Method and device for detecting pitch circle of sun gear of planetary reducer

Abstract

The application relates to the technical field of gear pitch circle detection, in particular to a pitch circle detection method and a detection device for calibrating a sun gear of a planetary reducer, which specifically comprises the following steps: performing pitch circle detection on the sun gear, collecting real-time temperatures of monitoring points on each tooth surface of the gear, and obtaining a pitch circle detection result containing temperature distribution data in the monitoring process; dividing pitch circle detection results of the same batch of sun gears based on all the collected data, judging and analyzing pitch circle detection errors under the influence of temperature based on the division result, and then correcting the pitch circle detection errors of the sun gear; the method avoids the problem that temperature fluctuations cause large errors in the pitch circle detection result in the batch production of the sun gear of the planetary reducer; the method improves the pitch circle detection result accuracy of the sun gear based on the planetary reducer and improves the accuracy of calibration of the sun gear machining and production.

Description

Technical Field

[0001] This application relates to the field of gear pitch circle detection technology, specifically to a pitch circle detection method and equipment for calibrating the sun gear of a planetary gear reducer. Background Technology

[0002] The transmission structure of a planetary reducer mainly consists of three parts: planetary gears, a sun gear, and an internal gear. It is often used in servo motors to reduce speed or increase torque. The sun gear is one of the core components for power output and transmission in the planetary reducer's transmission structure, and its pitch circle accuracy has a significant impact on the transmission's performance. The pitch circle is a key parameter for the meshing and rolling between the sun gear and planetary gears in the transmission structure. The accuracy of the pitch circle parameter is a crucial factor affecting gear meshing smoothness, transmission efficiency, and noise control. If there is a deviation in the sun gear's pitch circle, excessive or insufficient backlash during meshing with the planetary gears can easily cause impact vibration, exacerbate tooth surface wear, and generate frictional heat, leading to reduced transmission accuracy or tooth surface scuffing failure. Therefore, pitch circle testing of the sun gear is a crucial step in ensuring the transmission stability and operational reliability of the planetary reducer. It can improve the calibration accuracy of the sun gear, thereby improving manufacturing quality.

[0003] Currently, the production of sun gears for planetary gear reducers mostly adopts a mass production model. However, during mass production, environmental factors can cause significant errors in the actual workpiece inspection results, thus affecting the final pass rate of the sun gears produced by the planetary gear reducers. If the error in pitch circle detection during mass production is large, it will lead to a decrease in the accuracy of sun gear calibration. Due to factors such as equipment heat dissipation and climate fluctuations during mass production, the ambient temperature fluctuates during pitch circle detection, affecting the accuracy of the pitch circle detection results of the sun gears. This results in large calibration and adjustment errors during the sun gear production process, leading to poor consistency in mass production of sun gears and affecting the assembly and use of the sun gears. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a method and equipment for detecting the pitch circle of the sun gear in a planetary gear reducer. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a method for detecting the pitch circle of the sun gear in a planetary gear reducer, the method comprising the following steps:

[0006] Collect the pitch circle detection results of each gear, including the temperature of each preset monitoring point on each tooth surface of the gear at each time.

[0007] Based on the difference between all temperature data of each tooth surface and the standard temperature, a temperature deviation distribution matrix of each tooth surface is constructed to determine the first characteristic value of the temperature deviation of each tooth surface; based on the first characteristic value of each tooth surface of each gear and each tooth surface of any remaining gear, combined with the difference between the two corresponding temperature deviation distribution matrices, the influence coefficient of each tooth surface of each gear and each tooth surface of any remaining gear under temperature fluctuation is calculated.

[0008] The error values ​​of each gear are obtained by detecting the pitch circle of each gear; based on the difference distance between the error values ​​of all classes between any two gears, and combined with the influence coefficient, the clustering distance between any two gears is constructed, so as to cluster all gears by clustering algorithm;

[0009] Based on the difference distance between the pitch circle detection results of the current gear and the gears in each cluster, a control sample group for the current gear is determined; based on the overall distribution of the temperature data of the current gear, combined with the pitch circle detection results of the gears in the control sample group, the pitch circle detection results of the current gear are adjusted to correct and rework the current gear.

