Gear optimization and tolerance sensitivity analysis method and system

Abstract

The invention relates to a gear optimization and tolerance sensitivity analysis method, which is characterized by comprising the following steps of: based on an input initial parameter condition, screening out a primary tooth number scheme meeting the initial parameter condition from all possible tooth number sets; and based on the primary tooth number selection scheme, preset gear basic parameters and a preset initial optimization range, determining an optimization variable range including a pressure angle, a helical angle, a displacement coefficient and a modulus. An optimization target is focused on four core geometric variables, including a pressure angle, a helix angle, a displacement coefficient and a modulus, which have the most direct influence on gear performance, and a clear optimization variable range is set for the optimization target, so that the dimension and scale of an optimization problem are remarkably limited from the source, meanwhile, early-stage rapid screening of invalid gear geometric parameters is realized, and the optimal gear performance is obtained. And the convergence speed of the optimization process is obviously accelerated, and the consumption of computing resources is greatly reduced, so that a globally better gear geometric parameter solution can be obtained in a shorter design period.

Description

Technical Field

[0001] This invention relates to the field of gear transmission system optimization technology, specifically to a gear optimization and tolerance sensitivity analysis method and system. Background Technology

[0002] In the design of gear transmission systems in automobiles, wind power, and construction machinery, the parameter design of gears directly determines the system's performance, efficiency, lifespan, and manufacturing cost. Traditional design processes rely heavily on engineers' experience, typically employing a sequential iterative model of "experience-based initial selection - manual modeling - simulation verification - manual adjustment." This approach suffers from problems such as long design cycles, insufficient optimization, and difficulty in comprehensively balancing multiple performance indicators.

[0003] To improve design efficiency, existing technologies have developed secondary development solutions that combine professional simulation software with optimization algorithms. For example, Chinese patent CN202411537678.3 provides a method, system, and electronic equipment for optimizing gear parameters of reducers based on MASTA simulation. This method optimizes gear parameters by combining MASTA secondary development with MATLAB, requiring the definition of 7 to 10 types of optimization parameters. Although it can improve gear performance, the large number of parameters leads to long iteration cycles and high computational resource consumption. Furthermore, gear modeling requires manually creating and assembling shafts, gears, bearings, and other parts one by one, which is inefficient. The analysis of the impact of tolerances on gear meshing performance (such as contact ratio and strength) requires manually extracting MASTA calculation results and then importing them into tolerance analysis tools, resulting in a fragmented process that is prone to introducing errors. Therefore, there is an urgent need in this field for a gear design method and system that can achieve efficient parameter optimization, fully automated modeling, and deep integration of tolerance analysis while ensuring efficiency and design accuracy, so as to comprehensively improve design efficiency and reliability. Summary of the Invention

[0004] This application provides a gear optimization and tolerance sensitivity analysis method and system to solve the above problems.

[0005] In a first aspect, embodiments of this application provide a gear optimization and tolerance sensitivity analysis method, comprising the following steps: Based on the input initial parameter conditions, a preliminary tooth number scheme that satisfies the initial parameter conditions is selected from all possible tooth number sets; Based on the initial tooth count scheme, preset gear basic parameters, and preset initial optimization range, the optimization variable range including pressure angle, helix angle, displacement coefficient, and module is determined. Based on the module range and helix angle range, preset minimum axial overlap, and preset feasible tooth width range in the optimization variable range, the feasibility of the gear design space is determined. If the determination fails, the process is terminated. If the determination passes, the optimization step is executed: the particle swarm optimization algorithm is used to iteratively optimize within the optimization variable range. In each iteration, the preset multiple performance constraints are automatically determined for each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm, and the optimal gear geometric parameters are selected and output based on the preset selection conditions. The optimal gear geometry parameters are imported into the gear transmission system analysis software through a preset program interface, and the corresponding electric drive gear system simulation model is generated by the gear transmission system analysis software. Based on the deviation range of the input gear micro-modification parameters, the gear transmission system analysis software is driven by the program interface to perform simulation calculations, and analysis results reflecting the relationship between parameter deviations and gear performance indicators are generated based on the calculation results.

[0006] In conjunction with the first aspect, in one embodiment, the initial parameter conditions include: the number of motor pole slots, additional frequency avoidance order, frequency avoidance multiple, frequency avoidance, range of number of teeth, range of speed ratio, range of stage ratio, and maximum torque; the basic gear parameters include center distance, tooth tip clearance, and strength coefficient; the initial optimization range includes pressure angle range, helix angle range, displacement coefficient range, and tooth width range.

[0007] In conjunction with the first aspect, in one implementation, determining the range of optimization variables, including pressure angle, helix angle, displacement coefficient, and module, based on the initially selected tooth number scheme, preset gear basic parameters, and preset initial optimization range, specifically includes: The pressure angle range, helix angle range, and displacement coefficient range in the initial optimization range are directly used as the range of the corresponding variables in the optimization variable range; Based on the initially selected number of teeth scheme, the maximum torque under the initial parameter conditions, and the center distance and strength coefficient in the basic parameters, the feasible range of the module is calculated and determined as the module range in the optimization variable range.

