Design and optimization method of speed reducer gear and related product

Abstract

The invention discloses a design and optimization method of a speed reducer gear and a related product. Firstly, multiple groups of gear parameter sets can be initialized, and a three-dimensional model of a speed reducer gear pair is constructed; and obtaining model input parameters, taking the input parameters as input, and simulating the reducer gear pair three-dimensional model one by one based on the plurality of groups of gear parameter sets to obtain a plurality of groups of simulation results. Rejecting unqualified results in the plurality of groups of simulation results to obtain a plurality of groups of qualified results, and calculating a performance index set corresponding to each group of qualified results to obtain a plurality of performance index sets; and carrying out multi-objective optimization processing on the plurality of performance index sets to obtain a plurality of leading edge solution sets so as to determine an equilibrium solution set in the plurality of leading edge solution sets, and finally taking the equilibrium solution set as an optimal performance index set. Through the multi-objective optimization method, multiple performance indexes are comprehensively considered, the equilibrium solution set is selected from multiple leading edge solution sets, and optimization balance of high efficiency, low noise, light weight and high reliability is achieved.

Description

Technical Field

[0001] The present application relates to the field of mechanical engineering technology, and in particular to a design and optimization method for a reducer gear and related products. Background Art

[0002] A key challenge in the design of cylindrical gears for electric vehicle reducers is the difficulty of multi-objective optimization. Despite significant advances in modern design methods and technologies, in practice, most designs still focus on optimizing a single objective, such as improving transmission efficiency or reducing noise levels, making it difficult to simultaneously meet the comprehensive performance requirements of high efficiency, low noise, lightweight, and high reliability.

[0003] Therefore, how to achieve multi-objective collaborative optimization of the cylindrical gear of the electric vehicle reducer to simultaneously meet the comprehensive performance requirements of high efficiency, low noise, lightweight and high reliability is a technical problem that technicians in this field urgently need to solve. Summary of the Invention

[0004] Based on the above problems, this application provides a design and optimization method for reducer gears and related products, which can realize multi-objective collaborative optimization of electric vehicle reducer cylindrical gears to simultaneously meet the comprehensive performance requirements of high efficiency, low noise, light weight and high reliability.

[0005] The embodiments of this application disclose the following technical solutions:

[0006] A design and optimization method for a reducer gear, the method comprising:

[0007] Initializing N sets of gear parameter sets and constructing a three-dimensional model of a reducer gear pair; each set of the gear parameter sets includes a gear module, a first gear tooth number, a second gear tooth number, a first gear tooth width, a second gear tooth width, a gear pressure angle, and a gear helix angle;

[0008] Acquiring model input parameters, and using the model input parameters as input to simulate the three-dimensional model of the reducer gear pair based on multiple sets of gear parameter sets, one by one, to obtain multiple sets of simulation results; one set of gear parameter sets corresponds to one set of simulation results; the simulation results include a dynamic transmission error TEi, contact stress σH, bending stress σF, meshing frequency fm, and axial force Fα of the gear pair;

[0009] Eliminating unqualified results from the multiple groups of simulation results to obtain multiple groups of qualified results, and calculating a set of performance indicators corresponding to each group of qualified results to obtain multiple sets of performance indicators; the unqualified results are simulation results that do not meet noise, vibration and harshness (NVH) constraints and / or dynamic strength constraints; the performance indicators include transmission root mean square error f1, gear volume f2, and contact fatigue life f3;

[0010] Performing multi-objective optimization processing on the plurality of performance indicator sets to obtain a plurality of frontier solution sets;

[0011] A balanced solution set among the multiple frontier solution sets is determined, and the balanced solution set is used as an optimal performance indicator set.

[0012] In a possible implementation, performing multi-objective optimization on the plurality of performance indicator sets to obtain a plurality of frontier solution sets includes:

[0013] Using a non-dominated sorting genetic algorithm (IINSGA-II) mechanism, a non-dominated sorting and selection process is performed on the plurality of performance indicator sets to obtain an initial population;

[0014] Adaptively iteratively evolve the initial population by adjusting the crossover or mutation probability according to population diversity until the frontier change rate of the population for X consecutive generations of evolution is less than 1%, then stop; X is a positive integer;

[0015] X evolutionary populations whose frontier change rates of X consecutive evolutionary populations are less than 1% are taken as the plurality of frontier solution sets.

[0016] In a possible implementation, determining an equilibrium solution set from the multiple frontier solution sets includes:

[0017] Traversing the plurality of performance indicator sets, obtaining the minimum transmission error root mean square f1min, the minimum gear volume f2min, and the minimum contact fatigue life f3min in the plurality of performance indicator sets;

[0018] Calculate the comprehensive score of each performance indicator set based on the minimum transmission error root mean square f1min, the minimum gear volume f2min and the minimum contact fatigue life f3min;

[0019] The performance indicator set with the highest comprehensive score is used as the balanced solution set.

[0020] In one possible implementation,

[0021] The calculation formula of the transmission error root mean square f1 is: N is the number of groups of the gear parameter set, and N is a positive integer;

[0022] The calculation formula of the gear volume f2 is: m is the gear module, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, b1 is the tooth width of the first gear, and b2 is the tooth width of the second gear;

[0023] The calculation formula of the contact fatigue life f3 is:

[0024] In a possible implementation, the method further includes:

[0025] Verify whether the gear parameters in each gear parameter set meet the process constraints;

[0026] The gear parameter set with unqualified parameters is updated to use the updated gear parameter set to perform the steps of simulating the reducer gear pair three-dimensional model one by one based on multiple sets of gear parameter sets using the model input parameters as input to obtain multiple sets of simulation results, as well as subsequent steps; the unqualified parameters are gear parameters that do not meet the process constraints.

[0027] In a possible implementation, initializing multiple sets of gear parameter sets includes:

[0028] Setting a gear parameter value range set; the gear parameter value range set includes a gear module range, a first gear tooth number range, a second gear tooth number range, a first gear tooth width range, a second gear tooth width range, a gear pressure angle range, and a gear helix angle range;

[0029] Performing equal-interval sampling within the gear parameter value range set to obtain multiple sets of gear parameter sets;

[0030] Wherein, the gear module range is: T is the maximum torque of the electric vehicle motor, YF is the tooth form factor, [σF] is the allowable bending stress, z is the number of gear teeth, and dmax is the maximum pitch circle diameter limited by the reducer housing;

[0031] The number of teeth of the first gear is in the range of: [18, 35];

[0032] The number of teeth of the second gear is in the range of [18X, 35X], where X is the reduction ratio;

[0033] The tooth width range of the first gear is: ZE is the material elastic coefficient, ZH is the node area coefficient, Zε is the coincidence coefficient, u is the gear ratio, u=z2 / z1, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, d1 is the pinion pitch circle diameter, d1=m*z1, a is the safety factor, Y is a constant, Y∈[8,15];

[0034] The second gear tooth width range is: y is a constant, y∈[8,12];

[0035] The gear pressure angle range is: [20°, 25°];

[0036] The gear helix angle range is: [10°, 30°].

