Gear shaping methods, apparatus, media, and equipment based on non-uniform meshing

Abstract

This application discloses a gear shaping method, apparatus, medium, and equipment based on non-uniform meshing, relating to the field of gear shaping technology. The method includes: using an error prediction model to predict the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring to be machined, obtaining gear error prediction results; based on the mapping relationship between each motion axis of the gear shaping machine and the gear error, decoupling the gear error prediction results to the tool radial feed axis, tool rotation axis, and workpiece rotation axis of the gear shaping machine, obtaining error decoupling results for each axis; adjusting the machining process parameters of the tool radial feed axis and tool rotation axis according to the error decoupling results; designing a non-uniform meshing shaping process path, and generating a gear shaping compensation program based on the adjusted machining process parameters; importing the non-uniform meshing shaping process path and the gear shaping compensation program into a gear shaping simulation environment for simulation verification, thus completing the gear shaping based on non-uniform meshing.

Description

Technical Field

[0001] This application relates to the field of gear shaping technology, and in particular to a gear shaping method, apparatus, medium and equipment based on non-uniform meshing. Background Technology

[0002] Gears, as core transmission components in heavy equipment and precision devices, are crucial for the precise transmission of power and motion. Their machining accuracy directly determines the transmission performance, operational stability, and working efficiency of mechanical systems, and they are widely used in high-end equipment manufacturing fields such as aerospace, automotive manufacturing, precision machine tools, and high-end instruments. With the increasing precision requirements of modern industry for precision transmission equipment, the industry's requirements for key precision indicators such as cumulative total deviation of gear pitch and radial runout tolerance of gear rings have reached GB5 level and above. This places stringent demands on the precision control capabilities of gear machining processes and equipment.

[0003] Gear shaping is a core process for machining cylindrical gears, especially internal gears, large gears, asymmetric gears, and short-recessed gears. It is also a machining method that cannot be replaced by hobbing or turning in the machining of some special types of gears, making it an important technical approach for high-precision gear machining. Currently, conventional gear shaping processes rely on traditional CNC gear shaping machines, whose machining accuracy can only reach GB6-GB7 level. Moreover, it is limited by multiple factors such as the machining accuracy of machine tool parts, assembly accuracy, CNC system control accuracy, and equipment operation stability. Developing gear shaping machines with higher precision not only involves long development cycles and high manufacturing costs, but also faces many technical bottlenecks, making it difficult to quickly meet the machining requirements of high-end equipment for gears with GB5 level and above precision.

[0004] In current gear shaping processes, the industry mostly uses uniform meshing machining trajectories and lacks accurate prediction and targeted compensation methods for gear machining errors. It is impossible to predict the key error indicators of the gear to be processed in advance, and it is also difficult to establish an accurate mapping relationship between gear errors and the process parameters of each motion axis of the gear shaping machine. The machining process can only be adjusted after the fact, which makes it difficult to improve machining accuracy, resulting in low machining efficiency and high cost. At the same time, it is impossible to achieve advance compensation for machining errors, which has become the core technical problem restricting the improvement of gear shaping machining accuracy to GB5 level and above. Summary of the Invention

[0005] In view of this, this application provides a gear shaping method, apparatus, medium and equipment based on non-uniform meshing, which can realize comprehensive error compensation for gear shaping machines, improve the machining accuracy of CNC gear shaping machines and reduce the production cost of high-precision gears.

[0006] According to a first aspect of this application, a gear shaping method based on non-uniform meshing is provided, comprising:

[0007] An error prediction model is constructed, and the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring are predicted using the error prediction model to obtain the gear error prediction results.

[0008] Based on the mapping relationship between the motion axes of the gear shaper and the gear errors, the gear error prediction results are decoupled to the radial feed axis of the tool, the rotation axis of the tool and the rotation axis of the workpiece of the gear shaper to obtain the error decoupling results of each axis, and the machining process parameters of the radial feed axis of the tool and the rotation axis of the tool are adjusted according to the error decoupling results.

[0009] Design a non-uniform meshing planing process path with variable center distance and variable relative meshing position between the tool and the workpiece, and generate a gear shaping compensation program by combining the adjusted machining process parameters.

[0010] A gear shaping simulation environment was built, and the non-uniform meshing cutting process path and the gear shaping compensation program were imported into the gear shaping simulation environment for gear shaping simulation verification, thus completing the gear shaping based on non-uniform meshing.

[0011] According to a second aspect of this application, a gear shaping apparatus based on non-uniform meshing is provided, comprising:

[0012] The prediction module is used to construct an error prediction model. The error prediction model is used to predict the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring of the gear to be processed, so as to obtain the gear error prediction result.

[0013] The adjustment module is used to decouple the gear error prediction result to the tool radial feed axis, tool rotation axis and workpiece rotation axis of the gear shaper based on the mapping relationship between each motion axis and the gear error of the gear shaper, to obtain the error decoupling result of each axis, and to adjust the machining process parameters of the tool radial feed axis and the tool rotation axis according to the error decoupling result;

[0014] The generation module is used to design a non-uniform meshing planing process path with variable center distance and variable relative meshing position between the tool and the workpiece, and to generate a gear shaping compensation program in combination with the adjusted machining process parameters.

[0015] The simulation module is used to build a gear shaping simulation environment. The non-uniform meshing cutting process path and the gear shaping compensation program are imported into the gear shaping simulation environment for gear shaping simulation verification, thus completing the gear shaping based on non-uniform meshing.

[0016] According to a third aspect of this application, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described gear hobbing method based on non-uniform meshing.

[0017] According to a fourth aspect of this application, an electronic device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described gear hobbing method based on non-uniform meshing.

[0018] By employing the above technical solutions, the gear shaping method, apparatus, medium, and equipment based on non-uniform meshing provided in this application achieve accurate advance prediction of the cumulative total deviation of gear pitch and radial runout tolerance of the gear ring through the construction of an error prediction model. This overcomes the technical limitation of traditional gear shaping without advance error prediction. Furthermore, by relying on the mapping relationship between the various motion axes of the gear shaping machine and the gear error, the three-axis error decoupling is completed. By adjusting only the machining process parameters of the tool radial feed axis and the tool rotation axis, a precise correlation between gear error and machining process parameters can be established, solving the problem that traditional processes cannot accurately match errors with shaft system parameters and can only perform post-process inspection and adjustment. At the same time, a variable center is designed. The non-uniform meshing shaping process path with variable pitch and relative meshing position between the tool and the workpiece, combined with the adjusted parameters to generate a compensation program and complete the machining after simulation verification, replaces the traditional uniform meshing machining trajectory. It can fully tap the machining potential of existing gear shapers, and improve the gear shaping accuracy from GB6-GB7 to GB5 and above without developing a high-precision gear shaper. It can effectively solve the contradiction between the high-precision gear machining requirements of high-end equipment and the insufficient accuracy of existing gear shaping, while significantly improving gear shaping efficiency, reducing machining costs, and achieving advanced compensation for machining errors, fundamentally breaking through the core technical bottleneck that restricts the improvement of gear shaping accuracy.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 A schematic flowchart of a gear shaping method based on non-uniform meshing provided in an embodiment of this application is shown.

[0022] Figure 2 A schematic flowchart of a gear shaping method based on non-uniform meshing, according to another embodiment of this application, is shown.

[0023] Figure 3 This is a schematic diagram illustrating a DA-LSTM prediction model provided in an embodiment of this application;

[0024] Figure 4 This illustration shows a schematic diagram of the prediction result of the cumulative total deviation of the tooth pitch on the left tooth surface provided by an embodiment of this application;

[0025] Figure 5 This illustration shows a schematic diagram of the prediction result of the cumulative total deviation of the right tooth surface pitch provided by an embodiment of this application;

[0026] Figure 6 This diagram illustrates a prediction result of radial runout tolerance for a gear ring according to an embodiment of this application.

[0027] Figure 7 A schematic diagram of a gear shaping device based on non-uniform meshing provided in an embodiment of this application is shown.

[0028] Figure 8 A schematic diagram of a gear hobbing apparatus based on non-uniform meshing is shown in another embodiment of this application. Detailed Implementation

[0029] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0030] In current gear shaping processes, the industry mostly uses uniform meshing machining trajectories and lacks accurate prediction and targeted compensation methods for gear machining errors. It is impossible to predict the key error indicators of the gear to be processed in advance, and it is also difficult to establish an accurate mapping relationship between gear errors and the process parameters of each motion axis of the gear shaping machine. The machining process can only be adjusted after the fact, which makes it difficult to improve machining accuracy, resulting in low machining efficiency and high cost. At the same time, it is impossible to achieve advance compensation for machining errors, which has become the core technical problem restricting the improvement of gear shaping machining accuracy to GB5 level and above.

[0031] To address the aforementioned technical problems, embodiments of the present invention provide a gear shaping method based on non-uniform meshing, such as... Figure 1 As shown, the method includes:

[0032] Step 110: Construct an error prediction model. Use the error prediction model to predict the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring to be machined, and obtain the gear error prediction results.