[0008] In conjunction with the first aspect, in one implementation method, the feasibility assessment of the gear design space specifically includes: Based on the lower limit of the range of the module optimization variables, the lower limit of the range of the helix angle optimization variables, and the preset minimum axial overlap, the theoretical tooth width requirement is calculated. Determine whether the theoretical tooth width requirement is within the preset feasible tooth width range; If yes, then it is considered passed; if no, then it is considered failed.

[0009] In conjunction with the first aspect, in one implementation, each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm specifically includes: Based on the candidate pressure angle, helix angle, displacement coefficient, and module generated by the particle swarm optimization algorithm, and combined with the known number of teeth, center distance, tooth tip clearance, and tooth width generated within the preset feasible range of tooth width, a set of candidate gear geometric parameters is calculated in real time. The candidate gear geometric parameters include the pressure angle, helix angle, displacement coefficient, module, number of teeth, center distance, tooth tip circle diameter, and tooth root circle diameter of each stage in the two-stage gear pair.

[0010] In conjunction with the first aspect, in one implementation, the automatic determination of preset multiple performance constraints sequentially applied to each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm in each iteration specifically includes: Calculate the corresponding end face overlap, axial overlap, slip ratio, tooth tip thickness, and tooth root stress value based on the candidate gear's geometric parameters. Automatic determination of multiple preset performance constraints based on end face overlap, axial overlap, slip ratio, tooth tip thickness and tooth root stress value; the preset multiple performance constraints include overlap constraint, slip ratio constraint, undercut determination constraint and gear strength constraint determined in sequence.

[0011] In conjunction with the first aspect, in one implementation, the preset selection criteria include: selecting candidate optimization parameters with the largest end face overlap and axial overlap as the optimal gear geometry parameters.

[0012] In conjunction with the first aspect, in one embodiment, the gear micro-modification parameters include at least one of tooth profile inclination, tooth profile bulging amount, tooth direction inclination, tooth direction bulging amount, and tooth profile trimming amount.

[0013] In conjunction with the first aspect, in one implementation, the step of generating analysis results reflecting the relationship between parameter deviations and gear performance indicators based on calculation results specifically includes: generating at least one of univariate analysis charts, bivariate analysis charts, multivariate analysis charts, and statistical sampling analysis charts based on the number of gear micro-modification parameters.

[0014] Secondly, embodiments of this application provide a system for gear optimization and tolerance sensitivity analysis, comprising: The tooth count scheme design module is configured to: based on the input initial parameter conditions, select a preliminary tooth count scheme that meets the initial parameter conditions from all possible tooth count sets; The optimization module, connected to the tooth number scheme design module, is configured to: determine the range of optimization variables, including pressure angle, helix angle, displacement coefficient, and module, based on the initially selected tooth number scheme, preset basic gear parameters, and preset initial optimization range; perform a feasibility assessment of the gear design space based on the module range and helix angle range, preset minimum axial overlap, and preset feasible tooth width range within the optimization variable range; terminate the process if the assessment fails; and execute the optimization step if the assessment passes: iteratively optimize within the optimization variable range using a particle swarm optimization algorithm, and in each iteration, automatically determine preset multiple performance constraints for each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm, and filter and output the optimal gear geometric parameters based on preset selection conditions. The modeling module, which is connected to the optimization module, is configured to: import the optimal gear geometry parameters output by the optimization module into the gear transmission system analysis software through a preset program interface, and generate the corresponding electric drive gear system simulation model through the gear transmission system analysis software; The analysis module, which is connected to the modeling module, is configured to: drive the gear transmission system analysis software to perform simulation calculations based on the deviation range of the input gear micro-modification parameters through the program interface, and generate analysis results reflecting the relationship between parameter deviations and gear performance indicators based on the calculation results.

[0015] The beneficial effects of the technical solutions provided in this application include: 1. By focusing the optimization objective on the four core geometric variables that have the most direct impact on gear performance—pressure angle, helix angle, displacement coefficient, and module—and setting clear optimization variable ranges for them, the dimension and scale of the optimization problem are significantly limited from the source. At the same time, feasibility assessment through pre-designed gear space avoids starting optimization calculations that are destined to fail. Furthermore, the particle swarm optimization algorithm is used to automatically assess multiple performance constraints, including overlap, slip ratio, undercut, and strength, sequentially during the optimization process. If any assessment fails, subsequent calculations are immediately terminated. This achieves early and rapid screening of invalid gear geometric parameters, significantly accelerating the convergence speed of the optimization process and greatly reducing computational resource consumption. As a result, a globally superior gear geometric parameter solution can be obtained within a shorter design cycle.

[0016] 2. The optimal gear geometry parameters obtained by this method can be automatically imported into the gear transmission system analysis software through a preset program interface, and drive it to automatically generate a complete simulation model of the electric drive gear system. This process completely avoids the tedious manual modeling and assembly operations in the traditional process, eliminates human error, and ensures the consistency and reliability of the simulation model with the design intent.

[0017] 3. This method deeply embeds tolerance analysis into the design process. Users only need to input the deviation range of micro-modification parameters such as tooth profile inclination, tooth profile bulging, and tooth direction inclination. The system can automatically drive the simulation software to perform batch simulation calculations through the program interface, and intelligently generate single-variable, bivariate, multivariate, or statistical sampling analysis charts according to the number of variables. This intuitively reveals the sensitivity relationship between tolerance variation and gear performance indicators. This integrated analysis mode changes the fragmented state of traditional solutions that require cross-software platforms, manual data extraction, and further processing, enabling designers to quickly assess the impact of manufacturing errors during the design phase. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a diagram illustrating the main steps of the present invention; Figure 2 This is an example diagram of the preliminary tooth number selection scheme table in Embodiment 1 of the present invention; Figure 3 This is an example diagram of the two-dimensional tooth profile curve of the gear end face generated by CATIA software in Embodiment 1 of the present invention; Figure 4 This is an example diagram of the automatic filling of gear parameter definition interface in MASTA software according to Embodiment 1 of the present invention; Figure 5 This is an example graph showing the univariate analysis results of Embodiment 1 of the present invention; Figure 6 This is an example graph showing the bivariate analysis results of Embodiment 1 of the present invention; Figure 7 This is an example figure showing the results of multivariate analysis in Embodiment 1 of the present invention; Figure 8 This is an example diagram of the statistical sampling analysis results of Embodiment 1 of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0021] Example 1: Embodiment 1 of this application provides a gear optimization and tolerance sensitivity analysis method, including the following steps: S1. Based on the input initial parameter conditions, select the initial tooth number scheme that satisfies the initial parameter conditions from all possible tooth number sets; In a preferred embodiment of the present invention, the above-described automatic calculation and filtering process is implemented using Python programming; Specifically: The user inputs initial parameters within the program, which include: Number of poles and slots of the motor: In this embodiment, 6 poles and 54 slots are preferred; Additional frequency avoidance orders: user-specified key orders other than the main order to be avoided. In this embodiment, the preferred order is 48 or 96. Frequency avoidance factor: In this embodiment, it is preferably 3 times, and the gear meshing frequency should avoid the frequency band of 3 times the excitation frequency; Avoidance frequency: the allowable percentage deviation, preferably ±7% in this embodiment; Tooth count range: Set the upper and lower limits of the allowable number of teeth for each stage of the gear pair. In this embodiment, the first stage driving gear is preferred. ∈[25,30], driven wheel ∈[50,81]; Second-level active wheel ∈[17,22], driven wheel ∈[50,81]; Overall gear ratio range: The allowable range of the overall gear ratio of the transmission system is determined according to the power requirements of the vehicle. In this embodiment, it is preferably 11.4 to 11.7. Transmission ratio range: The ratio range of the transmission ratio of the first stage to the second stage. In this embodiment, it is preferably 0.8 to 1.6 to reasonably distribute the load of each stage.

[0022] Maximum torque: The maximum torque value that the system needs to transmit; After receiving the above initial parameters, the program will automatically perform the filtering according to the following logic: Combination generation: Based on the input range of tooth counts, a loop algorithm is used to automatically generate all possible combinations of four-level tooth counts. A set of.

[0023] Transmission ratio selection: For each combination in the set, calculate its overall speed ratio and stage ratio: ; ; Only combinations that simultaneously satisfy the requirements of a total speed ratio in the range of 11.4 to 11.7 and a stage ratio in the range of 0.8 to 1.6 are retained; NVH frequency avoidance verification: For the combination obtained in the previous step, calculate the meshing frequency of each gear according to the motor speed. Calculate the excitation frequency based on the input number of motor pole slots. The program determines whether the frequency avoidance rules for all specified frequency avoidance orders are satisfied based on the input frequency avoidance multiple and frequency avoidance number. Specifically, the following conditions must be met: ; in, The input frequency avoidance factor is 3; To avoid a frequency of 0.7.

[0024] The combination that passes all checks will be marked as qualified; Finally, the program outputs all combinations that have passed the above steps as preliminary tooth count schemes, and displays them in a table or diagram, as shown below. Figure 2 As shown.

[0025] S2. Based on the initially selected number of teeth scheme, the preset basic gear parameters, and the preset initial optimization range, determine the optimization variable range including pressure angle, helix angle, displacement coefficient, and module; based on the module range and helix angle range in the optimization variable range, the preset minimum axial overlap, and the preset feasible range of tooth width, perform a feasibility judgment on the gear design space; if the judgment fails, terminate the process; if the judgment passes, execute the optimization step: use the particle swarm optimization algorithm to perform iterative optimization within the optimization variable range, and in each iteration, perform automatic judgment of preset multiple performance constraints on each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm in sequence, and filter and output the optimal gear geometric parameters based on the preset selection conditions; S201, Determine the range of optimization variables: Preset basic gear parameters: In this embodiment, these are input by the user as fixed constraints in the optimization process, and include: Center distance of each gear pair: In this embodiment, the preferred center distance for the first stage is 88 mm, and the preferred center distance for the second stage is 129 mm; Tooth tip clearance: In this embodiment, it is preferably 0.23 mm; Strength coefficient: In this embodiment, the strength coefficient of the first-stage gear pair is preferably 110, and that of the second-stage gear pair is preferably 70.

[0026] Initial optimization range: the allowable fluctuation range of each key geometric parameter input by the user, which includes: Pressure angle range: In this embodiment, it is preferably 19°~20°; Helix angle range: In this embodiment, it is preferably 20°~26°; Displacement coefficient range: In this embodiment, it is preferably -0.9 to 1; Tooth width range: In this embodiment, it is preferably 38mm~40mm; The method for determining the range of the optimization variable is as follows: The pressure angle range, helix angle range, and displacement coefficient range mentioned above are directly used as the optimization variable range for the corresponding variables in the subsequent particle swarm optimization algorithm. The range of variables for optimizing the module needs to be based on the initially selected number of teeth, center distance, maximum torque, and strength coefficient determined in step S1. During the calculation process, the program will call a simplified design formula based on the well-known tooth surface contact fatigue strength design formula in this field for rapid estimation. The formula characterizes the pitch circle diameter. With strength coefficient Maximum torque Gear ratio and tooth width range The relationship between them can be specifically represented as follows: ; in, The strength coefficient is a dimensionless comprehensive empirical parameter. In this embodiment, its value is 110 / 70. This range is calibrated based on historical project data. This coefficient encapsulates the influence of multiple parameters involved in the tooth surface contact fatigue strength design, such as the node area coefficient, overlap coefficient, helix angle coefficient, allowable contact stress, and application coefficient, thereby achieving engineering simplification of complex formulas. The minimum pitch circle diameter obtained from the above calculation By combining the known initial number of teeth scheme and the range of optimization variables for the helix angle, the range of optimization variables for the module can be obtained by reverse deduction based on the well-known gear geometric parameter calculation formula in this field.

[0027] S202, Global Feasibility Assessment: Before performing the optimization step, the program performs a quick global feasibility check to avoid wasting resources in a design space that is fundamentally infeasible. The decision logic is as follows: The theoretical tooth width requirement is calculated based on the lower limit of the range of the module optimization variable, the lower limit of the range of the helix angle optimization variable, and the preset minimum axial overlap. ; in, The preset minimum axial overlap, This represents the lower limit of the range of variables used for modulo optimization. This represents the lower limit of the range of variables for optimizing the helix angle. Calculate the theoretical tooth width requirement: Based on the principles of gear geometry, calculate the minimum tooth width required to meet the preset minimum axial overlap ratio using the formula: The program determines whether the calculated minimum tooth width is within the preset tooth width range (38mm~40mm) in the initial optimization range. If it is, the program is considered to have passed, indicating that even under the most unfavorable conditions, the required tooth width has not exceeded the limit and the design space is feasible. Otherwise, the program is considered to have failed, indicating that a gear that meets the minimum performance requirements cannot be designed within the given tooth width range. The program then terminates and prompts the user to adjust the input parameters.