[0010] In one embodiment, the process of obtaining the temperature deviation distribution matrix is ​​as follows:

[0011] Calculate the difference between the temperature data of each monitoring point at each time on each tooth surface and the standard temperature, and use the matrix composed of the differences of all monitoring points at all times on each tooth surface as the temperature deviation distribution matrix of each tooth surface.

[0012] In one embodiment, the first characteristic value is the sum of the absolute values ​​of all elements in the temperature deviation distribution matrix of each tooth surface.

[0013] In one embodiment, the process of obtaining the influence coefficient is as follows:

[0014] The normalized value of the first characteristic value of each tooth surface is recorded as the second characteristic value. The mean of the second characteristic value of the x-th tooth surface of gear A and the second characteristic value of the y-th tooth surface of gear B is calculated. The SAD value between the temperature deviation distribution matrix of the x-th tooth surface and the y-th tooth surface is calculated. The influence coefficient between gear A and gear B is positively correlated with the mean value and the SAD value, respectively.

[0015] In one embodiment, the various error values ​​include: pitch circle diameter error, pitch circle roundness error, and coaxiality error, wherein the pitch circle diameter error is the difference between the pitch circle diameter data in the pitch circle detection result and the theoretical pitch circle diameter data.

[0016] In one embodiment, the process of obtaining the clustering distance is as follows:

[0017] The sum of all the influence coefficients between any two gears is recorded as the first distance value; the three-dimensional coordinates of each gear are constructed using the pitch circle diameter error, pitch circle roundness error, and coaxiality error of each gear, and the distance between the three-dimensional coordinates of any two gears is calculated and recorded as the second distance value; the product of the first distance value and the second distance value is used as the clustering distance between any two gears.

[0018] In one embodiment, the process of obtaining the control sample group is as follows:

[0019] Calculate the average distance between the current gear and the three-dimensional coordinates of all gears in each cluster, and take the cluster corresponding to the minimum value of the average distance as the control sample group of the current gear.

[0020] In one embodiment, the process of adjusting the pitch circle detection result of the current gear is as follows:

[0021] Calculate the variance of all temperature data for the current gear; if the variance is greater than or equal to a preset threshold, adjust the pitch circle detection result of the current gear; otherwise, do not adjust the pitch circle detection result of the current gear.

[0022] In one embodiment, the process of adjusting the current gear pitch circle detection result is as follows:

[0023] Using the average temperature of each gear in the control sample group as the x-axis and the average value of each error value of each gear as the y-axis, a fitting curve between temperature and each error value is constructed using a fitting algorithm to obtain the temperature-error value fitting curve of each control sample group.

[0024] Substitute the mean of all temperature data for the current gear into the temperature-error value fitting curve of the control sample group to obtain the control value for each error value of the current gear. Calculate the mean between the control value and the actual measured value of each error value of the current gear, and use this as the adjustment result for each error value of the current gear.

[0025] Secondly, embodiments of this application also provide a pitch circle detection device for calibrating the sun gear of a planetary gear reducer, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0026] The embodiments of this application have at least the following beneficial effects:

[0027] This application first performs pitch circle detection on the sun gear and then tracks and monitors the temperature in real time by setting monitoring points in the pitch circle detection area to obtain pitch circle detection results containing temperature distribution data during the monitoring process. Furthermore, it combines the real-time acquired temperature data with the calculated analysis results based on global tooth surface scanning data for a thorough comparative analysis, thereby accurately classifying the pitch circle detection results of the same batch of sun gears. Based on the classification results, it judges and analyzes the pitch circle detection error under the influence of temperature. Its beneficial effect lies in fully combining the differences in the influence characteristics of local temperature fluctuations on detection errors during batch production testing, accurately comparing and analyzing the errors in the sun gear pitch circle detection process, and thus correcting the pitch circle detection error of the sun gear. This avoids the problem of large errors in the pitch circle detection results caused by temperature fluctuations during the batch production pitch circle detection of sun gears in planetary gear reducers due to the influence of equipment heat dissipation and ambient temperature changes in the production testing area. This improves the accuracy of the pitch circle detection results of sun gears based on planetary gear reducers and enhances the accuracy of calibration for sun gear processing and production. Attached Figure Description

[0028] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating the steps of a method for calibrating the pitch circle of a planetary gear reducer sun gear according to an embodiment of this application.