[0033] Among them, the error prediction model refers to the algorithm model used to predict the machining error of the gear to be processed. It can establish the correlation between gear machining-related data and error indicators based on data processing, feature extraction and algorithm learning, so as to achieve accurate estimation of gear machining error; the cumulative total deviation of tooth pitch refers to the cumulative deviation value of the actual size of multiple tooth pitches on the gear relative to the theoretical size. It is the core indicator for measuring the gear tooth pitch machining accuracy and directly affects the smoothness and accuracy of gear transmission; the radial runout tolerance of the gear ring refers to the maximum allowable runout of the tooth groove or tooth tip on the gear ring in the radial direction when the gear rotates around the reference axis. It is a key indicator for evaluating the radial machining accuracy of the gear and determines the meshing accuracy of the gear and the mating parts; the gear error prediction result refers to the predicted values ​​of the cumulative total deviation of tooth pitch and the radial runout tolerance of the gear ring to be processed calculated by the error prediction model.

[0034] In this embodiment of the disclosure, an error prediction model suitable for gear shaping can be constructed by integrating historical inspection data and process-related data of gear processing. This model can rely on data mining and algorithm learning capabilities to establish the intrinsic correlation between gear processing data and key error indicators. Data on processing influencing factors related to the cumulative total deviation of gear pitch and radial runout tolerance of gear ring are input into the constructed error prediction model. The model uses internal calculation logic and correlation analysis to perform error deduction on the unprocessed gear to be processed, and finally outputs the gear error prediction results of the gear to be processed on the two core accuracy indicators of cumulative total deviation of gear pitch and radial runout tolerance of gear ring, providing accurate and advanced data support for subsequent gear shaping error compensation.

[0035] By constructing an error prediction model and accurately predicting the cumulative total deviation of the gear pitch and the radial runout tolerance of the gear ring, we can overcome the technical limitations of traditional gear shaping, which relies solely on post-processing inspection to detect errors without prior error prediction. This changes the passive mode of traditional gear shaping and enables the advance prediction of gear machining errors. It provides accurate data for subsequent targeted error compensation and optimization of machining process parameters, laying a data foundation for improving the accuracy of gear shaping from the source. At the same time, it effectively reduces workpiece rework and material waste caused by excessive machining errors, improves the overall efficiency of gear shaping, and reduces machining costs.

[0036] Step 120: Based on the mapping relationship between the motion axes of the gear shaper and the gear errors, decouple the gear error prediction results to the radial feed axis, the rotation axis and the workpiece rotation axis of the gear shaper to obtain the error decoupling results of each axis, and adjust the machining process parameters of the radial feed axis and the rotation axis according to the error decoupling results.

[0037] Among them, the mapping relationship refers to the inherent correlation between the motion errors of the radial feed axis, tool rotation axis, and workpiece rotation axis of the gear shaper and the cumulative total deviation of gear pitch and the radial runout tolerance of the gear ring. It is the theoretical basis for decomposing the overall gear error to each motion axis. Error decoupling refers to the process of decomposing and distributing the cumulative total deviation of gear pitch and the radial runout tolerance of the gear ring to the radial feed axis, tool rotation axis, and workpiece rotation axis of the gear shaper based on the mapping relationship between each axis and gear error, thereby realizing the transformation of the overall error to the single axis error. The error decoupling result refers to the error components corresponding to the radial feed axis, tool rotation axis, and workpiece rotation axis of the gear shaper after decoupling. The machining process parameters refer to the key parameters that control the motion of each axis of the gear shaper during the gear shaping process. For the radial feed axis, it is the feed amount parameter, and for the tool rotation axis, it is the angular velocity parameter. Their values ​​directly affect the accuracy of the gear shaping process.

[0038] In this embodiment of the invention, the mapping relationship between the radial feed axis of the gear shaper, the tool rotation axis, the workpiece rotation axis, the cumulative total deviation of the gear pitch, and the radial runout tolerance of the gear ring can be established based on the motion law of gear shaping. Based on this, the previously obtained gear error prediction results are decomposed and distributed to the three motion axes of the gear shaper according to the mapping relationship to obtain the error component corresponding to each axis, i.e., the error decoupling result. Then, based on the error decoupling result, the machining process parameters of the radial feed axis and the tool rotation axis of the gear shaper are adjusted specifically, while the process parameters of the workpiece rotation axis remain unchanged. The gear machining error is compensated by optimizing the parameters of the tool side axis system.

[0039] By establishing the mapping relationship between the motion axes of the gear shaper and the gear errors, and completing the three-axis decoupling of the gear error prediction results, the precise conversion of the overall gear error to the single-axis error component can be achieved. This establishes a precise correlation between gear errors and the machining process parameters of each axis of the gear shaper, overcoming the technical bottleneck of traditional processes that cannot effectively match gear errors with machining process parameters. Furthermore, by specifically adjusting the machining process parameters of the tool radial feed axis and the tool rotation axis, while preserving the workpiece rotation axis motion reference and avoiding disruption of the meshing motion matching relationship in the gear shaper process, precise compensation for gear machining errors can be achieved. This lays a precise parameter foundation for the subsequent implementation of non-uniform meshing shaper processes, improving the gear shaper machining accuracy from the perspective of axis motion control and solving the problem of lacking precise error basis for traditional process parameter adjustments.

[0040] Step 130: Design a non-uniform meshing planing process path with variable center distance and variable relative meshing position between the tool and the workpiece, and generate a gear shaping compensation program based on the adjusted machining process parameters.

[0041] Among them, the non-uniform meshing planing process path is a new type of planing process route that is different from the traditional uniform meshing machining method with fixed parameters. It changes the center distance between the tool and the workpiece in the planing process and adjusts the relative meshing position of the two to form the planing motion trajectory. It is adapted to error compensation. The center distance is the distance between the rotation axis of the planing cutter and the rotation axis of the gear to be machined during the planing process. It is a key geometric parameter that affects the gear meshing machining accuracy. The planing machining compensation program is a CNC program written in combination with error compensation requirements and adjusted machining process parameters. It can control each axis of the planing machine to complete the planing machining according to the optimized parameters and the non-uniform meshing process path, so as to achieve accurate compensation of machining errors.

[0042] In this embodiment of the invention, the specifications of the gear to be processed and the motion characteristics of the gear shaper can be combined to abandon the traditional uniform meshing processing method. Instead, a non-uniform meshing shaping process path can be designed that can dynamically change the center distance between the tool and the workpiece and flexibly adjust their relative meshing positions. At the same time, the processing parameters adjusted in the early stage for the radial feed axis and the rotation axis of the tool are used as the core basis. According to the instruction specifications of the gear shaper CNC system, the motion requirements of the process path and the compensation requirements for parameter adjustment are integrated to form a gear shaper processing compensation program that can be directly imported into the gear shaper. This allows the optimization of the process path and the compensation of the process parameters to form a coordinated processing control logic.

[0043] Designing a non-uniform meshing shaping process path with variable center distance and variable relative meshing position between the tool and the workpiece can overcome the accuracy limitations of traditional uniform meshing machining trajectories. It can fully tap the machining motion potential of the gear shaper and make the shaping process path more suitable for the actual needs of gear error compensation. At the same time, by combining the adjusted machining process parameters to generate a gear shaper machining compensation program, it can achieve an organic combination of process path optimization and process parameter compensation. It transforms the error compensation requirements into CNC machining instructions that the gear shaper can execute, so that the previous error prediction and parameter adjustment can be implemented into actual machining control actions. It can avoid the disconnect between process optimization and actual machining and provide executable process and program support for subsequent high-precision gear shaper machining. It effectively solves the technical problems of fixed process trajectories in traditional gear shaper machining and the inability to specifically adapt to error compensation.

[0044] Step 140: Build a gear shaping simulation environment, import the non-uniform meshing gear shaping process path and gear shaping compensation program into the gear shaping simulation environment for gear shaping simulation verification, and complete the gear shaping based on non-uniform meshing.

[0045] Among them, the gear shaping simulation environment is a digital environment built on simulation software that simulates the actual gear shaping process. It can reproduce the entire process of gear shaping machine movement, tool cutting, and gear machining, and serves as a digital platform for verifying machining technology and programs. The gear shaping simulation verification is a process of simulating the process path and control program of gear shaping in the completed digital simulation environment to verify the rationality of the process and program, the stability of the machining process, and the accuracy of the machining results. The non-uniform meshing gear shaping is a gear shaping method implemented after simulation verification, with non-uniform meshing cutting process path as the core and error compensation as the goal. It is different from the traditional uniform meshing gear shaping process.

[0046] In this embodiment of the present disclosure, a gear shaping simulation environment can be built using professional machining simulation software. This environment can accurately reproduce the motion characteristics of the CNC gear shaping machine, the cutting characteristics of the gear shaping cutter, and the machining process of the gear blank to be processed. The basic configuration of each model and machining parameter in the simulation environment is completed. Then, the designed non-uniform meshing gear shaping process path and the compiled gear shaping compensation program are simultaneously imported into the simulation environment. The entire process is simulated and run according to the actual gear shaping process and requirements. The rationality of the process path, the executability of the program, and the accuracy of the processed gear are comprehensively simulated and verified. After the verification is passed, the gear shaping based on non-uniform meshing can be completed on an actual CNC gear shaping machine using the process path and compensation program.

[0047] By establishing a simulation environment for gear shaping and simulating and verifying the non-uniform meshing gear shaping process path and gear shaping compensation program, digital pre-verification of the gear shaping process and program can be achieved. This allows for the timely identification of problems in the process path design and program compilation before actual machining, avoiding tool interference, collisions, and other failures caused by process or program defects during actual machining. Furthermore, it enables early prediction of whether the machining accuracy meets the expected requirements, reducing the number of trial cuts and workpiece scrap rates during actual machining, lowering machining costs, and improving machining efficiency. This ensures that the process optimization and error compensation scheme for non-uniform meshing gear shaping are fully verified before actual implementation, guaranteeing the stability and reliability of the actual gear shaping process based on non-uniform meshing. From the process implementation perspective, this provides a solid guarantee for improving the gear shaping accuracy to the target level.

[0048] In summary, the gear shaping method based on non-uniform meshing provided in this application achieves accurate advance prediction of the cumulative total deviation of gear pitch and the radial runout tolerance of the gear ring by constructing an error prediction model. This overcomes the technical limitation of traditional gear shaping without advance error prediction. Furthermore, by relying on the mapping relationship between the various motion axes of the gear shaping machine and the gear error, the three-axis error decoupling is completed. By adjusting only the machining process parameters of the tool radial feed axis and the tool rotation axis, a precise correlation between gear error and machining process parameters can be established. This solves the problem that traditional processes cannot accurately match errors with shaft system parameters and can only perform post-process inspection and adjustment. At the same time, the method designs variable center distance, variable tool and workpiece. The non-uniform meshing gear shaping process path at relative meshing positions, combined with adjusted parameters to generate a compensation program and complete the machining after simulation verification, replaces the traditional uniform meshing machining trajectory. It can fully tap the machining potential of existing gear shapers, and improve the gear shaping accuracy from GB6-GB7 to GB5 and above without developing a high-precision gear shaper. It can effectively solve the contradiction between the high-precision gear machining requirements of high-end equipment and the insufficient precision of existing gear shaping, while significantly improving gear shaping efficiency, reducing machining costs, and achieving advanced compensation for machining errors, fundamentally breaking through the core technical bottleneck that restricts the improvement of gear shaping accuracy.

[0049] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, and to fully illustrate the implementation of this embodiment, this embodiment also provides another gear shaping method based on non-uniform meshing, such as... Figure 2 As shown, the method includes:

[0050] Step 210: Construct an error prediction model. Use the error prediction model to predict the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring to be machined, and obtain the gear error prediction results.

[0051] The error prediction model is a Long Short-Term Memory (LSTM) network model based on data augmentation algorithms. It consists of an LSM layer, an attention layer, and a fully connected layer connected sequentially, with the LSM layer employing a single-layer LSM network structure. Data augmentation algorithms process raw data to increase its volume and density, making it more suitable for model training and improving generalization and prediction accuracy. The LSM network model is a neural network model capable of processing time-series data and storing long-term information. It effectively extracts feature information from time-series data and is suitable for predictive analysis of various types of time-series data. The LSM layer is the core computational layer of the LSM network model, responsible for feature extraction and information storage of the input time-series data. The attention layer is used to assign weights to data features, automatically identifying key information and increasing its weights, allowing the model to focus more on the analysis and processing of core features. The fully connected layer is used to perform comprehensive calculations and output of the extracted feature information, transforming the feature information from previous layers into result data that meets prediction requirements.

[0052] In this embodiment, a data augmentation-based long short-term memory network error prediction model can be constructed first, consisting of a single-layer long short-term memory network layer, an attention layer, and a fully connected layer connected sequentially. Then, the cumulative total deviation of gear pitch and radial runout tolerance of the gear ring are collected and calculated using a full-coordinate detection gear error evaluation algorithm as the original input data of the model. The original input data is augmented using a cubic spline interpolation curve interpolation algorithm to increase the density of data points and generate smooth and dense time-series training data. After normalization, the time-series training data is divided according to a preset time step and input into the long short-term memory network layer of the model to extract error feature information and output a hidden state sequence. This sequence is then input into the attention layer for weight allocation of key features to generate a context vector containing core error information. Finally, the context vector is input into the fully connected layer for comprehensive calculation, and the gear error prediction results of the cumulative total deviation of gear pitch and radial runout tolerance of the gear ring to be processed are finally output.

[0053] like Figure 3 As shown, a gear error evaluation algorithm based on full coordinate detection can be used to collect the cumulative total deviation of gear pitch and the radial runout tolerance of the gear ring, forming the original gear error dataset. Then, a data augmentation algorithm (DA) using cubic spline interpolation is used to process the original data, increasing the data point density and generating smooth and dense augmented gear error data. Finally, the augmented data is normalized and divided into multiple groups of time-series error data (i.e., time-series training data) x1, x2, ..., x... according to a preset time step. nThe data is then fed into a Long Short-Term Memory (LSTM) network layer composed of multiple LSTM units. Each LSTM unit extracts error features from the time-series data and outputs the corresponding hidden state sequence h1, h2, ..., h1. n The hidden state sequence is then fed into the attention layer, where different weights a1, a2, ..., a are assigned to each hidden state. n This approach focuses on key error information, generating context vectors M1, M2, ..., M that contain core error features. n Finally, the context vector is sequentially input into fully connected layer 1 and fully connected layer 2 for comprehensive calculation, and the final output model output (i.e. gear error prediction result) is output, thus completing the accurate prediction of gear error.

[0054] Accordingly, the implementation steps may include: collecting the cumulative total deviation data of gear pitch and the radial runout tolerance data of gear ring calculated by the gear error evaluation algorithm based on full coordinate detection, as the original input data of the error prediction model; performing data augmentation processing on the original input data using a curve interpolation algorithm to obtain augmented time-series training data. The curve interpolation algorithm is a cubic spline interpolation algorithm. The data augmentation processing is used to increase the data point density of the original input data to a preset multiple, generating smooth and dense time-series training data; after normalizing the time-series training data, dividing it according to a preset time step and inputting it into a long short-term memory network layer, extracting error feature information from the time-series training data and outputting a hidden state sequence; inputting the hidden state sequence into an attention layer for weight allocation, generating a context vector containing key error information, inputting the context vector into a fully connected layer for computation, and outputting the gear error prediction result.

[0055] Among them, the gear error evaluation algorithm based on full coordinate detection is an algorithm that calculates and evaluates gear machining errors by detecting the full coordinate position of the gear, and can accurately obtain error data such as the cumulative total deviation of the gear pitch and the radial runout tolerance of the gear ring; the curve interpolation algorithm is an algorithm that constructs a smooth curve from known data points and supplements new data points, which can improve the density and continuity of data; the cubic spline interpolation algorithm is a commonly used curve interpolation algorithm that can construct a smooth and continuous curve through piecewise cubic polynomials to achieve accurate data point supplementation; the time series training data is model training data with time series characteristics, which can reflect the change law of data with the processing process and adapt to the training needs of the long short-term memory network model; the normalization processing is a processing method for standardizing and scaling data, which can eliminate differences in data dimensions and improve the training efficiency and computational accuracy of the neural network model; the hidden state sequence is the sequence data reflecting the data characteristics output by the long short-term memory network layer after extracting features from the time series data; the context vector is the vector data that condenses the key feature information of the data after the attention layer assigns weights to the hidden state sequence.

[0056] A single-layer long short-term memory (LSTM) network error prediction model based on data augmentation algorithms is constructed. Accurate raw data is collected by combining a gear error evaluation algorithm based on full coordinate detection. Data augmentation is achieved through cubic spline interpolation, effectively enriching the quantity and density of training data and improving the model's generalization ability. Normalization ensures the model's computational efficiency and accuracy. Through hierarchical collaborative computation of the LSTM network layer, attention layer, and fully connected layer, precise extraction of the temporal features of gear errors and focused analysis of key features are achieved. This allows the error prediction model to accurately output the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring, significantly improving the prediction accuracy and reliability of gear machining errors. It enables precise advance prediction of key gear machining errors, providing high-precision data support for subsequent gear shaping error compensation and process parameter optimization. This ensures the effectiveness of gear shaping accuracy optimization from the data source, overcoming the technical limitations of traditional gear shaping without precise error prediction basis.

[0057] Step 220: Based on the motion law of gear shaping, establish a one-to-one mapping relationship between the radial feed axis of the tool, the rotation axis of the tool, the rotation axis of the workpiece, the cumulative total deviation of the tooth pitch, and the radial runout tolerance of the gear ring.

[0058] Among them, the motion law of gear shaping refers to the inherent correlation between the linkage mode and motion parameters of each motion axis of the gear shaping machine and the gear forming process, covering the influence logic of each axis motion on the gear accuracy index; the one-to-one mapping relationship is the unidirectional correspondence between the motion changes of the gear shaping machine's radial feed axis, tool rotation axis, and workpiece rotation axis and the two accuracy indexes of cumulative total deviation of gear pitch and radial runout tolerance of gear ring; the tool radial feed axis is the axis system on the gear shaping machine that controls the gear shaping cutter to make feed motion along the radial direction of the gear, and its motion parameters directly affect the radial machining dimension and accuracy of the gear; the tool rotation axis is the axis system on the gear shaping machine that controls the gear shaping cutter to make rotary cutting motion, and its motion parameters determine the cutting rhythm of the gear shaping cutter and the gear pitch machining accuracy; the workpiece rotation axis is the axis system on the gear shaping machine that controls the rotational motion of the gear to be processed, and is the core axis system for realizing gear indexing in gear shaping.

[0059] In this embodiment of the disclosure, the meshing motion principle between the tool and the workpiece during gear shaping can be analyzed in depth. The mechanism of the independent and linked motions of the tool radial feed axis, the tool rotation axis, and the workpiece rotation axis on the gear tooth profile, pitch, and radial dimensions can be sorted out. Combined with the formation principle of gear machining accuracy, the influence law of each axis motion deviation on the cumulative total pitch deviation and the radial runout tolerance of the gear ring can be quantitatively analyzed. Finally, a one-to-one mapping relationship between the three motion axes and the two core error indicators of the gear can be established, and the quantitative correlation logic of the gear error corresponding to the motion change of each axis can be clarified.

[0060] By establishing a one-to-one mapping relationship between each motion axis and the core error index of the gear based on the motion law of gear shaping, a precise quantitative correlation between the overall gear error and the single-axis motion of the gear shaping machine can be achieved. This breaks the technical status quo of gear error being disconnected from the motion of the equipment shaft system in traditional gear shaping. It can provide a scientific and precise theoretical basis for decoupling the overall gear error to each motion axis, so that the decomposition and compensation of gear error has a clear shaft system orientation, avoiding the blindness of error compensation. At the same time, it lays the core theoretical foundation for subsequent targeted adjustment of shaft system process parameters and precise error compensation, ensuring the effectiveness and accuracy of subsequent error decoupling and parameter adjustment from the principle level.

[0061] Step 230: Based on the mapping relationship, decouple the cumulative total deviation of tooth pitch and the radial runout tolerance of the gear ring to the tool radial feed axis, the tool rotation axis and the workpiece rotation axis respectively, and obtain the error components corresponding to each axis.

[0062] In the embodiments of this disclosure, based on the previously established one-to-one mapping relationship between the tool radial feed axis, tool rotation axis, workpiece rotation axis, cumulative total pitch deviation, and gear ring radial runout tolerance, the cumulative total pitch deviation is first decoupled into three axes and mapped to the tool radial feed axis to obtain its feed axis component of the cumulative total pitch deviation in the direction perpendicular to the gear pitch circle tangent. E x This is the difference between the theoretical center distance and the actual center distance between the tool and the workpiece when they are fully engaged. The pressure angle of the gear shaping cutter is given, and the cumulative total pitch deviation is mapped to the tool rotation axis and the workpiece rotation axis respectively to obtain the corresponding cumulative total pitch deviation tool rotation component F. py Total cumulative deviation of tooth pitch, workpiece rotation component F pc Next, the radial runout tolerance of the gear ring is decoupled into three axes and mapped to the radial feed axis of the tool to obtain the corresponding feed axis component F of the radial runout tolerance of the gear ring. rx =E x E x The value represents the difference between the theoretical and actual center distances between the tool and workpiece during full engagement. Simultaneously, the radial runout tolerance of the gear ring is mapped to the tool rotation axis and the workpiece rotation axis, respectively, yielding the corresponding tool rotation component F of the gear ring radial runout tolerance. ry Gear ring radial runout tolerance workpiece rotation component F rcFinally, the cumulative total pitch deviation feed axis component and the radial runout tolerance feed axis component of the gear ring, belonging to the same tool radial feed axis, are integrated as the error component of the tool radial feed axis. The cumulative total pitch deviation tool rotation component and the radial runout tolerance tool rotation component of the same tool rotation axis are integrated as the error component of the tool rotation axis. The cumulative total pitch deviation workpiece rotation component and the radial runout tolerance workpiece rotation component of the same workpiece rotation axis are integrated as the error component of the workpiece rotation axis, thus completing the precise decoupling of the gear core error to each motion axis of the gear shaping machine.

[0063] Accordingly, the implementation steps may include: based on the one-to-one mapping relationship between the tool radial feed axis, tool rotation axis, workpiece rotation axis and the cumulative total pitch deviation, mapping the cumulative total pitch deviation to the tool radial feed axis to obtain its feed axis component in the direction perpendicular to the gear pitch circle tangent; simultaneously mapping the cumulative total pitch deviation to the tool rotation axis and workpiece rotation axis respectively to obtain the corresponding tool rotation component and workpiece rotation component of the cumulative total pitch deviation; based on the one-to-one mapping relationship between the tool radial feed axis, tool rotation axis, workpiece rotation axis and gear ring radial runout tolerance, mapping the gear ring radial runout tolerance to the tool radial feed axis... The feed axis is used to obtain the corresponding feed axis component of the gear ring radial runout tolerance. At the same time, the gear ring radial runout tolerance is mapped to the tool rotation axis and the workpiece rotation axis respectively to obtain the corresponding tool rotation component and workpiece rotation component of the gear ring radial runout tolerance. The feed axis component of the cumulative total pitch deviation and the feed axis component of the gear ring radial runout tolerance are used as the error components of the tool radial feed axis. The tool rotation component of the cumulative total pitch deviation and the tool rotation component of the gear ring radial runout tolerance are used as the error components of the tool rotation axis. The workpiece rotation component of the cumulative total pitch deviation and the workpiece rotation component of the gear ring radial runout tolerance are used as the error components of the workpiece rotation axis.

[0064] Among them, the feed axis component of the cumulative total pitch deviation is the error component of the cumulative total pitch deviation decomposed to the tool radial feed axis according to the mapping relationship, which is the error value corresponding to the cumulative total pitch deviation generated by the axis along the direction perpendicular to the gear pitch circle tangent; the tool rotation component of the cumulative total pitch deviation is the error component of the cumulative total pitch deviation decomposed to the tool rotation axis according to the mapping relationship, which is the error value corresponding to the cumulative total pitch deviation generated by the rotational motion of the axis; the workpiece rotation component of the cumulative total pitch deviation is the error component of the cumulative total pitch deviation decomposed to the workpiece rotation axis according to the mapping relationship, which is the error value corresponding to the cumulative total pitch deviation generated by the rotational motion of the axis. The difference is as follows: The feed axis component of the gear ring radial runout tolerance is the error component of the gear ring radial runout tolerance decomposed to the tool radial feed axis according to the mapping relationship. It is the error value corresponding to the gear ring radial runout tolerance generated by the movement of this axis; The tool rotation component of the gear ring radial runout tolerance is the error component of the gear ring radial runout tolerance decomposed to the tool rotation axis according to the mapping relationship. It is the error value corresponding to the gear ring radial runout tolerance generated by the rotation movement of this axis; The workpiece rotation component of the gear ring radial runout tolerance is the error component of the gear ring radial runout tolerance decomposed to the workpiece rotation axis according to the mapping relationship. It is the error value corresponding to the gear ring radial runout tolerance generated by the rotation movement of this axis.

[0065] Based on the preset mapping relationship, the cumulative total deviation of tooth pitch and the radial runout tolerance of the gear ring are decoupled to the three motion axes of the gear shaper, and the corresponding error components of each axis are obtained. Through quantification formulas, the overall gear error can be accurately decomposed into single-axis error components, clarifying the specific quantitative relationship between each motion axis and the core gear error. This provides a concrete numerical basis for the decomposition of gear errors, avoiding the ambiguity and subjectivity of error decoupling. At the same time, the coaxial components after the decomposition of different gear errors are integrated to form the complete error components of each motion axis. This provides clear and specific numerical support for subsequent targeted error compensation and precise adjustment of machining parameters for each axis, realizing the accurate transformation of gear errors from the whole to the part. This gives the subsequent error compensation work clear direction and operability, ensuring the accuracy of gear shaper error compensation from the error decomposition level.

[0066] Step 240: Superimpose and calculate the error components of the tool radial feed axis to obtain the total feed deviation of the tool radial feed axis, and adjust the feed parameters of the tool radial feed axis according to the sign and value of the total feed deviation.

[0067] Among them, the total feed deviation is the comprehensive error value obtained by superimposing the cumulative total deviation of the tooth pitch on the radial feed axis of the tool and the radial runout tolerance of the gear ring feed axis. It is the core value reflecting the overall error of the axis. The feed parameter is the specific numerical parameter that controls the radial feed axis of the gear shaper tool to feed along the radial direction of the gear. Its value directly determines the radial feed distance of the tool and affects the radial machining accuracy and meshing effect of the gear.

[0068] In this embodiment of the disclosure, the cumulative total pitch deviation feed axis component and the radial runout tolerance feed axis component of the tool radial feed axis obtained from the previous error decoupling can be superimposed and calculated using a scalar superposition method, and the result can be obtained by formula F=F px +F rx The total feed rate deviation of the tool radial feed axis is obtained, where F is the total feed rate deviation. px F is the feed shaft component of the cumulative total pitch deviation. rx The radial runout tolerance of the gear ring is fed along the feed axis. Based on the positive or negative attribute and specific value of the calculated total feed deviation, the feed parameters of the tool's radial feed axis are adjusted accordingly. If the total feed deviation is positive, the feed amount corresponding to the absolute value is reduced; if the total feed deviation is negative, the feed amount corresponding to the absolute value is increased; if the total feed deviation is zero, the feed parameters are kept unchanged, thus completing the precise adjustment of the machining process parameters of the tool's radial feed axis.

[0069] By superimposing and calculating the total feed deviation by summing the error components of the radial feed axis of the tool, and adjusting the feed parameters accordingly, a comprehensive quantitative evaluation of multiple error components of the axis can be achieved. This avoids the problem of incomplete compensation caused by adjusting a single error component. Through clear numerical calculations and targeted parameter adjustment rules, the parameter adjustment of the radial feed axis of the tool can have a precise numerical basis and a unified execution standard. This abandons the empirical parameter adjustment method in traditional processes and enables precise and quantitative adjustment of the machining process parameters of the axis. It effectively offsets the impact of the radial feed axis error of the tool on the gear machining accuracy and improves the accuracy of gear shaping from the radial feed control level.

[0070] Step 250: Superimpose the error components of the tool rotation axis with the error components of the workpiece rotation axis to obtain the total angular velocity deviation of the tool rotation axis, and adjust the angular velocity parameters of the tool rotation axis according to the sign and value of the total angular velocity deviation.

[0071] Among them, the total angular velocity deviation is the comprehensive error value obtained by superimposing the error components of the tool rotation axis itself and the error components of the workpiece rotation axis. It is the core value reflecting the angular velocity deviation that the tool rotation axis needs to compensate for. The angular velocity parameter is the speed parameter that controls the rotational cutting motion of the gear shaper tool rotation axis. Its value directly determines the rotational cutting rhythm of the gear shaper cutter and affects the gear pitch machining accuracy and the smoothness of the meshing transmission.

[0072] In the embodiments of this disclosure, the error components of the tool rotation axis and the workpiece rotation axis obtained by decoupling from the previous error can be first calculated using the formula... The angular velocity deviation corresponding to the cumulative total pitch deviation is calculated, where F pyF represents the tool rotation component of the cumulative total pitch deviation. pc Z represents the workpiece rotation component of the cumulative total deviation of the tooth pitch, t is the workpiece rotation time corresponding to machining one theoretical tooth pitch, m is the module of the gear shaper, and Z is the number of teeth of the gear shaper. The pressure angle of the gear shaper cutter is then determined using the formula. The angular velocity deviation corresponding to the radial runout tolerance of the gear ring is calculated, where F ry F represents the tool rotation component of the radial runout tolerance of the gear ring. rc Let be the workpiece rotation component of the radial runout tolerance of the gear ring, t be the workpiece rotation time corresponding to machining one theoretical tooth pitch, m be the gear shaper module, and Z be the number of teeth of the gear shaper. This is the pressure angle of the gear shaping cutter. Then, using the formula... The total angular velocity deviation of the tool's rotation axis is calculated by superimposing the two angular velocity deviations. The total angular velocity deviation is calculated, and then the angular velocity parameters of the tool's rotary axis are adjusted accordingly based on the positive or negative value and the specific magnitude of the total angular velocity deviation. If the total angular velocity deviation is positive, the corresponding absolute value of the angular velocity is decreased; if the total angular velocity deviation is negative, the corresponding absolute value of the angular velocity is increased; if the total angular velocity deviation is zero, the angular velocity parameters are kept unchanged, thus completing the precise adjustment of the machining process parameters of the tool's rotary axis.

[0073] By superimposing the error components of the tool rotation axis and the workpiece rotation axis to calculate the total angular velocity deviation, and adjusting the angular velocity parameters of the tool rotation axis accordingly, a comprehensive quantitative assessment of the errors of the tool-side and workpiece-side rotation axes can be achieved. This allows the error of the workpiece rotation axis to be indirectly compensated through the adjustment of the tool rotation axis parameters, avoiding the problem of the meshing motion matching relationship being disrupted by adjusting the workpiece rotation axis parameters with a fixed reference. Through multiple sets of formulas, the error components are accurately quantified and converted into angular velocity deviations, allowing the adjustment of the tool rotation axis parameters to break away from empirical operations, with clear numerical basis and unified adjustment rules. This enables quantitative and precise adjustment of the machining process parameters of this axis, effectively offsetting the superimposed influence of the errors of the tool rotation axis and the workpiece rotation axis on the gear machining accuracy. From the perspective of rotary cutting control, this improves the pitch accuracy of gear shaping, ensures the smoothness of gear meshing transmission, and provides key shaft motion control guarantees for achieving the target level of gear shaping accuracy.

[0074] Step 260: Design a non-uniform meshing planing process path with variable center distance and variable relative meshing position between the tool and the workpiece, and generate a gear shaping compensation program based on the adjusted machining process parameters.

[0075] In this embodiment of the invention, the specifications of the gear to be processed and the motion characteristics of the gear shaper can be combined to abandon the traditional uniform meshing machining trajectory planning method. Instead, a non-uniform meshing shaping process path can be designed to dynamically change the center distance between the tool and the workpiece and flexibly adjust their relative meshing positions. The feed parameters of the radial feed axis and the angular velocity parameters of the tool rotation axis, which have been adjusted after error decoupling in the early stage, are used as the core control basis. Following the instruction compilation specifications of the gear shaper CNC system, the motion trajectory requirements of the non-uniform meshing shaping process path are integrated with the compensation control requirements of the core parameters. Compensation subroutines adapted to the motion control of the radial feed axis and the rotation axis of the tool are compiled separately. Then, the compensation subroutines of each axis are integrated and optimized to form a complete gear shaper machining compensation program that can be directly imported into the gear shaper and realize multi-axis collaborative error compensation.

[0076] Accordingly, the implementation steps may include: planning a variable center distance gear shaping feed trajectory based on the parameters of the gear to be processed and the motion characteristics of the gear shaper, while adjusting the relative meshing position of the tool and the workpiece to form a non-uniform meshing shaping process path; using the feed amount parameters of the tool radial feed axis after adjustment and the angular velocity parameters of the tool rotation axis after adjustment as the core parameters of the gear shaping compensation program; generating a first compensation subroutine for the tool radial feed axis and a second compensation subroutine for the tool rotation axis based on the CNC system instruction format of the gear shaper, combined with the non-uniform meshing shaping process path and the core parameters; and integrating the first compensation subroutine and the second compensation subroutine to generate a complete gear shaping compensation program.

[0077] Among them, the non-uniform meshing planing process path is different from the traditional uniform meshing machining method with fixed center distance and fixed meshing position. It is a personalized machining route adapted to error compensation requirements by dynamically adjusting the center distance between the tool and the workpiece and flexibly changing their relative meshing position during the planing process. The core parameters refer to the parameters that play a key control role in the planning of the planing machining compensation program. Here, they are the radial feed rate parameter of the tool and the angular velocity parameter of the tool rotation axis after error compensation adjustment, which directly determine the error correction effect of the compensation program. The planing machining compensation program is a CNC program compiled by combining the non-uniform meshing planing process path and the core parameters after error compensation. It can control each axis of the planing machine to complete the planing machining according to the optimized trajectory and parameters, so as to achieve precise compensation of machining errors.

[0078] Designing a non-uniform meshing shaping process path with variable center distance and variable relative meshing position between the tool and the workpiece can overcome the limitations of traditional fixed machining trajectories on gear shaping accuracy. This allows the machining trajectory to better meet the actual needs of gear error compensation, fully exploring the motion machining potential of the gear shaper. Simultaneously, by using the adjusted machining parameters as core parameters and integrating them with the process path to generate a gear shaping compensation program, a combination of process path optimization and shaft parameter compensation can be achieved. The results of previous error prediction and parameter adjustment are transformed into CNC machining instructions that the gear shaper can directly execute, ensuring that various error compensation requirements are translated into specific machining control actions. This avoids the disconnect between process optimization and actual machining execution. By using subroutines compiled separately for each axis and then integrating them, the accuracy of motion control for each axis and the coordination of multi-axis collaboration can be guaranteed. This provides directly implementable program support for subsequent high-precision non-uniform meshing gear shaping, effectively solving the technical problem that traditional gear shaping processes and programs cannot be specifically adapted to error compensation.

[0079] Step 270: Build a gear shaping simulation environment, import the non-uniform meshing gear shaping process path and gear shaping compensation program into the gear shaping simulation environment for gear shaping simulation verification, and complete the gear shaping based on non-uniform meshing.

[0080] In this embodiment of the present disclosure, a gear shaping simulation environment can be built using simulation software. First, a motion model of the CNC gear shaping machine, a gear shaping cutter structure model, and a gear blank model to be processed, matching the actual processing, are constructed within the environment. The parameters of each simulation model are then calibrated. Next, all calibrated models are integrated and imported into the gear shaping simulation environment. Simultaneously, the motion parameters, cutting parameters, and simulation processing parameters of the gear shaping machine CNC system, consistent with the actual processing, are configured. Subsequently, the designed non-uniform meshing gear shaping process path and the prepared gear shaping compensation program are imported into the simulation environment. The entire process of gear shaping is simulated and run according to the actual gear shaping process. During the simulation, it is monitored in real time for any abnormal processing problems such as tool interference and collision. At the same time, the various accuracy data of the gear after the simulation processing is completed are accurately recorded. The simulation verification of gear shaping is completed by comprehensively judging the abnormal processing conditions and accuracy data. After the verification is passed, gear shaping based on non-uniform meshing can be completed on an actual CNC gear shaping machine according to the process path and compensation program.

[0081] Accordingly, the implementation steps may include: constructing a motion model of the CNC gear shaper, a structural model of the gear shaper cutter, and a blank model of the gear to be machined in the simulation software, and completing the parameter calibration of the simulation model; importing all the constructed simulation models into the gear shaper machining simulation environment, configuring the motion parameters, cutting parameters, and process parameters of the CNC system of the gear shaper; importing the non-uniform meshing gear shaper process path and gear shaper machining compensation program into the gear shaper machining simulation environment, and performing the simulation operation of gear shaper machining; monitoring whether tool interference and collision problems occur during the simulation operation, and recording the gear accuracy data after simulation machining to complete the simulation verification.

[0082] Among them, the gear shaping simulation environment is a digital machining simulation platform built on professional simulation software. It can restore the entire machining process of gear shaping machine movement, tool cutting, and gear forming. It is a virtual machining scenario for verifying process paths and control programs. The gear shaping simulation verification is a process of simulating the running of process paths and compensation programs in the digital gear shaping simulation environment to verify the stability of the machining process, the executability of the program, and the accuracy of the machining results. The parameter calibration of the simulation model is an operation of calibrating and matching the parameters of various digital models related to gear shaping to ensure that the model parameters are consistent with the parameters of the actual machining equipment and workpiece, thus ensuring the authenticity of the simulation.

[0083] By establishing a simulation environment for gear shaping and simulating and verifying the non-uniform meshing cutting process path and gear shaping compensation program, digital pre-verification of the gear shaping process and program can be achieved. This allows for the timely detection of defects in the process path design and program compilation before actual machining, avoiding equipment failures and workpiece scrap due to process or program issues. Furthermore, it enables advance prediction of whether the machining accuracy meets the expected requirements, reducing the number of trial cuts and material waste during actual machining, significantly lowering machining costs and improving machining efficiency. This ensures that the rationality and effectiveness of the non-uniform meshing cutting process path and gear shaping compensation program are fully verified before actual implementation, guaranteeing the stability of the actual non-uniform meshing gear shaping process and the reliability of machining accuracy. From the process implementation perspective, this provides a solid digital guarantee for improving gear shaping accuracy to the target level, effectively solving the technical problems of traditional gear shaping processes and programs lacking advance verification and having high actual machining risks.

[0084] In specific application scenarios, as one possible approach, if the simulation runs without interference or collision issues and the gear accuracy after simulation machining reaches GB5 level or above, the non-uniform meshing shaping process path and gear shaping compensation program can be imported into the actual CNC gear shaping machine for on-site machining. As another possible approach, if the simulation encounters interference or collision issues or the gear accuracy does not meet requirements, the machining process parameters of the tool radial feed axis and tool rotation axis can be readjusted, and the non-uniform meshing shaping process path and gear shaping compensation program can be updated sequentially, followed by simulation verification. Interference and collision issues refer to abnormal contact or interference between the tool, workpiece, and machine tool components during gear shaping simulation or actual machining, which can affect machining stability and equipment safety.

[0085] By classifying and processing the simulation results and forming a closed-loop adjustment mechanism, the process path, compensation program, and process parameters can be continuously optimized before actual processing. This ensures that the final solution put into on-site processing is free from interference and collision risks and meets the accuracy standards. It effectively reduces the risk of equipment damage and workpiece scrap rate in actual processing, reduces trial cutting costs and processing cycle, and ensures that gear shaping based on non-uniform meshing can stably achieve processing accuracy of GB5 level or above. This makes the entire technical solution of error prediction, parameter compensation, process design, and program generation form a complete closed loop, greatly improving the reliability and practicality of gear shaping.

[0086] The following experiment uses a YK5132C CNC gear shaper as the experimental platform, with a gear shaper cutter module of 6mm, 21 teeth, and a pressure angle of 20°. A gear shaping experiment based on the principle of non-uniform meshing was conducted, followed by accuracy verification using a gear digital rolling inspection simulation platform built in the ADAMS environment.

[0087] First, the gear error evaluation algorithm based on full coordinate detection is used to evaluate the error of a batch of gear workpieces processed by the gear shaper, and the error of the gear to be processed is predicted. Based on the prediction results, the processing technology is optimized and the workpiece is test-cut. Then, the digital rolling inspection simulation experimental platform is used to compare the gear workpieces processed before and after the process optimization.

[0088] The results of the cumulative total deviation of gear pitch and radial runout tolerance of gear ring calculated by the gear error evaluation algorithm based on full coordinate detection are shown in Table 1 below:

[0089] Table 1 Gear Error Data Table

[0090]

[0091] Note: The table shows the error data for the first 12 teeth of an example gear with Z=30 teeth. The complete data includes the corresponding values ​​for all 30 teeth.

[0092] The error results calculated using the full-coordinate gear error evaluation method provide accurate error values ​​for each tooth, which are more suitable for providing data reference for constructing a gear shaping machine error compensation system.

[0093] The DA-LSTM prediction model was built using Matlab, with the Matlab compiler and Matlab deep learning toolbox as the runtime environment. The cumulative total pitch deviation and radial runout tolerance of the gear ring, after preprocessing and data augmentation, were obtained as a two-dimensional array with 30,000 data points. The Long Short-Term Memory (LSTM) network layer requires samples to be input as three-dimensional vectors, with the vector being (size, time, trait), where size is the batch size, time is the time series step size, and trait is the feature dimension of the input sample. This model uses the error data length of the gears processed in one hour as the time series step size, uses a sliding window to divide the input samples into three-dimensional vectors, sets the batch size to 5 (this can be adjusted if the data volume is large), and sets the feature dimension of the input samples to 4. The LSTM layer in the model has 300 output neurons, the first fully connected layer has 1200 neurons, and the second fully connected layer has 1 neuron. The ratio of training set to test set is set to 8:2. The sigmoid function is used as the activation function. 'Adam' is used as the optimizer for this model. The learning rate is set to 0.005, the number of network layers is 300, and the number of iterations is set to 300. This way, even after learning is complete, it will iterate to the last iteration, enhancing the model's generalization ability.

[0094] Data augmentation is performed using the cumulative total pitch deviation and radial runout tolerance of a set of gears in a time series. The augmented sample data is then used as training and testing data to train and predict the model.

[0095] The DA-LSTM prediction model's prediction results for the cumulative total pitch deviation of the left tooth surface, the cumulative total pitch deviation of the right tooth surface, and the radial runout tolerance of the gear ring are as follows: Figures 4-6 As shown.

[0096] The cumulative total deviation of gear pitch and the radial runout tolerance of gear ring are calculated using the formula F=Fpx+Frx in the direction perpendicular to the gear pitch circle tangent. Based on the calculation results, the feed amount of the radial feed axis (X-axis) of the CNC gear shaper is adjusted. The parameter changes are shown in Table 2 below.

[0097] Table 2 Parameter Variation Table

[0098]

[0099] According to the formula The components of the cumulative total deviation of gear pitch and radial runout tolerance of gear ring on the gear meshing line are calculated, and the machining parameters of the tool rotation axis (C2 axis) are adjusted according to the calculation results; the parameter changes are shown in Table 3 below.

[0100] Table 3 Parameter Variation Table

[0101]

[0102] The parameters of the machine tool and control system are set sequentially, the error compensation G code of the CNC gear shaping machine is imported, and the CNC program is reviewed and its syntax is checked. After completion, the CNC gear shaping simulation machining is performed on the workpiece blank to be shaped.

[0103] According to the simulation results, the CNC program designed based on the gear shaping accuracy optimization method of non-uniform meshing did not have problems such as interference or collision, the tool path was correct, and the gear shaping machine error compensation code ran smoothly.

[0104] To quantify the verification effect, the processing results of the same workpiece before and after process optimization were tested, and the key errors are compared as shown in Table 4 below:

[0105] Table 4 Comparison of Key Errors

[0106]

[0107] As shown in the table above, the F of the optimized gear p With F r The maximum values ​​all meet the tolerance requirements for grade 5 accuracy in GB / T 10095.1 standard, while the original accuracy only reached grade 6. The data directly proves the validity of this application.

[0108] The gears machined before and after the optimization process were digitally inspected using ADAMS kinematic analysis software. The results showed that the smoothness of the gear transmission and the accuracy of motion transmission were improved after the optimization of the gear shaping process. This verifies that the gear shaping process is improved by using the gear shaping accuracy optimization method based on non-uniform meshing.

