Reducer gear parameter optimization method and system based on MASTA simulation and electronic equipment

Abstract

The invention relates to the technical field of automobile speed reducers, and discloses a speed reducer gear parameter optimization method and system based on MASTA simulation and electronic equipment, and the method comprises the steps: building a speed reducer simulation model based on predetermined initial gear parameters and positioning requirements of speed reducer shaft gear parts; defining a range of iteration times and optimization variables, and generating an initial population; gear parameters generated by the initial population serve as input of a speed reducer simulation model, and gear optimization fitness is calculated based on gear performance parameters output by the speed reducer simulation model; based on a multi-objective genetic optimization algorithm, selecting individuals with relatively high fitness from each sub-group to form a new sub-group; iteratively calculating a gear parameter fitness value meeting a gear parameter constraint condition, and updating a previous generation of gear parameters; and when a preset termination condition is met, obtaining an optimal gear parameter. According to the scheme, an automatic simulation modeling and gear parameter optimization platform of the speed reducer is developed, and design and iterative updating of the speed reducer are accelerated.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile reducers, and in particular to a reducer gear parameter optimization method, system, storage medium and electronic equipment based on MASTA simulation. Background Art

[0002] Nowadays, pure electric vehicles are in a period of rapid development, so more and more pure electric reducer products are emerging. Traditional reducer gear design relies on manual simulation modeling and gear parameter design based on designer experience. This method has problems such as long cycle, large workload and difficult optimization in practical application.

[0003] With the increasing maturity of digital simulation and secondary development technology, the automatic modeling of reducer simulation models and the automatic optimization of gear parameters urgently need an effective way to achieve them. Summary of the invention

[0004] The purpose of the present invention is to provide a reducer gear parameter optimization method, system and electronic equipment based on MASTA simulation, which makes up for the problems of long cycle, large workload, difficult optimization, etc. caused by the prior art relying on manual simulation modeling and gear parameter design based on designer experience.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for optimizing reducer gear parameters based on MASTA simulation, the method comprising:

[0007] Based on the predetermined initial gear parameters and the positioning requirements of the reducer shaft gear parts, a reducer simulation model is constructed;

[0008] The number of iterations and the range of optimization variables are defined to generate an initial population; the gear parameters generated by the initial population are used as inputs of a reducer simulation model, and the gear optimization fitness is calculated based on the gear performance parameters output by the reducer simulation model;

[0009] Based on the multi-objective genetic optimization algorithm, individuals with higher fitness are selected from each sub-group to form a new sub-group; the fitness value of the gear parameters of each sub-group that meets the gear parameter constraints is iteratively calculated to update the previous generation gear parameters;

[0010] It is determined whether the preset termination condition is met, and when the preset termination condition is met, the optimal gear parameters are obtained.

[0011] Optionally, the construction of the reducer simulation model includes:

[0012] According to the requirements of reducer automatic simulation modeling and gear parameter optimization, build MASTA secondary development environment, write the corresponding function codes of automatic simulation modeling and gear parameter optimization on MATLAB, and define the model structure tree;

[0013] According to the reducer layout diagram, considering the transmission ratio and center distance requirements, the gear parameters are preliminarily confirmed to obtain the initial gear parameters;

[0014] Create a reducer shaft gear parts assembly under a predefined model structure tree; wherein the reducer shaft gear parts include: input shaft, intermediate shaft, gear and differential; the gear includes: gear-primary driven gear, main reducer driven gear;

[0015] The created input shaft assembly, gear assembly, intermediate shaft assembly and differential assembly are assembled respectively according to the initial gear parameters and the positioning requirements of the reducer shaft gear parts; and the MATLAB secondary development environment is used to use the parameters of the assembled reducer shaft gear parts as the input of the reducer simulation model, and the power flow is run to verify the model. After verifying that the reducer simulation model is correct, the MASTA reducer simulation model is saved and output.

