Variable gear shaft hole size parameter control method and system

Abstract

The invention discloses a variable gear shaft hole size parameter control method and system, and relates to the technical field of industrial part manufacturing. Calculating a size error according to the current shaft hole size and a preset target size; analyzing an error change rate and an error change trend according to the size error; determining a first proportional adjustment coefficient and a first integral adjustment coefficient according to the error change rate and the error change trend; evaluating the stability of shaft hole size adjustment to obtain a stability evaluation result; performing coefficient fusion processing according to the stability evaluation result, the first proportional adjustment coefficient and the first integral adjustment coefficient to obtain a fusion proportional adjustment coefficient and a fusion integral adjustment coefficient; adjusting the size of the shaft hole. According to the invention, the adjustment coefficient can be adjusted by combining the size error and the change characteristic, so that the shaft hole size parameter control is realized, and the accuracy and stability are improved.

Description

Technical Field

[0001] This invention relates to the field of industrial parts manufacturing technology, and in particular to a method and system for controlling the variable gear shaft hole size parameters. Background Technology

[0002] In precision industrial equipment, the diameter of the variable gear shaft bore needs to be adjusted to accommodate different drive shafts, compensate for wear, or optimize power transmission. To ensure dimensional accuracy, existing technologies use dimensional measuring probes to monitor the shaft bore inner diameter in real time and process the raw feedback information through preset digital filtering before adjusting the shaft bore. However, during long-term operation, uncertainties such as changes in the internal environment, alterations in sensor response characteristics, and operator experience lead to low control accuracy and stability.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a variable gear shaft hole size parameter control method and system, which can adjust the adjustment coefficient by combining size error and variation characteristics to achieve shaft hole size parameter control, thereby improving accuracy and stability.

[0005] On one hand, embodiments of the present invention provide a method for controlling the variable gear shaft bore size parameters, including the following steps:

[0006] Get the current shaft hole size;

[0007] Calculate the dimensional error based on the current shaft hole size and the preset target size;

[0008] Based on the stated dimensional error, analyze the rate of error change and the trend of error change;

[0009] Based on the error change rate and the error change trend, determine the first proportional adjustment coefficient and the first integral adjustment coefficient;

[0010] The stability of the shaft hole size adjustment is evaluated based on the first proportional adjustment coefficient and the first integral adjustment coefficient, and the stability evaluation result is obtained.

[0011] Based on the stability assessment results, the first proportional adjustment coefficient and the first integral adjustment coefficient, coefficient fusion processing is performed to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient.

[0012] The shaft hole size is adjusted according to the fusion ratio adjustment coefficient and the fusion integral adjustment coefficient.

[0013] On the other hand, embodiments of the present invention provide a variable gear shaft bore size parameter control system, comprising:

[0014] The data acquisition module is used to obtain the current shaft hole size;

[0015] The error calculation module is used to calculate the dimensional error based on the current shaft hole size and the preset target size;

[0016] The variation characteristic analysis module is used to analyze the error change rate and error change trend based on the size error;

[0017] The adjustment coefficient determination module is used to determine a first proportional adjustment coefficient and a first integral adjustment coefficient based on the error change rate and the error change trend.

[0018] The stability evaluation module is used to evaluate the stability of the shaft hole size adjustment based on the first proportional adjustment coefficient and the first integral adjustment coefficient, and obtain the stability evaluation result.

[0019] The coefficient fusion module is used to perform coefficient fusion processing based on the stability assessment result, the first proportional adjustment coefficient, and the first integral adjustment coefficient to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient.

[0020] The shaft hole size adjustment module is used to adjust the shaft hole size according to the fusion ratio adjustment coefficient and the fusion integral adjustment coefficient.

[0021] The embodiments of this application include at least the following beneficial effects: First, the current shaft hole size is obtained. Then, based on the current shaft hole size and the preset target size, the size error is calculated, and the error change rate and error change trend are analyzed. Then, based on the error change rate and error change trend, the first proportional adjustment coefficient and the first integral adjustment coefficient are determined, and the stability of the shaft hole size adjustment is evaluated to obtain the stability evaluation result. Finally, based on the stability evaluation result, the first proportional adjustment coefficient and the first integral adjustment coefficient, coefficient fusion processing is performed to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient, and the shaft hole size is adjusted. Thus, the adjustment coefficient can be adjusted by combining the size error and change characteristics to achieve shaft hole size parameter control, thereby improving accuracy and stability.

[0022] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0024] Figure 1 This is a flowchart of a variable gear shaft hole size parameter control method according to an embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of a variable gear shaft hole size parameter control system according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0027] In related technologies, the design of variable gear shaft bores is widely used in precision industrial equipment to achieve precise adjustment of the shaft bore diameter, thereby adapting to different drive shafts, compensating for wear, or optimizing power transmission. To ensure dimensional accuracy, existing systems monitor the shaft bore inner diameter in real time using a dimensional measurement probe, and the raw feedback information is then processed by a preset digital filter before being used to control the shaft bore adjustment. However, during long-term operation of the equipment, existing systems struggle to consistently maintain high precision and stability in dimensional control when faced with changes in the internal environment, alterations in sensor response characteristics, and operator intervention based on experience.

[0028] For example, in advanced industrial machinery, especially equipment requiring extremely high power transmission precision or complex material processing, the application of variable gear bores is becoming increasingly widespread. This design allows the diameter of the gear's central bore to be precisely adjusted, thereby accommodating drive shafts of different specifications, compensating for wear generated during long-term operation, or optimizing power transmission efficiency under varying operating loads. To achieve this precise dimensional adjustment, the system typically relies on an integrated dimensional measurement probe that continuously monitors the bore's inner diameter. To ensure stable operation of the control system and effectively suppress inherent measurement noise, the raw dimensional feedback information obtained from the probe undergoes a pre-defined digital filtering process. The parameters of this filter are carefully set at the factory based on the system's ideal operating characteristics and a series of typical operating conditions. This configuration ensures that the system maintains high dimensional accuracy and control responsiveness under standard, predictable environments.

[0029] However, during prolonged operation, especially under continuously varying loads and frequent shaft and bore size adjustments, subtle physical changes inevitably occur within such mechanical devices. Over time, minute wear occurs between the working surface of the shaft bore inner wall and the drive shaft, as well as between the sliding components inside the size adjustment actuator. These tiny metal particles generated by wear are not completely removed; instead, they remain suspended in the lubricating oil film of the adjustment mechanism. Over time, these suspended particles gradually accumulate near the sensitive area of ​​the built-in size measurement probe. For example, if the probe uses ultrasonic principles, the presence of these particles subtly alters the acoustic impedance and sound wave propagation speed of the local medium; if the probe is based on eddy current or capacitance principles, the dielectric properties or electromagnetic field distribution of the local medium also change due to particle accumulation. This alteration of the probe's surrounding environment results in a very slight extension of its response time to actual size changes; in other words, at specific frequencies of size changes, the hysteresis effect of the probe signal becomes slightly more noticeable.

[0030] The subtle response delay or hysteresis exhibited by this built-in dimensional measurement probe causes a slight deviation between the output raw dimensional feedback signal and the actual shaft and hole dimensions when the shaft and hole dimensions change rapidly or the system is under high-frequency vibration conditions. This deviation is particularly significant in dynamic scenarios. Since this hysteresis effect is not an ideal characteristic preset at the factory, the sensor reading calibration data table originally established based on ideal conditions cannot effectively compensate for this newly emerging, subtle signal hysteresis. As a result, the real-time dimensional information received by the control system is no longer completely accurate. This inaccuracy makes the control system's adjustment actions somewhat sluggish when rapid dimensional adjustments are needed or when the system load fluctuates frequently, failing to reflect the true physical state of the shaft and hole in a timely and accurate manner, leading to low control accuracy and low stability.

[0031] During long-term operation of the variable gear shaft bore, the accumulation of wear particles in the lubricating medium causes a slight delay in the dimensional measurement probe signal. Simultaneously, operators, seeking faster response times, increase key adjustment coefficients in the control system based on experience. These factors collectively induce high-frequency micro-oscillations in the control system near the target dimension, thereby accelerating localized uneven wear on the working surface of the drive shaft and the inner wall of the shaft bore. Under these conditions, existing technologies cannot achieve rapid and stable positioning of the target bore diameter in rapid changeover or high-load operating scenarios. Furthermore, they cannot maintain long-term high precision and stability of the mating clearance in a locked state, ultimately severely impacting the product's machining accuracy and consistency.

[0032] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0033] Figure 1 This is an optional flowchart of a variable gear shaft bore size parameter control method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S107.