[0037] A design and optimization device for a reducer gear, the device comprising:

[0038] Initialization unit, used to initialize multiple sets of gear parameter sets;

[0039] A model building unit is used to build a three-dimensional model of the reducer gear pair; each set of the gear parameter sets includes a gear module, a first gear tooth number, a second gear tooth number, a first gear tooth width, a second gear tooth width, a gear pressure angle, and a gear helix angle;

[0040] a simulation unit, configured to obtain model input parameters, and use the model input parameters as input to simulate the three-dimensional model of the reducer gear pair based on the multiple sets of gear parameter sets, one by one, to obtain multiple sets of simulation results; one set of gear parameter sets corresponds to one set of simulation results; the simulation results include a dynamic transmission error TEi, contact stress σH, bending stress σF, meshing frequency fm, and axial force Fα of the gear pair;

[0041] a rejection unit, configured to reject unqualified results from the plurality of groups of simulation results to obtain a plurality of qualified results; the unqualified results are simulation results that do not meet NVH constraints and / or dynamic strength constraints;

[0042] a performance indicator calculation unit, configured to calculate a performance indicator set corresponding to each group of qualified results to obtain a plurality of performance indicator sets; the performance indicators comprising a root mean square error f1, a gear volume f2, and a contact fatigue life f3;

[0043] an optimization processing unit, configured to perform multi-objective optimization processing on the plurality of performance indicator sets to obtain a plurality of frontier solution sets;

[0044] The first setting unit is configured to determine a balanced solution set among the multiple frontier solution sets, and use the balanced solution set as an optimal performance indicator set.

[0045] In a possible implementation, the optimization processing unit specifically includes:

[0046] a non-dominated sorting and selection unit, configured to perform non-dominated sorting and selection on the plurality of performance indicator sets using the NSGA-II mechanism to obtain a plurality of initial populations;

[0047] An adaptive evolution unit, configured to adjust the crossover or mutation probability according to population diversity to perform adaptive iterative evolution on the initial population until the frontier change rate of the population for X consecutive generations of evolution is less than 1%, then stopping; X is a positive integer;

[0048] The second setting unit is configured to select X evolutionary populations whose frontier change rates of X consecutive generations of evolutionary populations are less than 1% as the plurality of frontier solution sets.

[0049] A design and optimization device for a reducer gear comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the design and optimization method for the reducer gear as described above is implemented.

[0050] A computer-readable storage medium stores instructions, which, when executed on a terminal device, enable the terminal device to execute the above-mentioned method for designing and optimizing a reducer gear.

[0051] Compared with the existing technology, this application has the following beneficial effects:

[0052] The present application provides a design and optimization method for reducer gears and related products. Specifically, when executing the design and optimization method for reducer gears provided in the embodiments of the present application, first, initialize multiple sets of gear parameter sets and construct a three-dimensional model of the reducer gear pair. Each set of gear parameter sets includes parameters such as gear module, number of teeth, tooth width, pressure angle and helix angle. Then, obtain the model input parameters, and simulate the three-dimensional model of the reducer gear pair based on the multiple sets of gear parameter sets to obtain multiple sets of simulation results. The simulation results cover indicators such as dynamic transmission error, contact stress, bending stress, meshing frequency and axial force. Then, eliminate unqualified simulation results that do not meet NVH constraints and / or dynamic strength constraints, retain qualified results and calculate the performance index set corresponding to each set of qualified results. These performance indicators include the root mean square of transmission error, gear volume and contact fatigue life. Finally, perform multi-objective optimization processing on multiple performance index sets to obtain multiple frontier solution sets, and determine the equilibrium solution set as the optimal performance index set from them to ensure that the comprehensive performance reaches the optimal balance point. This application uses multi-objective optimization to simultaneously consider multiple performance indicators (such as transmission error, gear volume, and contact fatigue life), effectively overcoming the limitations of traditional design that focuses on a single objective, thereby comprehensively optimizing various performance requirements. Simultaneously, a balanced solution set is selected from multiple frontier solution sets as the optimal set of performance indicators, maximizing the overall performance balance and thus simultaneously meeting the requirements of high efficiency, low noise, lightweight, and high reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 A flow chart of a method for designing and optimizing a reducer gear provided in an embodiment of the present application;

[0055] Figure 2 A flow chart of a method for obtaining a frontier solution set provided in an embodiment of the present application;

[0056] Figure 3 A flow chart of a method for obtaining a balanced solution set provided in an embodiment of the present application;

[0057] Figure 4 A method flow chart of a method for initializing a gear parameter set provided in an embodiment of the present application;

[0058] Figure 5 A schematic structural diagram of a design and optimization device for a reducer gear provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] To facilitate understanding of the technical solutions provided by the embodiments of the present application, the background technology involved in the embodiments of the present application will be described below.

[0060] A key challenge in the design of cylindrical gears for electric vehicle reducers is the focus on single-objective optimization. Current design methods and technologies mostly focus on improving transmission efficiency or reducing noise levels, but struggle to simultaneously meet the comprehensive performance requirements of high efficiency, low noise, lightweight, and high reliability.

[0061] First, single-objective optimization has obvious limitations in practical applications. For example, to improve transmission efficiency, designers may choose to increase gear thickness or adopt a rougher tooth profile. While these measures can reduce energy loss to a certain extent, they may also lead to increased weight and noise levels. Similarly, to reduce noise levels, designers may need to optimize the gear tooth profile and machining accuracy, but this increases manufacturing costs and may affect the gear's strength and reliability.

[0062] Secondly, there are mutual constraints between various objectives. Optimizing a single objective often results in degradation of other performance indicators. For example, improving transmission efficiency may increase noise levels, or improving reliability may increase equipment weight, hindering lightweight design. This mutual constraint makes finding a balance between multiple objectives extremely difficult.

[0063] Furthermore, due to the lack of effective multi-objective collaborative optimization strategies, current technologies can lead to gear designs that are overly reliant on a single objective at the expense of other performance objectives. For example, excessive pursuit of lightweighting can lead to reduced reliability, or transmission efficiency can be sacrificed to achieve low noise levels. This compromised design approach fails to achieve optimal overall performance.