[0016] Optionally, creating a reducer shaft gear parts assembly under a predefined model structure tree includes:

[0017] According to the reducer layout diagram, determine the overall outline size of the reducer shaft gear parts, and separate the outline nodes based on the outline size;

[0018] According to the distribution pattern of the contour nodes, the contour node coordinates of the input shaft, the intermediate shaft, the gear and the differential housing are edited, and in combination with the layout requirements of the reducer, the input shaft assembly, the gear assembly, the intermediate shaft assembly and the differential assembly are created under the predefined model structure tree.

[0019] Optionally, creating the input shaft assembly includes:

[0020] Edit the outer diameter contour node and inner diameter contour node of the input shaft in sequence to create the input shaft;

[0021] According to the layout requirements of the reducer, the manufacturer information and model size of the front and rear bearings of the input shaft are edited in sequence to create the front and rear bearings of the input shaft; and an input shaft assembly consisting of the input shaft and the front and rear bearings of the input shaft is obtained;

[0022] The creating of the intermediate shaft assembly comprises:

[0023] Edit the outer diameter contour nodes and inner diameter contour nodes of the intermediate shaft in sequence to establish the front and rear bearings of the intermediate shaft; and,

[0024] Edit the outer diameter contour node and the inner diameter contour node of the first-level driven spoke in sequence to establish the first-level driven spoke; create a rigid connector, edit the length of the rigid connection, and establish a spoke rigid connection;

[0025] An intermediate shaft assembly consisting of the front and rear intermediate shaft bearings, the primary driven wheel spokes and the spokes rigidly connected is obtained.

[0026] Optionally, creating a gear assembly includes:

[0027] Edit and input the gear parameters of the primary driving gear, the secondary driving gear, and the secondary driving gear in sequence, establish the primary driving gear, the secondary driving gear, and the secondary driving gear; obtain the gear assembly consisting of the primary driving gear, the secondary driving gear, and the secondary driving gear; wherein the gear parameters include: center distance, module, pressure angle, helix angle, number of teeth, displacement coefficient, tooth width, tooth addendum coefficient, tooth root height coefficient, and hand direction.

[0028] Optionally, the creating a differential assembly includes:

[0029] Edit the outer diameter contour node and the inner diameter contour node of the differential housing in sequence to establish the differential housing; and,

[0030] Edit the outer diameter contour nodes and inner diameter contour nodes of the main reducer and driven gear spokes in sequence to establish the main reducer and driven gear spokes; select the manufacturer and model of the bearings according to the layout requirements of the reducer, edit the manufacturer information, model, and size of the front and rear bearings of the differential case, and establish the front and rear bearings of the differential; create a rigid connector, edit the length of the rigid connection of the differential case, and establish the rigid connection of the differential case;

[0031] A differential assembly is obtained which is composed of a differential case, a main reduction driven gear wheel spoke and a differential case which are rigidly connected.

[0032] Optionally, the calculating of the gear optimization fitness based on the gear performance parameters output by the reducer simulation model includes:

[0033] According to the actual working conditions, define the optimization items and ranges of gear parameters; including: module, pressure angle, helix angle, number of teeth, modification coefficient, tooth top height coefficient, and tooth top and root coefficient difference;

[0034] Based on the multi-objective genetic algorithm, the initial population is generated, and the number of individuals in the population, the number of gear parameters to be optimized, the number of iterations, the mutation probability, the crossover probability, and the value range of each optimized gear parameter are determined according to the actual situation;

[0035] The gear overlap adaptability F1 is obtained by weighted calculation of the end face overlap, longitudinal face overlap and total overlap of the first-stage gear pair and the second-stage gear pair;

[0036] The gear strength adaptability F2 is obtained by weighted calculation of the contact fatigue safety factor, bending fatigue safety factor, contact static safety factor, bending static safety factor, and tooth top thickness of the primary master-slave gear and the secondary master-slave gear.