[0028] S203, Perform the optimization step: Once the global feasibility assessment is passed, the particle swarm optimization algorithm is initiated to iteratively optimize within the range of optimization variables. In this embodiment, the solution with optimal transmission smoothness is sought by maximizing the sum of end face overlap and axial overlap as the selection criterion. Specifically: In each iteration of the particle swarm optimization algorithm, for the candidate pressure angle, helix angle, displacement coefficient, and module generated by the particle swarm optimization algorithm, combined with the known number of teeth, center distance, and tooth tip clearance, as well as the tooth width generated within the preset feasible range of tooth width, the set of candidate gear geometric parameters is calculated, which includes: the pressure angle, helix angle, displacement coefficient, module, number of teeth, center distance, tooth width, and tooth tip circle diameter and tooth root circle diameter of each stage in the two-stage gear pair; Specifically, the addendum circle diameter and dedendum circle diameter are calculated based on the standard formula of gear geometry. The four candidate variables are combined with the known number of teeth, center distance, tooth tip clearance and generated tooth width, and the pitch circle diameter, tooth tip height and tooth root height are calculated in sequence to finally obtain the addendum circle diameter and tooth root circle diameter. Based on the candidate gear geometric parameter set obtained from the above calculations, the program will sequentially calculate the corresponding end face overlap, axial overlap, slip ratio, tooth tip thickness, and tooth root stress values ​​using gear geometry, kinematics, and strength calculation methods known in the relevant technical field. Based on the intermediate parameters obtained from the above calculations, the program will then determine the performance constraints of the candidate gear geometric parameter set. Performance constraints are stored in the program's initialization ini file and serve as the standard for automatic determination. In this embodiment, performance constraint determination includes: Overlap constraint: In this embodiment, the first-level axial overlap is set to >4.05 and the second-level axial overlap is set to >2.6. The axial overlap is determined based on the above candidate gear geometric parameters. If the determination fails, all subsequent calculations for this scheme will be terminated immediately. Slip ratio constraint: In this embodiment, the absolute value of the maximum slip ratio of the tooth surface is set to ≤2. The slip ratio is determined based on the above candidate gear geometric parameters. If the determination fails, all subsequent calculations for this scheme will be terminated immediately. Undercut constraint: In this embodiment, it is set that (diameter of gear meshing start point - diameter of gear involute start circle) / 2 > 0.15. The tooth tip thickness calculated based on the above candidate gear geometric parameters is used for judgment. If the judgment fails, all subsequent calculations for this scheme will be terminated immediately. This condition is based on engineering experience and can effectively avoid undercut. When the tooth tip thickness calculated from the candidate gear parameters is small, the tooth tip circle diameter increases and the meshing start point decreases, resulting in the above difference being less than 0.15. Therefore, it is judged that there is a risk of undercut, and the subsequent calculations for this scheme will be terminated immediately, thereby improving optimization efficiency. Strength constraints: In this embodiment, the strength coefficient of the first-stage gear pair is preferably ≥110, and that of the second-stage gear pair is preferably ≥70. The strength is determined based on the root stress value calculated from the above candidate gear geometric parameters. If the determination fails, all subsequent calculations for this scheme will be terminated immediately. If all the above constraints are passed, the program will select the optimal gear geometry parameters from all the candidate gear geometry parameters that have passed the selection criteria (maximizing the sum of end face overlap and axial overlap) and output them. The final output optimal gear geometry parameters include: pressure angle, helix angle, displacement coefficient, module, number of teeth, tooth width, center distance, and addendum circle diameter and dedendum circle diameter for each stage of the two-stage gear pair. It is important to note that the order of the above performance constraints is based on the computational complexity and computational resource consumption of each performance indicator, designed from low to high and from fast to slow. This ensures that the most invalid solutions can be eliminated with the least computational cost in the early stages of optimization, which significantly speeds up the convergence of the optimization process, greatly reduces the consumption of computational resources, and greatly improves the overall optimization efficiency.

[0029] S3. Import the optimal gear geometry parameters into the gear transmission system analysis software through the preset program interface, and generate the corresponding electric drive gear system simulation model through the gear transmission system analysis software. The program interface is an automated script developed based on the Python language that can coordinate 3D modeling software and gear transmission system analysis software. In this embodiment, the 3D modeling software is preferably CATIA software, and the gear transmission system analysis software is preferably MASTA software. The specific implementation process is as follows: Calling the 3D modeling software: The CATIA software is started through the Python program interface, and the optimal gear geometry parameters finally obtained in step S2 are passed to the gear generation module of the CATIA software through its application programming interface (API); Generating accurate 2D tooth profiles: CATIA software automatically calculates and generates accurate 2D tooth profile curves for the gear end face based on optimal gear geometry parameters and its geometric kernel. Specifically, for example... Figure 3 As shown; Extracting contour data and generating scripts: The Python program interface extracts precise geometric data (such as coordinate point sequences or involute parameters) of the two-dimensional tooth profile curve from the CATIA software and converts this data into a dedicated contour definition script that can be recognized and executed by the MASTA software. Importing the contour script to complete modeling: The Python program interface drives the MASTA software to start, automatically opening its gear parameter definition interface and automatically filling the corresponding input boxes with the optimal gear geometry parameters. This embodiment provides a specific example as follows: Figure 4 As shown; at the same time, the Python program interface automatically imports or pastes the generated contour definition script into the MASTA software. The MASTA software kernel automatically reconstructs the corresponding 3D gear geometry in its own modeling environment based on the precise geometry defined by the script.

[0030] System-level assembly: MASTA software is based on a predefined parametric template for electric drive gear systems. This template defines the system's topology (such as shaft layout, bearing positions, and housing interfaces) and simulation baseline settings (such as standard materials, basic constraints, and load conditions). Based on this template, the program intelligently positions and assembles the automatically reconstructed gears onto the specified shaft segments, automatically establishing all necessary connections and constraints between the gears and shafts, and between the bearings and the housing. After assembly, the system automatically inherits and adapts the material properties, boundary conditions, and load settings in the template, ultimately generating a complete simulation model of the electric drive gear system.

[0031] S4. Based on the deviation range of the input gear micro-modification parameters, the gear transmission system analysis software is driven by the program interface to perform simulation calculations, and analysis results reflecting the relationship between parameter deviations and gear performance indicators are generated based on the calculation results.

[0032] The user provides the deviation range of key gear micro-modification parameters, which in this embodiment includes, but is not limited to: Tooth profile inclination In this embodiment, the range is set to -15μm to 15μm; Toothed drum shape measurement In this embodiment, the range is set to 0μm~12μm; Tooth profile trimming amount In this embodiment, the range is set to 0μm~15μm; Tooth tilt In this embodiment, the range is set to -15μm to 15μm; Tooth-to-drum shape In this embodiment, the range is set to 0μm~12μm; After receiving the aforementioned gear micro-modification parameters, the system automatically generates a large number of parameter combination samples within the corresponding deviation range based on the Monte Carlo method or experimental design method, forming a sample set for statistical analysis. The Python program interface then automatically drives the MASTA software and performs the following operations: Iterative simulation: For each set of parameter samples in the sample set, the Python program interface automatically assigns them to the electric drive gear system simulation model generated in step S3, and drives the MASTA software to perform performance simulation calculations. Performance index monitoring: In each simulation, the preset key gear performance indexes are automatically monitored and extracted. These performance indexes include, but are not limited to, transmission error, tooth surface contact stress, and tooth root bending stress. Data recording: Each set of input parameter samples and its corresponding output performance index results are automatically stored as a mapping relationship to form a dataset for subsequent analysis.

[0033] After completing the simulation of all sample sets, the system automatically analyzes the dataset and intelligently selects and generates corresponding analysis charts based on the number of variables analyzed, to intuitively reveal the relationship between parameter deviations and performance indicators. This embodiment includes, but is not limited to: When performing univariate analysis, the system generates a univariate analysis graph, such as... Figure 5 As shown in the figure, the chart displays the response curves of several key performance indicators, including tooth surface contact stress, peak-to-peak value of transmitted error, and edge contact coefficient, when the tooth profile camber parameter varies within its tolerance range. This is used to determine the sensitive range and optimal nominal value of the parameter. When performing bivariate analysis, the system generates a bivariate analysis graph, such as... Figure 6 As shown in the figure, the horizontal and vertical axes represent the variations of the tooth profile inclination parameter and the tooth direction inclination parameter within their respective tolerance ranges, and their values ​​within the scope of their investigation. The graph, composed of color gradients and contour lines, intuitively shows the numerical distribution of a key gear performance index (peak-to-peak transmission error in this example) under all possible combinations of these two parameters. The transition of color from dark green to red-orange in the graph clearly marks a "basin" region (dark green) with optimal performance and a region where performance gradually deteriorates. When there are more than two variables being analyzed, the system generates a multivariate analysis graph, such as... Figure 7 As shown, the figure adopts a matrix-style layout of multiple sub-figures. In this figure, each sub-figure shows the influence trend of the tooth profile tilt parameter on the transmission error under a specific combination of a pair of tooth profile modification parameters. Through the layout of the entire matrix and the legend, the figure also intuitively presents the changes in the above-mentioned influence relationship when the third or even the fourth modification parameter changes. In addition, the system can also generate statistical sampling analysis charts, such as Figure 8 As shown, this figure is an intuitive visualization of the Monte Carlo simulation results, used to evaluate the performance consistency and reliability of the product under a given tolerance scheme. Each gray line in the figure represents an independent random sampling simulation, and its result corresponds to the performance curve of a virtual gear randomly generated within the tolerance zone. A large number of gray curves together outline the statistical fluctuation range of key performance indicators (such as dynamic transmission error). In addition, the system will intelligently identify and highlight several samples whose performance is at the boundary or the most representative (as shown by the colored curves in the figure), and label their quantitative indicators (such as mean square error, MSE). Finally, the system automatically integrates the analysis charts and key conclusions (such as the most sensitive parameters and suggested tolerance adjustment directions) to generate a structured tolerance sensitivity analysis report, providing a quantitative basis for tolerance design.

[0034] Example 2: This second embodiment further provides a system for implementing the above-mentioned gear optimization and tolerance sensitivity analysis method. Through modular design, the system implements the automated process of the first embodiment at the hardware and / or software level. Those skilled in the art will understand that the functions of each module of the system can directly correspond to the method steps of the first embodiment, and its collaborative workflow is also consistent with the method flow.

[0035] The gear optimization and tolerance sensitivity analysis system provided in this embodiment mainly includes: a tooth number scheme design module, an optimization module, a modeling module, and an analysis module. Specifically: Tooth Count Scheme Design Module: This module is configured to execute step S1 in Example 1. It receives initial parameter conditions input by the user (such as the number of motor pole slots, frequency avoidance requirements, tooth count range, speed ratio range, etc.), and automatically calculates and outputs a preliminary tooth count scheme that satisfies all constraints from all possible tooth count sets through built-in combination generation and filtering logic. The output result of this module is as follows: Figure 2 The table showing the initial tooth number scheme will be used as input for the optimization module.