[0064] In order to solve this problem, an embodiment of the present application provides a design and optimization method for reducer gears and related products. First, multiple sets of gear parameter sets are initialized, and a three-dimensional model of the reducer gear pair is constructed based on these parameters. For each set of parameters, simulation is performed by inputting model parameters to obtain multiple sets of simulation results including dynamic transmission error, contact stress, bending stress, meshing frequency and axial force. Subsequently, unqualified results that do not meet NVH constraints and / or dynamic strength constraints are eliminated, qualified results are screened out and corresponding performance indicators are calculated, such as the root mean square of transmission error, gear volume and contact fatigue life. By performing multi-objective optimization on multiple performance indicator sets, a series of frontier solution sets are obtained, and then an equilibrium solution set is determined from them as the final optimal performance indicator, thereby achieving comprehensive performance optimization design of reducer gears. The present application adopts a multi-objective optimization method, which takes into account multiple performance indicators such as transmission error, gear volume and contact fatigue life at the same time, breaking through the limitation of traditional design that only focuses on a single target. This method can perform comprehensive optimization among multiple performance requirements to ensure that the overall performance is optimized while meeting different design requirements. In addition, by selecting a balanced solution set from multiple cutting-edge solution sets, the performance indicators are ensured to reach the optimal balance point, thereby maximizing the balance between multiple requirements such as high efficiency, low noise, lightweight and high reliability.

[0065] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0066] See also Figure 1 , which is a flow chart of a method for designing and optimizing a reducer gear provided by an embodiment of the present application, as shown in FIG. Figure 1 As shown, the design and optimization method of the reducer gear may include steps S101-S105:

[0067] S101: Initialize multiple sets of gear parameter sets and construct a three-dimensional model of the reducer gear pair.

[0068] In order to achieve efficient design and optimization of reducer gears, it is first necessary to initialize multiple sets of gear parameter sets and construct a three-dimensional model of the reducer gear pair.

[0069] Each set of gear parameters includes key parameters such as the gear module, the number of teeth on the first and second gears, the first and second gear tooth widths, the gear pressure angle, and the gear helix angle. The selection and combination of these parameters is based on a comprehensive consideration of various factors in gear design, ensuring comprehensive coverage of diverse design requirements and potential application scenarios. This approach lays a solid foundation for subsequent simulation analysis and performance evaluation, enabling precise optimization of reducer gear performance.

[0070] It should be noted that, since the reducer gear pair includes two gears, when initializing the parameters, each set of gear parameter sets needs to initialize two gear tooth numbers and two gear tooth widths.

[0071] It's also worth noting that gear module is a parameter that measures the size of a gear, indicating the ratio of the size and spacing of the gear teeth. It is defined as the ratio of the number of teeth on a gear to its diameter. The larger the module, the larger the teeth on the gear and the greater the power it can transmit.

[0072] The number of teeth on the first gear: refers to the number of teeth on the first gear (usually the driving gear) in the reducer. The number of teeth directly affects the speed ratio of the gear transmission.

[0073] Second gear teeth: refers to the number of teeth on the second gear (usually the passive gear) in the reducer. The number of teeth on the second gear is opposite to the number of teeth on the first gear, and the two together determine the gear ratio.

[0074] First gear tooth width: refers to the width of the first gear tooth surface in the gear axial direction. The size of the tooth width affects the contact area and load capacity of the gear. The larger the tooth width, the greater the gear's load capacity.

[0075] Second gear tooth width: Similar to the first gear tooth width, the second gear tooth width refers to the width of the second gear tooth surface in the gear axial direction. It affects the contact strength, load capacity and operating stability of the gear.

[0076] Gear pressure angle: This is the angle between the normal line of the gear tooth surface and the tangent line of the base circle. The pressure angle determines the direction and magnitude of the contact force during gear meshing. Common pressure angles include 20° and 25°. A larger pressure angle improves gear transmission stability but increases friction between the tooth surfaces.

[0077] Gear helix angle: Usually applies to helical gears, it refers to the helix angle of the tooth surface. The size of the helix angle affects the meshing characteristics of the gear. A larger helix angle can improve the smoothness of the gear transmission and reduce noise, but it also affects the gear's load capacity.

[0078] In one possible implementation, 50 sets of gear parameter sets can be initialized, but are not limited to 50 sets of gear parameter sets. This application does not impose any specific restrictions on the number of initialized gear parameter sets, and users can adjust them according to actual needs.

[0079] S102: Acquire model input parameters, and use the model input parameters as input to simulate the three-dimensional model of the reducer gear pair one by one based on multiple sets of gear parameter sets to obtain multiple sets of simulation results.

[0080] After setting multiple sets of gear parameter sets, you also need to obtain a series of model input parameters. These parameters will be used as input for simulation analysis. In this way, the simulation will analyze the 3D model of the reducer gear pair based on multiple different sets of gear parameter sets (such as gear module, number of teeth, etc.).

[0081] For each set of gear parameters, a set of simulation results will be obtained, reflecting the performance of the gear design. These simulation results may include but are not limited to multiple key performance indicators:

[0082] Dynamic transmission error (TEi): describes the error generated by the gear during dynamic transmission, affecting the transmission accuracy.

[0083] Contact stress (σH): The pressure on the contact surface when gears mesh, which determines the durability and wear of the gears.

[0084] Bending stress (σF): The bending stress generated by the gear under load affects the strength and reliability of the gear.

[0085] Meshing frequency (fm): The frequency at which gears mesh, which may affect the noise and vibration of gear operation.

[0086] Axial force (Fα): The force applied to the gear in the axial direction directly affects the stability and service life of the gear.

[0087] In one possible implementation, the torque-speed curve under a typical operating condition of an electric vehicle (such as the WLTC cycle) can be discretized into a time series T(t) and used as an input parameter of the model.

[0088] In this way, the performance of the reducer gear pair can be systematically evaluated by inputting different operating conditions and design parameters, thus providing a basis for further design optimization.

[0089] S103: Eliminate unqualified results from the multiple groups of simulation results to obtain multiple groups of qualified results, and calculate the performance indicator set corresponding to each group of qualified results to obtain multiple performance indicator sets.

[0090] After performing multiple simulation analyses, you can filter out unqualified results and discard them, retaining only qualified results that meet the standards. Unqualified results are those that fail to meet NVH (Noise, Vibration, and Harshness) constraints and / or dynamic strength constraints, meaning they fall short of the performance standards in these areas.

[0091] Next, for each set of qualified simulation results, we need to calculate the corresponding performance index set. These performance indexes are key parameters used to evaluate the performance of the reducer gear pair, including:

[0092] Transmission error root mean square (f1): Indicates the average level of gear transmission error. The lower the root mean square value, the higher the transmission accuracy.

[0093] Gear volume (f2): A measure of the size of the gear. Smaller gear volume generally means more compact design and better space utilization.

[0094] Contact fatigue life (f3): Evaluates the durability of gears under long-term operation and reflects the fatigue resistance of the gear surface under high load.

[0095] Ultimately, calculations yielded multiple sets of performance indicators that can help engineers further analyze and optimize the design of gear pairs, ensuring optimal performance while meeting requirements for strength, durability, and noise.

[0096] In one possible implementation, the NVH constraints include:

[0097] (1) Avoidance of meshing frequency and natural frequency: meshing frequency The meshing frequency, fm, must avoid being an integer multiple of the gear system's natural frequency, fn. That is, fm ≠ k*fn (k = 1, 2, 3). Here, r is the gear speed, and fm depends on the gear speed and number of teeth. To avoid resonance, the difference between the meshing frequency and the natural frequency must be greater than a certain safety margin, Δf. Specifically, |fm - k*fn| ≥ Δf. Δf is typically between 50 and 200 Hz. This prevents the system from generating excessive vibration and noise at specific speeds.

[0098] (2) Gear meshing order avoidance: The gear meshing order (the operating frequency of the gear) needs to be 7% away from the meshing order of other gears and the main order of the motor. This is to prevent the frequencies of multiple gears and motors from overlapping, which can cause resonance and lead to noise and vibration problems.

[0099] (3) Peak-to-peak transmission error: Transmission error (TE) refers to the deviation caused by factors such as manufacturing and assembly errors during gear meshing. To ensure smooth operation of the system, the maximum peak-to-peak value of the transmission error, TEpeak, should be less than or equal to 0.7 μm, that is, TEpeak ≤ 0.7 μm, where μ is the friction coefficient, which represents the friction characteristics during gear meshing; and m is the module of the gear. This constraint is based on the noise experience of electric vehicles. Controlling the transmission error can reduce vibration and noise.

[0100] The peak-to-peak value of the transmission error TEpeak is ≤ 0.7 μm (based on the experience of electric vehicle noise, TEpeak is the maximum value of the dynamic transmission error TEi).

[0101] (4) Total contact constraint: Total contact is an important parameter in the gear meshing process, including the contact condition of the gears. The total contact is required to be ∈*γ=∈α+∈β≥3.7, where α is the pressure angle and β is the helix; ∈α≥1.7 and ∈β≥2.0. These parameters ensure the stability of the gear meshing, avoid uneven meshing or excessive friction, and thus reduce noise and vibration.

[0102] Through these constraints, the noise and vibration of the gear transmission system can be effectively reduced during the design process, and the stability and comfort of the system can be improved. It is particularly suitable for applications with high noise requirements such as electric vehicles.

[0103] In one possible implementation, the dynamic strength constraint includes:

[0104] (1) Contact stress constraint: Contact stress refers to the stress distribution in the tooth surface contact area when the gears are meshing. In order to avoid local plastic deformation or damage on the gear surface due to excessive stress, the contact stress must be controlled. The dynamic contact stress peak is set to σH, the allowable contact stress is [σH], and a safety factor a1 is introduced, which is generally taken as 1.0. The dynamic contact stress peak σH calculated by the simulation software needs to meet the following constraint: σH≤a1[σH]. This constraint ensures that the gear does not exceed the allowable contact stress during operation, avoiding premature wear and failure of the tooth surface.

[0105] (2) Bending stress constraint: Bending stress refers to the bending stress generated at the root of the gear teeth due to external forces when the gear is loaded. In order to avoid gear fracture or fatigue failure, the bending stress needs to be controlled. The dynamic bending stress peak is set to σF, the allowable bending stress is [σF], and a safety factor a2 is introduced, which is generally taken as 1.4. The dynamic bending stress peak σF calculated by the simulation software needs to meet the following constraint: σF ≤ a2 [σF]. This constraint ensures that the gear will not be damaged when it is loaded, maintaining its long-term reliability and stability.

[0106] (3) Axial force constraint: The axial force Fα refers to the force acting on the gear shaft along the axial direction. For bearings, the magnitude of the axial force is also limited, usually determined by the rated axial force load capacity of the bearing. For example, if the allowable axial force of the bearing is [Fα], it may be set to 5kN for a deep groove ball bearing. It is necessary to ensure that the axial force in the gear system does not exceed this value: Fα≤[Fα]. This constraint helps prevent excessive axial force from damaging the bearing and ensures smooth operation of the system.

[0107] These dynamic strength constraints can effectively ensure that the gear system does not suffer from excessive wear, fatigue or failure during high-load and high-speed operation, thereby improving the reliability and service life of the system.

[0108] S104: Perform multi-objective optimization processing on the multiple performance indicator sets to obtain multiple frontier solution sets.

[0109] After obtaining multiple performance indicator sets, these sets need to be subjected to multi-objective optimization. Multi-objective optimization involves simultaneously considering multiple optimization objectives (such as performance indicators) and finding an optimal balance. In gear pair design, performance indicators often constrain each other. For example, improving transmission accuracy may increase size, while extending contact fatigue life may affect dynamic strength.

[0110] Therefore, through multi-objective optimization, we can find an optimal compromise between these conflicting objectives, achieving the best possible balance between each performance indicator, without simply optimizing one aspect and degrading others. The result of optimization is a set of multiple frontier solutions—a set of different design solutions, each of which achieves an optimal balance across multiple objectives.

[0111] These frontier solution sets usually reflect the best design options under different design constraints and requirements.

[0112] S105: Determine a balanced solution set among the multiple frontier solution sets, and use the balanced solution set as an optimal performance indicator set.

[0113] After multi-objective optimization, multiple frontier solution sets are obtained. Each frontier solution set represents a set of solutions that achieve a balance between different performance indicators. To select the optimal design from these frontier solution sets, it is necessary to further determine an equilibrium solution set.

[0114] A balanced solution set is a set of solutions that finds the optimal balance between multiple performance indicators (such as the root mean square of transmission error f1, gear volume f2, and contact fatigue life f3). This balanced solution set achieves the optimal compromise between various indicators while ensuring that all key performance requirements are met.

[0115] Ultimately, the determined equilibrium solution set is used as the optimal set of performance indicators. This ensures that the selected design solution not only performs well in a single performance indicator, but also achieves optimal overall performance, thus providing reliable design parameters for practical applications.

[0116] By determining the equilibrium solution set and using it as the optimal set of performance indicators, it is ensured that the design scheme achieves the optimal balance among multiple key performance indicators.

[0117] Based on the contents of S101-S105, it can be seen that by initializing multiple sets of gear parameter sets and constructing a three-dimensional model of the reducer gear pair, combined with simulation analysis, the simulation results of dynamic transmission error, contact stress, bending stress, meshing frequency and axial force under multiple gear parameter combinations are obtained, and qualified results that meet the NVH constraints and dynamic strength requirements are screened out, and the corresponding performance indicators (such as the root mean square of transmission error, gear volume and contact fatigue life) are calculated. Then, through multi-objective optimization processing, multiple frontier solution sets are obtained, and the balanced solution set is selected from them as the optimal performance indicator set to achieve the comprehensive performance optimization of the reducer gear. This application uses multi-objective optimization processing to consider multiple performance indicators (such as transmission error, gear volume and contact fatigue life, etc.) at the same time, which can effectively solve the limitations of traditional design that only focuses on a single target, thereby achieving comprehensive optimization of various performance requirements. At the same time, by selecting the balanced solution set as the optimal performance indicator set from multiple frontier solution sets, it can ensure that the comprehensive performance reaches the optimal balance point, thereby meeting the various requirements of high efficiency, low noise, lightweight and high reliability as much as possible.