[0037] Optionally, the iterative calculation of the fitness value of each subgroup gear parameter that satisfies the gear parameter constraint condition and updating the previous generation gear parameters comprises:

[0038] According to the requirements of different reducers for adaptability and strength, different weights are selected to adjust the gear parameter optimization results;

[0039] The gear parameters generated by the initial population are written into the reducer simulation model through the API, the reducer simulation model is run through the code for simulation, and the simulation results are read out through the API, and the fitness value of each generation of gear parameters is solved based on the gear optimization fitness calculation method;

[0040] A multi-objective genetic algorithm is used to iteratively calculate the fitness value that satisfies the gear parameter constraints. When the preset number of iterations is reached, the fitness values ​​of each generation calculated are sorted in descending order, and the gear parameter groups with the highest priority are selected as the result parameters of this optimization.

[0041] The optimization result parameters are input into the MASTA reducer simulation model through API to complete the gear parameter optimization work.

[0042] Optionally, define the gear parameter constraints as follows:

[0043]

[0044] In the formula, gear s e is the tooth top thickness, ε α is the end face overlap, ε β is the longitudinal surface overlap, ε γ is the total overlap, S FH is the contact fatigue safety factor, S FF is the bending fatigue safety factor, S SH is the static contact safety factor, S SF is the static safety factor of bending, ds is the effective involute starting circle diameter, df is the root circle diameter, db is the base circle diameter, S1 and S2 refer to the safety factors of the primary gear and the secondary gear respectively.

[0045] In a second aspect, the present application also provides a reducer gear parameter optimization system based on MASTA simulation, the system comprising:

[0046] A construction module, used for constructing a reducer simulation model based on predetermined initial gear parameters and positioning requirements of reducer shaft gear parts;

[0047] A determination module is used to define the number of iterations and the range of optimization variables to generate an initial population; the gear parameters generated by the initial population are used as inputs of a reducer simulation model, and the gear optimization fitness is calculated based on the gear performance parameters output by the reducer simulation model;

[0048] An iterative calculation module is used to select individuals in each sub-group with higher fitness to form a new sub-group based on a multi-objective genetic optimization algorithm; iteratively calculate the fitness value of the gear parameters of each sub-group that meets the gear parameter constraint conditions, and update the previous generation gear parameters;

[0049] The optimization module is used to determine whether the preset termination conditions are met, and when the preset termination conditions are met, the optimal gear parameters are obtained.

[0050] The beneficial effects of the present invention are embodied in:

[0051] The above-mentioned reducer gear parameter optimization method, system and electronic device based on MASTA simulation are secondary developed based on MASTA simulation software to realize data interaction between design parameters and simulation parameters, give constraints through design experience and establish fitness function for parameter optimization, deploy multi-objective genetic optimization algorithm on the basis of automated modeling, and complete gear parameter optimization design through iterative optimization. It can quickly generate simulation models by only inputting design parameters, which significantly shortens modeling time and improves work efficiency.

[0052] The above-mentioned reduction gear parameter optimization method, system and electronic equipment based on MASTA simulation realizes the automatic simulation modeling and gear parameter optimization design of automobile reduction gears using the MATALB programming platform; specifically, it includes the overall design process of setting up the secondary development environment of MASTA simulation software and MATLAB programming platform, automatic parametric modeling of reduction gears, deployment of multi-objective genetic optimization algorithm, gear parameter optimization, reduction gear simulation model and gear parameter verification. Thus, the full automation of the simulation modeling of the transmission components of the reduction gear is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0054] Figure 1A flow chart of a method for optimizing reducer gear parameters based on MASTA simulation provided by the present invention;

[0055] Figure 2 The automatic modeling and gear parameter optimization flow chart for the pure electric reducer provided by the present invention;

[0056] Figure 3 A schematic diagram of a simulation model of a reducer provided by the present invention;

[0057] Figure 4 A flow chart of the multi-objective genetic algorithm provided by the present invention;

[0058] Figure 5 A schematic diagram of the structure of a reducer gear parameter optimization system based on MASTA simulation provided by the present invention;

[0059] Figure 6 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] The embodiment of the present application provides a reducer gear parameter optimization method, system and electronic device based on MASTA simulation, which are mainly used in the electric drive reducer design system of electric vehicles. The method can be applied to a terminal, a server, or a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc.