[0034] Step S101: Obtain the current shaft hole size;

[0035] Step S102: Calculate the dimensional error based on the current shaft hole size and the preset target size;

[0036] Step S103: Based on the dimensional error, analyze the rate of error change and the trend of error change;

[0037] Step S104: Determine the first proportional adjustment coefficient and the first integral adjustment coefficient based on the error change rate and error change trend;

[0038] Step S105: Evaluate the stability of shaft hole size adjustment based on the first proportional adjustment coefficient and the first integral adjustment coefficient, and obtain the stability evaluation result;

[0039] Step S106: Based on the stability assessment results, the first proportional adjustment coefficient and the first integral adjustment coefficient, perform coefficient fusion processing to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient.

[0040] Step S107: Adjust the shaft hole size according to the fusion ratio adjustment coefficient and the fusion integral adjustment coefficient.

[0041] Steps S101 to S107 shown in the embodiments of this application can adjust the adjustment coefficient by combining dimensional error and variation characteristics to achieve control of shaft hole size parameters, thereby improving accuracy and stability.

[0042] In some embodiments, steps S101-S107 may involve first obtaining the current shaft hole size. For example, a high-precision laser diameter gauge can be used to perform non-contact measurement of the shaft hole's inner diameter, providing fast and accurate dimensional data. Alternatively, a contact probe, such as an LVDT (Linear Variable Differential Transformer) sensor, can be used, extending into the shaft hole for measurement. This method may offer higher anti-interference capabilities under certain operating conditions. Image recognition technology can also be used, combined with a high-resolution industrial camera, to photograph the shaft hole and extract its inner diameter using image processing algorithms.

[0043] Then, based on the current shaft hole size and the preset target size, the dimensional error is calculated. A simple arithmetic subtraction between the current measured value and the preset target value yields a numerical value representing the degree of deviation. For example, if the target size is 100mm and the current measured size is 100.1mm, the dimensional error is 0.1mm. Based on the dimensional error, the rate of error change and the trend of error change are analyzed. The rate of error change can be obtained by differential calculation of the dimensional error at consecutive time points; for example, calculating the ratio of the change in error between two adjacent measurements to the time interval. Signal processing methods such as moving average and Kalman filtering can be used to smooth the error sequence, obtain the error change trend, and thus identify whether the error is continuously increasing, continuously decreasing, or fluctuating around the target value. For example, a time window can be set to observe the average direction of error change within that window.

[0044] Then, based on the error change rate and trend, the first proportional and first integral control coefficients are determined. This can be achieved using a pre-defined lookup table. For example, appropriate proportional and integral control coefficients can be selected from a pre-defined lookup table based on the rate of error change and the direction of the error change trend. When the error is large and continuously deviates from the target, larger proportional and integral control coefficients may be needed to accelerate the response speed; when the error is small and close to the target, smaller coefficients may be needed to avoid overshoot. The pre-defined lookup table can be obtained by statistically analyzing a large amount of historical experimental data. It is understood that the first proportional and first integral control coefficients are two key parameters in the proportional-integral-derivative (PID) control algorithm, used to adjust the system's response strength to the current error and eliminate steady-state error.

[0045] The smoothness of the shaft hole size adjustment is evaluated based on the first proportional adjustment coefficient and the first integral adjustment coefficient, yielding a smoothness assessment result. This can be accomplished through simulation or actual testing. For example, the currently determined adjustment coefficients can be applied to a mathematical model of a shaft hole adjustment mechanism to simulate its adjustment process, and parameters such as overshoot, oscillation frequency, and settling time in the simulation results can be analyzed to evaluate the smoothness of the adjustment. Alternatively, small-amplitude test adjustments can be performed in an actual system, and the response curve of the shaft hole size can be monitored; the smoothness can then be judged based on the characteristics of the response curve.

[0046] Based on the stationarity assessment results, the first proportional adjustment coefficient, and the first integral adjustment coefficient, coefficient fusion processing is performed to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient. Methods such as weighted averaging, fuzzy inference, or neural networks can be used. For example, different weights can be assigned to the first proportional adjustment coefficient and the first integral adjustment coefficient according to the quality of the stationarity assessment results, and a weighted average can be performed to obtain the final fused adjustment coefficient.

[0047] Finally, the shaft hole size is adjusted based on the fused proportional and integral adjustment coefficients. For example, the diameter of the shaft hole can be adjusted using a precision lead screw mechanism driven by a stepper motor. The control system inputs the fused adjustment coefficients into the proportional-integral-derivative (PID) controller. The PID controller calculates the control quantity based on these coefficients and sends it to the actuator, thereby achieving precise adjustment of the shaft hole size.

[0048] This embodiment, by introducing analysis of the rate of change and trend of dimensional error, can dynamically determine the first proportional adjustment coefficient and the first integral adjustment coefficient, thereby enabling the control system to respond more intelligently to the current operating conditions. For example, when the error is large and deviates rapidly from the target, the system can quickly adjust the coefficients to accelerate the response; when the error is small and close to the target, the coefficients can be reduced to avoid overshoot. Furthermore, by evaluating the stability of shaft and hole size adjustment, this embodiment can further optimize the adjustment coefficients, ensuring the stability and accuracy of the adjustment process. The fusion of the stability evaluation results with the first proportional and first integral adjustment coefficients allows the final fused proportional and fused integral adjustment coefficients to comprehensively consider the current error state, the dynamic changes in error, and the system's own stability requirements, thus achieving more robust and precise shaft and hole size control. This dynamic and adaptive control strategy significantly improves the long-term high accuracy and stability of shaft and hole size control, effectively avoiding the problems of adjustment oscillation and increased wear caused by signal lag and empirical intervention in traditional methods.

[0049] Through the above technical solution, this embodiment effectively solves the problems of decreased control accuracy and system instability caused by signal lag, empirical intervention, and fixed parameters in existing technologies. For example, when wear of the shaft hole causes sensor response lag, this embodiment can identify this lag effect by analyzing the error change rate and trend, and adjust the adjustment coefficient accordingly to avoid over-correction. Simultaneously, stability assessment ensures the smoothness of the adjustment process, reduces fretting wear on the shaft hole and transmission shaft, thereby extending the service life of the equipment and improving production efficiency and product quality. This dynamic and adaptive control strategy is significantly superior to traditional control methods that rely on experience and fixed parameters, providing a more advanced and reliable solution for the variable gear shaft hole size control of precision industrial equipment.

[0050] In some embodiments, in step S104, determining the first proportional adjustment coefficient and the first integral adjustment coefficient based on the error change rate and error change trend may include, but is not limited to, the following steps:

[0051] Initialize the first proportional adjustment coefficient and the first integral adjustment coefficient;

[0052] Determine whether the dimensional error exceeds a preset error threshold;

[0053] If the size error is greater than the preset error threshold, then determine whether the error change rate is greater than the preset change rate threshold and whether the error change trend is deviating from the target.

[0054] If the error rate of change is greater than the preset rate of change threshold and the error trend is deviating from the target, then the first proportional adjustment coefficient and the first integral adjustment coefficient are increased.

[0055] If the size error is less than the preset error threshold, the error change rate and error change trend are judged.

[0056] If the error change rate is less than the preset change rate threshold and the error change trend is close to the target, then reduce the first proportional adjustment coefficient and the first integral adjustment coefficient.

[0057] If the error rate of change is greater than the preset rate of change threshold and the error trend is frequent oscillation, then the first integral adjustment coefficient is set to 0.

[0058] In some embodiments, if the determination of the first proportional adjustment coefficient and the first integral adjustment coefficient lacks a precise assessment of the current dimensional error state, it may lead to poor response speed, overshoot, or stability during the adjustment process. Therefore, the first proportional adjustment coefficient and the first integral adjustment coefficient can be initialized first. Initial values ​​for these two key adjustment parameters can be set when the control system is started or reset. These initial values ​​can be determined based on expert experience, with the aim of providing a benchmark for subsequent dynamic adjustments.

[0059] Then, it checks whether the dimensional error exceeds a preset error threshold, aiming to distinguish the severity of the current dimensional error. The preset error threshold can be set according to the control accuracy requirements of the actual application; for example, it can be set to 90% of the allowable tolerance range of the shaft and hole dimensions.