[0109] Furthermore, as Figure 1 and Figure 2 The specific implementation of the method shown in this embodiment provides a gear shaping device based on non-uniform meshing, such as... Figure 7 As shown, the device includes: a prediction module 71, an adjustment module 72, a generation module 73, and a simulation module 74.

[0110] Prediction module 71 can be used to construct an error prediction model. The error prediction model is used to predict the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring of the gear to be machined, and the gear error prediction result is obtained.

[0111] The adjustment module 72 can be used to decouple the gear error prediction results to the tool radial feed axis, tool rotation axis and workpiece rotation axis of the gear shaper based on the mapping relationship between each motion axis and the gear error of the gear shaper, to obtain the error decoupling results of each axis, and adjust the machining process parameters of the tool radial feed axis and the tool rotation axis according to the error decoupling results;

[0112] The generation module 73 can be used to design non-uniform meshing planing process paths with varying center distances and varying relative meshing positions of the tool and workpiece, and generate a gear shaping compensation program in combination with the adjusted machining process parameters.

[0113] The simulation module 74 can be used to build a gear shaping simulation environment. The non-uniform meshing gear shaping process path and gear shaping compensation program can be imported into the gear shaping simulation environment for gear shaping simulation verification, and the gear shaping based on non-uniform meshing can be completed.

[0114] In some embodiments of this application, the error prediction model is a long short-term memory network model based on data augmentation algorithm. The error prediction model is composed of a long short-term memory network layer, an attention layer and a fully connected layer connected in sequence, and the long short-term memory network layer adopts a single-layer long short-term memory network structure.

[0115] The prediction module 71 is specifically used to collect the cumulative total deviation data of gear pitch and the radial runout tolerance data of gear ring calculated by the gear error evaluation algorithm based on full coordinate detection, as the original input data of the error prediction model; the original input data is augmented using a curve interpolation algorithm to obtain augmented time-series training data. The curve interpolation algorithm is a cubic spline interpolation algorithm. The data augmentation process is used to increase the data point density of the original input data to a preset multiple, generating smooth and dense time-series training data; after normalizing the time-series training data, it is divided according to a preset time step and input into the Long Short-Term Memory network layer to extract the error feature information in the time-series training data and output the hidden state sequence; the hidden state sequence is input into the attention layer for weight allocation to generate a context vector containing key error information, and the context vector is input into the fully connected layer for operation to output the gear error prediction result.

[0116] In some embodiments of this application, the adjustment module 72 can be specifically used to establish a one-to-one mapping relationship between the tool radial feed axis, tool rotation axis, workpiece rotation axis and the cumulative total pitch deviation and the radial runout tolerance of the gear ring, based on the motion law of gear shaping. According to the mapping relationship, the cumulative total pitch deviation and the radial runout tolerance of the gear ring are decoupled to the tool radial feed axis, tool rotation axis and workpiece rotation axis respectively to obtain the error components corresponding to each axis. The error components of the tool radial feed axis are superimposed to obtain the total feed deviation of the tool radial feed axis, and the feed parameters of the tool radial feed axis are adjusted according to the sign and value of the total feed deviation. The error components of the tool rotation axis and the error components of the workpiece rotation axis are superimposed to obtain the total angular velocity deviation of the tool rotation axis, and the angular velocity parameters of the tool rotation axis are adjusted according to the sign and value of the total angular velocity deviation.

[0117] In some embodiments of this application, when the cumulative total pitch deviation and the radial runout tolerance of the gear ring are decoupled to the tool radial feed axis, the tool rotation axis, and the workpiece rotation axis according to the mapping relationship, and the error components corresponding to each axis are obtained, the adjustment module 72 can be specifically used to map the cumulative total pitch deviation to the tool radial feed axis according to the one-to-one mapping relationship between the tool radial feed axis, the tool rotation axis, the workpiece rotation axis and the cumulative total pitch deviation, to obtain its feed axis component of the cumulative total pitch deviation in the direction perpendicular to the gear pitch circle tangent, and simultaneously map the cumulative total pitch deviation to the tool rotation axis and the workpiece rotation axis respectively, to obtain the corresponding tool rotation component and workpiece rotation component of the cumulative total pitch deviation; according to the tool radial feed axis, the tool rotation axis, the workpiece rotation axis and the tool rotation axis, the cumulative total pitch deviation is mapped to the tool rotation axis and the workpiece rotation axis respectively, to obtain the corresponding tool rotation component and workpiece rotation component of the cumulative total pitch deviation; according to the tool radial feed axis, the tool rotation axis, the workpiece rotation axis and the tool rotation axis, the cumulative total pitch deviation is mapped to the tool rotation axis and the workpiece rotation axis respectively, to obtain the corresponding tool rotation component and workpiece rotation component of the cumulative total pitch deviation; according to the tool radial feed axis, the tool rotation axis, the workpiece rotation axis and the workpiece ... A one-to-one mapping relationship between the workpiece rotation axis and the radial runout tolerance of the gear ring is established. The radial runout tolerance of the gear ring is mapped to the radial feed axis of the tool to obtain its corresponding feed axis component. At the same time, the radial runout tolerance of the gear ring is mapped to the tool rotation axis and the workpiece rotation axis respectively to obtain the corresponding tool rotation component and workpiece rotation component of the radial runout tolerance of the gear ring. The feed axis component of the cumulative total pitch deviation and the feed axis component of the radial runout tolerance of the gear ring are used as the error components of the tool radial feed axis. The tool rotation component of the cumulative total pitch deviation and the tool rotation component of the radial runout tolerance of the gear ring are used as the error components of the tool rotation axis. The workpiece rotation component of the cumulative total pitch deviation and the workpiece rotation component of the radial runout tolerance of the gear ring are used as the error components of the workpiece rotation axis.

[0118] In some embodiments of this application, the generation module 73 is specifically used to plan a variable center distance gear shaping feed trajectory based on the parameters of the gear to be processed and the motion characteristics of the gear shaper, while adjusting the relative meshing position of the tool and the workpiece to form a non-uniform meshing shaping process path; the feed amount parameters after adjusting the radial feed axis of the tool and the angular velocity parameters after adjusting the rotation axis of the tool are used as the core parameters of the gear shaping compensation program; based on the CNC system instruction format of the gear shaper, combined with the non-uniform meshing shaping process path and the core parameters, a first compensation subroutine for the radial feed axis of the tool and a second compensation subroutine for the rotation axis of the tool are generated; the first compensation subroutine and the second compensation subroutine are integrated to generate a complete gear shaping compensation program.

[0119] In some embodiments of this application, the simulation module 74 can be specifically used to construct a motion model of the CNC gear shaper, a structural model of the gear shaper cutter, and a blank model of the gear to be machined in the simulation software, and complete the parameter calibration of the simulation model; import all constructed simulation models into the gear shaper machining simulation environment, configure the motion parameters, cutting parameters, and process parameters of the CNC system of the gear shaper; import the non-uniform meshing gear shaper process path and gear shaper machining compensation program into the gear shaper machining simulation environment, and perform simulation operation of gear shaper machining; monitor whether tool interference and collision problems occur during the simulation operation, and record the gear accuracy data after simulation machining to complete the simulation verification.

[0120] In some embodiments of this application, such as Figure 8 As shown, the device may further include: a processing module 75 and a verification module 76;

[0121] The machining module 75 can be used to import the non-uniform meshing shaping process path and gear shaping compensation program into the actual CNC gear shaping machine for on-site machining if there are no interference or collision problems during the simulation and the gear accuracy after simulation reaches GB5 level or above.

[0122] The verification module 76 can be used to readjust the machining process parameters of the tool radial feed axis and the tool rotation axis if interference or collision problems occur during the simulation or the gear accuracy does not meet the requirements, and to update the non-uniform meshing planing process path and the gear planing compensation program in sequence, and then perform simulation verification again.

[0123] It should be noted that other corresponding descriptions of the functional units involved in the gear hobbing device based on non-uniform meshing provided in this embodiment can be found in [reference]. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0124] Based on the above, Figure 1 and Figure 2Accordingly, this embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method. Figure 1 and Figure 2 The illustrated gear shaping method is based on non-uniform meshing.

[0125] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause an electronic device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0126] Based on the above, Figure 1 and Figure 2 The method shown, and Figure 7 , Figure 8 To achieve the above objectives, the present application also provides an electronic device, specifically a personal computer, tablet computer, server, or other network device, as shown in the virtual device embodiment. This device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described objectives. Figure 1 and Figure 2 The illustrated gear shaping method is based on non-uniform meshing.

[0127] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0128] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0129] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platform, or it can be implemented by hardware.