[0062] The following embodiment takes the automobile end as an example to elaborate the design process of the method in detail. Through the optimization method proposed in the embodiment of the present invention, the construction of the reducer simulation model and the optimization of the reducer gear parameters are realized.

[0063] In one embodiment, Figure 1 As shown, an embodiment of the present invention provides a method for optimizing reducer gear parameters based on MASTA simulation, the method comprising:

[0064] S101 builds a reducer simulation model based on the predetermined initial gear parameters and the positioning requirements of the reducer shaft gear parts;

[0065] S102 defines the number of iterations and the range of optimization variables to generate an initial population; uses the gear parameters generated by the initial population as input to the reducer simulation model, and calculates the gear optimization fitness based on the gear performance parameters output by the reducer simulation model;

[0066] S103 selects individuals from each sub-group with higher fitness to form a new sub-group based on a multi-objective genetic optimization algorithm; iteratively calculates the fitness value of the gear parameters of each sub-group that meets the gear parameter constraint conditions, and updates the previous generation gear parameters;

[0067] S104 determines whether a preset termination condition is met, and when the preset termination condition is met, obtains the optimal gear parameters.

[0068] In the above embodiment, in step S101, based on the predetermined initial gear parameters and the positioning requirements of the reducer shaft gear parts, constructing the reducer simulation model includes:

[0069] According to the requirements of reducer automatic simulation modeling and gear parameter optimization, build MASTA secondary development environment, write the corresponding function codes of automatic simulation modeling and gear parameter optimization on MATLAB, and define the model structure tree;

[0070] According to the reducer layout diagram, considering the transmission ratio and center distance requirements, the gear parameters are preliminarily confirmed to obtain the initial gear parameters;

[0071] Create a reducer shaft gear parts assembly under a predefined model structure tree; wherein the reducer shaft gear parts include: input shaft, intermediate shaft, gear and differential; the gear includes: gear-primary driven gear, main reducer driven gear;

[0072] The created input shaft assembly, gear assembly, intermediate shaft assembly and differential assembly are assembled respectively according to the initial gear parameters and the positioning requirements of the reducer shaft gear parts; and the MATLAB secondary development environment is used to use the parameters of the assembled reducer shaft gear parts as the input of the reducer simulation model, and the power flow is run to verify the model. After verifying that the reducer simulation model is correct, the MASTA reducer simulation model is saved and output.

[0073] In the above embodiment, creating a reducer shaft gear parts assembly under a predefined model structure tree includes:

[0074] According to the reducer layout diagram, determine the overall outline size of the reducer shaft gear parts, and separate the outline nodes based on the outline size;

[0075] According to the distribution pattern of the contour nodes, the contour node coordinates of the input shaft, the intermediate shaft, the gear and the differential housing are edited, and in combination with the layout requirements of the reducer, the input shaft assembly, the gear assembly, the intermediate shaft assembly and the differential assembly are created under the predefined model structure tree.

[0076] In the above embodiment, creating the input shaft assembly includes:

[0077] Edit the outer diameter contour node and inner diameter contour node of the input shaft in sequence to create the input shaft;

[0078] According to the layout requirements of the reducer, the manufacturer information and model size of the front and rear bearings of the input shaft are edited in sequence to create the front and rear bearings of the input shaft; and an input shaft assembly consisting of the input shaft and the front and rear bearings of the input shaft is obtained.

[0079] In the above embodiment, creating the intermediate shaft assembly includes:

[0080] Edit the outer diameter contour nodes and inner diameter contour nodes of the intermediate shaft in sequence to establish the front and rear bearings of the intermediate shaft; and,

[0081] Edit the outer diameter contour node and the inner diameter contour node of the first-level driven spoke in sequence to establish the first-level driven spoke; create a rigid connector, edit the length of the rigid connection, and establish a spoke rigid connection;

[0082] An intermediate shaft assembly consisting of the front and rear intermediate shaft bearings, the primary driven wheel spokes and the spokes rigidly connected is obtained.

