steel billets and perform pre-finish machining; Step 2: Grind the groove using CNC end face grinding wheel precision grinding process; Step 3: Perform ultra-precision finishing of the channel surface using magnetorheological polishing technology; Step 4: Perform nanoscale finishing on the channel surface using low-temperature plasma finishing technology to release residual stress; Step 5: Perform micro-texture analysis on the channel surface using a white light interferometer and an adaptive texture algorithm to ensure the uniformity and directionality of the texture distribution. Step 6: Construct a process evaluation model and monitor the processing parameters in real time through a closed-loop control system to ensure that the processing quality meets the target standards.

2. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 1, characterized in that, The specific steps of step 1 include: The bearing steel billet selected has an alloy composition of GCr15SiMn and a carbon content of 0.95%-1.05%. Pre-finish turning is performed on a CNC lathe to achieve a surface roughness of ≤0.8 micrometers for the groove base surface, with a feed rate of 0.02-0.05 mm / r. Induction heating and controlled cooling processes are used for tempering to ensure that the billet microstructure consists of fine pearlite and ferrite.

3. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 1, characterized in that, The specific steps of step 2 include: CNC face grinding wheel is used for precision grinding, with the grinding wheel linear speed controlled at 30-35 m / s and the feed rate at 0.02-0.05 mm / r; The grinding fluid temperature is kept constant at 25±1℃ to ensure that the temperature fluctuation during processing does not exceed ±1℃.

4. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 1, characterized in that, The specific steps of step 3 include: Using dual diamond rollers and magnetorheological polishing technology, the particle size is controlled to 1 micrometer, ensuring a surface roughness of ≤0.03 micrometers; The magnetic induction intensity of the magnetorheological fluid is controlled at 0.3-0.6 T to achieve a uniform smoothing effect on the channel surface.

5. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 1, characterized in that, The specific steps of step 4 include: Low-temperature plasma finishing technology is used, with a frequency range of 40-60 Hz and a processing time of 30-60 s, to ensure the smoothness of the channel surface and control of residual stress. Monitor the residual stress on the channel surface; the residual stress should The formula for calculating residual stress ≤80 MPa is: ； Where E is the elastic modulus of the material, ΔL is the channel deformation, and L0 is the original length of the channel.

6. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 1, characterized in that, The specific steps of step 5 include: Microscopic texture detection of the channel surface was performed using a white light interferometer, and the surface power spectral density was measured. An adaptive texture distribution algorithm is used to analyze the roughness anisotropy index β of the channel surface. The calculation formula is as follows: ； Where Ra1 represents the surface roughness of the channel in the horizontal direction, Ra2 represents the surface roughness of the channel in the vertical direction, β less than 1 indicates that the surface roughness is large in the vertical direction, β greater than 1 indicates that the surface roughness is large in the horizontal direction, and if β is greater than 1.2, it is judged that there are directional wear marks on the surface.

7. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 1, characterized in that, The specific steps of step 6 include: Based on roughness Ra and residual stress Curvature ρ constructs a comprehensive evaluation function Q: ； in, These are the weighting coefficients for each indicator, ρ0 is the target curvature, and Q is the comprehensive evaluation value of processing quality. When Q exceeds the set threshold Q t At the same time, the grinding pressure and finishing time are adjusted and corrected through a closed-loop control system.

8. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 7, characterized in that, The set threshold Q t This represents the maximum allowable error limit during the processing, and Q... t The setting is based on the standard roughness Ra t Residual stress σ{r,t} and curvature error ρ t The factors are dynamically adjusted.

9. The ultra-precision machining process for the inner and outer flange grooves of a wheel hub bearing according to claim 1, characterized in that, The ultra-precision machine is equipped with a multi-channel monitoring module, including a surface temperature sensor, a vibration accelerometer, and an optical roughness sensor. It achieves real-time status monitoring and provides early warning of abnormalities in the processing through a data fusion algorithm.

10. The ultra-precision machining process for the inner and outer flange raceways of a wheel hub bearing according to claim 1, characterized in that, The closed-loop optimization system employs a self-learning parameter adjustment module based on a genetic algorithm to update the grinding parameter set {v, f, B, t} in real time to minimize the objective function Q, thereby ensuring the stability of the ultra-precision machining process.

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