Spline machining device and method for eccentric shaft of speed reducer

Abstract

The invention relates to the technical field of spline machining, in particular to a spline machining device and method for an eccentric shaft of a speed reducer, and the method comprises the steps that the amplitude-frequency difference between all cutting force data and all vibration data in a frequency domain and the correlation between all cutting force data and all temperature data within a preset duration before all moments are analyzed; determining a cutting risk value; comprehensively analyzing a cutting risk value, an energy ratio, a dominant frequency amplitude difference between all vibration data and all cutting force data in a frequency domain and a change relation of a cutting related index along with the machining quality in a preset period before the current moment, predicting a machining quality score to determine a machining instability index, and combining the energy ratio and the machining quality score to determine the machining instability index. And determining a main shaft rotating speed correction value and a cutter feeding amount. The problem of compensation failure of traditional PID control is solved, the spline machining quality is improved, and the service life of a workpiece is prolonged.

Description

Technical Field

[0001] This application relates to the technical field of spline machining, and specifically relates to a spline machining device and method for the eccentric shaft of a speed reducer. Background Art

[0002] As the core transmission component of an RV speed reducer, the eccentric shaft realizes power transmission and speed reduction functions through its precise eccentric design. Its machining accuracy directly determines the smoothness, noise level, and service life of the transmission system. In grinding machining, the linkage control between the grinding wheel frame and the workpiece is prone to reciprocating impact vibration due to commutation inertia, resulting in an increased risk of collision between the grinding wheel and the workpiece, seriously affecting the surface quality and threatening the safety of the equipment.

[0003] In the spline machining of the eccentric shaft of a speed reducer, due to the sudden change in the inertia of the cutting system and uneven force caused by the asymmetric geometric structure of the eccentric shaft, the cutting force shows periodic violent fluctuations. This not only causes low-frequency vibration of the machine tool, resulting in tooth surface vibration marks and microcracks, but also induces deterioration of the metallographic structure on the surface layer of the material due to instantaneous temperature rise, seriously reducing the fatigue life of the spline. However, by integrating a piezoelectric cutting force sensor and a vibration accelerometer to monitor the machining state in real time, and dynamically adjusting the spindle speed and feed rate based on the PID control algorithm, this technology is limited by the preset linear control model and is difficult to accurately predict the non-linear load mutation caused by the variable cross-section of the eccentric shaft. Moreover, it is prone to adjustment lag in the later stage of tool wear, resulting in compensation failure, reducing the spline machining quality and the workpiece life. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a spline machining device and method for the eccentric shaft of a speed reducer, and the specific technical solutions adopted are as follows: In the first aspect, an embodiment of this application provides a spline machining method for the eccentric shaft of a speed reducer, and this method includes the following steps: Obtain the cutting force data, vibration data, and temperature data in real time during the spline machining process of the eccentric shaft of the speed reducer; Analyze the amplitude-frequency difference in the frequency domain between all the cutting force data and all the vibration data within a preset time period before each moment, and determine the cutting vibration difference value of the spline at each moment; by analyzing the correlation between all the cutting force data and all the temperature data within the preset time period, determine the cutting correlation index of the spline at each moment, and combine the cutting vibration difference value to determine the cutting risk value of the spline at each moment; Perform modal decomposition on all cutting risk values within a preset time period before each moment. By analyzing the energy difference between a preset number of high-frequency modal components and all modal components, determine the energy proportion at each moment. By analyzing the dispersion degree of all cutting force data within a preset time period before each moment, determine the machining quality of the spline at each moment. Comprehensively analyze the cutting risk value, energy proportion, the main frequency amplitude difference in the frequency domain between all vibration data and all cutting force data, and the variation relationship of cutting-related indices with machining quality within a preset time period before the current moment, so as to predict the machining quality score of the spline at the current moment, and combine the energy proportion at the current moment to determine the machining instability index of the spline at the current moment. Based on the machining instability index, judge the necessity of adjusting the spindle speed and tool feed rate during the machining process of the spline at the current moment, so as to judge whether to combine the energy proportion at the current moment and the machining quality score to determine the spindle speed correction value and tool feed rate during the machining process of the spline at the current moment.