[0083] In the above embodiment, creating the gear assembly includes:

[0084] Edit and input the gear parameters of the primary driving gear, the secondary driving gear, and the secondary driving gear in sequence, establish the primary driving gear, the secondary driving gear, and the secondary driving gear; obtain the gear assembly consisting of the primary driving gear, the secondary driving gear, and the secondary driving gear; wherein the gear parameters include: center distance, module, pressure angle, helix angle, number of teeth, displacement coefficient, tooth width, tooth addendum coefficient, tooth root height coefficient, and hand direction.

[0085] In the above embodiment, the step of creating a differential assembly includes:

[0086] Edit the outer diameter contour nodes and inner diameter contour nodes of the differential case in sequence to establish the differential case; and edit the outer diameter contour nodes and inner diameter contour nodes of the main reducer and driven gear spokes in sequence to establish the main reducer and driven gear spokes; and according to the layout requirements of the reducer, select the manufacturer and model of the bearing, edit the manufacturer information, model, and size of the front and rear bearings of the differential case, and establish the front and rear bearings of the differential; create a rigid connector, edit the length of the rigid connection of the differential case, and establish the rigid connection of the differential case;

[0087] A differential assembly is obtained which is composed of a differential case, a main reduction driven gear wheel spoke and a differential case which are rigidly connected.

[0088] In step S102 of the above embodiment, the calculation of the gear optimization fitness based on the gear performance parameters output by the reducer simulation model includes:

[0089] According to the actual working conditions, define the optimization items and ranges of gear parameters; including: module, pressure angle, helix angle, number of teeth, modification coefficient, tooth top height coefficient, and tooth top and root coefficient difference;

[0090] Based on the multi-objective genetic algorithm, the initial population is generated, and the number of individuals in the population, the number of gear parameters to be optimized, the number of iterations, the mutation probability, the crossover probability, and the value range of each optimized gear parameter are determined according to the actual situation;

[0091] The gear overlap adaptability F1 is obtained by weighted calculation of the end face overlap, longitudinal face overlap and total overlap of the first-stage gear pair and the second-stage gear pair;

[0092] The gear strength adaptability F2 is obtained by weighted calculation of the contact fatigue safety factor, bending fatigue safety factor, contact static safety factor, bending static safety factor, and tooth top thickness of the primary master-slave gear and the secondary master-slave gear.

[0093] In step S103 of the above embodiment, the iterative calculation of the fitness value of each subgroup gear parameter that satisfies the gear parameter constraint condition and updating the previous generation gear parameters includes:

[0094] According to the requirements of different reducers for adaptability and strength, different weights are selected to adjust the gear parameter optimization results;

[0095] The gear parameters generated by the initial population are written into the reducer simulation model through API, the reducer simulation model is run through the code for simulation, and the simulation results are read out through API, and the fitness value of each generation of gear parameters is solved based on the gear optimization fitness calculation method;

[0096] A multi-objective genetic algorithm is used to iteratively calculate the fitness value that satisfies the gear parameter constraints. When the preset number of iterations is reached, the fitness values ​​of each generation calculated are sorted in descending order, and the gear parameter groups with the highest priority are selected as the result parameters of this optimization.

[0097] The optimization result parameters are input into the MASTA reducer simulation model through API to complete the gear parameter optimization work.

[0098] In the above embodiment, the gear parameter constraint condition is defined by the following formula:

[0099]

[0100] In the formula, gear s e is the tooth top thickness, ε α is the end face overlap, ε β is the longitudinal surface overlap, ε γ is the total overlap, S FH is the contact fatigue safety factor, S FF is the bending fatigue safety factor, S SH is the static contact safety factor, S SF is the static safety factor of bending, ds is the effective involute starting circle diameter, df is the root circle diameter, db is the base circle diameter, S1 and S2 refer to the safety factors of the first-stage gear and the second-stage gear respectively.