[0118] In a possible implementation, the present application also provides a method for obtaining a frontier solution set, see Figure 2 , Figure 2 A flow chart of a method for obtaining a frontier solution set provided in an embodiment of the present application. Accordingly, step S104 performs multi-objective optimization processing on multiple performance indicator sets to obtain multiple frontier solution sets, which can be specifically achieved through steps S201-S203:

[0119] S201: Using the NSGA-II mechanism, non-dominated sorting and selection are performed on the plurality of performance indicator sets to obtain a plurality of initial populations.

[0120] In the multi-objective optimization process, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is first applied to sort and select multiple given performance metrics. The goal is to select the optimal solution from multiple possible solutions. Specifically, non-dominated sorting compares the advantages and disadvantages of different solutions and selects those that are not dominated by other solutions, called non-dominated solutions. These solutions represent the optimal solutions that achieve a balance between various objectives. This sorting method generates an initial population, in which each individual represents a solution, and these initial solutions are selected based on their superiority with respect to various objectives. Therefore, the non-dominated sorting and selection process ensures the diversity and effectiveness of the initial population, laying the foundation for the subsequent evolutionary process.

[0121] In one possible implementation, the specific steps for processing multiple performance indicator sets using the NSGA-II mechanism are as follows:

[0122] A1: First, multiple performance indicator sets are input into the NSGA-II algorithm. These performance indicator sets include key performance indicators under different design parameter combinations, such as the root mean square error f1, gear volume f2, and contact fatigue life f3.

[0123] A2: Next, these performance metric sets are evaluated using non-dominated sorting. Non-dominated sorting classifies them into different frontier layers based on the relative strengths of the various performance metrics. Specifically, if one set is not inferior to another set in all performance metrics, the latter is dominated; otherwise, the two sets are non-dominated and placed in the same frontier layer.

[0124] A3: Then, a selection operation is performed based on the results of the non-dominated sorting, selecting the best-performing set as the initial population. During this selection process, NSGA-II not only considers the quality of performance metrics but also balances the diversity of solutions to avoid excessive concentration or convergence of solutions in a particular region. The selected optimal set will form the initial population for subsequent genetic operations. This step ensures that the selected set not only performs well on a single performance metric but also achieves a good balance across multiple performance metrics.

[0125] In this way, the initial population can be effectively screened and generated from multiple performance indicator sets, providing a basis for subsequent adaptive evolution and optimization.

[0126] The NSGA-II mechanism is used to perform non-dominated sorting and selection on multiple performance indicator sets, which can efficiently generate the initial population, which represents the potential good solutions in the multi-objective optimization problem.

[0127] S202: Adjust the crossover or mutation probability according to population diversity to perform adaptive iterative evolution on the initial population until the frontier change rate of the population for X consecutive generations of evolution is less than 1%, and then stop.

[0128] During multi-objective optimization, the probabilities of crossover and mutation can be dynamically adjusted based on the diversity of the population, allowing the initial population to undergo adaptive iterative evolution. Specifically, if the population diversity is high, the crossover probability is reduced to avoid excessive mixing of different characteristics; if the population diversity is low, the crossover probability is increased to increase diversity. In this way, each generation of the population is optimized based on the current diversity. This process continues until the rate of change in the frontier of the evolving population is less than 1% for X consecutive generations (X is a positive integer). This means that the frontier solution of the population has remained virtually unchanged over these X consecutive generations, indicating that the population has reached a stable optimal state.

[0129] It should be noted that each evolving population is a frontier solution set, so the frontier change rate of two adjacent generations of evolving populations can be calculated.

[0130] In a possible implementation, X may be, but is not limited to, set to 20. This application does not impose any specific restrictions on the number of consecutive generations X, and users may adjust it according to actual needs.

[0131] S203: X evolved populations whose frontier change rates of X consecutive evolved populations are less than 1% are taken as the plurality of frontier solution sets.

[0132] By observing the rate of change of the frontier of a population over X consecutive generations, if the rate of change over these generations is less than 1%, the solutions in these X populations can be considered as multiple frontier solution sets. Specifically, the frontier change rate refers to the degree of change in the set of non-dominated solutions (i.e., frontier solutions) in the population between generations. When the rate of change of the frontier solution set is very small (less than 1%), it indicates that the population's evolution has stabilized during this period of evolution and the quality of the frontier solution set has reached a stable state. Therefore, these solutions are considered as multiple frontier solution sets. These frontier solution sets represent a relatively ideal, optimal solution set and can be used as the final solution set for further analysis or decision-making.

[0133] Through steps S201-S203, a diverse initial population can be efficiently generated, genetic operations can be dynamically adjusted to balance exploration and development capabilities, and the stability and high quality of the frontier solution set can be ensured, thereby improving the efficiency and effectiveness of multi-objective optimization.

[0134] In a possible implementation, the present application also provides a method for obtaining a balanced solution set, see Figure 3 , Figure 3 This is a flow chart of a method for obtaining a balanced solution set provided in an embodiment of the present application. Accordingly, determining a balanced solution set among the multiple frontier solution sets in step S105 can be specifically implemented through steps S301-S303:

[0135] S301: traverse the plurality of performance indicator sets to obtain the minimum transmission error root mean square f1min, the minimum gear volume f2min, and the minimum contact fatigue life f3min in the plurality of performance indicator sets.

[0136] To obtain a balanced solution set, we first need to traverse multiple performance indicator sets. Specifically, we need to extract the minimum values ​​of three key indicators from these performance indicator sets: minimum root mean square error (RMS) f1min, minimum gear volume f2min, and minimum contact fatigue life f3min. By traversing each performance indicator set and extracting these minimum values, we can ensure a comprehensive assessment of each set's performance across different performance indicators, thus providing a basis for further comprehensive scoring and the final selection of a balanced solution set.

[0137] S302: Calculate a comprehensive score for each of the performance indicator sets based on the minimum transmission error root mean square f1min, the minimum gear volume f2min, and the minimum contact fatigue life f3min.

[0138] The calculation of the comprehensive score is achieved by comparing the minimum performance indicator value with the actual value in the current performance indicator set. Specifically, the score of each performance indicator set is calculated separately by the minimum transmission error root mean square f1min, the minimum gear volume f2min and the minimum contact fatigue life f3min. Each score is calculated by weighted average, where the transmission error accounts for 40%, the gear volume accounts for 30% and the contact fatigue life accounts for 30%. The specific calculation formula is: Comprehensive score Among them, f1, f2, and f3 are the root mean square of transmission error, gear volume, and contact fatigue life of the corresponding performance index set, respectively.

[0139] Through this weighted approach, the pros and cons of these performance indicators can be comprehensively considered, and ultimately a comprehensive score can be obtained, which can quantify the comprehensive performance of each performance indicator set and help determine the optimal solution set.