[0005] Preferably, the method for determining the cutting vibration difference value of the spline at each moment is as follows: Calculate the ratio of the main frequency amplitude to the corresponding frequency of all cutting force data in the frequency domain within a preset time period before each moment, and the ratio of the main frequency amplitude to the corresponding frequency of all vibration data in the frequency domain, and record them as the first ratio and the second ratio respectively. The result of dividing the first ratio by the second ratio is used as the cutting vibration difference value of the spline at each moment.

[0006] Preferably, the cutting-related index of the spline at each moment is the correlation coefficient between all cutting force data and all temperature data within a preset time period before each moment.

[0007] Preferably, the expression of the cutting risk value of the spline at each moment is: ; where represents the cutting risk value of the spline at moment i; represents the cutting difference value of the spline at moment i; represents the cutting-related index of the spline at moment i.

[0008] Preferably, the energy proportion at each moment is the result of dividing the total energy of the preset number of high-frequency modal components by the total energy of all modal components.

[0009] Preferably, the machining quality of the spline at each moment is: the exponential function value with the natural constant as the base and the opposite number of the dispersion degree of all cutting force data within a preset time period before each moment as the independent variable.

[0010] Preferably, predicting the machining quality score of the spline at the current moment includes: Taking the cutting risk values at all moments within a preset period before the current moment, the energy ratios at all moments, the main frequency amplitude differences in the frequency domain between all vibration data and all cutting force data at each moment, and the cutting-related indices at all moments as the four independent variables in the random forest algorithm, and taking the machining quality at all moments as the dependent variable in the random forest algorithm, the machining quality score of the spline at the current moment is obtained.

[0011] Preferably, the expression of the machining instability index of the spline at the current moment is: ; In the formula, represents the machining quality score of the spline at the current moment; E represents the energy ratio at the current moment; norm( ) represents the normalization function.

[0012] Preferably, judging the necessity of adjusting the spindle speed and the tool feed rate during the machining process of the spline at the current moment based on the machining instability index, and determining the spindle speed correction value and the tool feed rate during the machining process of the spline at the current moment by combining the energy ratio and the machining quality score at the current moment, includes: If the machining instability index of the spline at the current moment is greater than the preset threshold, then adjust the spindle and the tool feed rate during the machining process of the spline, otherwise, do not adjust the spindle and the tool feed rate during the machining process of the spline; The process of adjusting the spindle and the tool feed rate during the machining process of the spline includes: The expression of the spindle speed correction value during the machining process is: ; represents the spindle speed collected at the current moment; represents the energy ratio at the current moment; The tool feed rate during the machining process of the spline at the current moment The expression is: ; In the formula,

[0013] The present application has at least the following beneficial effects: In view of the problems of unbalanced vibration energy distribution and thermo-mechanical coupling effect caused by sudden changes in the inertia and uneven force of the cutting system, this application analyzes the correlation between cutting force, vibration, and temperature data to evaluate the risk of spline machining, constructs a cutting risk value, quantifies the synergistic effect of vibration energy difference and thermal damage risk, solves the limitation of traditional linear models in predicting sudden changes in non-linear loads, helps to reveal the impact of thermo-mechanical coupling effect on material thermal damage, thereby optimizing the machining process, improving the quality of spline machining and the workpiece life; further, in view of the problem of the coupling risk of progressive performance decline and sudden disturbances, this application evaluates and predicts the quality of spline machining and the risk of machining instability through multi-dimensional data analysis, can monitor and predict the quality and stability in the spline machining process in real time, timely discover potential problems and take corresponding measures, eliminate the adjustment lag effect of a single linear control model in the later stage of tool wear, and improve the quality of spline machining and the workpiece life; further, this application dynamically adjusts the spindle speed by the energy ratio to suppress sudden changes in cutting force, combines the machining quality score to optimize the feed rate to balance vibration suppression and machining efficiency, solves the problem of compensation failure of traditional PID control, realizes the synergistic suppression of non-linear load and thermo-mechanical coupling effect, and improves the quality of spline machining and the workpiece life. Brief Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a flowchart of the steps of a method for machining a spline of a reducer eccentric shaft provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the process of extracting the machining quality score provided by an embodiment of the present application. Detailed Embodiments

[0016] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of the spline machining device and method for a reducer eccentric shaft according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs.

[0018] The following specifically describes the specific solutions of the spline machining device and machining method for the eccentric shaft of the speed reducer provided in this application in conjunction with the accompanying drawings.

[0019] Please refer to Figure 1 , which shows a flowchart of the steps of the spline machining method for the eccentric shaft of the speed reducer provided in an embodiment of this application. The method includes the following steps: Step S1: Real-time obtain the cutting force data, vibration data, and temperature data during the spline machining process of the eccentric shaft of the speed reducer.

[0020] In the spline machining device for the eccentric shaft of the speed reducer, in order to accurately monitor the key parameters during the spline machining process, a piezoelectric cutting force sensor is integrated at the tool holder to collect the three-axis cutting forces (Fx, Fy, Fz) in real time, which are used to analyze the periodic fluctuation characteristics of the cutting force and its influence on the tool load; fiber Bragg grating vibration sensors are arranged at the spindle and workpiece ends to obtain vibration data in real time, which are used to identify the low-frequency vibration patterns and inertial mutation characteristics caused by the asymmetric structure; at the same time, a non-contact infrared thermal imager is installed in the spline machining area to dynamically capture the temperature data of the tool-workpiece contact area, which is used to evaluate the risk of deterioration of the surface metallographic structure of the material caused by the instantaneous temperature rise.

[0021] Specifically, in the spline machining device for the eccentric shaft of the speed reducer, the three-axis cutting forces collected by the voltage-type cutting force sensor are vectorially synthesized into a comprehensive cutting force, which is briefly recorded as the cutting force and is used to characterize the real-time dynamic change of the overall tool load; the vibration data can be directly obtained through the fiber Bragg grating vibration sensor, and the infrared image obtained by the infrared thermal imager is used to quantify the instantaneous temperature rise gradient of the tool-workpiece contact area and its risk degree of thermal damage to the material by calculating the temperature distribution range in the infrared image.

[0022] Among them, vector synthesis is a well-known technology to those skilled in the art, and the specific principle will not be elaborated here.

[0023] In this embodiment, the acquisition frequency of all the above data is set to 100 Hz. In the actual application process, as other implementation manners, the implementer can also set the data acquisition frequency according to the specific situation. Further, in order to eliminate the influence of dimensions, various collected data are respectively normalized.

[0024] It should be noted that there are many commonly used normalization algorithms. In this embodiment, the z-score normalization method is used to normalize the data. In actual application processes, as other implementation manners, the implementer can also use the maximum-minimum normalization method to normalize the data. Regarding the selection of the normalization method, this embodiment does not make special restrictions.

[0025] Among them, the z-score normalization method is a well-known technology, and its specific principle will not be elaborated here.

[0026] Step S2: Analyze the amplitude-frequency differences in the frequency domain between all cutting force data and all vibration data within a preset time period before each moment to determine the cutting vibration difference value of the spline at each moment; determine the cutting correlation index of the spline at each moment by analyzing the correlation between all cutting force data and all temperature data within the preset time period, and combine the cutting vibration difference value to determine the cutting risk value of the spline at each moment.

[0027] Due to the asymmetric geometric structure of the eccentric shaft of the reducer, the inertia of the cutting system mutates and the force is unevenly distributed, resulting in periodic severe fluctuations in cutting force, low-frequency vibration, and instantaneous temperature rise, causing tooth surface vibration marks, microscopic cracks, and deterioration of the metallographic structure of the material surface layer, seriously reducing the machining quality and fatigue life of the spline.

[0028] There are time-varying and periodic characteristics between vibration data and cutting force data, such as low-frequency vibration marks and instantaneous cutting force fluctuations; while the relationship between temperature and cutting force pays more attention to the linear correlation of the overall trend. Therefore, based on the correlation characteristics between cutting force data and vibration data, and the correlation between cutting force data and temperature data, in this embodiment, the amplitude-frequency differences in the frequency domain between all cutting force data and all vibration data within a preset time period before each moment are analyzed to determine the cutting vibration difference value of the spline at each moment; the cutting correlation index of the spline at each moment is determined by analyzing the correlation between all cutting force data and all temperature data within the preset time period, and the cutting risk value of the spline at each moment is determined by combining the cutting vibration difference value. The specific process is as follows: In this embodiment, the ratio of the main frequency amplitude to the corresponding frequency of all cutting force data in the frequency domain within a preset time period before each moment is calculated respectively, and the ratio of the main frequency amplitude to the corresponding frequency of all vibration data in the frequency domain is calculated respectively, and are denoted as the first ratio and the second ratio respectively. The result of dividing the first ratio by the second ratio is used as the cutting vibration difference value of the spline at each moment; among them, the first ratio and the second ratio quantify the vibration energy accumulation effect of cutting force data and vibration data. The closer the ratio of the first ratio to the second ratio is to 1, that is, the closer the cutting vibration difference value is to 1, the more matching the vibration and cutting force energy densities are.

[0029] Furthermore, in this embodiment, the correlation coefficient between all cutting force data and all temperature data within a preset time period before each moment is used as the cutting correlation index of the spline at each moment, which is used to characterize the linear correlation degree between the temperature gradient and the cutting force fluctuation, and reveal the influence of the thermo-mechanical coupling effect on the thermal damage of the material. If the cutting correlation index is larger, it means that the correlation between the temperature gradient and the cutting force is stronger, and the influence of temperature on the cutting force is more significant, that is, the thermo-mechanical coupling effect is significant.

[0030] It should be noted that there are many common calculation methods for the correlation coefficient. In this embodiment, the Pearson correlation coefficient between all cutting force data and all temperature data within a preset time period before each moment is used as the correlation coefficient between all cutting force data and all temperature data within a preset time period before each moment. In the actual application process, as other implementation manners, the implementer can also combine specific situations and adopt other correlation coefficient calculation methods such as the Spearman correlation coefficient or the Kendall rank correlation coefficient. Regarding the selection of the correlation coefficient calculation method, this embodiment does not make special restrictions.

[0031] Among them, the calculation method of the Pearson correlation coefficient is a well-known technology, and its specific calculation process will not be elaborated here.

[0032] Furthermore, based on the cutting vibration difference value and the cutting correlation index, this embodiment determines the cutting risk value of the spline at each moment, which is used to characterize the combined influence of the vibration energy difference and the thermo-mechanical coupling effect on the spline machining quality. Specifically: As an implementation manner, in this embodiment, the cutting risk value of the spline at moment i has the following expression: ; in the formula, represents the cutting difference value of the spline at moment i; represents the cutting correlation index of the spline at moment i.

[0033] It can be understood from the cutting risk values of the spline at each moment that the greater the cutting vibration difference value, the less matching the energy density between the cutting force and vibration data, and the greater the cutting-related index, the more significant the thermo-mechanical coupling effect, and the greater the obtained cutting risk value, indicating that the asymmetry of the vibration energy distribution and the thermo-mechanical coupling effect synergistically increase during the spline machining process. The two jointly lead to system dynamic instability, thus greatly increasing the combined risk of tooth surface vibration marks, crack propagation, and deterioration of the metallographic structure, and the machining quality and workpiece life of the spline drop sharply; conversely, if the cutting vibration difference value is closer to 1, it indicates that the energy density between the cutting force and vibration data is more matched, and the cutting force-related index is smaller, and the thermo-mechanical coupling effect is less significant. Finally, a smaller cutting risk value is obtained, indicating that the vibration energy distribution is relatively symmetric during the spline machining process, the thermo-mechanical coupling effect is weak, the system dynamic stability is good, and the risks of tooth surface vibration marks, crack propagation, and deterioration of the metallographic structure are low, thus keeping the machining quality and workpiece life of the spline at a relatively high level.