[0030] Figure 2 This is a schematic diagram illustrating the process of obtaining the influence coefficient. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the planetary gear reducer sun gear calibration method and testing equipment proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] 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 pertains.

[0033] The following, in conjunction with the accompanying drawings, details the specific scheme of the pitch circle detection method and detection equipment for calibrating the sun gear of the planetary reducer provided in this application.

[0034] Please see Figure 1 The diagram illustrates a flowchart of a method for calibrating the pitch circle of a planetary gear reducer sun gear according to an embodiment of this application. The method includes the following steps:

[0035] Step S1: Collect the pitch circle detection results of each gear, including the temperature of each preset monitoring point on each tooth surface of the gear at each time.

[0036] In the mass production process of the sun gear of the planetary reducer, the pitch circle detection and calibration of the sun gear needs to be completed in a constant temperature testing environment. Therefore, during the production process, the temperature of the pitch circle detection area of ​​the sun gear is controlled at (20±0.5)℃, and monitoring points are evenly set around the center of the detection area during the detection process. Temperature data is collected in real time at the monitoring points through temperature sensors. Pitch circle detection is performed using a gear pitch circle detection device, which consists of a positioning mandrel, a sapphire probe, and a grating displacement sensor. The device is preheated for 30 minutes before the detection to avoid large initial detection deviations. Furthermore, the sun gear to be tested is pre-treated. Specifically, the gear tooth surface and inner hole reference surface are cleaned with anhydrous ethanol to remove oil and iron filings. The gear and positioning mandrel are placed together in a constant temperature laboratory and left to stand for 2 hours to ensure that the gear and the ambient temperature are completely balanced, avoiding detection deviations caused by temperature lag.

[0037] Based on the above process, during the production and processing of planetary reducers, the pitch circle detection process for each sun gear involves inserting a mandrel into the inner hole of the sun gear and fixing it on the clamping assembly of the detection device for positioning and calibration. The radial runout of the gear is detected using a dial indicator until the radial runout value is less than or equal to 0.005 mm. After positioning, the parameters of the sun gear are obtained, including the module, number of teeth, pressure angle, and theoretical pitch circle diameter. During the detection process, the tooth profile is scanned point by point. Specifically, a sapphire probe scans along the tooth profile at a speed of 0.3 mm / s, and a grating displacement sensor collects the displacement data of the probe in the X, Y, and Z axes in real time. Each set of data is recorded at 0.008 mm intervals, and all monitoring points are simultaneously monitored. Temperature data is collected using a temperature sensor; five tooth surfaces of each sun gear are scanned to avoid data deviation caused by defects on a single tooth surface; the pitch circle diameter, pitch circle roundness error, and coaxiality error are calculated based on the scanned data. The specific pitch circle detection calculation process is well known to those skilled in the art, and the detailed process will not be elaborated here. The pitch circle detection results for each sun gear are obtained. The detection results include sample number, ambient temperature data, theoretical parameter data, actual detection data, and deviation data. Among them, the ambient temperature data is the temperature data of the monitoring point, the theoretical parameter data is the module, number of teeth, pressure angle, and theoretical pitch circle diameter, the actual detection data is the calculated pitch circle diameter, and the deviation data is the pitch circle roundness error and coaxiality error.

[0038] Step S2: Construct a temperature deviation distribution matrix for each tooth surface based on the difference between all temperature data of each tooth surface and the standard temperature to determine the first characteristic value of the temperature deviation of each tooth surface; Based on the first characteristic value of each tooth surface of each gear and each tooth surface of any remaining gear, and combined with the difference between the two corresponding temperature deviation distribution matrices, calculate the influence coefficient between each tooth surface of each gear and each tooth surface of any remaining gear under temperature fluctuation.

[0039] In the mass production of sun gears for planetary gear reducers, the pitch circle monitoring of sun gears typically employs a global fitting method using scanned data from a portion of the tooth surfaces to obtain the actual pitch circle diameter data of the produced sun gears. However, temperature variations during equipment operation can cause errors in the calculated pitch circle diameter, affecting the accuracy of sun gear calibration. Therefore, based on the pitch circle testing results of the same batch of sun gears, this study compares and analyzes the distribution characteristics of temperature deviations on different tooth surfaces at different times during the pitch circle testing process. Furthermore, based on the comparative analysis results, it precisely analyzes the impact of environmental interference on the testing error during the pitch circle testing process, and then performs a classification error analysis on the pitch circle testing of the same batch of sun gears to accurately extract the interference characteristics during the sun gear pitch circle testing process.

[0040] Based on the above analysis, and according to the test results of the pitch circle detection of the same batch of sun gears, a comparative analysis of the influence characteristics of the detection error of the sun gears is conducted. The specific analysis process is as follows:

[0041] First, considering that even small temperature fluctuations during the pitch circle detection of the sun gear can cause significant errors, the temperature deviation distribution characteristics of different tooth surfaces during scanning at different times are analyzed based on the temperature data collected during the pitch circle detection of each sun gear. Specifically, for each sun gear pitch circle detection result, the sequence of temperature data from all monitoring points at each moment during the scanning process of each tooth surface is used as a row element in a matrix. The matrix formed by the sequence of temperature data corresponding to all moments is used as the temperature monitoring matrix for scanning detection of each tooth surface. The difference between each element in the temperature monitoring matrix and the set standard temperature value is calculated. The larger the absolute value of the difference, the greater or smaller the local temperature fluctuation during the detection process. Based on the above calculation, the elements in the temperature monitoring matrix are updated by replacing each element with the difference between each element and the set standard temperature value. The updated matrix result is used as the temperature deviation distribution matrix for scanning detection of each tooth surface.

[0042] Based on the above process, the temperature deviation distribution matrix of each tooth surface during the pitch circle detection of each sun gear is obtained. Furthermore, during the pitch circle detection analysis of the same batch of sun gears, the influence of temperature differences on the comparison of detection results is analyzed by combining the temperature deviation distribution during the scanning detection of different tooth surfaces. Specifically, for each sample in the detection distribution set, the sum of the absolute values ​​of all elements in the temperature deviation distribution matrix corresponding to each tooth surface in the sample is calculated. This sum is used as the first characteristic value of the temperature deviation for each tooth surface. The larger the first characteristic value, the greater the impact of the temperature deviation on the pitch circle detection, considering the temperature deviation analysis of all positions and all times. All the first characteristic values ​​are used as input, and the Softmax function is used to obtain the second characteristic value of the temperature deviation corresponding to each tooth surface. The Softmax function is a known technique, and its specific process will not be elaborated further.

[0043] It should be noted that this application provides only one normalization method for the normalization of the first eigenvalue. There are many existing normalization methods, and implementers may also use other normalization algorithms to normalize the first eigenvalue. This application does not impose any specific restrictions.

[0044] Furthermore, for each tooth surface between any two samples, based on the second characteristic value of the temperature deviation distribution between different tooth surfaces, the comprehensive characteristic value of the deviation influence of pitch circle detection between different samples is calculated, and the calculation relationship is as follows:

[0045]

[0046] in, Represents the first of sample A The tooth surface and the sample B's first tooth surface The comprehensive characteristic value of the influence of deviation in pitch circle detection between individual tooth surfaces; and These represent the first and second halves of sample A, respectively. The tooth surface and the sample B's first tooth surface The second feature value corresponding to each tooth surface. The larger the calculated comprehensive feature value, the greater the comprehensive analysis of the temperature deviation during the tooth surface scanning process between different samples, and the greater the possibility that the temperature deviation will cause pitch circle detection error between samples.

[0047] Furthermore, based on the above analysis, a comparative analysis of the temperature deviation distribution differences during the pitch circle detection scanning process for different samples is conducted. Specifically, for any two samples A and B in the detection distribution set, the SAD value (sum of absolute errors) between the temperature deviation distribution matrices of each tooth surface of sample A and each tooth surface of sample B is calculated. The SAD value is used as the deviation value of the temperature distribution of different tooth surfaces between different samples. The larger the deviation value, the greater the impact of temperature change differences on the pitch circle detection error during the scanning process due to the randomness of temperature fluctuations. The calculation of the SAD value is a well-known technique, and the specific process will not be elaborated further.