[0140] S303: Taking the performance indicator set with the highest comprehensive score as the balanced solution set.

[0141] Among all performance indicator sets, the comprehensive score obtained through the aforementioned calculation method is used to evaluate the performance of each set. A higher comprehensive score indicates that the performance indicator set is closer to optimal across all key factors, thus achieving better balance. Ultimately, the performance indicator set with the highest comprehensive score is selected as the balanced solution set. This selected solution set achieves the best balance among multiple aspects, such as transmission error, gear volume, and contact fatigue life, achieving the optimal overall effect across different indicators. This balanced solution set exhibits relatively ideal overall performance, suitable for practical application requirements.

[0142] Steps S301-S303 traverse multiple performance indicator sets, accurately calculating the comprehensive score for each set. Taking into account key indicators such as the root mean square error (RMS) of transmission errors, gear volume, and contact fatigue life, they select the most balanced solution set across all aspects. This approach ensures that the resulting balanced solution set achieves a good balance across multiple optimization objectives, avoiding the potential imbalances associated with single-objective optimization, and helping to improve the overall performance and reliability of the system.

[0143] In one possible implementation,

[0144] The calculation formula of the transmission error root mean square f1 is: N is the number of groups of the gear parameter set, and N is a positive integer;

[0145] The calculation formula of the gear volume f2 is: m is the gear module, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, b1 is the tooth width of the first gear, and b2 is the tooth width of the second gear;

[0146] The calculation formula of the contact fatigue life f3 is:

[0147] In one possible implementation, in order to ensure that all gear parameter sets meet process constraints and improve the reliability of simulation results, the method further includes:

[0148] First, verify whether the gear parameters in each gear parameter set meet the process constraints. Then, update the gear parameter sets containing unqualified parameters. The updated gear parameter sets are used to execute step S102, "Using the model input parameters as input, simulating the reducer gear pair three-dimensional model based on the multiple sets of gear parameter sets one by one to obtain multiple sets of simulation results," and subsequent steps. Unqualified parameters are gear parameters that do not meet the process constraints.

[0149] Specifically, first, verifying whether the gear parameters in each gear parameter set meet the process constraints is to ensure that the selected gear parameters can operate stably in actual production and will not cause equipment failure or performance problems due to unqualified parameters. Next, if it is found that some gear parameters do not meet the process constraints (i.e., unqualified parameters), these parameters need to be updated, and the unqualified gear parameter set is updated to a qualified gear parameter set. The updated gear parameter set will be used for the next simulation to ensure that the design of the gear pair meets actual requirements. In step S102, these updated gear parameter sets will be input into the three-dimensional model of the reducer gear pair for simulation analysis to obtain multiple simulation results, and subsequent design optimization and verification will be carried out based on these results. In this way, the final gear design not only meets the process requirements, but also maintains an efficient and stable working state in actual applications.

[0150] In one possible implementation, the process constraints include:

[0151] (1) Tooth tip diameter constraint: The tooth tip diameter da = m(z + 2) ≤ dmax, where dmax is the maximum tooth tip diameter within the reducer housing size limit. A safety gap of approximately 5 mm is required. This constraint ensures that the gear tooth tip diameter is within the design range and avoids interference with the reducer housing due to an excessively large tooth tip diameter, thereby ensuring the normal operation of the gear transmission system.

[0152] (2) Total contact constraint: The total contact is required to be ∈*γ = ∈α + ∈β ≥ 2.0, where α is the pressure angle and β is the helix angle. This constraint ensures transmission continuity during gear meshing, avoids uneven gear meshing or excessive backlash, reduces unnecessary vibration and noise, and improves the stability and comfort of the transmission system.

[0153] Through these process constraints, the reliability and stability of the gear transmission system can be effectively guaranteed, mechanical failures caused by improper design can be avoided, and the noise and vibration of the system can be controlled. It is suitable for applications requiring high precision and stability.

[0154] In a possible implementation, the present application also provides a method for initializing a gear parameter set, see Figure 4 , Figure 4 A method flow chart of a method for initializing a gear parameter set provided in an embodiment of the present application. Accordingly, initializing multiple sets of gear parameter sets in step S101 can be specifically implemented through steps S401-S402:

[0155] S401: Setting a gear parameter value range set.

[0156] In order to initialize multiple sets of gear parameter sets, you first need to set the gear parameter value range set. This set includes a variety of key gear parameter ranges, as follows:

[0157] Gear module range: defines the value range of the gear module to ensure that it is within a reasonable range.

[0158] First gear tooth number range: defines the first gear tooth number range, ensuring it is within the appropriate range.

[0159] Second gear tooth number range: defines the second gear tooth number range, ensuring it is within the appropriate range.

[0160] First gear tooth width range: defines the tooth width range of the first gear, ensuring it is within the appropriate range.

[0161] Second gear tooth width range: defines the second gear tooth width range, ensuring it is within the appropriate range.

[0162] Gear pressure angle range: defines the pressure angle range of the gear to ensure it is within the appropriate range.

[0163] Gear helix angle range: defines the helix angle range of the gear to ensure it is within the appropriate range.

[0164] By setting the value range of these parameters, we can ensure that each gear parameter has a reasonable numerical distribution during the simulation process, thereby improving the accuracy and reliability of the simulation. The setting of these parameter ranges provides the basis for subsequent equal-interval sampling and parameter initialization.

[0165] S402: Performing equal-interval sampling within the gear parameter value range set to obtain multiple sets of gear parameter sets.

[0166] When initializing multiple gear parameter sets, the gear parameter value ranges are first set, including the gear module range, the first gear tooth number range, the second gear tooth number range, the first gear tooth width range, the second gear tooth width range, the gear pressure angle range, and the gear helix angle range. Next, equally spaced samples are taken within these set value ranges to generate multiple gear parameter sets.

[0167] Specifically, the process of equally spaced sampling involves evenly selecting several values ​​within the range of each parameter, ensuring that these values ​​are evenly distributed across the entire range. This method yields multiple sets of gear parameter combinations, each within its predetermined range. This approach not only ensures parameter diversity and representativeness but also improves simulation accuracy and reliability.

[0168] Wherein, the gear module range is: T is the maximum torque of the electric vehicle motor, YF is the tooth form factor, [σF] is the allowable bending stress, z is the number of gear teeth, and dmax is the maximum pitch circle diameter limited by the reducer housing;

[0169] The number of teeth of the first gear is in the range of: [18, 35];

[0170] The number of teeth of the second gear is in the range of [18X, 35X], where X is the reduction ratio;

[0171] The tooth width range of the first gear is: ZE is the material elastic coefficient, ZH is the node area coefficient, Zε is the coincidence coefficient, u is the gear ratio, u=z2 / z1, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, d1 is the pinion pitch circle diameter, d1=m*z1, a is the safety factor, Y is a constant, Y∈[8,15];

[0172] The second gear tooth width range is: y is a constant, y∈[8,12];

[0173] The gear pressure angle range is: [20°, 25°];

[0174] The gear helix angle range is: [10°, 30°].