[0036] Optimization Module: This module is connected to the tooth count scheme design module and is configured to execute step S2 in Embodiment 1. It receives the initial tooth count scheme from the tooth count scheme design module and combines it with the user-preset gear basic parameters (center distance, tooth tip clearance, etc.) and initial optimization range (pressure angle, helix angle, etc.). Specifically: This module first determines the range of optimization variables (corresponding to step S201 in Implementation Example 1).

[0037] Subsequently, a feasibility assessment of the gear design space is performed (corresponding to step S202 in Implementation Example 1). This assessment is based on the lower limit of the module and helix angle range and the minimum axial overlap requirement. The theoretical tooth width requirement is calculated. If the assessment fails, the process is terminated. If it passes, the optimization process begins. During the optimization phase, the module uses the particle swarm optimization (PSO) algorithm for iterative optimization and performs automatic determination of multiple performance constraints in each iteration (corresponding to step S203 in Embodiment 1). Finally, the optimal gear geometry parameters are selected and output based on preset selection conditions.

[0038] Modeling module: This module is connected to the optimization module and is configured to execute step S3 in Example 1. It receives the optimal gear geometry parameters output by the optimization module and drives the gear transmission system analysis software (such as MASTA) to automatically generate the simulation model of the electric drive gear system through a preset Python-based program interface. Specifically, this module can call 3D modeling software (such as CATIA) to generate accurate gear profiles, such as... Figure 3 The tooth profile curve shown is then used to import parameters and profile data into the MASTA software via an interface, automatically completing the parameter entry (interface example shown). Figure 4 The process involves model reconstruction, ultimately generating a complete system simulation model.

[0039] Analysis Module: This module is connected to the modeling module and is configured to execute step S4 in Example 1. It receives the deviation range of the gear micro-modification parameters input by the user and then automatically drives the MASTA software to perform a large number of Monte Carlo simulation calculations through the program interface. After the simulation is completed, the module performs intelligent post-processing on the calculation results and automatically generates corresponding analysis charts based on the number of variables analyzed, such as... Figure 5 The univariate analysis chart shown Figure 6 The bivariate analysis plot shown Figure 7 The multivariate analysis plot shown and Figure 8 The statistical sampling analysis chart shown is shown.

[0040] These charts visually reveal the sensitivity of parameter deviations to gear performance (such as transmission error and contact stress), and are ultimately automatically integrated to generate a tolerance sensitivity analysis report.

[0041] The workflow of this invention's system embodiment is as follows: the tooth count scheme design module first completes the scheme selection; the result triggers the optimization module to perform parameter optimization and feasibility determination; the optimization result is sent to the modeling module for automatic modeling; finally, the analysis module performs tolerance sensitivity analysis based on the built model and generates a report. These four modules are sequentially linked, with automatic data flow, forming a fully automated and intelligent design closed loop from macro-level selection to micro-level tolerance optimization, effectively solving the problems of process fragmentation and low efficiency mentioned in the background technology.

[0042] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0043] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0044] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for gear optimization and tolerance sensitivity analysis, characterized in that, Includes the following steps: Based on the input initial parameter conditions, a preliminary tooth number scheme that satisfies the initial parameter conditions is selected from all possible tooth number sets; Based on the initial tooth count scheme, the preset basic gear parameters, and the preset initial optimization range, the optimization variable range including pressure angle, helix angle, displacement coefficient, and module is determined; based on the module range and helix angle range in the optimization variable range, the preset minimum axial overlap, and the preset feasible tooth width range, the feasibility of the gear design space is determined. If the decision fails, the process will be terminated. If the determination is successful, the optimization step is executed: the particle swarm optimization algorithm is used to perform iterative optimization within the scope of the optimization variables. In each iteration, the preset multiple performance constraints are automatically determined for each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm, and the optimal gear geometric parameters are selected and output based on the preset selection conditions. The optimal gear geometry parameters are imported into the gear transmission system analysis software through a preset program interface, and the corresponding electric drive gear system simulation model is generated by the gear transmission system analysis software. Based on the deviation range of the input gear micro-modification parameters, the gear transmission system analysis software is driven by the program interface to perform simulation calculations, and analysis results reflecting the relationship between parameter deviations and gear performance indicators are generated based on the calculation results.

2. The gear optimization and tolerance sensitivity analysis method according to claim 1, characterized in that, The initial parameter conditions include: number of motor pole slots, additional frequency avoidance order, frequency avoidance multiple, frequency avoidance, range of number of teeth, range of speed ratio, range of stage ratio, and maximum torque; the basic gear parameters include center distance, tooth tip clearance, and strength coefficient; the initial optimization range includes pressure angle range, helix angle range, displacement coefficient range, and tooth width range.