[0048] Furthermore, based on the comprehensive comparative analysis of the temperature deviations of different tooth surfaces among different samples in the detection distribution set and the analysis results of distribution differences, the influence coefficient of different tooth surfaces among different samples under temperature fluctuations during the pitch circle detection process is calculated. The formula for its calculation is as follows:

[0049]

[0050] in, Represents the first of sample A The tooth surface and the sample B's first tooth surface The influence coefficient between individual tooth surfaces under temperature fluctuations; Represents the first of sample A The tooth surface and the sample B's first tooth surface Deviation in temperature distribution between individual tooth surfaces; Represents the first of sample A The tooth surface and the sample B's first tooth surface The comprehensive characteristic value of the influence of deviation in pitch circle detection between individual tooth surfaces. The larger the calculated influence coefficient, the greater the impact of temperature fluctuations on the detection error during the detection process.

[0051] Step S3: Obtain various error values ​​for each gear through the pitch circle detection results of each gear; Based on the difference distance between the error values ​​of all classes between any two gears, and combined with the influence coefficient, construct the clustering distance between any two gears, so as to cluster all gears through a clustering algorithm.

[0052] Because local temperature changes have a significant impact on scanning data errors during pitch circle detection, large differences in local temperature deviation distributions across different tooth surfaces during global fitting of the scanning results can lead to substantial pitch circle detection errors, affecting the accuracy of sun gear calibration. Therefore, for each sun gear, the difference between the pitch circle diameter data and the theoretical pitch circle diameter data in the pitch circle detection results is calculated. The ratio of this difference to the theoretical pitch circle diameter data is used as the pitch circle diameter error data. The arrays of pitch circle diameter error data, pitch circle roundness error data, and coaxiality error data for each sun gear are used as three-dimensional coordinates. Each sun gear is considered a detection sample, and all detection samples are mapped to a three-dimensional Cartesian coordinate system according to their corresponding three-dimensional coordinates. The X-axis, Y-axis, and Z-axis coordinates represent the pitch circle diameter error data, pitch circle roundness error data, and coaxiality error data, respectively. The mapped sample set is then used as the detection distribution set.

[0053] Based on the above analysis, spatial mapping is performed on the errors in three aspects—pitch circle diameter, pitch circle roundness, and coaxiality—of the test samples according to the pitch circle test results of the same batch of sun gears. For the pitch circle test results of the same batch of sun gears, the impact of temperature fluctuations during the testing process on the test results is compared and analyzed, thereby enabling precise classification analysis of the monitoring results of the same batch. Specifically, according to the above analysis and calculation process, the sum of all the aforementioned influence coefficients between any two samples in the test distribution set is calculated, serving as the first distance value for the comparative analysis between any two samples in the test distribution set. On the other hand, the Euclidean distance between any two samples in the test distribution set is calculated, specifically the Euclidean distance between the three-dimensional coordinates of any two samples, and this Euclidean distance is used as the second distance value for the comparative analysis between any two samples.

[0054] Furthermore, the product of the first and second distance values ​​calculated above is used as the clustering distance between any two samples, denoted as the clustering distance. The larger the clustering distance, the lower the probability of similarity in the detection results between different samples, considering both the temperature fluctuation distribution characteristics during the nodal circle detection process and the differences in nodal circle detection results. Therefore, based on the calculated clustering distance, the detection distribution set is clustered using the density peak clustering algorithm to obtain each cluster. The density peak clustering algorithm is a well-known technique, and its specific process will not be elaborated further.

[0055] It should be noted that this application provides only one clustering method for all samples. There are many existing clustering methods, and implementers may also use other clustering algorithms to cluster samples. This application does not impose any specific restrictions.

[0056] Step S4: Based on the difference distance between the pitch circle detection results of the current gear and the gears in each cluster, determine the control sample group for the current gear; based on the overall distribution of the temperature data of the current gear, and combined with the pitch circle detection results of the gears in the control sample group, adjust the pitch circle detection results of the current gear to correct and rework the current gear.