[0101] In the above embodiment, step S104 determines whether a preset termination condition is met, where the preset termination condition includes an expected indicator or a maximum number of custom iterations.

[0102] Optionally, a specific embodiment is provided based on the above content:

[0103] Taking the pure electric reducer as an example, Figure 2 The process of automatic simulation modeling and gear parameter optimization for reducer:

[0104] 1. According to the requirements of reducer automatic simulation modeling and gear parameter optimization, build the MASTA secondary development environment, and write the corresponding function codes for automatic simulation modeling and gear parameter optimization on MATLAB.

[0105] 2. According to the reducer layout diagram, consider the transmission ratio and center distance requirements, and make a preliminary confirmation of the macro parameters of the gear. The specific gear parameters are as follows: Figure 3 As shown, the initial parameters that need to be determined are: center distance, module, pressure angle, helix angle, number of teeth, modification coefficient, tooth width, tooth top height coefficient, tooth root height coefficient, and hand direction.

[0106] 3. Based on the preliminarily determined gear parameters and the layout of the shaft and gear parts, use MATLAB and MASTA secondary development environment to automatically create a reducer simulation model. Figure 3 The simulation model of the reducer is shown in Figure 1. All the following steps are to automatically create the simulation model using code in MATLAB:

[0107] 1) Create a new MASTA file, name the model file, and add a model design "Design". All the following model data are under the Design structure tree.

[0108] 2) Determine the overall outline dimensions of the input shaft, intermediate shaft, gear-first driven gear, main reducer driven gear according to the layout diagram, separate the outline dimensions into outline nodes, and edit the node coordinates of each shaft and gear outline in the form of (length, diameter) from left to right.

[0109] 3) Create "Input Shaft Assembly" under the Design model tree. First, edit the outer diameter contour node of the input shaft, then edit the inner diameter contour node of the input shaft to complete the creation of the input shaft model. Secondly, according to the layout requirements of the reducer, select the manufacturer and model of the bearing, then edit the manufacturer information of the front and rear bearings of the input shaft, and then edit the model size of the front and rear bearings of the input shaft to complete the creation of the front and rear bearing models of the input shaft.

[0110] 4) Create "Gear Assembly" under the Design model tree. First, edit the parameters of the primary driving gear and the primary driven gear according to the preliminary confirmation of the gear parameters. The input gear parameters include: center distance, module, pressure angle, helix angle, number of teeth, displacement coefficient, tooth width, tooth top height coefficient, tooth root height coefficient, and hand direction. Similarly, the secondary gear parameters are also edited and input according to this step, and finally the creation of the primary gear pair and the secondary gear pair model is completed.

[0111] 5) Create "Intermediate Shaft Assembly" under the Design model tree. First, edit the outer diameter contour node of the intermediate shaft, then edit the inner diameter contour node of the intermediate shaft to complete the creation of the intermediate shaft model. Next, edit the outer diameter contour node of the gear-first-level driven spoke, then edit the inner diameter contour node of the gear-first-level driven spoke to complete the creation of the gear-first-level driven spoke model. Finally, create a rigid connection, edit and input the length of the rigid connection, and complete the creation of the rigid connection model.

[0112] 6) Create "Differential Assembly" under the Design model tree. This embodiment does not perform detailed design of the differential, so it does not involve the planetary gears and half-axle gears inside the differential. First, edit the outer diameter contour node of the differential case, and then edit the inner diameter contour node of the differential case to complete the creation of the differential case model. Next, edit the outer diameter contour node of the main reducer and driven gear spokes, and then edit the inner diameter contour node of the main reducer and driven gear spokes to complete the creation of the main reducer and driven gear spoke models. Next, according to the layout requirements of the reducer, select the manufacturer and model of the bearing, and then edit the manufacturer information, model, and size of the front and rear bearings of the differential to complete the creation of the front and rear bearing models of the differential. