Spline processing device and processing method for eccentric shaft of reducer

Abstract

The present application relates to the field of spline processing technology, and specifically to a spline processing device and processing method for an eccentric shaft of a reducer. The method comprises: analyzing the amplitude-frequency difference in the frequency domain between all cutting force data and all vibration data within a preset time period before each moment, as well as the correlation between all cutting force data and all temperature data, to determine the cutting risk value; comprehensively analyzing the cutting risk value, energy proportion, the main frequency amplitude difference in the frequency domain between all vibration data and all cutting force data within a preset period before the current moment, and the relationship between the cutting-related index and the processing quality, predicting the processing quality score to determine the processing instability index, and combining the energy proportion and the processing quality score to determine the spindle speed correction value and the tool feed rate. The present application solves the compensation failure problem of traditional PID control and improves the spline processing quality and workpiece life.

Description

Technical Field

[0001] The present application relates to the technical field of spline processing, and in particular to a spline processing device and method for an eccentric shaft of a reducer. Background Art

[0002] As the core transmission component of an RV reducer, the eccentric shaft achieves both power transmission and reduction through its precise eccentric design. Its machining accuracy directly determines the smoothness, noise level, and service life of the transmission system. During grinding, the coordinated control of the grinding wheel head and workpiece is susceptible to reciprocating impact vibrations caused by switching inertia. This increases the risk of collision between the grinding wheel and the workpiece, severely impacting surface quality and threatening equipment safety.

[0003] In the processing of the eccentric shaft spline of the reducer, the inertia mutation and uneven force of the cutting system caused by the asymmetric geometric structure of the eccentric shaft lead to periodic and violent fluctuations in the cutting force, which not only triggers low-frequency vibration of the machine tool, causing chatter marks and micro cracks on the tooth surface, but also induces degradation of the metallographic structure of the material surface due to instantaneous temperature rise, seriously reducing the fatigue life of the spline. However, by integrating piezoelectric cutting force sensors and vibration accelerometers to monitor the processing status 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 nonlinear load mutation caused by the variable cross-section of the eccentric shaft. In addition, adjustment lag is prone to occur in the later stage of tool wear, resulting in compensation failure, which reduces the spline processing quality and 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 processing device and processing method for the eccentric shaft of a reducer. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for processing a spline of an eccentric shaft of a reducer, the method comprising the following steps:

[0006] Real-time acquisition of cutting force data, vibration data, and temperature data during the spline machining process of the reducer eccentric shaft;

[0007] Analyze the amplitude-frequency difference 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 determine the cutting risk value of the spline at each moment in combination with the cutting vibration difference value;

[0008] Perform modal decomposition on all cutting risk values ​​within a preset time period before each moment, and determine the energy proportion at each moment by analyzing the energy difference between a preset number of high-frequency modal components and all modal components; determine the processing quality of the spline at each moment by analyzing the degree of discreteness of all cutting force data within a preset time period before each moment; comprehensively analyze the cutting risk value, energy proportion, the difference in the main frequency amplitude between all vibration data and all cutting force data in the frequency domain, and the relationship between the cutting-related index and the processing quality within the preset period before the current moment to predict the processing quality score of the spline at the current moment, and determine the processing instability index of the spline at the current moment in combination with the energy proportion at the current moment;

[0009] Based on the machining instability index, the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment is judged to determine 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.

[0010] Preferably, the method for determining the cutting vibration difference value of the spline at each moment is:

[0011] Calculate the ratio of the main frequency amplitude to the corresponding frequency in the frequency domain of all cutting force data within the preset time before each moment, as well as the ratio of the main frequency amplitude to the corresponding frequency in the frequency domain of all vibration data, and record them as the first ratio and the second ratio respectively. Divide the first ratio by the second ratio as the cutting vibration difference value of the spline at each moment.

[0012] Preferably, the cutting correlation index of the spline at each moment is a correlation coefficient between all cutting force data and all temperature data within a preset time period before each moment.

[0013] 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 time i; represents the cutting difference value of the spline at time i; Represents the cutting-related index of the spline at time i.

[0014] 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.

[0015] Preferably, the processing quality of the spline at each moment is: an exponential function value with a natural constant as the base and the inverse of the discrete degree of all cutting force data in a preset time period before each moment as the independent variable.

[0016] Preferably, the prediction of the processing quality score of the spline at the current moment includes:

[0017] The cutting risk value at all moments in the preset period before the current moment, the energy proportion at all moments, the main frequency amplitude difference between all vibration data and all cutting force data in the frequency domain at each moment, and the cutting related index at all moments are used as four independent variables in the random forest algorithm, and the processing quality at all moments is used as the dependent variable in the random forest algorithm to obtain the processing quality score of the spline at the current moment.

[0018] Preferably, the expression of the machining instability index of the spline at the current moment is: Where, represents the processing quality score of the spline at the current moment; E represents the energy proportion at the current moment; norm( ) represents the normalization function.

[0019] Preferably, the necessity of adjusting the spindle speed and the tool feed rate during the spline machining process at the current moment is judged based on the machining instability index, so as to judge whether to determine the spindle speed correction value and the tool feed rate during the spline machining process at the current moment in combination with the energy proportion at the current moment and the machining quality score, including:

[0020] If the machining instability index of the spline at the current moment is greater than the preset threshold, the spindle and tool feed rates during the spline machining process are adjusted; otherwise, the spindle and tool feed rates during the spline machining process are not adjusted;

[0021] The process of adjusting the spindle and tool feeds during spline machining includes:

[0022] Spindle speed correction value during machining The expression is: ; Indicates the spindle speed collected at the current moment; Indicates the energy ratio at the current moment;

[0023] The tool feed rate during spline machining at the current moment The expression is: Where, represents a preset compensation gain coefficient; Q represents the spline processing quality score at the current moment. In a second aspect, an embodiment of the present application further provides a spline processing device for a reducer eccentric shaft, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any of the above-mentioned methods for processing a spline of a reducer eccentric shaft are implemented.

[0024] This application has at least the following beneficial effects:

[0025] This application aims to solve the problem of vibration energy distribution imbalance and thermal-mechanical coupling effect caused by inertia mutation and uneven force of cutting system. By analyzing the correlation between cutting force, vibration and temperature data, the spline processing risk is evaluated, and a cutting risk value is constructed. The synergistic effect of vibration energy difference and thermal damage risk is quantified, which solves the limitation of traditional linear model in predicting nonlinear load mutation, helps to reveal the influence of thermal-mechanical coupling effect on material thermal damage, thereby optimizing the processing process and improving spline processing quality and workpiece life. Furthermore, this application aims to solve the problem of progressive performance degradation and sudden disturbance coupling risk through multi-dimensional data analysis. Analysis is used to evaluate and predict the spline processing quality and processing instability risk. It can monitor and predict the quality and stability of the spline processing process in real time, discover potential problems in time and take corresponding measures, eliminate the adjustment lag effect of the single linear control model in the later stage of tool wear, and improve the spline processing quality and workpiece life; further, the application dynamically adjusts the spindle speed through the energy proportion to suppress the sudden change of cutting force, optimizes the feed rate in combination with the processing quality score to balance vibration suppression and processing efficiency, solves the compensation failure problem of traditional PID control, realizes the coordinated suppression of nonlinear load and thermomechanical coupling effect, and improves the spline processing quality and workpiece life. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A flowchart of a method for processing a spline of an eccentric shaft of a reducer according to an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the processing quality score extraction process provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the spline machining device and method for the eccentric shaft of a reducer proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0031] The specific scheme of the spline processing device and processing method of the eccentric shaft of the reducer provided in this application is described in detail below with reference to the accompanying drawings.

[0032] See also Figure 1 , which shows a flowchart of a method for processing a spline of an eccentric shaft of a reducer provided by an embodiment of the present application, the method comprising the following steps:

[0033] Step S1: obtaining cutting force data, vibration data, and temperature data during the spline machining process of the reducer eccentric shaft in real time.

[0034] In the spline processing device of the reducer eccentric shaft, in order to accurately monitor the key parameters in the spline processing process, a piezoelectric cutting force sensor is integrated in the tool holder to collect three-dimensional cutting forces (Fx, Fy, Fz) in real time, which is used to analyze the periodic fluctuation characteristics of the cutting force and its impact on the tool load; fiber grating vibration sensors are arranged on the spindle and workpiece ends to obtain vibration data in real time, which is used to identify low-frequency vibration marks and inertia mutation characteristics caused by asymmetric structures; at the same time, a non-contact infrared thermal imager is commercially installed in the spline processing area to dynamically capture the temperature data of the tool-workpiece contact area, which is used to evaluate the risk of degradation of the surface metallographic structure of the material due to instantaneous temperature rise.

[0035] Specifically, in the reducer eccentric shaft spline processing device, the three-dimensional cutting force collected by the voltage-type cutting force sensor is converted into a comprehensive cutting force through vector synthesis and abbreviated as cutting force, which is used to characterize the real-time dynamic changes of the overall tool load; vibration data can be directly obtained through the fiber grating vibration sensor, and the infrared image obtained by the infrared thermal imager is used to quantify the instantaneous temperature rise gradient in the tool-workpiece contact area and its risk of thermal damage to the material by calculating the extreme temperature distribution in the infrared image.

[0036] Here, vector synthesis is a technology well known to those skilled in the art, and the specific principles will not be described in detail.

[0037] In this embodiment, the acquisition frequency of all the above data is set to 100 Hz. In actual application, as other implementation methods, the implementer can also set the data acquisition frequency according to the specific situation. Furthermore, in order to eliminate the influence of dimension, the various collected data are normalized respectively.

[0038] 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, as other implementation methods, the implementer may also use the maximum and minimum value normalization method to normalize the data. Regarding the selection of the normalization method, this embodiment does not impose any special restrictions.

[0039] Among them, the z-score normalization method is a well-known technology, and its specific principle will not be described in detail.

[0040] Step S2: Analyze the amplitude-frequency difference in the frequency domain between all cutting force data and all 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 cutting force data and all 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.

[0041] Due to the asymmetric geometric structure of the eccentric shaft of the reducer, the inertia of the cutting system changes suddenly and the force is uneven, which causes periodic and violent fluctuations in the cutting force, low-frequency vibration and instantaneous temperature rise, resulting in chatter marks, micro cracks and deterioration of the metallographic structure of the material surface, seriously reducing the processing quality and fatigue life of the spline.

[0042] The vibration data and cutting force data have time-varying and periodic characteristics, such as low-frequency vibration marks and instantaneous cutting force fluctuations; the relationship between temperature and cutting force focuses more on the linear correlation of the overall trend. Therefore, based on the correlation characteristics between the cutting force data and the vibration data, as well as the correlation between the cutting force data and the temperature data, this embodiment determines the cutting vibration difference value of the spline at each moment by analyzing the amplitude-frequency difference in the frequency domain between all cutting force data and all vibration data within a preset time period before each moment; by analyzing the correlation between all cutting force data and all temperature data within the preset time period, the cutting correlation index of the spline at each moment is determined, and combined with the cutting vibration difference value, the cutting risk value of the spline at each moment is determined. The specific process is as follows:

[0043] In this embodiment, the ratios of the main frequency amplitudes of all cutting force data in the frequency domain to the corresponding frequencies within a preset time period before each moment, as well as the ratios of the main frequency amplitudes of all vibration data in the frequency domain to the corresponding frequencies are calculated respectively, and recorded 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; wherein, the first ratio and the second ratio quantify the cumulative effect of the vibration energy of the cutting force data and the 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 closely the vibration and cutting force energy density are matched.

