High-precision processing method and processing equipment for internal spline of planetary gear

Abstract

The application relates to the technical field of internal spline machining, in particular to an internal spline high-precision machining method and machining equipment suitable for a planetary gear, which comprises the following steps: obtaining periodic metrics and a trend line function; then obtaining a cutting force fluctuation coefficient and a cutting influence coefficient of each internal spline machining; based on the cutting influence coefficient, obtaining the particle weight of each particle in a particle swarm optimization algorithm and the updated inertia weight of each iteration, and performing high-precision machining on the internal spline of the planetary gear. The application improves the machining precision of the internal spline machining of the planetary gear.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of internal spline machining, in particular to an internal spline high-precision machining method and machining equipment suitable for planetary gears. BACKGROUND

[0002] An RV reducer is a high-precision reduction device widely used in the industrial machinery field, has the advantages of compact structure, large torque and high transmission efficiency, and is widely used in the fields of machine tools, robots, automatic equipment and the like. The planetary gear is an important component in the RV reducer, and the precision of the planetary gear is one of the keys to ensuring the quality of the RV reducer. The internal spline is one of the key components of the planetary gear, and the machining precision requirement of the internal spline is high, and the machining difficulty is great. Therefore, how to ensure the high-precision machining of the internal spline becomes the focus in the machining of the planetary gear. In order to ensure the precision of the internal spline, the variable needs to be controlled, and the broaching parameters need to be repeatedly corrected through repeated experiments, so as to finally determine the optimal parameters.

[0003] With the gradual introduction of computer technology into the mechanical industry, in order to improve the efficiency, the optimal solution is often obtained through an optimization algorithm in the prior art to reduce the number of experiments. However, when the optimization algorithm is used, the weights of different machining data are the same, which requires that the machining parameters of different particles are uniform enough, so as to ensure good global search ability and local search ability. However, in the actual internal spline machining, the experimental conditions are often set according to experience, so that the machining parameters of different particles have high similarity. This similarity makes the global search ability poor when the optimal machining parameters are iterated by using the optimization algorithm, and it is easy to fall into local optimization, so that the optimal parameters obtained are not accurate enough, and thus the machining precision of the internal spline is affected. SUMMARY

[0004] In order to solve the above technical problems, the purpose of the present application is to provide an internal spline high-precision machining method and machining equipment suitable for planetary gears, and the technical scheme adopted is as follows:

[0005] In a first aspect, the embodiments of the present application provide an internal spline high-precision machining method suitable for planetary gears, which comprises the following steps:

[0006] Collecting the vibration sequence, temperature sequence, tangential force sequence, radial force sequence, axial force sequence and cutting speed of the cutting tool at each collection time in the internal spline machining process of each planetary gear;

[0007] According to the change of the cutting speed, the tangential force sequence is segmented to obtain the cutting preparation sequence, cutting sequence and cutting completion sequence;

[0008] Based on the autocorrelation of the cutting sequence, the periodicity measure of the cutting sequence is obtained.

[0009] obtaining a trend line function according to the cutting sequence;

[0010] obtaining a cutting force fluctuation coefficient of each internal spline machining based on the mutation of the cutting preparation sequence and the cutting completion sequence, the periodicity measure of the cutting sequence, the slope of the trend line function, and the average of the elements in the tangential force sequence, the radial force sequence and the axial force sequence;

[0011] obtaining a cutting influence coefficient of each internal spline machining based on the cutting force fluctuation coefficient, the discrete degree of the vibration sequence and the temperature sequence, the correlation between the tangential force sequence and the vibration sequence, and the difference between adjacent elements in the tangential force sequence and the temperature sequence;

[0012] obtaining the particle weight of each particle in the particle swarm optimization algorithm based on the cutting influence coefficient;

[0013] obtaining the updated inertia weight of each iteration in the particle swarm optimization algorithm based on the particle weight, and performing high-precision machining on the internal spline of the planetary gear.

[0014] Further, the obtaining method of the cutting preparation sequence, the cutting sequence and the cutting completion sequence is:

[0015] For the cutting speed of each collection time, the time period from the time when the internal spline starts machining to the collection time when the cutting speed reaches the preset normal cutting speed for the first time is taken as the cutting start time period, the time period from the collection time when the cutting speed reaches the preset normal cutting speed for the first time to the collection time when the cutting speed reaches the preset normal cutting speed for the last time is taken as the cutting stable time period, and the time period from the collection time when the cutting speed reaches the preset normal cutting speed for the last time to the time when the internal spline stops machining is taken as the cutting end time period.