[0034] So far, in this embodiment, by analyzing the correlation between cutting force, vibration, and temperature data, the spline machining risk is evaluated, and a cutting risk value is constructed, which helps to reveal the influence of the thermo-mechanical coupling effect on material thermal damage, thereby optimizing the machining process and improving the spline machining quality and workpiece life.

[0035] Step S3: Perform modal decomposition on all cutting risk values within a preset time period before each moment. By analyzing the energy difference between a preset number of high-frequency modal components and all modal components, determine the energy proportion at each moment; by analyzing the dispersion degree of all cutting force data within a preset time period before each moment, determine the machining quality of the spline at each moment; comprehensively analyze the variation relationships of the cutting risk value, energy proportion, the main frequency amplitude difference in the frequency domain between all vibration data and all cutting force data, and the cutting-related index with the machining quality within a preset time period before the current moment to predict the machining quality score of the spline at the current moment, and combine the energy proportion at the current moment to determine the machining instability index of the spline at the current moment.

[0036] In modern manufacturing, the reducer is a key component widely used in various mechanical equipment, and its performance and life directly affect the working efficiency and reliability of the entire equipment. The spline connection in the reducer is a common transmission method, and its machining quality is directly related to the transmission accuracy and load-bearing capacity of the reducer. However, due to the asymmetric geometric structure of the eccentric shaft of the reducer, problems such as periodic severe fluctuations in cutting force, low-frequency vibration, and instantaneous temperature rise are likely to occur during the cutting process. These problems will cause tooth surface vibration marks, microscopic cracks, and deterioration of the material surface metallographic structure, thus seriously reducing the machining quality and fatigue life of the spline.

[0037] To solve the above problems, it is necessary to monitor and evaluate the quality of spline machining in real time. However, due to reasons such as the lag in micro-defect detection, the irreversibility of the dynamic process, and the monitoring blind spots of multi-physical field coupling, it is very difficult to directly obtain the machining quality data. Therefore, in this embodiment, by analyzing the energy difference between a preset number of high-frequency modal components and all modal components, the energy ratio at each moment is determined; by analyzing the dispersion degree of all cutting force data within a preset time period before each moment, the machining quality of the spline at each moment is determined; comprehensively analyzing the cutting risk value, energy ratio, the main frequency amplitude difference in the frequency domain between all vibration data and all cutting force data, and the change relationship of the cutting-related index with the machining quality within a preset period before the current moment, to predict the machining quality score of the spline at the current moment, and combining the energy ratio at the current moment, determine the machining instability index of the spline at the current moment, to predict the change trend of machining quality and the risk of machining instability. The specific process is as follows: (1) In this embodiment, modal decomposition is performed on all cutting risk values within a preset time period before each moment. By analyzing the energy difference between a preset number of high-frequency modal components and all modal components, the energy ratio at each moment is determined, specifically as follows: In this embodiment, all cutting risk values within a preset time period before each moment are used as the input of the empirical mode decomposition algorithm, and all modal components are output. All modal components are arranged in descending order of frequency. The first preset number of modal components in the arrangement result are used as high-frequency modal components. The result of dividing the total energy of the preset number of high-frequency modal components by the total energy of all modal components is used as the energy ratio at each moment, which reflects the risk of cutting force mutation and instantaneous temperature rise. The larger the energy ratio, the greater the risk of cutting force mutation and instantaneous temperature rise.

[0038] It should be noted that as an implementation method, in this embodiment, the empirical mode decomposition algorithm is used for modal decomposition. In the actual application process, as other implementation methods, implementers can also use other modal decomposition algorithms such as variational mode decomposition in combination with specific situations. Regarding the selection of the modal decomposition algorithm, this embodiment does not make special restrictions.

[0039] Among them, the empirical mode decomposition algorithm is a well-known technology, and its specific principle will not be elaborated here.

[0040] In addition, it is further explained that the value of the preset number is set artificially. In this embodiment, the value of the preset number is 3. In the actual application process, as other implementation methods, implementers can also set it by themselves in combination with specific situations. This embodiment does not make special restrictions.

[0041] (2) Further, in this embodiment, by analyzing the dispersion degree of all cutting force data within a preset time period before each moment, the machining quality of the spline at each moment is determined, specifically as follows: In this embodiment, taking the natural constant as the base and the negative of the degree of dispersion of all cutting force data within a preset period before each moment as the independent variable, the exponential function value is used as the machining quality of the spline at each moment, indirectly characterizing the change trend of the machining quality. When the coefficient of variation of the cutting force is smaller, the result after the negative exponential mapping is closer to 1, indicating that the cutting force is more stable and the machining quality is better.

[0042] (3) Further, in order to more comprehensively and accurately predict the machining quality and predict the risk of machining instability, by comprehensively analyzing the cutting risk value, energy proportion, the difference in the main frequency amplitude in the frequency domain between all vibration data and all cutting force data, and the relationship between the cutting-related index and the machining quality within a preset period before the current moment, to predict the machining quality score of the spline at the current moment, and combining the energy proportion at the current moment, determine the machining instability index of the spline at the current moment, specifically: In this embodiment, the cutting risk values at all moments within a preset period before the current moment, the energy proportions at all moments, the differences in the main frequency amplitude in the frequency domain between all vibration data and all cutting force data at each moment, and the cutting-related indices at all moments are used as four independent variables in the random forest algorithm, and the machining quality at all moments is used as the dependent variable in the random forest algorithm. Among them, in this embodiment, the depth of the tree is set to 10 and the number of trees is set to 100 to obtain the machining quality score of the spline at the current moment, which is used to characterize the combined influence of non-linear thermal-mechanical coupling and energy distribution on the machining risk of the spline.

[0043] Among them, the random forest algorithm is a well-known technology, and the specific process of using it to predict the machining quality will not be elaborated here.

[0044] Preferably, the schematic diagram of the process for extracting the machining quality score provided in this embodiment is as Figure 2 shown.

[0045] Further, based on the machining quality score of the spline at the current moment in this embodiment, and combining the energy proportion at the current moment, determine the machining instability index of the spline at the current moment, which is used to characterize the coupling risk of stability deterioration and instantaneous impact, specifically: As an implementation manner, in this embodiment, the machining instability index of the spline at the current moment has the following expression: ; in the formula, represents the machining quality score of the spline at the current moment; E represents the energy proportion at the current moment; norm( ) represents the normalization function.

[0046] It can be understood from the machining instability index of the spline at the current moment that the machining instability index is used to characterize the coupling risk of stability deterioration and instantaneous impact. The machining quality score characterizes the stability and health status of the spline machining process. The greater the difference between the machining quality score and 1, the worse the current spline machining quality; the greater the energy proportion, the greater the energy of the short-time mutation and instantaneous impact components in the corresponding signal, indicating that inertial mutations or instantaneous temperature rises frequently occur during the spline machining process, resulting in dynamic instability of the machining process. The corresponding machining instability index is larger, indicating that the spline machining process faces both progressive performance degradation and sudden disturbances at the same time, and the two synergistically increase the risk of spline machining failure; On the contrary, the machining quality score is close to 1, which indicates that the stability and health status of the spline machining process are good, the machining quality is high, and the energy proportion is smaller, and the energy of the short-time mutation and instantaneous impact components is smaller: This shows that during the spline machining process, the occurrence frequency of inertial mutations or instantaneous temperature rises is lower, the dynamic stability is better, and the finally obtained machining instability index is smaller, indicating that the progressive performance degradation and sudden disturbances faced by the spline machining process are smaller, and the machining failure risk under the synergistic action of the two is lower.