[0044] Furthermore, this embodiment uses the correlation coefficient between all cutting force data and all temperature data within a preset time period before each moment as the cutting correlation index of the spline at each moment, which is used to characterize the degree of linear correlation between the temperature gradient and the cutting force fluctuation, and to reveal the influence of the thermomechanical 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 thermomechanical coupling effect is significant.

[0045] It should be noted that there are many commonly used methods for calculating the correlation coefficient. In this embodiment, the Pearson correlation coefficient between all cutting force data and all temperature data within the preset time before each moment is used as the correlation coefficient between all cutting force data and all temperature data within the preset time before each moment. In actual application, as other implementation methods, the implementer may also adopt other correlation coefficient calculation methods such as the Spearman correlation coefficient or the Kendall rank correlation coefficient based on the specific circumstances. Regarding the selection of the correlation coefficient calculation method, this embodiment does not impose any special restrictions.

[0046] The calculation method of the Pearson correlation coefficient is a well-known technique, and the specific calculation process will not be described in detail.

[0047] Furthermore, this embodiment determines the cutting risk value of the spline at each moment based on the cutting vibration difference value and the cutting correlation index, which is used to characterize the combined impact of the vibration energy difference and the thermal-mechanical coupling effect on the spline processing quality, specifically:

[0048] As an implementation method, in this embodiment, the cutting risk value of the spline at time i is The expression is: Where, represents the cutting difference value of the spline at time i; Represents the cutting-related index of the spline at time i.

[0049] According to the cutting risk value of the spline at each moment, it can be understood that if the cutting vibration difference value is larger, it means that the energy density between the cutting force and the vibration data is more mismatched, and the larger the cutting correlation index is, the more significant the thermal-mechanical coupling effect is, and the larger the cutting risk value is, indicating that the asymmetry of the vibration energy distribution during the spline processing and the thermal-mechanical coupling effect are synergistically enhanced, and the two together lead to dynamic instability of the system, thereby greatly increasing the combined risk of tooth surface vibration marks, crack propagation and metallographic structure degradation, and the spline processing quality and workpiece life drop sharply; on the contrary, if the cutting vibration difference value is closer to 1, it means that the energy density between the cutting force and the vibration data is more matched, and the smaller the cutting force correlation index is, the less significant the thermal-mechanical coupling effect is, and the final cutting risk value is smaller, indicating that the vibration energy distribution during the spline processing is more symmetrical, the thermal-mechanical coupling effect is weak, the system dynamic stability is better, and the risk of tooth surface vibration marks, crack propagation and metallographic structure degradation is low, thereby maintaining the spline processing quality and workpiece life at a high level.

[0050] Thus, this embodiment evaluates the spline machining risk by analyzing the correlation between cutting force, vibration, and temperature data, and constructs a cutting risk value, which helps to reveal the influence of the thermomechanical coupling effect on thermal damage of the material, thereby optimizing the machining process and improving the spline machining quality and workpiece life.

[0051] Step S3: Perform modal decomposition on all cutting risk values ​​within the preset time period before each moment, and determine the energy proportion at each moment by analyzing the energy difference between a preset number of high-frequency modal components and all modal components; determine the processing quality of the spline at each moment by analyzing the discrete degree of all cutting force data within the preset time period before each moment; comprehensively analyze the cutting risk value, energy proportion, the difference in the main frequency amplitude between all vibration data and all cutting force data in the frequency domain, and the relationship between the cutting-related index and the processing quality within the preset period before the current moment to predict the processing quality score of the spline at the current moment, and determine the processing instability index of the spline at the current moment in combination with the energy proportion at the current moment.

[0052] In modern manufacturing, reducers are key components widely used in various mechanical equipment. Their performance and lifespan directly affect the efficiency and reliability of the entire equipment. The spline connection in reducers is a common transmission method, and its processing quality is directly related to the transmission accuracy and load-bearing capacity of the reducer. However, due to the asymmetric geometry of the reducer's eccentric shaft, the cutting process is prone to problems such as periodic and severe fluctuations in cutting force, low-frequency vibration, and transient temperature rise. These problems can lead to chatter marks on the tooth surface, microcracks, and deterioration of the material surface metallographic structure, which seriously reduces the processing quality and fatigue life of the spline.

[0053] In order to solve the above problems, it is necessary to monitor and evaluate the spline processing quality in real time. However, due to the lag in micro-defect detection, the irreversibility of the dynamic process, and the blind spots of multi-physical field coupling monitoring, it is very difficult to directly obtain processing quality data. Therefore, this embodiment determines the energy proportion at each moment by analyzing the energy difference between a preset number of high-frequency modal components and all modal components; determines the processing quality of the spline at each moment by analyzing the discrete degree of all cutting force data in the preset time period before each moment; comprehensively analyzes the cutting risk value, energy proportion, the difference in the main frequency amplitude between all vibration data and all cutting force data in the frequency domain, and the relationship between the cutting-related index and the processing quality in the preset period before the current moment to predict the processing quality score of the spline at the current moment, and determines the processing instability index of the spline at the current moment in combination with the energy proportion at the current moment to predict the trend of processing quality changes and the risk of processing instability. The specific process is as follows:

[0054] (1) In this embodiment, modal decomposition is performed on all cutting risk values ​​within a preset period before each moment. By analyzing the energy difference between a preset number of high-frequency modal components and all modal components, the energy proportion at each moment is determined, specifically:

[0055] In this embodiment, all cutting risk values ​​within a preset time period before each moment are used as input to the empirical mode decomposition algorithm, all modal components are output, all modal components are arranged in descending order of frequency, and the first preset number of modal components in the arrangement result are used as high-frequency modal components. The total energy of the preset number of high-frequency modal components is divided by the total energy of all modal components as the energy proportion at each moment, which reflects the risk of sudden change in cutting force and instantaneous temperature rise. The larger the energy proportion, the greater the risk of sudden change in cutting force and instantaneous temperature rise.

[0056] It should be noted that, as an implementation method, in this embodiment, the empirical mode decomposition algorithm is used for modal decomposition. In actual application, as other implementation methods, the implementer may also adopt other modal decomposition algorithms such as variational modal decomposition based on specific circumstances. Regarding the selection of modal decomposition algorithm, this embodiment does not impose any special restrictions.

[0057] Among them, the empirical mode decomposition algorithm is a well-known technology, and its specific principle will not be repeated here.

[0058] In addition, it should be noted that the value of the preset number is set manually. In this embodiment, the value of the preset number is 3. In actual application, as other implementation methods, the implementer can also set it by himself based on the specific situation. This embodiment does not impose any special restrictions.

[0059] (2) Furthermore, this embodiment determines the processing quality of the spline at each moment by analyzing the discrete degree of all cutting force data within a preset period before each moment, specifically:

[0060] In this embodiment, an exponential function value with a natural constant as the base and the opposite of the discrete degree of all cutting force data in a preset time period before each moment as the independent variable is used as the processing quality of the spline at each moment to indirectly characterize the trend of changes in processing quality. When the coefficient of variation of the cutting force is smaller, the result after negative exponential mapping is closer to 1, which means that the cutting force is more stable and the processing quality is better.

[0061] (3) Furthermore, in order to more comprehensively and accurately predict the processing quality and the risk of processing instability, the cutting risk value, energy proportion, the difference in the main frequency amplitude between all vibration data and all cutting force data in the frequency domain, and the relationship between the cutting-related index and the processing quality in the preset period before the current moment are comprehensively analyzed to predict the processing quality score of the spline at the current moment. Combined with the energy proportion at the current moment, the processing instability index of the spline at the current moment is determined, specifically:

[0062] In this embodiment, the cutting risk value at all moments in a preset period before the current moment, the energy proportion at all moments, the main frequency amplitude difference between all vibration data and all cutting force data in the frequency domain at each moment, and the cutting related index at all moments are used as four independent variables in the random forest algorithm, and the processing quality at all moments is used as the dependent variable in the random forest algorithm. In this embodiment, the depth of the tree is set to 10 and the number of trees is 100, and the processing quality score of the spline at the current moment is obtained, which is used to characterize the synergistic effect of nonlinear thermal-mechanical coupling and energy distribution on the spline processing risk.

[0063] Among them, the random forest algorithm is a well-known technology, and the specific process of using it to predict processing quality will not be described in detail.

[0064] Preferably, the schematic diagram of the processing quality score extraction process provided in this embodiment is as follows: Figure 2 shown.

[0065] Furthermore, this embodiment determines the spline machining instability index at the current moment based on the machining quality score of the spline at the current moment and in combination with the energy proportion at the current moment, which is used to characterize the coupling risk of stability degradation and transient impact. Specifically:

[0066] As an implementation method, in this embodiment, the machining instability index of the lower spline at the current moment is The expression is: Where, represents the processing quality score of the spline at the current moment; E represents the energy proportion at the current moment; norm( ) represents the normalization function.

[0067] According to the machining instability index of the spline at the current moment, it can be understood that the machining instability index is used to characterize the coupling risk of stability degradation and transient 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-term mutation and transient impact components in the corresponding signal, indicating that inertia mutations or transient temperature rises frequently occur during the spline machining process, resulting in dynamic instability of the machining process. The larger the corresponding machining instability index, the more the spline machining process faces both progressive performance degradation and sudden disturbances, which synergistically exacerbate the risk of spline machining failure.

[0068] On the contrary, the processing quality score is close to 1, which indicates that the stability and health of the spline processing process are good, the processing quality is high, and the smaller the energy proportion, the smaller the energy of short-term mutations and instantaneous impact components: this shows that during the spline processing process, the frequency of inertia mutations or instantaneous temperature rise is low, the dynamic stability is good, and the final processing instability index is small, indicating that the spline processing process faces less progressive performance degradation and sudden disturbances, and the risk of processing failure under the synergistic effect of the two is low.

[0069] Thus, this embodiment uses multi-dimensional data analysis to evaluate and predict the spline processing quality and processing instability risk. It can monitor and predict the quality and stability of the spline processing process in real time, discover potential problems in time and take corresponding measures, thereby improving processing quality and efficiency and extending the life of the workpiece.

[0070] Step S4: Based on the machining instability index, the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment is judged to determine 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.

[0071] Since traditional adaptive control systems rely on linear PID control models, it is difficult to accurately predict the nonlinear load mutations caused by the variable cross-section of the eccentric shaft of the reducer, and adjustment lag is prone to occur in the later stages of tool wear, resulting in periodic fluctuations in cutting force, low-frequency vibrations and instantaneous temperature rise that cannot be effectively suppressed, which in turn causes tooth surface chatter marks, microcracks and deterioration of the metallographic structure of the material surface, seriously reducing the spline processing quality and fatigue life.

[0072] Therefore, this embodiment judges the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment based on the machining instability index, so as to determine 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:

[0073] If the machining instability index of the spline at the current moment is greater than the preset threshold, it indicates that the risk of dynamic instability in the spline machining process is high, and the spindle and tool feed rates 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, the spindle and tool feed rates during the spline machining process are not adjusted.

[0074] The process of adjusting the spindle and tool feeds during spline machining includes:

[0075] Spindle speed correction value during machining The expression is: ; Indicates the spindle speed collected at the current moment; represents the energy proportion at the current moment. Based on this relationship, the adaptive spindle system equipped with magnetic bearings adjusts the speed to suppress sudden changes in cutting force.

[0076] Among them, the spindle speed can be obtained in real time through the photoelectric encoder.

[0077] The tool feed rate during spline machining at the current moment The expression is: Where, represents the preset compensation gain coefficient; Q represents the spline machining quality score at the current moment. Based on this relationship, the online compensation module adjusts the tool feed rate to alleviate vibration accumulation.

[0078] Among them, the value of the preset compensation gain coefficient is set artificially. In this embodiment, the value of the preset compensation gain coefficient is 0.3, so that the feed rate can suppress the risk of vibration and thermal damage while avoiding a significant decrease in spline processing efficiency due to excessive feed rate adjustment, thereby achieving a balance between stability and sensitivity.

[0079] At this point, through the above control adjustments, the real-time suppression of nonlinear load mutations and thermal-mechanical coupling effects is achieved, ensuring the stability of the spline machining process and the reliability of the workpiece.

[0080] Based on the same inventive concept as the above-mentioned method, an embodiment of the present application also provides a spline processing device for a reducer eccentric shaft, comprising 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-mentioned methods for processing the spline of the reducer eccentric shaft are implemented.

[0081] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0083] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A spline processing method for an eccentric shaft of a reducer, characterized in that: The method comprises the following steps: Real-time acquisition of cutting force data, vibration data, and temperature data during the spline machining process of the reducer eccentric shaft; Analyze the amplitude-frequency difference 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 determine the cutting risk value of the spline at each moment in combination with the cutting vibration difference value; Perform modal decomposition on all cutting risk values ​​within a preset time period before each moment, and determine the energy proportion at each moment by analyzing the energy difference between a preset number of high-frequency modal components and all modal components; determine the processing quality of the spline at each moment by analyzing the degree of discreteness of all cutting force data within a preset time period before each moment; comprehensively analyze the cutting risk value, energy proportion, the difference in the main frequency amplitude between all vibration data and all cutting force data in the frequency domain, and the relationship between the cutting-related index and the processing quality within the preset period before the current moment to predict the processing quality score of the spline at the current moment, and determine the processing instability index of the spline at the current moment in combination with the energy proportion at the current moment; Based on the machining instability index, the necessity of adjusting the spindle speed and tool feed rate during the spline machining process at the current moment is judged to determine 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.

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

3. The spline processing method of the eccentric shaft of the reducer according to claim 1, characterized in that: The cutting correlation 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.

4. The spline processing method of the eccentric shaft of the reducer according to claim 1, characterized in that: The expression of the cutting risk value of the spline at each moment is: Where, represents the cutting risk value of the spline at time i; represents the cutting difference value of the spline at time i; Represents the cutting-related index of the spline at time i.

5. The spline processing method of the eccentric shaft of the 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 modal components.

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

7. The spline processing method of the eccentric shaft of the reducer according to claim 1, characterized in that: The predicted processing quality score of the spline at the current moment includes: The cutting risk value at all moments in the preset period before the current moment, the energy proportion at all moments, the main frequency amplitude difference between all vibration data and all cutting force data in the frequency domain at each moment, and the cutting related index at all moments are used as four independent variables in the random forest algorithm, and the processing quality at all moments is used as the dependent variable in the random forest algorithm to obtain the processing quality score of the spline at the current moment.

8. The spline processing method of the eccentric shaft of the reducer according to claim 1, characterized in that: The expression of the machining instability index of the spline at the current moment is: Where, represents the processing 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 method for processing the spline of the eccentric shaft of the reducer according to claim 1, wherein: The necessity of adjusting the spindle speed and the tool feed rate during the spline machining process at the current moment is judged based on the machining instability index, 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 the 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, the spindle and tool feed rates during the spline machining process are adjusted; otherwise, the spindle and tool feed rates during the spline machining process are not adjusted; The process of adjusting the spindle and tool feeds during spline machining includes: Spindle speed correction value during machining The expression is: ; Indicates the spindle speed collected at the current moment; Indicates the energy ratio at the current moment; The tool feed rate during spline machining at the current moment The expression is: Where, represents the preset compensation gain coefficient; Q represents the processing quality score of the spline at the current moment.

10. A spline machining device for an eccentric shaft of a reducer, 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, the steps of the spline processing method for the eccentric shaft of the reducer as described in any one of claims 1 to 9 are implemented.

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