[0060] If the dimensional error exceeds a preset error threshold, it indicates that the current shaft and hole dimensions deviate significantly from the target dimensions. In this case, it's necessary to further determine whether the error change rate exceeds a preset change rate threshold and whether the error trend is deviating from the target. If the error change rate exceeds the preset change rate threshold and the error trend is deviating from the target, it means the dimensional error is not only large but also rapidly moving away from the target, which usually indicates the system needs stronger corrective force. In this situation, the first proportional adjustment coefficient and the first integral adjustment coefficient can be increased. The purpose is to enhance the system's response speed and its ability to eliminate steady-state errors, thereby accelerating the convergence of the shaft and hole dimensions towards the target value.

[0061] If the dimensional error is less than the preset error threshold, it indicates that the current shaft and hole size is close to the target size, and the adjustment should be more precise. It is necessary to judge the error change rate and trend. If the error change rate is less than the preset change rate threshold and the error change trend is close to the target, it means that the dimensional error is slowly and steadily approaching the target value, and the system is in the fine-tuning stage. In this case, the first proportional adjustment coefficient and the first integral adjustment coefficient can be reduced. The purpose is to reduce the system overshoot and suppress oscillations, improving the smoothness of the adjustment and the final control accuracy. If the error change rate is greater than the preset change rate threshold and the error change trend is frequent oscillations, it indicates that the system may have overshoot or instability, and the cumulative effect of the integral term may exacerbate this oscillation. In this case, the first integral adjustment coefficient can be set to 0. The purpose is to eliminate the adverse effects of the integral term on system stability, avoid integral saturation, thereby suppressing oscillations and allowing the system to recover stability as quickly as possible.

[0062] To illustrate this technical solution more clearly, a specific example is used below. Assume that in the variable gear shaft bore size parameter control process, there is a significant size error between the current shaft bore size obtained at the initial moment and the preset target size. First, the system initializes the first proportional adjustment coefficient and the first integral adjustment coefficient, for example, set to... and Subsequently, the system determines the dimensional error. If it exceeds a preset error threshold, and analysis shows that the error change rate exceeds the preset change rate threshold and the error trend deviates from the target, this indicates that the shaft hole size is rapidly deviating from the target value. At this point, the system increases the first proportional adjustment coefficient and the first integral adjustment coefficient, for example, adjusting them to 1.2*... and 1.2* This enhances the controller's responsiveness and accelerates the convergence of the shaft hole dimensions towards the target value.

[0063] As adjustments proceed, the dimensional error gradually decreases. When the dimensional error falls below a preset error threshold, the system further assesses the rate and trend of error change. If the rate of error change is below the preset threshold and the trend is approaching the target, it indicates that the shaft and hole dimensions are steadily approaching the target value. To avoid overshoot, the system reduces the first proportional and first integral adjustment coefficients, for example, adjusting them to 0.8*. and 0.8* This makes the adjustment process smoother and improves the final control accuracy. In another scenario, if the system detects that the error change rate exceeds the preset threshold and the error changes with frequent oscillations during adjustment, this is usually due to excessive integral action or system parameter mismatch. In this case, the system immediately sets the first integral adjustment coefficient to 0 to eliminate the cumulative effect of the integral term, quickly suppressing oscillations and restoring stability to the shaft hole size adjustment process. Through this dynamic adjustment, the system can flexibly switch control strategies according to the real-time status of the shaft hole size, thereby achieving precise, rapid, and stable control of the shaft hole size parameters.

[0064] Through the above technical solution, this embodiment can adaptively adjust the first proportional adjustment coefficient and the first integral adjustment coefficient according to the actual error of the shaft hole size, thereby significantly improving the performance of the shaft hole size parameter control method. Specifically, this embodiment can effectively solve the problems of slow response, large overshoot, or easy oscillation caused by simply adjusting PID parameters, making the shaft hole size adjustment process have a faster response speed, smaller overshoot, and higher stability. Especially when facing complex and changing machining environments and wear conditions, this adaptive adjustment mechanism can ensure that the system always operates with the optimal control strategy, thereby achieving accurate, efficient, and robust control of the variable gear shaft hole size parameters.

[0065] In some embodiments, in step S105, the smoothness of the shaft hole size adjustment is evaluated based on the first proportional adjustment coefficient and the first integral adjustment coefficient to obtain a smoothness evaluation result, which may include, but is not limited to, the following steps:

[0066] Step S201: Monitor the wear condition of the shaft hole, the type of material being processed, and the operating temperature of the shaft hole adjustment mechanism;

[0067] Step S202: Determine the first safety upper limit of the first proportional adjustment coefficient and the second safety upper limit of the first integral adjustment coefficient based on the type of processed material, the wear condition of the shaft hole and the operating environment temperature.

[0068] Step S203: Update the first proportional adjustment coefficient and the first integral adjustment coefficient according to the first safety upper limit and the second safety upper limit;

[0069] Step S204: Evaluate the smoothness of shaft hole size adjustment based on the updated first proportional adjustment coefficient and first integral adjustment coefficient, and obtain the smoothness evaluation result.

[0070] In some embodiments, if various dynamic factors in the actual operating environment are not fully considered, such as the wear state of the shaft hole, the characteristics of the processed material, and the operating temperature of the shaft hole adjustment mechanism, the stability assessment results may not be accurate enough or may not reflect the potential risks of the system in a timely manner, thereby affecting the accuracy of the adjustment coefficient and the reliability and safety of the shaft hole size adjustment.

[0071] To address this, the wear condition of the shaft hole, the type of material being processed, and the operating temperature of the shaft hole adjustment mechanism can be monitored first. Various sensors or detection methods can be used to acquire the wear degree and pattern of the shaft hole and its adjustment mechanism in real time or periodically to obtain the shaft hole wear condition. For example, acoustic emission sensor arrays, piezoelectric strain sensors, or miniature piezoresistive sensor arrays can be used to collect wear-related physical signals and analyze them to assess the wear condition. The type of material being processed refers to the type and characteristics of the material being processed, such as hardness, toughness, and coefficient of thermal expansion. These parameters have a significant impact on the response characteristics of the shaft hole size adjustment. The operating temperature of the shaft hole adjustment mechanism refers to the ambient temperature of the environment in which the adjustment mechanism operates. Temperature changes may affect the mechanical performance of the mechanism and the measurement accuracy of the sensors.

[0072] Then, based on the type of material being processed, the wear condition of the shaft and bore, and the ambient temperature, a first safety upper limit for the first proportional control coefficient and a second safety upper limit for the first integral control coefficient are determined. The maximum allowable values ​​for the proportional and integral control coefficients in the PID controller can be dynamically set based on these monitored operating parameters. For example, when the shaft and bore are severely worn or the material being processed is hard, the safety upper limits will be appropriately tightened to avoid over-adjustment leading to system instability or damage; conversely, under ideal operating conditions, the safety upper limits can be relaxed to allow for a faster response speed. These safety upper limits can be determined using pre-established empirical models, lookup tables, or machine learning-based predictive models.

[0073] Then, based on the first and second safety upper limits, update the first proportional adjustment coefficient and the first integral adjustment coefficient. The currently calculated first proportional adjustment coefficient and first integral adjustment coefficient can be compared with their corresponding safety upper limits. If the calculated coefficient exceeds its safety upper limit, it is limited to the safety upper limit value to ensure the stability and safety of the adjustment process. For example, if the calculated value of the first proportional adjustment coefficient is... The first safety limit is Then the minimum of the two can be taken as the updated first proportional adjustment coefficient.

[0074] Finally, based on the updated first proportional adjustment coefficient and first integral adjustment coefficient, the smoothness of the shaft hole size adjustment is evaluated, and a smoothness evaluation result is obtained. After considering the limitations of actual operating conditions, a smoothness evaluation is performed using an adjustment coefficient constrained by a safety upper limit, thereby obtaining a more reliable smoothness evaluation result that better reflects actual operating conditions.

[0075] To illustrate this technical solution more clearly, a specific example is used below. Suppose that in the machining process of a variable gear, high-precision control of the shaft hole dimensions is required. In the initial stage, the shaft hole wear condition is good, the machining material is standard steel, and the ambient temperature is moderate. At this time, the system determines a relatively lenient first safety upper limit for the first proportional adjustment coefficient and a second safety upper limit for the first integral adjustment coefficient based on these parameters. As the machining time increases, the wear sensor of the shaft hole adjustment mechanism detects a gradual increase in wear. Simultaneously, the machining material is changed to a harder alloy material, and the workshop ambient temperature rises due to seasonal changes.

[0076] Specifically, the system monitors these changes in real time: the wear condition of the shaft hole changes from "slight" to "moderate," the type of processed material changes from "standard steel" to "high-hardness alloy," and the operating environment temperature changes from "25℃" to "35℃." Based on these updated operating parameters, the system recalculates and tightens the first safety upper limit of the first proportional adjustment coefficient and the second safety upper limit of the first integral adjustment coefficient. For example, the first safety upper limit may be adjusted from 0.8 to 0.6, and the second safety upper limit may be adjusted from 0.5 to 0.3. If the first proportional adjustment coefficient calculated based on the dimensional error is 0.7 at this time, it will be limited to 0.6 in the update step to prevent over-adjustment or oscillation due to increased wear and material hardness. Similarly, if the first integral adjustment coefficient is calculated to be 0.4, it will be limited to 0.3. Subsequently, the system will use these adjustment coefficients updated with safety upper limits to evaluate the smoothness of the shaft hole size adjustment, thereby ensuring that the shaft hole size adjustment process maintains a high degree of stability and safety under changing operating conditions.

[0077] Through the above technical solution, this embodiment monitors the wear state of the shaft hole, the type of processed material, and the operating environment temperature in real time, and dynamically sets and updates the safety upper limit of the adjustment coefficient accordingly. This makes the stability assessment results of the shaft hole size adjustment more accurate and reliable. This not only significantly improves the system's adaptability and robustness to complex working conditions, effectively avoiding system instability or equipment damage caused by improper adjustment coefficients, but also extends the service life of the shaft hole adjustment mechanism, ensuring the safety and efficiency of the variable gear shaft hole size parameter control process.

[0078] In some embodiments, step S203, updating the first proportional adjustment coefficient and the first integral adjustment coefficient based on the first safety upper limit and the second safety upper limit, may include, but is not limited to, the following steps:

[0079] Step S301: With the shaft hole in a heavy-load locked state, apply a perturbation signal to the shaft hole through the shaft hole adjustment mechanism;

[0080] Step S302: Acquire the response signal of the shaft hole inner diameter to the perturbation signal;

[0081] Step S303: Perform spectral analysis on the response signal to obtain the perturbation response spectrum;

[0082] Step S304: Extract spectral features reflecting the health status of the contact interface from the perturbation response spectrum;

[0083] Step S305: Compare the spectral features with the healthy reference spectrum to obtain the comparison results;

[0084] Step S306: If the comparison result shows that the spectral characteristics deviate from the healthy reference spectrum, then calculate the characteristic deviation based on the spectral characteristics and the healthy reference spectrum;

[0085] Step S307: Update the first proportional adjustment coefficient and the first integral adjustment coefficient based on the characteristic deviation, the first safety upper limit, and the second safety upper limit.

[0086] In some embodiments, a perturbation signal can be applied to the shaft hole through the shaft hole adjustment mechanism while the shaft hole is in a heavy-load locked state. The shaft hole being in a heavy-load locked state means that the shaft hole adjustment mechanism is under a large load and locked in a certain position. At this time, the dimensional adjustment function of the shaft hole is temporarily inactive, but its internal contact interface is subjected to continuous stress. In this state, a perturbation signal is applied to the shaft hole through the shaft hole adjustment mechanism. This perturbation signal is typically a small-amplitude, high-frequency excitation, the purpose of which is not to affect the locked state of the shaft hole and to stimulate a weak response at the contact interface. For example, the perturbation signal can be a periodic or random small force applied by a piezoelectric actuator or a micro-vibrator.

[0087] Then, the response signal of the shaft bore inner diameter to the perturbation signal is acquired. This response signal reflects the dynamic behavior of the shaft bore inner diameter under the action of perturbation, and it can be monitored and acquired in real time using high-precision displacement sensors, laser rangefinders, or eddy current sensors. The characteristics of the response signal, such as amplitude, frequency, and phase, are affected by the health of the contact interface. Spectral analysis is then performed on the response signal to obtain the perturbation response spectrum. Spectral analysis is the process of converting a time-domain signal into a frequency-domain signal, for example, using methods such as Fast Fourier Transform (FFT). The perturbation response spectrum can reveal the vibration energy distribution of the shaft bore inner diameter at different frequencies, containing frequency components related to the characteristics of the contact interface.

[0088] Next, spectral features reflecting the health status of the contact interface are extracted from the perturbation response spectrum. These features, related to health issues such as wear, fatigue, and poor lubrication at the shaft-hole contact interface, can be identified and quantified. They may include specific frequency peaks, bandwidth, harmonic components, or energy decay modes. These spectral features are the fingerprints of the physical state of the contact interface. The spectral features are then compared with a health reference spectrum to obtain the comparison results. The health reference spectrum is a perturbation response spectrum pre-collected and established when the shaft-hole adjustment mechanism is in a brand-new or ideal health state. By comparison, it can be determined whether the current health status of the shaft-hole contact interface deviates from the normal state.

[0089] If the comparison result shows that the spectral characteristics deviate from the healthy reference spectrum, then the characteristic deviation is calculated based on the spectral characteristics and the healthy reference spectrum. The characteristic deviation quantifies the degree of difference between the current health condition and the ideal health condition; for example, it can calculate the frequency shift of spectral peaks, amplitude changes, or energy differences.

[0090] Finally, based on the characteristic deviation, the first safety upper limit, and the second safety upper limit, the first proportional adjustment coefficient and the first integral adjustment coefficient are updated. This means that when a deviation in the health condition of the contact interface is detected, the first proportional adjustment coefficient and the first integral adjustment coefficient can be dynamically adjusted according to the magnitude and direction of the deviation, as well as the preset safety upper limit, to ensure the smoothness and safety of the shaft hole size adjustment. For example, if the characteristic deviation indicates a deterioration in the health condition of the contact interface, it may be necessary to reduce the adjustment coefficient to avoid over-adjustment or increased wear.

[0091] To illustrate this technical solution more clearly, a specific example is used below. Suppose a variable gear shaft hole machining system requires high-precision control of the shaft hole dimensions. After the system has been running for a period of time, to ensure the smoothness of the shaft hole dimension adjustment, the first proportional adjustment coefficient and the first integral adjustment coefficient need to be updated. At this point, the shaft hole adjustment mechanism is first placed in a heavy-load locked state; for example, the shaft hole is fixed in a certain position by a hydraulic system and subjected to a preset machining load. Subsequently, a piezoelectric actuator integrated inside the shaft hole adjustment mechanism applies a sweep frequency perturbation signal with a frequency range of 1kHz to 10kHz and an extremely small amplitude to the shaft hole. Simultaneously, a laser displacement sensor installed near the inner wall of the shaft hole is used to acquire the response signal of the shaft hole's inner diameter to this perturbation signal in real time.

[0092] The acquired response signal is transmitted to a signal processing unit, which performs a Fast Fourier Transform (FFT) on the response signal to generate a perturbation response spectrum. In the perturbation response spectrum, the system focuses on the energy peaks and their corresponding full width at half maximum (FWHM) within a specific frequency range; these are defined as spectral characteristics reflecting the health of the contact interface. For example, if a healthy reference spectrum has a significant energy peak at 5 kHz and a FWHM of 100 Hz, while the currently acquired spectral characteristic shows a peak at 5.2 kHz and a FWHM of 150 Hz, this indicates that the spectral characteristic deviates from the healthy reference spectrum. The system calculates a characteristic deviation value based on this frequency shift and change in FWHM. For example, the weighted sum of the frequency shift and the change in FWHM can be used as the characteristic deviation.

[0093] Finally, based on the calculated characteristic deviation and combined with the preset first and second safety upper limits, the system dynamically adjusts the first proportional adjustment coefficient and the first integral adjustment coefficient. For example, if the characteristic deviation indicates slight wear at the contact interface, the system may fine-tune the first proportional adjustment coefficient by 5% and the first integral adjustment coefficient by 3% to make the shaft hole size adjustment process smoother and avoid aggravating wear. If the characteristic deviation indicates severe wear, the adjustment coefficients may be significantly reduced, or even a maintenance alarm may be triggered to prevent equipment damage. In this way, the updates to the adjustment coefficients can more accurately adapt to the actual health condition of the shaft hole adjustment mechanism, thereby ensuring the smoothness and reliability of the entire size control process.

[0094] Through the above technical solution, this embodiment, by introducing perturbation signals and spectrum analysis, can monitor the microscopic health status of the contact interface of the shaft hole adjustment mechanism in real time and non-invasively. This health assessment based on spectral characteristics allows the updates of the first proportional adjustment coefficient and the first integral adjustment coefficient to more accurately reflect the actual operating status and potential risks of the system, thereby significantly improving the smoothness and reliability of shaft hole size adjustment. When early wear or fatigue signs appear on the contact interface, the adjustment coefficient can be adjusted in a timely and accurate manner, effectively preventing excessive oscillation, adjustment lag, or accelerated damage to mechanical parts that may be caused by mismatched adjustment parameters, extending the service life of the equipment, and ensuring machining accuracy.

[0095] In some embodiments, step S307, updating the first proportional adjustment coefficient and the first integral adjustment coefficient based on the characteristic deviation, the first safety upper limit, and the second safety upper limit, may include, but is not limited to, the following steps:

[0096] The response signal is segmented to obtain multiple segmented data.

[0097] Feature extraction is performed on multiple segmented data to obtain stationarity features;

[0098] The first proportional adjustment coefficient is updated based on the stationarity characteristics, characteristic deviation, and the first safety upper limit.

[0099] The first integral adjustment coefficient is updated based on the stationarity characteristics, characteristic deviation, and second safety upper limit.

[0100] In some embodiments, relying solely on a single characteristic deviation for coefficient updates may not adequately capture the dynamic stability changes during shaft and hole size adjustment, especially when the system faces complex disturbances or is in a critical state, potentially leading to insufficient adjustment accuracy or sluggish response. To address this, the response signal can be segmented to obtain multiple segmented data. Continuous response signals can be divided according to preset time windows or data volumes to obtain multiple discrete segmented data. For example, the response signal can be segmented at certain time intervals (e.g., 1 second, 0.5 seconds), or segmented after collecting a certain number of data points (e.g., 1024 points). The purpose of this segmentation is to enable analysis of the local characteristics of the response signal over different time periods and to capture its transient changes.

[0101] Then, feature extraction is performed on multiple segmented data to obtain stationarity features. Indicators characterizing the system's operational stability can be calculated from each segmented data. These stationarity features can include time-domain statistical features such as mean, variance, standard deviation, kurtosis, skewness, energy, entropy, zero-crossing rate, waveform factor, and impulse factor; or frequency-domain or time-frequency-domain features extracted based on methods such as wavelet transform and empirical mode decomposition, such as energy distribution in different frequency bands and dominant frequency components. The aim is to quantify the dynamic behavior of the shaft-hole system over various time periods from multiple dimensions, thereby reflecting its stationarity status more precisely.

[0102] Then, based on the stationarity characteristics, characteristic deviation, and the first safety upper limit, the first proportional control coefficient is updated. And based on the stationarity characteristics, characteristic deviation, and the second safety upper limit, the first integral control coefficient is updated. When updating the proportional and integral control coefficients of the PID controller, in addition to considering the overall characteristic deviation (a macroscopic indicator reflecting the health of the contact interface), more detailed stationarity characteristics (microscopic indicators reflecting the dynamic behavior of the system) are introduced. For example, various fusion algorithms such as weighted averaging, fuzzy logic inference, and neural networks can be used. When the stationarity characteristics indicate that the system has slight oscillations or response hysteresis, even if the characteristic deviation is small, the proportional and integral control coefficients can be adjusted appropriately to improve the system's response speed or suppress oscillations. Conversely, when the stationarity characteristics show high stability, the coefficients can be adjusted more aggressively within the safety upper limit to accelerate the adjustment process. The first and second safety upper limits serve as boundary conditions for coefficient adjustment, ensuring the safety of the adjustment process.

[0103] To illustrate this technical solution more clearly, a specific example is used below. Assume that the shaft hole is in a heavy-load locked state, and a perturbation signal is applied to the shaft hole through a shaft hole adjustment mechanism, and the response signal of the shaft hole's inner diameter is acquired. This response signal is continuously acquired, for example, 10 seconds of data are acquired at a sampling rate of 10kHz. First, the 10-second response signal is segmented. For example, a sliding window approach can be used, extracting a 1-second data segment every 0.1 seconds, resulting in 91 segmented data points. Second, feature extraction is performed on each of these 91 segmented data points to obtain stationarity features. For each segmented data point, its root mean square (RMS), standard deviation, kurtosis coefficient, and energy entropy can be calculated as stationarity features. For example, if the RMS value of a certain segmented data point suddenly increases, it may indicate that the system vibration has intensified and stationarity has decreased during that time period. Simultaneously, the perturbation response spectrum is obtained through spectral analysis, and spectral features reflecting the health status of the contact interface are extracted. After comparison with a healthy reference spectrum, the feature deviation is calculated.

[0104] Finally, combining these stationarity characteristics, feature deviations, and preset first and second safety upper limits, the first proportional adjustment coefficient and the first integral adjustment coefficient are updated. For example, a fuzzy logic controller can be designed, including: inputs of feature deviation, root mean square value of piecewise data, and kurtosis coefficient of piecewise data; and output of the adjustment amount of the first proportional adjustment coefficient. Adjustment amount of the first integral adjustment coefficient Its fuzzy rule is: if the feature deviation is large and the root mean square value is high (indicating system instability), then the value is significantly increased. and If the characteristic deviation is small but the root mean square value fluctuates slightly (indicating a decrease in the local stationarity of the system), then increase it slightly. Adjust according to the kurtosis coefficient To suppress potential oscillations. If the eigenbia and all stationary characteristics perform well, then maintain... and The adjustment coefficient remains unchanged or is finely adjusted according to the optimization objective, but never exceeds the first and second safety limits. In this way, even if the overall characteristic deviation has not reached the critical value, if the local stability characteristics show abnormalities, the system can adjust the adjustment coefficient in a timely and precise manner, thereby more effectively maintaining the stability of the shaft hole size adjustment.

[0105] Through the above technical solution, this embodiment introduces segmented processing of the response signal and extraction of multidimensional stationarity features, enabling the adjustment of the adjustment coefficient to fully consider the dynamic behavior and local stability of the system at different time scales. This not only improves the accuracy and timeliness of coefficient updates, allowing the system to better adapt to complex and changing working environments and wear conditions, but also helps to detect and correct potential instability factors at an early stage, thereby significantly improving the stability, accuracy, and robustness of shaft hole size adjustment, effectively extending the service life of the equipment and reducing maintenance costs.

[0106] In some embodiments, monitoring the wear condition of the shaft hole in step S201 may include, but is not limited to, the following steps:

[0107] After deploying an acoustic emission sensor array in the wear-sensitive area of ​​the shaft hole adjustment mechanism, acoustic emission signals are collected through the acoustic emission sensor array;

[0108] Time-frequency analysis of acoustic emission signals is performed to extract characteristic parameters reflecting the initiation and propagation of microcracks;

[0109] If the characteristic parameter is greater than the preset wear precursor threshold, the location of the first wear occurrence and the severity of the first wear are identified.

[0110] The wear state of the shaft hole is generated based on the location and severity of the first wear.

[0111] In some embodiments, an acoustic emission sensor array can be deployed in the wear-sensitive area of ​​the shaft hole adjustment mechanism to collect acoustic emission signals. The wear-sensitive area of ​​the shaft hole adjustment mechanism refers to the part most prone to material wear, fatigue, or crack initiation during the shaft hole size adjustment process due to factors such as mechanical contact, friction, and vibration. For example, these areas may include the contact surface between the shaft hole and gear, bearings, guide rails, and meshing points of transmission components. An acoustic emission sensor array is a network of multiple acoustic emission sensors strategically arranged in the wear-sensitive area to monitor in real time the transient elastic wave signals generated inside or on the surface of the material due to stress release. Its purpose is to capture early, microscopic wear events, such as the initiation and propagation of microcracks.

[0112] Then, time-frequency analysis is performed on the acoustic emission signal to extract characteristic parameters reflecting the initiation and propagation of microcracks. Signal processing techniques such as Short-Time Fourier Transform (STFT), Wavelet Transform, or Hilbert-Huang Transform can be used to transform the acquired acoustic emission signal from the time domain to the frequency domain, and analyze its energy distribution and variation patterns at different times and frequencies. Through time-frequency analysis, characteristic parameters related to the initiation and propagation of microcracks can be extracted, such as the amplitude, energy, duration, rise time, ring count, and intensity of specific frequency components of the acoustic emission event. These parameters can effectively characterize the type and severity of internal damage in the material.

[0113] If the characteristic parameters exceed the preset wear precursor threshold, it indicates the presence of wear precursors. The location of the first wear event can be identified by analyzing the location information of the acoustic emission signal source (utilizing the geometric configuration of the sensor array and the signal arrival time difference) and signal characteristics; for example, it may be a specific area or component within the shaft hole. Simultaneously, the severity of the first wear event can be assessed based on the amplitude, frequency characteristics, and deviation from the threshold of the characteristic parameters; for example, it may be minor, moderate, or severe wear. The preset wear precursor threshold is a critical value pre-set based on extensive experimental data, historical operating experience, and material properties.