[0016] For the tangential force sequence, the tangential force sequence in the cutting start time period is taken as the cutting preparation sequence, the tangential force sequence in the cutting stable time period is taken as the cutting sequence, and the tangential force sequence in the cutting end time period is taken as the cutting completion sequence.

[0017] Further, the obtaining method of the periodicity measure is:

[0018] The autocorrelation function is used to obtain the autocorrelation coefficient of the cutting sequence and the lag of the autocorrelation coefficient, the autocorrelation function graph is constructed with the autocorrelation coefficient as the vertical coordinate and the lag of the autocorrelation coefficient as the horizontal coordinate, the peak value detection algorithm is used to obtain each peak value in the autocorrelation function graph, and the peak value greater than the preset correlation threshold is taken as each significant peak value.

[0019] Taking the bit sequence of each significant peak in all peaks in the autocorrelation function diagram as the horizontal coordinate, and taking the lag amount of the significant peak as the vertical coordinate, a linear fitting is performed to obtain a lag amount fitting straight line;

[0020] A product of the fitting degree of the lag amount fitting straight line and the average of all significant peaks in the autocorrelation function diagram is taken as the periodicity measure of the cutting sequence.

[0021] Further, the trend line function obtained according to the cutting sequence comprises:

[0022] The cutting sequence is taken as the input of a time series decomposition sequence algorithm, and a trend sequence is output; and a linear fitting algorithm is used to obtain a trend line function of the trend sequence.

[0023] Further, the method for obtaining the cutting force fluctuation coefficient comprises:

[0024] A first-order difference sequence of the cutting preparation sequence is obtained, and then the average of all elements in the first-order difference sequence of the cutting preparation sequence is taken as a first mutation threshold value, and the elements greater than the first mutation threshold value in the first-order difference sequence are taken as the mutation points of the first-order difference sequence of the cutting preparation sequence; a first-order difference sequence of the cutting completion sequence is obtained, and then the average of all elements in the first-order difference sequence of the cutting completion sequence is taken as a second mutation threshold value, and the elements less than the second mutation threshold value in the first-order difference sequence of the cutting completion sequence are taken as the mutation points of the first-order difference sequence of the cutting completion sequence.

[0025] The ratio of the number of the mutation points in the first-order difference sequence of the cutting preparation sequence to the number of all elements in the first-order difference sequence of the cutting preparation sequence is taken as a first mutation ratio value; and the first-order difference sequence of the cutting completion sequence is processed in the same way as the first-order difference sequence of the cutting preparation sequence to obtain a second mutation ratio value.

[0026] The calculation formula of the cutting force fluctuation coefficient is: ; in the formula, is the cutting force fluctuation coefficient of each internal spline machining, , the first mutation threshold value and the second mutation threshold value, respectively, is the sum of the first mutation ratio value and the second mutation ratio value, is the periodicity measure of the cutting sequence, is the slope of the trend line function, is the average of all elements in the tangential force sequence and the radial force sequence, is the average of all elements in the axial force sequence.

[0027] Further, the calculation formula of the cutting influence coefficient is: ; in the formula, is the cutting influence coefficient of each internal spline processing; is the cutting force fluctuation coefficient of internal spline machining, 、 are the variances of all elements in the vibration sequence and the variances of all elements in the temperature sequence, is the Pearson correlation coefficient between the tangential force series and the vibration series, is the mean of the absolute values ​​of the differences between all adjacent elements in the tangential force sequence, is the mean of the absolute values ​​of the differences between all adjacent elements in the temperature series.

[0028] Furthermore, the particle weight is obtained as follows:

[0029] Each internal spline machining process is regarded as each particle in the particle swarm optimization algorithm, and the particle weight of each particle is calculated as follows: Where, It is The particle weight of each particle, 、 It is Second internal spline processing process, Cutting influence coefficient of secondary internal spline machining process, is the total number of internal spline machining times.

[0030] Furthermore, the method for obtaining the updated inertia weight is:

[0031] The particle swarm optimization algorithm is used to iterate all particles in combination with the particle weight of each particle. For each iteration process, the sum of the particle weights of all particles within the preset neighborhood radius of the optimal solution particle during the iteration process and the preset initial inertia weight are calculated as the updated inertia weight of each iteration process.

[0032] Furthermore, the high-precision processing of the internal splines of the planetary gear includes:

[0033] According to the updated inertia weight of each iteration process, the particle swarm optimization algorithm is used to iterate all particles to obtain the optimal solution particle. The preset machining parameters of the tool for the internal spline machining process corresponding to the optimal solution particle are used as the optimal machining parameters, and the internal spline of the planetary gear is machined with high precision using the optimal machining parameters.

[0034] In the second aspect, an embodiment of the present application also provides a high-precision processing device for internal splines of planetary gears, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0035] This application has at least the following beneficial effects:

[0036] The application calculates a cutting force fluctuation coefficient according to the change trend and characteristics of the cutting force in the internal spline machining process, which is helpful to identify the advantages and disadvantages of the machining parameters, and quantifies the stability and reliability in the machining process. Then, the cutting influence coefficient is calculated by collecting the vibration and temperature fluctuation in the machining process, which more comprehensively evaluates the advantages and disadvantages of the machining parameters, and is helpful to identify the influence of the vibration and temperature change caused by the cutting force on the machining precision, and quantifies the influence of the machining parameters on the machining quality. Finally, the particle weight is calculated by using the cutting influence coefficient to obtain the optimal machining parameters through optimization iteration. In this way, according to the influence of the machining parameters on the cutting force, stability and temperature in the internal spline machining process, the advantages and disadvantages of the machining parameters are quantified according to the fluctuation of the data in the machining process, and then each particle is weighted according to the quantification result, so as to improve the sensitivity of the particle difference identification, and then the corresponding inertia weight can be adaptively constructed according to the difference between the particles in the subsequent optimization algorithm, which improves the globality of the optimization algorithm, reduces the probability of falling into local optimum, ensures the accuracy of the optimal solution, is helpful to obtain the optimal machining parameters, and further improves the machining precision of the internal spline machining of the planetary gear. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0038] Figure 1 The step flow chart of the internal spline high-precision machining method suitable for the planetary gear provided by an embodiment of the present application is shown in the figure.

[0039] Figure 2 The particle weight acquisition flow chart provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0040] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the internal spline high-precision machining method and machining equipment suitable for the planetary gear according to the present application, its specific implementation, structure, characteristics and effects are described in detail as follows. 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.

[0041] Unless otherwise defined, 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.

[0042] The application provides a high-precision machining method and machining equipment for internal spline of a planetary gear.

[0043] Referring to Figure 1 The application provides a high-precision machining method and machining equipment for internal spline of a planetary gear.

[0044] In step S1, vibration sequence, temperature sequence, tangential force sequence, radial force sequence, axial force sequence and cutting speed of the tool at each collection time are collected during machining of the internal spline of the planetary gear.

[0045] In the machining of the internal spline of the planetary gear, the preset machining parameters of the tool are set, and then the internal spline is machined. At this time, the vibration data of the tool during cutting is collected through the acceleration sensor installed on the tool handle, the temperature change of the tool during cutting is monitored through the temperature sensor on the tool, and the cutting force of the tool in the tangential, radial and axial directions is monitored through the three-axis force sensor of the tool handle. The collection frequency of all data is 1 kHz, and the cutting speed of the tool at each collection time is obtained.

[0046] According to the above analysis, for each kind of data collected during machining of the internal spline of the planetary gear, the data at all collection times are respectively constituted into corresponding data sequences in chronological order, which are vibration sequence, temperature sequence, tangential force sequence, radial force sequence and axial force sequence.

[0047] In step S2, the tangential force sequence is divided according to the change of the cutting speed to obtain a cutting preparation sequence, a cutting sequence and a cutting completion sequence; the periodicity measure of the cutting sequence is obtained based on the autocorrelation of the cutting sequence; the trend line function is obtained according to the cutting sequence; and the cutting force fluctuation coefficient of each internal spline machining is obtained based on the mutation of the cutting preparation sequence and the cutting completion sequence, the periodicity measure of the cutting sequence, the slope of the trend line function and the average of the elements in the tangential force sequence, the radial force sequence and the axial force sequence.

[0048] In the broaching machining of the internal spline, the tool is used to cut and polish the workpiece, and the cutting speed, feed rate and number of tool teeth of the tool all have an impact on the machining quality of the internal spline. Therefore, how to select the optimal cutting speed, feed rate and number of tool teeth becomes the key to high-precision machining of the internal spline.

[0049] In order to improve production efficiency, the optimal algorithm (such as particle swarm optimization algorithm) is selected to obtain the optimal processing parameters, but the processing parameters are determined according to experience in the internal spline broaching process, which leads to the relatively close processing parameters between the particles in the particle swarm optimization algorithm, and the weights of the particles in the traditional particle swarm optimization algorithm are the same, so that the particles have a great influence on the acquisition of the optimal solution, and are easy to fall into local optimum, and the global search ability is weak, thereby affecting the accuracy of the optimal solution.

[0050] Because the cutting speed, feed rate and number of teeth will affect the vibration data, temperature data and cutting force changes in the internal spline machining process, and it is because of the changes of these data that the machining process and the tool are affected, thereby ultimately affecting the precision of the internal spline machining, so by monitoring the changes of these data, the advantages and disadvantages of the corresponding processing parameters can be determined.

[0051] In the internal spline machining process, under normal circumstances, there are three directions of cutting force, tangential, radial and axial. The tangential force is the force along the cutting direction, and the radial force is the force perpendicular to the cutting direction. The forces in these two directions are the main cutting forces that ensure the cutting efficiency of the tool. The axial force is the force along the axial direction of the workpiece, and its main function is to maintain the stability of the workpiece in the axial direction, reduce vibration and deformation. The size of the force in the three directions is in the order of tangential force being the largest, radial force being the second, and axial force being the smallest. Therefore, in order to ensure the machining efficiency of the internal spline, the tangential force and the radial force should not be too small, and a smaller axial force is needed to ensure the stability of the workpiece. Therefore, the more optimal the processing parameters in the internal spline machining process, the greater the difference between the tangential force, the radial force and the axial force, and the greater the machining precision of the internal spline.

[0052] Secondly, in the internal spline machining, as the teeth of the tool enter the workpiece in turn, friction is generated between the tool and the workpiece, resulting in an increase in the broaching load and the cutting force. Therefore, when the tool starts to work, the cutting force shows a stepwise increasing trend. As the tool penetrates the workpiece, the number of working teeth reaches the maximum, and the tool gradually accelerates to the preset normal cutting speed. The tool enters a stable working state at the normal cutting speed, and the cutting force shows a periodic fluctuation trend with the cycle of the tool teeth. Because the friction is larger during rough machining, the broaching load is larger, and the cutting force is also larger. As the machining proceeds, the machining enters the finishing stage, and the friction decreases, resulting in a gradual decrease in the cutting force. That is, in the stable working state of the tool at the normal cutting speed, the cutting force shows a periodic change and a overall decreasing trend. Finally, the cutting ends, the cutting speed gradually decreases, and the tool teeth exit the workpiece in turn, so that the cutting force shows a stepwise decreasing trend.

[0053] Further, for each cutting speed at each collection time, a time period from the time when the internal spline starts to be machined to the collection time when the cutting speed reaches the preset normal cutting speed for the first time is taken as a cutting start time period, a time period from the collection time when the cutting speed reaches the preset normal cutting speed for the first time to the collection time when the cutting speed reaches the preset normal cutting speed for the last time is taken as a cutting stable time period, and a time period from the collection time when the cutting speed reaches the preset normal cutting speed for the last time to the time when the internal spline stops to be machined is taken as a cutting end time period. In the embodiment, the preset normal cutting speed is 45 m / min, and other values can be selected according to actual conditions.

[0054] For the tangential force sequence, the tangential force sequence in the cutting start time period is taken as a cutting preparation sequence, the tangential force sequence in the cutting stable time period is taken as a cutting sequence, and the tangential force sequence in the cutting end time period is taken as a cutting completion sequence.

[0055] The cutting sequence is taken as input, and STL decomposition is adopted to output a trend sequence, and then a linear fitting is adopted to obtain a fitting straight line function of the trend sequence, which is referred to as a trend line function.

[0056] Further, the cutting sequence is taken as input of an autocorrelation function, and each autocorrelation coefficient of the cutting sequence is output, each autocorrelation coefficient corresponds to a different lag, and the difference between adjacent lags is 1. In the embodiment, the value range of the lag is [1, 500], and other values can be selected according to actual conditions.

[0057] According to all autocorrelation coefficients of the cutting sequence and the lags of the autocorrelation coefficients, an autocorrelation function graph is constructed, the abscissa of the autocorrelation function graph is the lag, and the ordinate is the autocorrelation coefficient. Each peak value in the autocorrelation function graph is obtained by using a peak value detection algorithm, and a peak value greater than a preset correlation threshold is taken as each significant peak value. In the embodiment, the value of the preset correlation threshold is 0.7, and other values can be selected according to actual conditions.

[0058] Further, a straight line fitting is performed on the horizontal coordinates of the order of each significant peak value in all peak values in the autocorrelation function graph and the vertical coordinates of the lags of the significant peak values, to obtain a lag fitting straight line. Wherein, the STL decomposition, the autocorrelation function and the linear fitting are known technologies, and the specific process is not described again.

[0059] Further, the first-order difference sequence of the cutting preparation sequence is obtained, and the mean of all elements in the first-order difference sequence of the cutting preparation sequence is taken as the first mutation threshold. Elements greater than the first mutation threshold in the first-order difference sequence are taken as the mutation points of the first-order difference sequence of the cutting preparation sequence. Similarly, the first-order difference sequence of the cutting completion sequence is obtained, and the mean of all elements in the first-order difference sequence of the cutting completion sequence is taken as the second mutation threshold. Elements less than the second mutation threshold in the first-order difference sequence of the cutting completion sequence are taken as the mutation points of the first-order difference sequence of the cutting completion sequence.

[0060] The ratio of the number of mutation points in the first-order difference sequence of the cutting preparation sequence to the number of all elements in the first-order difference sequence of the cutting preparation sequence is taken as the first mutation ratio. The first-order difference sequence of the cutting completion sequence is processed in the same way as the first-order difference sequence of the cutting preparation sequence, and the second mutation ratio is obtained. Further, the product of the fitting goodness of the lag amount fitting straight line and the mean of all significant peaks in the autocorrelation function diagram is taken as the periodicity measure of the cutting sequence.

[0061] Based on the above analysis, in order to measure the time sequence change trend of the cutting force in the internal spline machining process, the cutting force fluctuation coefficient is calculated, and the calculation formula is: ; in the formula, is the cutting force fluctuation coefficient of each internal spline machining, , are the first mutation threshold and the second mutation threshold, respectively, is the sum of the first mutation ratio and the second mutation ratio, is the periodicity measure of the cutting sequence, is the slope of the trend line function, is the mean of all elements in the tangential force sequence and the radial force sequence, is the mean of all elements in the axial force sequence.

[0062] It should be noted that when the cutting parameters are optimal in internal spline machining, the tangential and radial cutting forces are greater than the axial cutting force, and the greater the difference, the better. The greater the tangential and radial cutting forces, although they can ensure high machining efficiency, but at the same time, they will also lead to increased vibration, thereby affecting the internal spline accuracy, so the tangential and radial cutting forces cannot be too large, i.e. is small; secondly, the cutting force corresponding to the cutting preparation and cutting completion stages changes in a stepwise manner, so there are fewer mutation points in the first-order difference sequence, and the mutation threshold of the cutting preparation stage is small, and the mutation threshold of the cutting completion stage is large, i.e. , is small, is large; the cutting force in the cutting stage changes periodically, and the overall trend decreases, so The value of the periodicity is larger, so when the inner spline is machined, the cutting force fluctuation coefficient is smaller under the condition of better cutting parameters. On the contrary, when the cutting parameters are worse, the cutting force changes more sharply in the machining process, so that the possibility and degree of deviation from the normal condition are greater, and the cutting force fluctuation coefficient is larger.

[0063] Step S3, based on the cutting force fluctuation coefficient, the discrete degree of the vibration sequence and the temperature sequence, the correlation between the tangential force sequence and the vibration sequence, and the difference between adjacent elements in the tangential force sequence and the temperature sequence, the cutting influence coefficient of each inner spline machining is obtained; the particle weight of each particle in the particle swarm optimization algorithm is obtained based on the cutting influence coefficient.

[0064] The cutting force fluctuation coefficient only measures the change of the cutting force, and the influence of the cutting force on the inner spline machining precision is small. The vibration and temperature change caused by the cutting force in the machining process ultimately affect the machining precision of the inner spline or cause deformation, so there is a certain limitation in considering only the change of the cutting force. Only when the cutting force causes the change of vibration and temperature, can the influence of the cutting force on the machining precision be accurately judged.

[0065] Under normal circumstances, when the inner spline machining parameters are better, with the change of the cutting force, the vibration and temperature also change, and the change frequency is relatively close, and the fluctuation of vibration and temperature is small, so that the final inner spline precision is higher. When the inner spline machining parameters are poor, with the change of the cutting force, the instantaneous change of vibration and temperature is relatively severe, so that the change frequency has obvious difference, and the fluctuation of vibration and temperature is large, so that the final inner spline precision is poor.

[0066] Based on the above analysis, in order to improve the accuracy of data change characteristic measurement in the inner spline machining process, the cutting influence coefficient is calculated, and the calculation formula is: ; In the formula, is the cutting influence coefficient of each inner spline machining; is the cutting force fluctuation coefficient of the inner spline machining, , is the variance of all elements in the vibration sequence and the variance of all elements in the temperature sequence, is the Pearson correlation coefficient between the tangential force sequence and the vibration sequence, is the mean value of the absolute value of the difference between all adjacent elements in the tangential force sequence, is the mean value of the absolute value of the difference between all adjacent elements in the temperature sequence.

[0067] ​It should be noted that when the internal spline machining parameters are optimal, the cutting force fluctuation coefficient is small, and the change of cutting force causes the change of vibration and temperature, and the change frequencies are relatively close, so there is a high correlation between the tangential force sequence and the vibration sequence, that is, Secondly, the change of cutting force will cause the fluctuation of instantaneous temperature. When the processing parameters are better, the changes of the two are closer and the difference between the change rates is relatively small, that is, Small; vibration and temperature fluctuations are small, that is 、 Small; so when the internal spline processing parameters are better, the cutting influence coefficient On the contrary, when the internal spline processing parameters are poor, it will cause drastic changes in vibration and temperature, which will increase the difference between the changes in cutting force and the cutting influence coefficient. Larger.

[0068] When using the original particle swarm optimization algorithm to iterate internal spline machining parameters, each particle has the same weight, resulting in a high risk of the final optimal solution falling into a local optimum. To avoid this problem, based on the characteristics of the internal spline machining process, by analyzing the changes in various monitoring data during the internal spline machining process, the cutting influence coefficient is calculated to measure the quality of the corresponding internal spline machining parameters. This weight is then assigned to each particle, enhancing the global search capability of the optimization algorithm and improving the accuracy of the optimal machining parameters.

[0069] According to the above analysis, each internal spline machining process is regarded as a particle. Furthermore, in order to reflect the machining parameters of the tool in each internal spline machining process, the particle weight of each particle is obtained based on the cutting influence coefficient. The calculation formula is:

[0070] Where, It is The particle weight of each particle, 、 It is Second internal spline processing process, Cutting influence coefficient of secondary internal spline machining process, Is the total number of internal spline processing. In this embodiment, the value of n is 100, and the implementer can select other values ​​according to actual conditions. Among them, the particle weight acquisition flow chart is as follows Figure 2 shown.

[0071] It should be noted that for the i-th internal spline machining process, when the tool machining parameters are poor, Larger, so that the particle weight When the particle swarm optimization is performed later, the weight is used to construct a larger inertia weight, which helps to enhance the global search capability and avoid falling into the local optimum, thereby improving the accuracy of the optimal solution. On the contrary, when the processing parameters of the tool are good, the particle weight The smaller the θ, the smaller the inertia weight of subsequent constructions, which helps to increase the iteration speed while maintaining a good iteration effect.

[0072] Step S4: obtaining the updated inertia weight of each iteration in the particle swarm optimization algorithm based on the particle weight, and performing high-precision processing on the internal spline of the planetary gear.

[0073] The set of parameter settings for each internal spline machining is considered as a particle. (In this embodiment ), particle dimensions (In this embodiment , respectively cutting speed, feed rate, number of teeth), the maximum number of iterations of the system (In this embodiment ), preset initial inertia weight , learning factor , preset neighborhood radius (In this embodiment ) as input, the particle swarm optimization algorithm is used to output the optimal solution. In this process, each time an iteration is completed, the inertia weight needs to be updated according to the optimal solution obtained by the iteration. Specifically, when the After the iteration is completed, according to the position of the current optimal solution, Particles within the neighborhood radius are then updated with inertia weights. Based on the above analysis, the calculation formula for updating inertia weights is: Where, It is After the iteration is completed, the inertia weight is updated. is the preset initial inertia weight, It is The sum of the particle weights of all particles within the preset neighborhood radius of the optimal solution particle at the iteration.

[0074] When The optimal solution of the iteration If the processing parameters of particles within the neighborhood radius are poor, Larger, thus increasing the inertia weight , which helps to jump out of the current local area, enhance the global search capability, and enhance the ability to obtain the global optimal solution in subsequent iterations. Conversely, if the processing parameters of the particles around the current optimal solution are good, reduce the inertia weight to improve the search efficiency.

[0075] After the optimal solution is iterated out, the machining parameters in the optimal solution are set according to the machining parameters, so as to realize high-precision machining of the internal spline. In this way, according to the data change in the internal spline machining process, each particle is assigned a weight, which is then used to adjust the inertia weight, so that the optimization algorithm can maintain good global search ability and high search efficiency. At the same time, even if the difference between the machining parameters of the particles is relatively small, the sensitivity of identifying small differences is enhanced through this way, thereby enhancing the accuracy of obtaining the optimal machining parameters.

[0076] Based on the same inventive concept as the above method, the embodiments of the present application also provide an internal spline high-precision machining device for planetary gears, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above-mentioned internal spline high-precision machining methods for planetary gears when executing the computer program.

[0077] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present application. 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, multi-task processing and parallel processing are possible or may be advantageous.

[0078] Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

[0079] The above is only the preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A high-precision machining method for the internal splines of planetary gears, characterized in that: The method comprises the following steps: Collect the vibration sequence, temperature sequence, tangential force sequence, radial force sequence, axial force sequence of the tool cutting during each planetary gear internal spline processing, and the cutting speed of the tool cutting at each collection moment; The tangential force sequence is segmented according to the change of cutting speed to obtain the cutting preparation sequence, cutting sequence and cutting completion sequence; Obtaining a periodicity measure of the cutting sequence based on the autocorrelation of the cutting sequence; Get trend line function based on cutting sequence; Based on the mutation of the cutting preparation sequence and the cutting completion sequence, the periodicity measurement of the cutting sequence, the slope of the trend line function, and the average of the elements in the tangential force sequence, radial force sequence, and axial force sequence, the cutting force fluctuation coefficient of each internal spline machining is obtained; The cutting influence coefficient of each internal spline machining is obtained based on the cutting force fluctuation coefficient, the discrete degree of the vibration sequence and the temperature sequence, the correlation between the tangential force sequence and the vibration sequence, and the difference between adjacent elements in the tangential force sequence and the temperature sequence. Obtain the particle weight of each particle in the particle swarm optimization algorithm based on the cutting influence coefficient; The updated inertia weight of each iteration in the particle swarm optimization algorithm is obtained based on the particle weight, and the internal splines of the planetary gear are processed with high precision.

2. The high-precision machining method for internal splines of planetary gears according to claim 1, characterized in that: The method for obtaining the cutting preparation sequence, cutting sequence and cutting completion sequence is as follows: For the cutting speed at each acquisition moment, the time period from the moment the internal spline starts to be machined to the acquisition moment when the cutting speed first reaches the preset normal cutting speed is taken as the cutting start time period; the time period from the acquisition moment when the cutting speed first reaches the preset normal cutting speed to the acquisition moment when the cutting speed last reaches the preset normal cutting speed is taken as the cutting stabilization time period; the time period from the acquisition moment when the cutting speed last reaches the preset normal cutting speed to the moment when the internal spline stops machining is taken as the cutting end time period; For the tangential force sequence, the tangential force sequence in the cutting start time period is regarded as the cutting preparation sequence, the tangential force sequence in the cutting stable time period is regarded as the cutting sequence, and the tangential force sequence in the cutting end time period is regarded as the cutting completion sequence.

3. The high-precision machining method for internal splines of planetary gears according to claim 1, characterized in that: The method for obtaining the periodic metric is: Using the autocorrelation function to obtain the respective correlation coefficients and lags of the autocorrelation coefficients of the cutting sequences, constructing an autocorrelation function graph with the autocorrelation coefficient as the ordinate and the lags of the autocorrelation coefficient as the abscissa, using a peak detection algorithm to obtain peaks in the autocorrelation function graph, and taking peaks greater than a preset correlation threshold as significant peaks; The order of each significant peak in all peaks of the autocorrelation function diagram is used as the abscissa, and the hysteresis of the significant peak is used as the ordinate to perform straight line fitting to obtain the hysteresis fitting line; The goodness of fit of the hysteresis fitting line multiplied by the mean of all significant peaks in the autocorrelation function graph was used as a measure of the periodicity of the cutting sequence.

4. The high-precision machining method for internal splines of planetary gears according to claim 1, characterized in that: The step of obtaining a trend line function according to a cutting sequence includes: The cutting sequence is used as the input of the time series decomposition algorithm to output the trend sequence; the trend line function of the trend sequence is obtained using the straight line fitting algorithm.

5. The high-precision machining method for internal splines of planetary gears according to claim 1, characterized in that: The method for obtaining the cutting force fluctuation coefficient is: Obtain a first-order difference sequence of the cutting preparation sequence, then use the mean of all elements in the first-order difference sequence of the cutting preparation sequence as a first mutation threshold, and use elements in the first-order difference sequence that are greater than the first mutation threshold as mutation points of the first-order difference sequence of the cutting preparation sequence; obtain a first-order difference sequence of the cutting completion sequence, then use the mean of all elements in the first-order difference sequence of the cutting completion sequence as a second mutation threshold, and use elements in the first-order difference sequence of the cutting completion sequence that are less than the second mutation threshold as mutation points of the first-order difference sequence of the cutting completion sequence; The ratio of the number of mutation points in the first-order difference sequence of the cutting preparation sequence to the number of all elements in the first-order difference sequence of the cutting preparation sequence is calculated as the first mutation ratio; the first-order difference sequence of the cutting completion sequence is processed using the same method as that used to obtain the first mutation ratio, to obtain the second mutation ratio; The calculation formula of the cutting force fluctuation coefficient is: Where, is the cutting force fluctuation coefficient of each internal spline processing, 、 They are the first mutation threshold and the second mutation threshold, is the sum of the first mutation ratio and the second mutation ratio, is a measure of the periodicity of the cutting sequence, is the slope of the trendline function, is the mean of all elements in the tangential force series and radial force series, is the mean of all elements in the axial force series.

6. The high-precision machining method for internal splines of planetary gears according to claim 1, characterized in that: The calculation formula of the cutting influence coefficient is: Where, is the cutting influence coefficient of each internal spline processing; is the cutting force fluctuation coefficient of internal spline machining, 、 are the variances of all elements in the vibration sequence and the variances of all elements in the temperature sequence, is the Pearson correlation coefficient between the tangential force series and the vibration series, is the mean of the absolute values ​​of the differences between all adjacent elements in the tangential force sequence, is the mean of the absolute values ​​of the differences between all adjacent elements in the temperature series.

7. The high-precision machining method for internal splines of planetary gears according to claim 1, characterized in that: The method for obtaining the particle weight is: Each internal spline machining process is regarded as each particle in the particle swarm optimization algorithm, and the particle weight of each particle is calculated as follows: Where, It is The particle weight of each particle, 、 It is Second internal spline processing process, Cutting influence coefficient of secondary internal spline machining process, is the total number of internal spline machining times.

8. The high-precision machining method for internal splines of planetary gears according to claim 7, characterized in that: The method for obtaining the updated inertia weight is: The particle swarm optimization algorithm is used to iterate all particles in combination with the particle weight of each particle. For each iteration process, the sum of the particle weights of all particles within the preset neighborhood radius of the optimal solution particle during the iteration process and the preset initial inertia weight are calculated as the updated inertia weight of each iteration process.

9. The high-precision machining method for internal splines of planetary gears according to claim 8, characterized in that: The high-precision processing of the internal splines of the planetary gear includes: According to the updated inertia weight of each iteration process, the particle swarm optimization algorithm is used to iterate all particles to obtain the optimal solution particle. The preset machining parameters of the tool for the internal spline machining process corresponding to the optimal solution particle are used as the optimal machining parameters, and the internal spline of the planetary gear is machined with high precision using the optimal machining parameters.

10. A high-precision machining device for internal splines of planetary gears, 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 high-precision machining method for internal splines of planetary gears according to any one of claims 1 to 9 are implemented.

Images