[0175] By following these steps, we can efficiently initialize multiple sets of gear parameters, providing a solid foundation for subsequent 3D model simulation of the reducer gear pair. This method not only simplifies the parameter initialization process but also improves the accuracy and reliability of the simulation.

[0176] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of a design and optimization device for a reducer gear provided in an embodiment of the present application. Figure 5 As shown, the design and optimization device of the reducer gear includes:

[0177] Initialization unit 501, used to initialize multiple sets of gear parameter sets;

[0178] The model building unit 502 is used to build a three-dimensional model of the reducer gear pair; each set of the gear parameter sets includes a gear module, a first gear tooth number, a second gear tooth number, a first gear tooth width, a second gear tooth width, a gear pressure angle, and a gear helix angle;

[0179] The simulation unit 503 is configured to obtain model input parameters and use the model input parameters as input to simulate the three-dimensional model of the reducer gear pair based on the multiple sets of gear parameter sets, one by one, to obtain multiple sets of simulation results; one set of gear parameter sets corresponds to one set of simulation results; the simulation results include the dynamic transmission error TEi, contact stress σH, bending stress σF, meshing frequency fm, and axial force Fα of the gear pair;

[0180] A rejection unit 504 is configured to reject unqualified results from the plurality of groups of simulation results to obtain a plurality of qualified results; the unqualified results include simulation results that do not meet NVH constraints and / or dynamic strength constraints;

[0181] A performance indicator calculation unit 505 is configured to calculate a set of performance indicators corresponding to each set of qualified results to obtain a plurality of sets of performance indicators; the performance indicators include a root mean square error f1, a gear volume f2, and a contact fatigue life f3;

[0182] An optimization processing unit 506 is configured to perform multi-objective optimization processing on the plurality of performance indicator sets to obtain a plurality of frontier solution sets;

[0183] The first setting unit 507 is configured to determine a balanced solution set from the multiple frontier solution sets, and use the balanced solution set as an optimal performance indicator set.

[0184] In a possible implementation, the optimization processing unit 506 specifically includes:

[0185] a non-dominated sorting and selection unit, configured to perform non-dominated sorting and selection on the plurality of performance indicator sets using the NSGA-II mechanism to obtain a plurality of initial populations;

[0186] An adaptive evolution unit, configured to adjust the crossover or mutation probability according to population diversity to perform adaptive iterative evolution on the initial population until the frontier change rate of the population for X consecutive generations of evolution is less than 1%, then stopping; X is a positive integer;

[0187] The second setting unit is configured to select X evolutionary populations whose frontier change rates of X consecutive generations of evolutionary populations are less than 1% as the plurality of frontier solution sets.

[0188] In a possible implementation, determining an equilibrium solution set from the multiple frontier solution sets includes:

[0189] a minimum index obtaining unit, configured to traverse the plurality of performance index sets and obtain the minimum transmission error root mean square f1min, the minimum gear volume f2min, and the minimum contact fatigue life f3min from the plurality of performance index sets;

[0190] A comprehensive score calculation unit, configured to calculate a comprehensive score for each of the performance indicator sets based on the minimum transmission error root mean square f1min, the minimum gear volume f2min, and the minimum contact fatigue life f3min;

[0191] The third setting unit is configured to use the performance indicator set with the highest comprehensive score as the balanced solution set.

[0192] In one possible implementation,

[0193] The calculation formula of the transmission error root mean square f1 is: N is the number of groups of the gear parameter set, and N is a positive integer;

[0194] The calculation formula of the gear volume f2 is: m is the gear module, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, b1 is the tooth width of the first gear, and b2 is the tooth width of the second gear;

[0195] The calculation formula of the contact fatigue life f3 is:

[0196] In a possible implementation, the apparatus further includes:

[0197] A verification unit, used to verify whether the gear parameters in each gear parameter set meet the process constraints;

[0198] An updating execution unit is used to update the gear parameter set containing unqualified parameters, so as to use the updated gear parameter set to execute the steps of simulating the three-dimensional model of the reducer gear pair one by one based on multiple sets of the gear parameter sets using the model input parameters as input to obtain multiple sets of simulation results, as well as subsequent steps; the unqualified parameters are gear parameters that do not meet the process constraints.

[0199] In a possible implementation, the initialization unit 501 specifically includes:

[0200] A value range setting unit is used to set a gear parameter value range set; the gear parameter value range set includes a gear module range, a first gear tooth number range, a second gear tooth number range, a first gear tooth width range, a second gear tooth width range, a gear pressure angle range, and a gear helix angle range;

[0201] An equidistant sampling unit, configured to perform equidistant sampling within the gear parameter value range set to obtain multiple sets of gear parameter sets;

[0202] Wherein, the gear module range is: T is the maximum torque of the electric vehicle motor, YF is the tooth form factor, [σF] is the allowable bending stress, z is the number of gear teeth, and dmax is the maximum pitch circle diameter limited by the reducer housing;

[0203] The number of teeth of the first gear is in the range of: [18, 35];

[0204] The number of teeth of the second gear is in the range of [18X, 35X], where X is the reduction ratio;

[0205] The tooth width range of the first gear is: ZE is the material elastic coefficient, ZH is the node area coefficient, Zε is the coincidence coefficient, u is the gear ratio, u=z2 / z1, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, d1 is the pinion pitch circle diameter, d1=m*z1, a is the safety factor, Y is a constant, Y∈[8,15];

[0206] The second gear tooth width range is: y is a constant, y∈[8,12];

[0207] The gear pressure angle range is: [20°, 25°];

[0208] The gear helix angle range is: [10°, 30°].

[0209] In addition, an embodiment of the present application also provides a design and optimization device for a reducer gear, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the design and optimization method for the reducer gear as described above is implemented.

[0210] In addition, an embodiment of the present application further provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes the reducer gear design and optimization method as described above.

[0211] This application adopts a multi-objective optimization method, comprehensively considering multiple performance indicators such as transmission error, gear volume and contact fatigue life, effectively breaking through the limitations of traditional design that only focuses on a single performance target. Through the collaborative optimization of multiple performance indicators, a coordinated balance between various performance indicators is achieved. Furthermore, this application selects a balanced solution set from multiple frontier solution sets as the final optimal performance solution, so that the reducer gear reaches the overall optimal state in terms of efficiency, noise, weight and reliability, thereby meeting the comprehensive requirements of electric vehicle reducers for multiple performance.