[0047] So far, in this embodiment, multi-dimensional data analysis is used to evaluate and predict the spline machining quality and machining instability risk, and the quality and stability during the spline machining process can be monitored and predicted in real time, potential problems can be discovered in time and corresponding measures can be taken, so as to improve the machining quality and efficiency and extend the service life of the workpiece.

[0048] Step S4: Based on the machining instability index, judge the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment, so as to judge whether to combine the energy proportion at the current moment and the machining quality score to determine the spindle speed correction value and tool feed rate during the spline machining process at the current moment.

[0049] Since the traditional adaptive control system relies on a linear PID control model, it is difficult to accurately predict the non-linear load mutation caused by the variable cross-section of the eccentric shaft of the reducer, and it is easy to have a regulation lag in the later stage of tool wear, resulting in periodic fluctuations of the cutting force, low-frequency vibration and instantaneous temperature rise that cannot be effectively suppressed, and then causing tooth surface vibration marks, micro-cracks and deterioration of the metallographic structure of the material surface, seriously reducing the spline machining quality and fatigue life.

[0050] Therefore, in this embodiment, based on the machining instability index, judge the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment, so as to judge whether to combine the energy proportion at the current moment and the machining quality score to determine the spindle speed correction value and tool feed rate during the spline machining process at the current moment. The specific process is as follows: If the machining instability index of the spline at the current moment is greater than the preset threshold, it indicates that the dynamic instability risk in the spline machining process is relatively high, then the spindle speed and the tool feed rate during the spline machining process are adjusted; conversely, if the machining instability index of the spline at the current moment is less than or equal to the preset threshold, then the spindle speed and the tool feed rate during the spline machining process are not adjusted.

[0051] The process of adjusting the spindle speed and the tool feed rate during the spline machining process includes: The correction value of the spindle speed during the machining process The expression is: ; represents the spindle speed collected at the current moment; represents the energy ratio at the current moment. Through this relational expression, the speed is adjusted by the magnetic levitation bearing equipped in the adaptive spindle system, thereby suppressing the sudden change of the cutting force.

[0052] Among them, the spindle speed can be obtained in real time through an optical encoder.

[0053] The tool feed rate during the spline machining process at the current moment The expression is: ; In the formula, represents the preset compensation gain coefficient; Q represents the machining quality score of the spline at the current moment. Through this relational expression, the feed rate of the tool feed speed is adjusted by the online compensation module to relieve the vibration accumulation.

[0054] Among them, the value of the preset compensation gain coefficient is set manually. In this embodiment, the value of the preset compensation gain coefficient is 0.3, which enables the feed rate to balance stability and sensitivity while suppressing the risks of vibration and thermal damage and avoiding a significant decrease in the spline machining efficiency due to an excessive adjustment amplitude of the feed rate.

[0055] Thus, through the above control and adjustment, the real-time suppression of the non-linear load mutation and the thermal-mechanical coupling effect is achieved, ensuring the stability of the spline machining process and the reliability of the workpiece.

[0056] Based on the same inventive concept as the above method, the embodiment of the present application also provides a spline machining device for the eccentric shaft of a reducer, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above spline machining methods for the eccentric shaft of the reducer are implemented.