[0131] This invention achieves accurate advance prediction of the cumulative total deviation of gear pitch and the radial runout tolerance of the gear ring by constructing an error prediction model. This overcomes the technical limitations of traditional gear shaping machining, which lacks advance error prediction. Furthermore, it achieves three-axis error decoupling by relying on the mapping relationship between the various motion axes of the gear shaping machine and the gear errors. By adjusting only the machining process parameters of the tool radial feed axis and the tool rotation axis, a precise correlation between gear errors and machining process parameters can be established. This solves the problem that traditional processes cannot accurately match errors with shaft system parameters and can only perform post-process inspection and adjustment. At the same time, it designs a non-uniform system with variable center distance and variable relative meshing position between the tool and the workpiece. The meshing and shaping process path, combined with the adjusted parameters to generate a compensation program and complete the machining after simulation verification, replaces the traditional uniform meshing machining trajectory. It can fully tap the machining potential of existing gear shapers, and improve the gear shaping accuracy from GB6-GB7 to GB5 and above without developing a high-precision gear shaper. It can effectively solve the contradiction between the high-precision gear machining requirements of high-end equipment and the insufficient precision of existing gear shaping, while significantly improving gear shaping efficiency, reducing machining costs, and achieving advanced compensation for machining errors, fundamentally breaking through the core technical bottleneck that restricts the improvement of gear shaping accuracy.

[0132] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0133] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A gear shaping method based on non-uniform meshing, characterized in that, include: An error prediction model is constructed, and the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring are predicted using the error prediction model to obtain the gear error prediction results. Based on the mapping relationship between the motion axes of the gear shaper and the gear errors, the gear error prediction results are decoupled to the radial feed axis, the rotary axis, and the workpiece rotary axis of the gear shaper to obtain the error decoupling results for each axis. The machining process parameters of the radial feed axis and the rotary axis are then adjusted according to these decoupling results. This includes: establishing a one-to-one mapping relationship between the radial feed axis, the rotary axis, the workpiece rotary axis, and the cumulative total pitch deviation and the radial runout tolerance of the gear ring, based on the motion law of gear shaper machining; and adjusting the cumulative total pitch deviation and the radial runout tolerance of the gear ring according to the mapping relationship. The dynamic tolerance is decoupled to the tool radial feed axis, the tool rotation axis, and the workpiece rotation axis to obtain the error components corresponding to each axis. The error components of the tool radial feed axis are superimposed to obtain the total feed deviation of the tool radial feed axis. The feed parameters of the tool radial feed axis are adjusted according to the sign and value of the total feed deviation. The error components of the tool rotation axis and the error components of the workpiece rotation axis are superimposed to obtain the total angular velocity deviation of the tool rotation axis. The angular velocity parameters of the tool rotation axis are adjusted according to the sign and value of the total angular velocity deviation. Specifically, based on the mapping relationship, the cumulative total pitch deviation and the radial runout tolerance of the gear ring are decoupled to the tool radial feed axis, the tool rotation axis, and the workpiece rotation axis, respectively, to obtain the error components corresponding to each axis. This includes: based on the one-to-one mapping relationship between the tool radial feed axis, the tool rotation axis, the workpiece rotation axis, and the cumulative total pitch deviation, mapping the cumulative total pitch deviation to the tool radial feed axis to obtain its feed axis component in the direction perpendicular to the gear pitch circle tangent; simultaneously mapping the cumulative total pitch deviation to the tool rotation axis and the workpiece rotation axis to obtain the corresponding tool rotation component and workpiece rotation component of the cumulative total pitch deviation; and based on the one-to-one mapping relationship between the tool radial feed axis, the tool rotation axis, the workpiece rotation axis, and the... A one-to-one mapping relationship for the radial runout tolerance of the gear ring is established. The radial runout tolerance of the gear ring is mapped to the radial feed axis of the tool to obtain its corresponding radial runout tolerance feed axis component. Simultaneously, the radial runout tolerance of the gear ring is mapped to the tool rotation axis and the workpiece rotation axis to obtain the corresponding radial runout tolerance tool rotation component and radial runout tolerance workpiece rotation component. The cumulative total pitch deviation feed axis component and the radial runout tolerance feed axis component are used as the error components of the tool radial feed axis. The cumulative total pitch deviation tool rotation component and the radial runout tolerance tool rotation component are used as the error components of the tool rotation axis. The cumulative total pitch deviation workpiece rotation component and the radial runout tolerance workpiece rotation component are used as the error components of the workpiece rotation axis. Design a non-uniform meshing planing process path with variable center distance and variable relative meshing position between the tool and the workpiece, and generate a gear shaping compensation program by combining the adjusted machining process parameters. A gear shaping simulation environment was built, and the non-uniform meshing cutting process path and the gear shaping compensation program were imported into the gear shaping simulation environment for gear shaping simulation verification, thus completing the gear shaping based on non-uniform meshing.

2. The method according to claim 1, characterized in that, The error prediction model is a long short-term memory network model based on data augmentation algorithm. The error prediction model is composed of a long short-term memory network layer, an attention layer and a fully connected layer connected in sequence, and the long short-term memory network layer adopts a single-layer long short-term memory network structure. The error prediction model is constructed, and the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring are predicted using the error prediction model to obtain the gear error prediction results, including: The cumulative total deviation of gear pitch and radial runout tolerance of gear ring, calculated by the gear error evaluation algorithm based on full coordinate detection, are collected as the original input data of the error prediction model. The original input data is augmented using a curve interpolation algorithm to obtain augmented time-series training data. The curve interpolation algorithm is a cubic spline interpolation algorithm. The data augmentation process is used to increase the data point density of the original input data to a preset multiple, generating smooth and dense time-series training data. After normalizing the time-series training data, it is divided according to a preset time step and input into the long short-term memory network layer. Error feature information in the time-series training data is extracted and the hidden state sequence is output. The hidden state sequence is input into the attention layer for weight allocation, generating a context vector containing key error information. The context vector is then input into the fully connected layer for computation, outputting the gear error prediction result.

3. The method according to claim 1, characterized in that, Design a non-uniform meshing planing process path with varying center distance and varying relative meshing position between the tool and workpiece, and generate a gear planing compensation program based on the adjusted machining process parameters, including: Based on the parameters of the gear to be processed and the motion characteristics of the gear shaper, a gear shaper feed trajectory with variable center distance is planned, and the relative meshing position of the tool and the workpiece is adjusted to form a non-uniform meshing shaper process path. The feed rate parameter after adjusting the radial feed axis of the tool and the angular velocity parameter after adjusting the rotation axis of the tool are used as the core parameters of the gear shaping compensation program. Based on the CNC system instruction format of the gear shaper, combined with the non-uniform meshing shaper process path and the core parameters, a first compensation subroutine for the radial feed axis of the tool and a second compensation subroutine for the rotation axis of the tool are generated. The first compensation subroutine and the second compensation subroutine are integrated to generate a complete gear hobbing compensation program.

4. The method according to claim 1, characterized in that, The process involves establishing a gear shaping simulation environment, importing the non-uniform meshing cutting process path and the gear shaping compensation program into the simulation environment for gear shaping simulation verification, and completing gear shaping based on non-uniform meshing, including: In the simulation software, construct the motion model of the CNC gear shaper, the structural model of the gear shaper cutter, and the blank model of the gear to be processed, and complete the parameter calibration of the simulation model; Import all constructed simulation models into the gear shaping simulation environment, and configure the motion parameters, cutting parameters, and process parameters of the gear shaping machine CNC system. The non-uniform meshing cutting process path and gear shaping compensation program are imported into the gear shaping simulation environment for simulation operation. Monitor whether tool interference or collision occurs during the simulation process, and record the gear accuracy data after simulation machining to complete the simulation verification.

5. The method according to claim 4, characterized in that, The method further includes: If the simulation runs without interference or collision issues and the gear precision after simulation machining reaches GB5 level or above, then the non-uniform meshing shaping process path and the gear shaping compensation program will be imported into the actual CNC gear shaping machine for on-site machining. If interference or collision issues occur during the simulation or the gear accuracy does not meet the requirements, the machining process parameters of the tool radial feed axis and the tool rotation axis should be readjusted, and the non-uniform meshing planing process path and the gear planing compensation program should be updated sequentially, and the simulation verification should be performed again.

6. A gear shaping apparatus based on non-uniform meshing, characterized in that, The apparatus is used to implement the gear shaping method based on non-uniform meshing as described in any one of claims 1 to 5, comprising: The prediction module is used to construct an error prediction model. The error prediction model is used to predict the cumulative total deviation of the tooth pitch and the radial runout tolerance of the gear ring of the gear to be processed, so as to obtain the gear error prediction result. The adjustment module is used to decouple the gear error prediction result to the tool radial feed axis, tool rotation axis and workpiece rotation axis of the gear shaper based on the mapping relationship between each motion axis and the gear error of the gear shaper, to obtain the error decoupling result of each axis, and to adjust the machining process parameters of the tool radial feed axis and the tool rotation axis according to the error decoupling result; The generation module is used to design a non-uniform meshing planing process path with variable center distance and variable relative meshing position between the tool and the workpiece, and to generate a gear shaping compensation program in combination with the adjusted machining process parameters. The simulation module is used to build a gear shaping simulation environment. The non-uniform meshing cutting process path and the gear shaping compensation program are imported into the gear shaping simulation environment for gear shaping simulation verification, thus completing the gear shaping based on non-uniform meshing.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

8. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

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