[0212] The above is a detailed introduction to the design and optimization method of a reducer gear and related products provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0213] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0214] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

Claims

1. A design and optimization method for a reducer gear, characterized in that: The method comprises: Initializing N sets of gear parameter sets and constructing a three-dimensional model of a reducer gear pair; each set of the gear parameter sets includes a gear module, a first gear tooth number, a second gear tooth number, a first gear tooth width, a second gear tooth width, a gear pressure angle, and a gear helix angle; Acquiring model input parameters, and using the model input parameters as input to simulate the three-dimensional model of the reducer gear pair based on multiple sets of gear parameter sets, one by one, to obtain multiple sets of simulation results; one set of gear parameter sets corresponds to one set of simulation results; the simulation results include a dynamic transmission error TEi, contact stress σH, bending stress σF, meshing frequency fm, and axial force Fα of the gear pair; Eliminating unqualified results from the multiple groups of simulation results to obtain multiple groups of qualified results, and calculating a set of performance indicators corresponding to each group of qualified results to obtain multiple sets of performance indicators; the unqualified results are simulation results that do not meet noise, vibration and harshness (NVH) constraints and / or dynamic strength constraints; the performance indicators include transmission root mean square error f1, gear volume f2, and contact fatigue life f3; Performing multi-objective optimization processing on the plurality of performance indicator sets to obtain a plurality of frontier solution sets; A balanced solution set among the multiple frontier solution sets is determined, and the balanced solution set is used as an optimal performance indicator set.

2. The method according to claim 1, characterized in that The multi-objective optimization process is performed on the plurality of performance indicator sets to obtain a plurality of frontier solution sets, including: Using a non-dominated sorting genetic algorithm (IINSGA-II) mechanism, a non-dominated sorting and selection process is performed on the plurality of performance indicator sets to obtain an initial population; Adaptively iteratively evolve the initial population by adjusting the crossover or mutation probability according to population diversity until the frontier change rate of the population for X consecutive generations of evolution is less than 1%, then stop; X is a positive integer; X evolutionary populations whose frontier change rates of X consecutive evolutionary populations are less than 1% are taken as the plurality of frontier solution sets.

3. The method according to claim 1, characterized in that Determining an equilibrium solution set from the plurality of frontier solution sets comprises: Traversing the plurality of performance indicator sets, obtaining the minimum transmission error root mean square f1min, the minimum gear volume f2min, and the minimum contact fatigue life f3min in the plurality of performance indicator sets; Calculate the comprehensive score of each performance indicator set based on the minimum transmission error root mean square f1min, the minimum gear volume f2min and the minimum contact fatigue life f3min; The performance indicator set with the highest comprehensive score is used as the balanced solution set.

4. The method according to claim 1, wherein The calculation formula of the transmission error root mean square f1 is: N is the number of groups of the gear parameter set, and N is a positive integer; The calculation formula of the gear volume f2 is: m is the gear module, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, b1 is the tooth width of the first gear, and b2 is the tooth width of the second gear; The calculation formula of the contact fatigue life f3 is:

5. The method according to claim 1, wherein The method further comprises: Verify whether the gear parameters in each gear parameter set meet the process constraints; The gear parameter set with unqualified parameters is updated to use the updated gear parameter set to perform the steps of simulating the reducer gear pair three-dimensional model one by one based on multiple sets of gear parameter sets using the model input parameters as input to obtain multiple sets of simulation results, as well as subsequent steps; the unqualified parameters are gear parameters that do not meet the process constraints.

6. The method according to claim 1, characterized in that Initializing multiple sets of gear parameter sets includes: Setting a gear parameter value range set; the gear parameter value range set includes a gear module range, a first gear tooth number range, a second gear tooth number range, a first gear tooth width range, a second gear tooth width range, a gear pressure angle range, and a gear helix angle range; Performing equal-interval sampling within the gear parameter value range set to obtain multiple sets of gear parameter sets; Wherein, the gear module range is: T is the maximum torque of the electric vehicle motor, YF is the tooth form factor, [σF] is the allowable bending stress, z is the number of gear teeth, and dmax is the maximum pitch circle diameter limited by the reducer housing; The number of teeth of the first gear is in the range of: [18, 35]; The number of teeth of the second gear is in the range of [18X, 35X], where X is the reduction ratio; The tooth width range of the first gear is: ZE is the material elastic coefficient, ZH is the node area coefficient, Zε is the coincidence coefficient, u is the gear ratio, u=z2 / z1, z1 is the number of teeth of the first gear, z2 is the number of teeth of the second gear, d1 is the pinion pitch circle diameter, d1=m*z1, a is the safety factor, Y is a constant, Y∈[8,15]; The second gear tooth width range is: y is a constant, y∈[8,12]; The gear pressure angle range is: [20°, 25°]; The gear helix angle range is: [10°, 30°].

7. A design and optimization device for a reducer gear, characterized in that: The device comprises: Initialization unit, used to initialize multiple sets of gear parameter sets; A model building unit is used to build a three-dimensional model of the reducer gear pair; each set of the gear parameter sets includes a gear module, a first gear tooth number, a second gear tooth number, a first gear tooth width, a second gear tooth width, a gear pressure angle, and a gear helix angle; a simulation unit, configured to obtain model input parameters, and use the model input parameters as input to simulate the three-dimensional model of the reducer gear pair based on the multiple sets of gear parameter sets, one by one, to obtain multiple sets of simulation results; one set of gear parameter sets corresponds to one set of simulation results; the simulation results include a dynamic transmission error TEi, contact stress σH, bending stress σF, meshing frequency fm, and axial force Fα of the gear pair; a rejection unit, configured to reject unqualified results from the plurality of groups of simulation results to obtain a plurality of qualified results; the unqualified results are simulation results that do not meet NVH constraints and / or dynamic strength constraints; a performance indicator calculation unit, configured to calculate a performance indicator set corresponding to each group of qualified results to obtain a plurality of performance indicator sets; the performance indicators comprising a root mean square error f1, a gear volume f2, and a contact fatigue life f3; an optimization processing unit, configured to perform multi-objective optimization processing on the plurality of performance indicator sets to obtain a plurality of frontier solution sets; The first setting unit is configured to determine a balanced solution set among the multiple frontier solution sets, and use the balanced solution set as an optimal performance indicator set.

8. The device according to claim 7, characterized in that The optimization processing unit specifically includes: a non-dominated sorting and selection unit, configured to perform non-dominated sorting and selection on the plurality of performance indicator sets using the NSGA-II mechanism to obtain a plurality of initial populations; An adaptive evolution unit, configured to adjust the crossover or mutation probability according to population diversity to perform adaptive iterative evolution on the initial population until the frontier change rate of the population for X consecutive generations of evolution is less than 1%, then stopping; X is a positive integer; The second setting unit is configured to select X evolutionary populations whose frontier change rates of X consecutive generations of evolutionary populations are less than 1% as the plurality of frontier solution sets.

9. A design and optimization device for reducer gears, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for designing and optimizing the reducer gear according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the method for designing and optimizing a reducer gear according to any one of claims 1 to 6.

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