[0114] Based on the location and severity of the first wear event, the wear status of the shaft hole is then generated. This status information can be used for subsequent stability assessments and updates to adjustment coefficients. For example, structured data containing wear area coordinates, wear type, wear level, and wear development trend can be generated.

[0115] To illustrate this technical solution more clearly, a specific example is used below. Assume that in a variable gear shaft bore size parameter control system, a key wear-sensitive area of ​​the shaft bore adjustment mechanism (e.g., the contact surface between the shaft bore and the adjusting sleeve) is equipped with an array of six acoustic emission sensors. During shaft bore size adjustment, these sensors continuously collect acoustic emission signals. When the system detects that the energy peak and specific frequency components (e.g., signals in the 200kHz-400kHz range) of an acoustic emission signal collected by a certain sensor, after time-frequency analysis, consistently exceed a preset wear precursor threshold, the system immediately determines that wear precursors exist. By triangulating the arrival time difference of the array signals, the system identifies the location of the first wear occurrence as the lower region of the shaft bore adjusting sleeve. Simultaneously, based on the signal amplitude and duration, the severity of the first wear is assessed as the initiation stage of a minor fatigue crack. Based on this precise wear status information, the stability assessment module can more accurately determine the applicability of the current adjustment coefficient and guide the coefficient fusion module to appropriately lower its upper limit when updating the first proportional adjustment coefficient and the first integral adjustment coefficient, so as to avoid applying excessive stress to the wear area. This effectively protects the shaft hole structure without affecting the adjustment efficiency, ensuring the stability of the adjustment process and the long-term reliable operation of the equipment.

[0116] Through the above technical solution, this embodiment can achieve early and high-precision monitoring of shaft hole wear. Since acoustic emission technology can detect the initiation and propagation of microscopic damage within materials, potential problems can be identified in the early stages of wear, significantly improving the timeliness of wear warnings. This precise and timely wear status information allows for a more accurate reflection of the actual health condition of the shaft hole when assessing the smoothness of shaft hole size adjustment. Therefore, the safe upper limits of the first proportional adjustment coefficient and the first integral adjustment coefficient can be determined more reasonably, and more accurate coefficient updates can be performed, thereby effectively improving the smoothness and reliability of shaft hole size adjustment, extending equipment lifespan, and reducing the risk of failure due to wear.

[0117] In some embodiments, monitoring the wear condition of the shaft hole in step S201 may include, but is not limited to, the following steps:

[0118] After embedding a piezoelectric strain sensor in the internal bearing component of the shaft hole adjustment mechanism, the stress fluctuation of the internal bearing component is collected through the piezoelectric strain sensor.

[0119] Based on stress fluctuations, identify frequency components associated with microcrack initiation and propagation;

[0120] Amplitude analysis is performed on the frequency components to obtain the amplitude analysis results;

[0121] If the amplitude analysis results show that there is an amplitude anomaly, then identify the location of the second wear and the severity of the second wear.

[0122] The wear status of the shaft hole is generated based on the location of the second wear occurrence and the severity of the second wear.

[0123] In some embodiments, a piezoelectric strain sensor can be embedded in the internal load-bearing component of the shaft hole adjustment mechanism to collect stress fluctuations in the internal load-bearing component. Dynamic stress response data of the internal load-bearing component under operating conditions can be continuously acquired. This stress fluctuation data contains characteristic information of the component during normal operation, fatigue accumulation, and the early stages of wear. Specifically, the piezoelectric sensor can be directly mounted or integrated into the structural components bearing the main loads inside the shaft hole adjustment mechanism. These internal load-bearing components can be bearing seats, gear shafts, connecting rods, etc., with the aim of directly sensing the minute deformations of these key components caused by force changes during operation. The piezoelectric strain sensor can convert mechanical strain into an electrical signal, thereby achieving real-time, high-sensitivity monitoring of internal stress fluctuations.

[0124] Then, based on stress fluctuations, frequency components related to the initiation and propagation of microcracks are identified. Time-frequency analysis, such as Fourier transform, can be performed on the acquired stress fluctuation signals to reveal their frequency domain characteristics. The initiation and propagation of microcracks typically cause changes in the internal structure of the material, thereby affecting the propagation characteristics of stress waves and exhibiting energy concentration or anomalies within specific frequency ranges. By analyzing these frequency components, signals caused by wear can be effectively distinguished from normal operating noise.

[0125] Next, amplitude analysis is performed on the frequency components to obtain the amplitude analysis results. The energy or intensity of the identified wear-related frequency components can be quantitatively evaluated to obtain the amplitude analysis results. If the amplitude analysis results show an amplitude anomaly, it means that wear is accelerating or cracks are further developing. The location of the second wear event and the severity of the second wear event can be identified. This means that when the amplitude of a specific frequency component exceeds a preset health threshold, the system can locate the specific area where wear occurs (i.e., the location of the second wear event) based on the layout of the sensor array and signal propagation characteristics, and quantify the severity of wear (i.e., the severity of the second wear event) based on the degree of amplitude anomaly.

[0126] Finally, based on the location and severity of the second wear event, the wear status of the shaft hole is generated. The purpose is to provide a comprehensive and accurate internal wear assessment report, offering a reliable basis for subsequent stability assessments and adjustments.

[0127] To illustrate this technical solution more clearly, a specific example is used below. Assume that in a variable gear shaft bore size parameter control system, multiple piezoelectric strain sensors are embedded in the internal load-bearing component of the shaft bore adjustment mechanism (e.g., a critical bearing housing). During system operation, these sensors continuously collect stress fluctuation data of the bearing housing. When micro-fatigue cracks begin to appear inside the bearing housing, these cracks cause minute changes in the local stress distribution of the material, thereby generating specific high-frequency stress waves. By performing real-time spectral analysis on the collected stress fluctuation signals, the system can identify specific frequency components related to the initiation and propagation of these micro-cracks, such as energy anomalies in the range of 20kHz to 50kHz. Furthermore, the amplitude of these frequency components is continuously monitored, and once the amplitude of a certain frequency component consistently exceeds a preset health threshold, the system determines that an amplitude anomaly exists. At this point, based on the sensor location and signal characteristics of the anomaly, the system can accurately identify the location of the second wear (e.g., a specific area of ​​the bearing housing) and the severity of the second wear (e.g., initial cracks or moderate fatigue damage). Based on this information, the system can generate a detailed report on the wear status of the shaft and hole, and use it for subsequent stability assessment. This allows for timely adjustment of the first proportional adjustment coefficient and the first integral adjustment coefficient to prevent further wear and ensure the stability of the shaft and hole size adjustment.

[0128] Through the above technical solution, this embodiment can directly sense stress changes in the internal material, thereby detecting potential wear problems in the early stages of microcrack initiation and propagation. This significantly improves the timeliness and accuracy of wear condition assessment, avoids sudden failures caused by wear accumulation, extends the service life of the shaft hole adjustment mechanism, and ensures the long-term stability and reliability of the shaft hole size adjustment process.

[0129] In some embodiments, monitoring the wear condition of the shaft hole in step S201 may include, but is not limited to, the following steps:

[0130] After deploying a miniature piezoresistive sensor array in the wear-sensitive area of ​​the shaft hole adjustment mechanism, microscopic pressure distribution data is collected through the miniature piezoresistive sensor array;

[0131] Based on micro-pressure distribution data, identify local stress concentration modes associated with grain slip or dislocation stacking;

[0132] Based on the local stress concentration pattern, identify the local stress concentration region and local stress intensity;

[0133] Based on the local stress concentration area and local stress intensity, identify the location and severity of the third wear;

[0134] The wear status of the shaft hole is generated based on the location and severity of the third wear.

[0135] In some embodiments, relying solely on macroscopic changes in physical quantities or indirect signal analysis may result in insufficient sensitivity and accuracy in identifying early, microscopic wear mechanisms. For example, if wear is judged only by vibration or temperature anomalies, the optimal intervention time may have been missed, affecting the smoothness and reliability of shaft hole size adjustment. This could lead to a lag in wear condition assessment, which in turn affects the accurate updating of the adjustment coefficient, and ultimately may cause unexpected damage or performance degradation to the shaft hole adjustment mechanism.

[0136] To address this, a miniature piezoresistive sensor array can be deployed in the wear-sensitive area of ​​the shaft hole adjustment mechanism to collect microscopic pressure distribution data. This array is a sensor network composed of multiple miniature piezoresistive sensors that converts changes in microscopic pressure into electrical signals, thereby enabling precise measurement of the microscopic pressure distribution on the object's surface. This array allows for real-time, high-resolution acquisition of microscopic pressure distribution data on the shaft hole surface, reflecting the material's stress at the microscopic level.