[0057] It should be noted that: The above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the specific embodiments of this specification have been described above. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The embodiments in this specification are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0059] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for machining the spline of the eccentric shaft of a speed reducer, characterized in that, The method includes the following steps: Obtain the cutting force data, vibration data, and temperature data in real time during the spline machining process of the eccentric shaft of the reducer; Analyze the amplitude-frequency difference in the frequency domain between all the cutting force data and all the vibration data within a preset time period before each moment to determine the cutting vibration difference value of the spline at each moment; by analyzing the correlation between all the cutting force data and all the temperature data within the preset time period, determine the cutting correlation index of the spline at each moment, and combine the cutting vibration difference value to determine the cutting risk value of the spline at each moment; Perform modal decomposition on all the cutting risk values within a preset time period before each moment. By analyzing the energy difference between a preset number of high-frequency modal components and all the modal components, determine the energy proportion at each moment; by analyzing the dispersion degree of all the cutting force data within a preset time period before each moment, determine the machining quality of the spline at each moment; comprehensively analyze the cutting risk value, energy proportion, main frequency amplitude difference in the frequency domain between all the vibration data and all the cutting force data, and the variation relationship of the cutting correlation index with the machining quality within a preset time period before the current moment to predict the machining quality score of the spline at the current moment, and combine the energy proportion at the current moment to determine the machining instability index of the spline at the current moment; Based on the machining instability index, judge the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment, and determine the spindle speed correction value and tool feed rate during the spline machining process at the current moment by combining the energy proportion at the current moment and the machining quality score.

2. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that, The method for determining the cutting vibration difference value of the spline at each moment is as follows: Calculate the ratio of the main frequency amplitude to the corresponding frequency of all the cutting force data in the frequency domain and the ratio of the main frequency amplitude to the corresponding frequency of all the vibration data in the frequency domain within a preset time period before each moment, and record them as the first ratio and the second ratio respectively. Take the result of dividing the first ratio by the second ratio as the cutting vibration difference value of the spline at each moment.

3. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that, The cutting correlation index of the spline at each moment is the correlation coefficient between all the cutting force data and all the temperature data within a preset time period before each moment.

4. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that, The expression for the cutting risk value of the spline at each moment is as follows: ; In the formula, represents the cutting risk value of the spline at moment i; represents the cutting difference value of the spline at moment i; represents the cutting correlation index of the spline at moment i.

5. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that The energy proportion at each moment is the result of dividing the total energy of the preset number of high-frequency modal components by the total energy of all the modal components.

6. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that The machining quality of the spline at each moment is: the exponential function value with the natural constant as the base and the opposite number of the dispersion degree of all the cutting force data within a preset time period before each moment as the independent variable.

7. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that, The prediction of the machining quality score of the spline at the current moment includes: Take the cutting risk values at all the moments within a preset time period before the current moment, the energy proportions at all the moments, the main frequency amplitude differences in the frequency domain between all the vibration data and all the cutting force data at each moment, and the cutting correlation indexes at all the moments as four independent variables in the random forest algorithm, and take the machining quality at all the moments as the dependent variable in the random forest algorithm to obtain the machining quality score of the spline at the current moment.

8. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that, The expression for the machining instability index of the spline at the current moment is as follows: ; In the formula, represents the machining quality score of the spline at the current moment; E represents the energy proportion at the current moment; norm( ) represents the normalization function.

9. The spline machining method of the eccentric shaft of the speed reducer according to claim 1, characterized in that, Based on the machining instability index, determine the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment, and determine whether to combine the energy ratio at the current moment and the machining quality score to determine the spindle speed correction value and tool feed rate during the spline machining process at the current moment, including: If the machining instability index of the spline at the current moment is greater than the preset threshold, adjust the spindle and tool feed rate during the spline machining process; otherwise, do not adjust the spindle and tool feed rate during the spline machining process; The process of adjusting the spindle and tool feed rate during the spline machining process includes: Spindle speed correction value during the machining process The expression is as follows: ; represents the spindle speed at the currently collected moment; represents the energy percentage at the current moment; The tool feed during the spline machining process at the current moment has the following expression: ; where represents the preset compensation gain coefficient; Q represents the machining quality score of the spline at the current moment.

10. A spline machining device for a reducer eccentric shaft, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the spline machining method of the eccentric shaft of the reducer according to any one of claims 1-9.

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