3. The gear optimization and tolerance sensitivity analysis method according to claim 2, characterized in that, Based on the initially selected tooth number scheme, preset gear basic parameters, and preset initial optimization range, the optimization variable range, including pressure angle, helix angle, displacement coefficient, and module, is determined, specifically including: The pressure angle range, helix angle range, and displacement coefficient range in the initial optimization range are directly used as the range of the corresponding variables in the optimization variable range; Based on the initially selected number of teeth scheme, the maximum torque under the initial parameter conditions, and the center distance and strength coefficient in the basic parameters, the feasible range of the module is calculated and determined as the module range in the optimization variable range.

4. The gear optimization and tolerance sensitivity analysis method according to claim 1, characterized in that, The feasibility assessment of the gear design space specifically includes: Based on the lower limit of the range of the module optimization variables, the lower limit of the range of the helix angle optimization variables, and the preset minimum axial overlap, the theoretical tooth width requirement is calculated. Determine whether the theoretical tooth width requirement is within the preset feasible tooth width range; If yes, then it is considered passed; if no, then it is considered failed.

5. The gear optimization and tolerance sensitivity analysis method according to claim 2, characterized in that, The geometric parameters of each set of candidate gears generated by the particle swarm optimization algorithm specifically include: Based on the candidate pressure angle, helix angle, displacement coefficient, and module generated by the particle swarm optimization algorithm, and combined with the known number of teeth, center distance, tooth tip clearance, and tooth width generated within the preset feasible range of tooth width, a set of candidate gear geometric parameters is calculated in real time. The candidate gear geometric parameters include the pressure angle, helix angle, displacement coefficient, module, number of teeth, center distance, tooth width, and tooth tip circle diameter and tooth root circle diameter for each stage of the two-stage gear pair.

6. The gear optimization and tolerance sensitivity analysis method according to claim 1, characterized in that, The automatic determination of preset multiple performance constraints for each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm in each iteration specifically includes: Based on the geometric parameters of the candidate gears, calculate their corresponding end face overlap, axial overlap, slip ratio, tooth tip thickness, and tooth root stress value. Automatic determination of multiple preset performance constraints based on end face overlap, axial overlap, slip ratio, tooth tip thickness and tooth root stress value; the preset multiple performance constraints include overlap constraint, slip ratio constraint, undercut determination constraint and gear strength constraint determined in sequence.

7. The gear optimization and tolerance sensitivity analysis method according to claim 1, characterized in that, The preset selection criteria include: selecting the candidate optimization parameters with the largest end face overlap and axial overlap as the optimal gear geometry parameters.

8. The gear optimization and tolerance sensitivity analysis method according to claim 1, characterized in that, The gear micro-modification parameters include at least one of the following: tooth profile inclination, tooth profile bulging amount, tooth direction inclination, tooth direction bulging amount, and tooth profile trimming amount.

9. The gear optimization and tolerance sensitivity analysis method according to claim 1, characterized in that, The analysis results generated based on the calculation results, reflecting the relationship between parameter deviation and gear performance indicators, specifically include: generating at least one of the following: univariate analysis chart, bivariate analysis chart, multivariate analysis chart, and statistical sampling analysis chart, based on the number of gear micro-modification parameters.

10. A system based on the gear optimization and tolerance sensitivity analysis method of claim 1, characterized in that, include: The tooth count scheme design module is configured to: based on the input initial parameter conditions, select a preliminary tooth count scheme that meets the initial parameter conditions from all possible tooth count sets; The optimization module, connected to the tooth number scheme design module, is configured to: determine the range of optimization variables, including pressure angle, helix angle, displacement coefficient, and module, based on the initially selected tooth number scheme, preset gear basic parameters, and preset initial optimization range; and determine the feasibility of the gear design space based on the module range and helix angle range, preset minimum axial overlap, and preset feasible tooth width range in the optimization variable range. If the decision fails, the process will be terminated. If the determination is successful, the optimization step is executed: the particle swarm optimization algorithm is used to perform iterative optimization within the scope of the optimization variables. In each iteration, the preset multiple performance constraints are automatically determined for each set of candidate gear geometric parameters generated by the particle swarm optimization algorithm, and the optimal gear geometric parameters are selected and output based on the preset selection conditions. The modeling module, which is connected to the optimization module, is configured to: import the optimal gear geometry parameters output by the optimization module into the gear transmission system analysis software through a preset program interface, and generate the corresponding electric drive gear system simulation model through the gear transmission system analysis software; The analysis module, which is connected to the modeling module, is configured to: drive the gear transmission system analysis software to perform simulation calculations based on the deviation range of the input gear micro-modification parameters through the program interface, and generate analysis results reflecting the relationship between parameter deviations and gear performance indicators based on the calculation results.

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