[0137] Then, based on the microscopic pressure distribution data, local stress concentration modes associated with grain slip or dislocation accumulation are identified. Grain slip and dislocation accumulation are two fundamental microscopic mechanisms of plastic deformation and fatigue damage in materials under stress, and they are precursors to macroscopic wear. Local stress concentration modes refer to specific spatial or temporal patterns of pressure variation presented in the microscopic pressure distribution data. These modes are closely related to the relative movement between grains or the accumulation of dislocations within the material. By identifying these modes, early detection of microscopic damage within the material can be achieved.

[0138] Based on the local stress concentration pattern, the local stress concentration areas and local stress intensities are identified. Local stress concentration areas refer to specific locations on the shaft / hole surface where micro-pressure is abnormally high or unevenly distributed; these locations are typically where wear occurs first or is most severe. Local stress intensity refers to the degree or magnitude of stress concentration within these areas. By accurately identifying these areas and intensities, the extent of micro-damage can be quantified.

[0139] Finally, based on the local stress concentration area and local stress intensity, the location and severity of the third wear are identified. The location of the third wear refers to the specific physical location on the shaft hole adjustment mechanism where wear occurs, while the severity of the third wear is a quantitative assessment of the wear condition at that location, such as being classified as slight, moderate, or severe. Based on the location and severity of the third wear, a shaft hole wear state is generated. This state provides a comprehensive description of the current wear condition of the shaft hole, offering an accurate basis for subsequent updates to the adjustment coefficients.

[0140] To illustrate this technical solution more clearly, a specific example is used below. Assume that in a variable gear system of a high-precision CNC machine tool, the inner wall of the shaft hole adjustment mechanism is the main wear-sensitive area. An array of 10x10 miniature piezoresistive sensors is deployed on this inner wall. During machine tool operation, this sensor array continuously collects microscopic pressure distribution data of the inner wall of the shaft hole. When microscopic fatigue damage begins to appear on the inner wall of the shaft hole, such as grain slippage occurring in a specific area, the sensor array will detect subtle changes in the local pressure distribution in that area, forming a specific stress concentration pattern. For example, in the sensor area from coordinates (5,5) to (5,7), the pressure reading is consistently higher than the surrounding area and exhibits periodic fluctuations. Based on a preset algorithm, the system identifies this pattern as a local stress concentration pattern related to grain slippage. Furthermore, the system identifies the local stress concentration area as a specific sector of the inner wall based on this pattern and determines that the local stress intensity is high. Based on this information, the system identifies the location of the third wear as this sector, and the severity of the third wear as "minor fatigue wear." Finally, the system generates the shaft hole wear status as "early warning of localized minor fatigue wear on the inner wall" and inputs it into the stability evaluation module so that the adjustment coefficients can be adjusted in a timely manner. For example, the first proportional adjustment coefficient and the first integral adjustment coefficient can be appropriately reduced to slow down the wear process in this area, thereby ensuring the long-term stability of the shaft hole size adjustment.

[0141] Through the above technical solution, this embodiment can detect potential wear risks earlier, thereby providing more reliable and forward-looking data support for the stability assessment of shaft hole size adjustment. This helps to adjust the first proportional adjustment coefficient and the first integral adjustment coefficient in a timely manner, avoiding inaccurate adjustment or mechanism damage caused by wear accumulation, and significantly improving the reliability and service life of the variable gear shaft hole size parameter control system.

[0142] In some embodiments, in step S106, coefficient fusion processing is performed based on the stationarity assessment result, the first proportional adjustment coefficient, and the first integral adjustment coefficient to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient. This may include, but is not limited to, the following steps:

[0143] Obtain the second proportional adjustment coefficient and the second integral adjustment coefficient input by the operator;

[0144] Based on the stability assessment results, the first proportional adjustment coefficient and the second proportional adjustment coefficient are weighted and fused to obtain the fused proportional adjustment coefficient.

[0145] Based on the stability assessment results, the first integral adjustment coefficient and the second integral adjustment coefficient are weighted and fused to obtain the fused integral adjustment coefficient.

[0146] In some embodiments, the second proportional adjustment coefficient and the second integral adjustment coefficient input by the operator can be obtained first. The second proportional adjustment coefficient and the second integral adjustment coefficient input by the operator refer to adjustment parameters manually set by experienced operators based on their understanding of the equipment operating status, the type of material being processed, historical data, or specific production requirements. These parameters can supplement or correct the results of the system's automated calculations to address complex situations that the automated model may not fully cover. For example, when processing special materials or when the equipment is in a specific wear stage, the operator can input adjustment coefficients that are more suitable for the current operating conditions based on experience.

[0147] Then, based on the stability assessment results, the first proportional adjustment coefficient and the second proportional adjustment coefficient are weighted and fused to obtain the fused proportional adjustment coefficient. Similarly, based on the stability assessment results, the first integral adjustment coefficient and the second integral adjustment coefficient are weighted and fused to obtain the fused integral adjustment coefficient. Weighted fusion refers to combining the first and second proportional adjustment coefficients according to a certain weight ratio. Simultaneously, the first and second integral adjustment coefficients are also combined according to a certain weight ratio. The stability assessment results are used to determine these weights. For example, when the stability assessment results show that the system is operating well and stably, the automatically determined first proportional adjustment coefficient and first integral adjustment coefficient can be assigned higher weights; conversely, when the stability assessment results show that the system has potential instability risks or requires manual intervention, the weights of the second proportional adjustment coefficient and second integral adjustment coefficient input by the operator can be appropriately increased to better utilize human experience for intervention and adjustment.

[0148] To illustrate this technical solution more clearly, a specific example is used below. Assume that in the process of controlling the variable gear shaft bore size parameters, the system, by analyzing the size error, the rate of error change, and the trend of error change, initially determines the first proportional adjustment coefficient as follows: The first integral adjustment coefficient is Meanwhile, the stability assessment module evaluated the stability of the current shaft hole size adjustment. The results showed that the current system exhibited a slight oscillation trend, but had not yet reached a level of severe instability. At this point, based on their understanding of the current machining material (e.g., a new alloy whose machining characteristics differ from conventional materials) and their historical experience, the experienced operator determined that a fine-tuning of the adjustment intensity was necessary, and thus manually entered the second proportional adjustment coefficient. Second integral adjustment coefficient For example, the operator might think it necessary to slightly reduce the proportional action to reduce oscillations and appropriately adjust the integral action to eliminate steady-state error. Slightly smaller ,and Possibly with Approximately or slightly adjusted. During coefficient fusion, the system determines the appropriate coefficient based on the stationarity assessment results. , and , The weighting ratio. Because the evaluation results show slight oscillations, the system may assign the operator's input a weighting ratio. and Relatively high weighting, for example, the fusion ratio adjustment coefficient. Fusion integral adjustment coefficient Through this weighted fusion, the final fusion ratio adjustment coefficient and fusion integral adjustment coefficient not only include the system's automated judgment based on real-time data analysis, but also incorporate the operator's experience-based corrections for specific working conditions. This allows the shaft hole size adjustment process to adapt more smoothly and accurately to the current complex machining environment.

[0149] Through the above technical solution, this embodiment combines the automatically determined first proportional and first integral adjustment coefficients with the operator-input second proportional and second integral adjustment coefficients using a weighted fusion method. This allows the system to comprehensively consider real-time data analysis and human experience judgment, thereby generating more accurate and stable fused proportional and integral adjustment coefficients under various complex and changing operating conditions. This not only avoids the rigidity and inadequacy that may occur with purely automated control but also allows human experience to participate in control decisions in a structured and quantitative manner. This effectively reduces adjustment deviations and risks caused by a single control source, ultimately ensuring the smoothness, accuracy, and reliability of the shaft hole size adjustment process.

[0150] The beneficial effects of implementing the embodiments of the present invention include: First, the current shaft hole size is obtained. Then, based on the current shaft hole size and the preset target size, the size error is calculated, and the error change rate and error change trend are analyzed. Then, based on the error change rate and error change trend, the first proportional adjustment coefficient and the first integral adjustment coefficient are determined, and the stability of the shaft hole size adjustment is evaluated to obtain the stability evaluation result. Finally, based on the stability evaluation result, the first proportional adjustment coefficient and the first integral adjustment coefficient, coefficient fusion processing is performed to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient, and the shaft hole size is adjusted. Thus, the adjustment coefficient can be adjusted by combining the size error and change characteristics to achieve shaft hole size parameter control, thereby improving accuracy and stability.

[0151] like Figure 2 As shown, this embodiment of the invention also provides a variable gear shaft bore size parameter control system, including:

[0152] Data acquisition module 401 is used to acquire the current shaft hole size;

[0153] Error calculation module 402 is used to calculate dimensional error based on the current shaft hole size and the preset target size;

[0154] The variation characteristic analysis module 403 is used to analyze the error change rate and error change trend based on the size error;

[0155] The adjustment coefficient determination module 404 is used to determine the first proportional adjustment coefficient and the first integral adjustment coefficient based on the error change rate and error change trend.

[0156] The stability evaluation module 405 is used to evaluate the stability of the shaft hole size adjustment based on the first proportional adjustment coefficient and the first integral adjustment coefficient, and obtain the stability evaluation result.

[0157] The coefficient fusion module 406 is used to perform coefficient fusion processing based on the stability assessment results, the first proportional adjustment coefficient and the first integral adjustment coefficient to obtain the fusion proportional adjustment coefficient and the fusion integral adjustment coefficient.

[0158] The shaft hole size adjustment module 407 is used to adjust the shaft hole size according to the fusion ratio adjustment coefficient and the fusion integral adjustment coefficient.

[0159] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0160] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A variable gear shaft hole size parameter control method characterized by, The method comprises the following steps: acquiring a current shaft hole size; calculating a size error according to the current shaft hole size and a preset target size; analyzing an error change rate and an error change trend according to the size error; determining a first proportional adjustment coefficient and a first integral adjustment coefficient according to the error change rate and the error change trend; evaluating a stability of shaft hole size adjustment according to the first proportional adjustment coefficient and the first integral adjustment coefficient to obtain a stability evaluation result; performing coefficient fusion processing according to the stability evaluation result, the first proportional adjustment coefficient and the first integral adjustment coefficient to obtain a fused proportional adjustment coefficient and a fused integral adjustment coefficient; performing shaft hole size adjustment according to the fused proportional adjustment coefficient and the fused integral adjustment coefficient.

2. The method of claim 1, wherein, The method of determining the first proportional adjustment coefficient and the first integral adjustment coefficient according to the error change rate and the error change trend comprises: initializing the first proportional adjustment coefficient and the first integral adjustment coefficient; judging whether the size error is greater than a preset error threshold; if the size error is greater than the preset error threshold, judging whether the error change rate is greater than a preset change rate threshold and the error change trend is deviating from a target; if the error change rate is greater than the preset change rate threshold and the error change trend is deviating from the target, increasing the first proportional adjustment coefficient and the first integral adjustment coefficient; if the size error is less than the preset error threshold, judging the error change rate and the error change trend; if the error change rate is less than the preset change rate threshold and the error change trend is approaching the target, decreasing the first proportional adjustment coefficient and the first integral adjustment coefficient; if the error change rate is greater than the preset change rate threshold and the error change trend is frequent oscillation, setting the first integral adjustment coefficient to 0.

3. The method of claim 1, wherein, The method of evaluating the stability of shaft hole size adjustment according to the first proportional adjustment coefficient and the first integral adjustment coefficient to obtain the stability evaluation result comprises: monitoring a shaft hole wear state, a processing material type and an operating environment temperature of a shaft hole adjustment mechanism; determining a first safety upper limit of the first proportional adjustment coefficient and a second safety upper limit of the first integral adjustment coefficient according to the processing material type, the shaft hole wear state and the operating environment temperature; updating the first proportional adjustment coefficient and the first integral adjustment coefficient according to the first safety upper limit and the second safety upper limit; evaluating the stability of shaft hole size adjustment according to the updated first proportional adjustment coefficient and the first integral adjustment coefficient to obtain the stability evaluation result.

4. The method of claim 3, wherein, The method of updating the first proportional adjustment coefficient and the first integral adjustment coefficient according to the first safety upper limit and the second safety upper limit comprises: applying a perturbation signal to the shaft hole through the shaft hole adjustment mechanism when the shaft hole is in a heavy load locking state; collecting a response signal of the shaft hole inner diameter to the perturbation signal; performing frequency spectrum analysis on the response signal to obtain a perturbation response spectrum; extracting a spectrum feature reflecting a contact interface health condition from the perturbation response spectrum; The spectral features are compared with the healthy reference spectrum to obtain the comparison results; If the comparison result is that the spectral feature deviates from the healthy reference spectrum, then the feature deviation is calculated based on the spectral feature and the healthy reference spectrum; The first proportional adjustment coefficient and the first integral adjustment coefficient are updated based on the characteristic deviation, the first safety upper limit, and the second safety upper limit.

5. The method of claim 4, wherein, The step of updating the first proportional adjustment coefficient and the first integral adjustment coefficient based on the characteristic deviation, the first safety upper limit, and the second safety upper limit includes: The response signal is segmented to obtain multiple segmented data. Feature extraction is performed on the multiple segmented data to obtain stationarity features; The first proportional adjustment coefficient is updated based on the stability characteristic, the characteristic deviation, and the first safety upper limit. The first integral adjustment coefficient is updated based on the stability characteristic, the characteristic deviation, and the second safety upper limit.

6. The method of claim 3, wherein, The monitoring of shaft hole wear status includes: After deploying an acoustic emission sensor array in the wear-sensitive area of ​​the shaft hole adjustment mechanism, acoustic emission signals are collected through the acoustic emission sensor array; Time-frequency analysis was performed on the acoustic emission signal to extract characteristic parameters reflecting the initiation and propagation of microcracks; If the characteristic parameter is greater than the preset wear precursor threshold, then the location of the first wear occurrence and the severity of the first wear are identified; The wear state of the shaft hole is generated based on the location of the first wear occurrence and the severity of the first wear.

7. The method of claim 3, wherein, The monitoring of shaft hole wear status includes: After embedding a piezoelectric strain sensor in the internal bearing component of the shaft hole adjustment mechanism, the stress fluctuation of the internal bearing component is collected through the piezoelectric strain sensor; Based on the stress fluctuations, identify the frequency components associated with microcrack initiation and propagation; Amplitude analysis was performed on the frequency components to obtain the amplitude analysis results; If the amplitude analysis result indicates the presence of amplitude anomalies, then the location of the second wear and the severity of the second wear are identified. The wear state of the shaft hole is generated based on the location of the second wear occurrence and the severity of the second wear.

8. The method of claim 3, wherein, The monitoring of shaft hole wear status includes: After deploying a miniature piezoresistive sensor array in the wear-sensitive area of ​​the shaft hole adjustment mechanism, microscopic pressure distribution data is collected through the miniature piezoresistive sensor array; Based on the micro-pressure distribution data, identify local stress concentration patterns associated with grain slip or dislocation stacking; Based on the local stress concentration pattern, identify the local stress concentration region and the local stress intensity; Based on the local stress concentration area and the local stress intensity, the location and severity of the third wear are identified; The wear state of the shaft hole is generated based on the location of the third wear and the severity of the third wear.

9. The method of claim 1, wherein, The step of performing coefficient fusion processing based on the stability assessment result, the first proportional adjustment coefficient, and the first integral adjustment coefficient to obtain fused proportional adjustment coefficient and fused integral adjustment coefficient includes: Obtain the second proportional adjustment coefficient and the second integral adjustment coefficient input by the operator; Based on the stability assessment results, the first proportional adjustment coefficient and the second proportional adjustment coefficient are weighted and fused to obtain the fused proportional adjustment coefficient. Based on the stability assessment results, the first integral adjustment coefficient and the second integral adjustment coefficient are weighted and fused to obtain the fused integral adjustment coefficient.

10. A variable gear bore size parameter control system characterized by, include: The data acquisition module is used to obtain the current shaft hole size; The error calculation module is used to calculate the dimensional error based on the current shaft hole size and the preset target size; The variation characteristic analysis module is used to analyze the error change rate and error change trend based on the size error; The adjustment coefficient determination module is used to determine a first proportional adjustment coefficient and a first integral adjustment coefficient based on the error change rate and the error change trend. The stability evaluation module is used to evaluate the stability of the shaft hole size adjustment based on the first proportional adjustment coefficient and the first integral adjustment coefficient, and obtain the stability evaluation result. The coefficient fusion module is used to perform coefficient fusion processing based on the stability assessment result, the first proportional adjustment coefficient, and the first integral adjustment coefficient to obtain the fused proportional adjustment coefficient and the fused integral adjustment coefficient. The shaft hole size adjustment module is used to adjust the shaft hole size according to the fusion ratio adjustment coefficient and the fusion integral adjustment coefficient.

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