[0057] Based on the classification results of the same batch of sun gears, the mean value of all temperature data for each sample in each cluster was calculated. The mean value was used as the x-axis, and the pitch circle diameter error data, pitch circle roundness error data, and coaxiality error data for each sample were used as the y-axis. The least squares method was used to construct temperature-pitch circle diameter error fitting curves, temperature-pitch circle roundness error fitting curves, and temperature-coaxiality error fitting curves for each cluster. This yielded the temperature-error value fitting curve for each cluster. The process of curve fitting using the least squares method is a well-known technique, and its specific steps will not be elaborated further.

[0058] Furthermore, error analysis is performed based on the test results of the same batch of sun gears during the current pitch circle detection process. First, the difference between the current pitch circle detection result of the sun gear and the detection results in each cluster sample is calculated, that is, the distance between the current pitch circle detection result of the sun gear and the three-dimensional coordinates of each sample in each cluster is calculated, and the cluster with the smallest mean of all the distance values ​​is used as the control sample group for the current sun gear pitch circle detection error analysis. The mean and variance of all temperature data collected during the current sun gear pitch circle detection process are calculated. Furthermore, for the mean of temperature data collected for each sun gear in the same batch of production process, the variance of all the means is used as the judgment threshold for the impact of temperature fluctuation on pitch circle detection. The purpose is to combine the temperature fluctuation characteristics in the historical production process to accurately judge the error caused by temperature fluctuation in the current pitch circle detection.

[0059] Furthermore, if the variance is greater than or equal to the judgment threshold, it indicates that the temperature fluctuation during the detection process is relatively large compared to the production and detection of the same batch of sun gears, which may have a significant impact on the detection results. Therefore, the mean value is substituted into the temperature-pitch circle diameter error fitting curve, temperature-pitch circle roundness error fitting curve, and temperature-coaxiality error fitting curve in the control sample group to obtain the corresponding control values ​​for pitch circle diameter error, pitch circle roundness error, and coaxiality error. To reduce the impact of temperature changes on pitch circle detection error, the control values ​​for pitch circle diameter error, pitch circle roundness error, and coaxiality error are... The average value of the pitch circle error is calculated by comparing it with the current measured value of the sun gear pitch circle. The three average values ​​are then used as the output test result, and the sun gear is corrected and repaired based on the test result. On the other hand, if the variance is less than the judgment threshold, it means that the temperature fluctuation during the test is relatively small compared to the production and testing of the same batch of sun gears, and may have a small impact on the test result. Therefore, the current measured value of the sun gear pitch circle is used as the output test result, and the sun gear is corrected and repaired based on the test result. The purpose is to avoid over-adjustment of the pitch circle detection error, which could lead to correction deviation.

[0060] A schematic diagram illustrating the process of obtaining the influence coefficient is shown below. Figure 2 As shown.

[0061] Based on the same inventive concept as the above methods, this application also provides a pitch circle detection device for calibrating the sun gear of a planetary gear reducer, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for calibrating the sun gear of a planetary gear reducer.

[0062] In summary, this application provides a pitch circle detection method for calibrating the sun gear of a planetary gear reducer. First, the pitch circle of the sun gear is detected, and real-time temperature tracking is performed by setting monitoring points in the pitch circle detection area to obtain pitch circle detection results containing temperature distribution data during the monitoring process. Furthermore, the influence characteristics of the calculated analysis results based on the global tooth surface scanning data, combined with the real-time acquired temperature data, are fully compared and analyzed to accurately classify the pitch circle detection results of the same batch of sun gears. Based on the classification results, the pitch circle detection error under the influence of temperature is judged and analyzed. Its beneficial effect lies in fully combining the differences in the influence characteristics of local temperature fluctuations on detection errors during batch production testing, accurately comparing and analyzing the errors in the sun gear pitch circle detection process, and thus correcting the pitch circle detection error of the sun gear. This avoids the problem of temperature fluctuations during the detection process of sun gears in the batch production of planetary gear reducers, which can cause large errors in the pitch circle detection results due to the influence of equipment heat dissipation and ambient temperature changes in the production testing area. This improves the accuracy of the pitch circle detection results of the sun gear based on the planetary gear reducer and enhances the accuracy of calibration for sun gear processing and production.

[0063] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0064] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0065] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for calibrating the pitch circle of the sun gear in a planetary gear reducer, characterized in that, The method includes the following steps: Collect the pitch circle detection results of each gear, including the temperature of each preset monitoring point on each tooth surface of the gear at each time. Based on the difference between all temperature data of each tooth surface and the standard temperature, a temperature deviation distribution matrix of each tooth surface is constructed to determine the first characteristic value of the temperature deviation of each tooth surface; based on the first characteristic value of each tooth surface of each gear and each tooth surface of any remaining gear, combined with the difference between the two corresponding temperature deviation distribution matrices, the influence coefficient of each tooth surface of each gear and each tooth surface of any remaining gear under temperature fluctuation is calculated. The error values ​​of each gear are obtained by detecting the pitch circle of each gear; based on the difference distance between the error values ​​of all classes between any two gears, and combined with the influence coefficient, the clustering distance between any two gears is constructed, so as to cluster all gears by clustering algorithm; Based on the difference distance between the pitch circle detection results of the current gear and the gears in each cluster, a control sample group for the current gear is determined; based on the overall distribution of the temperature data of the current gear, combined with the pitch circle detection results of the gears in the control sample group, the pitch circle detection results of the current gear are adjusted to correct and rework the current gear.

2. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 1, characterized in that, The process of obtaining the temperature deviation distribution matrix is ​​as follows: Calculate the difference between the temperature data of each monitoring point at each time on each tooth surface and the standard temperature, and use the matrix composed of the differences of all monitoring points at all times on each tooth surface as the temperature deviation distribution matrix of each tooth surface.

3. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 1, characterized in that, The first characteristic value is the sum of the absolute values ​​of all elements in the temperature deviation distribution matrix of each tooth surface.

4. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 1, characterized in that, The process of obtaining the influence coefficient is as follows: The normalized value of the first characteristic value of each tooth surface is recorded as the second characteristic value. The mean of the second characteristic value of the x-th tooth surface of gear A and the second characteristic value of the y-th tooth surface of gear B is calculated. The SAD value between the temperature deviation distribution matrix of the x-th tooth surface and the y-th tooth surface is calculated. The influence coefficient between gear A and gear B is positively correlated with the mean value and the SAD value, respectively.

5. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 1, characterized in that, The various error values ​​include: pitch circle diameter error, pitch circle roundness error, and coaxiality error. Among them, the pitch circle diameter error is the difference between the pitch circle diameter data in the pitch circle detection result and the theoretical pitch circle diameter data.

6. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 5, characterized in that, The process of obtaining the cluster distance is as follows: The sum of all the influence coefficients between any two gears is recorded as the first distance value; the three-dimensional coordinates of each gear are constructed using the pitch circle diameter error, pitch circle roundness error, and coaxiality error of each gear, and the distance between the three-dimensional coordinates of any two gears is calculated and recorded as the second distance value; the product of the first distance value and the second distance value is used as the clustering distance between any two gears.

7. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 6, characterized in that, The process for obtaining the control sample group is as follows: Calculate the average distance between the current gear and the three-dimensional coordinates of all gears in each cluster, and take the cluster corresponding to the minimum value of the average distance as the control sample group of the current gear.

8. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 1, characterized in that, The process of adjusting the current gear pitch circle detection result is as follows: Calculate the variance of all temperature data for the current gear; if the variance is greater than or equal to a preset threshold, adjust the pitch circle detection result of the current gear; otherwise, do not adjust the pitch circle detection result of the current gear.

9. The method for detecting the pitch circle of the sun gear in a planetary gear reducer as described in claim 8, characterized in that, The process of adjusting the current gear pitch circle detection result is as follows: Using the average temperature of each gear in the control sample group as the x-axis and the average value of each error value of each gear as the y-axis, a fitting curve between temperature and each error value is constructed using a fitting algorithm to obtain the temperature-error value fitting curve of each control sample group. Substitute the mean of all temperature data for the current gear into the temperature-error value fitting curve of the control sample group to obtain the control value for each error value of the current gear. Calculate the mean between the control value and the actual measured value of each error value of the current gear, and use this as the adjustment result for each error value of the current gear.

10. A pitch circle testing device for calibrating the sun gear of a planetary gear 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, it implements the steps of the method as described in any one of claims 1-9.

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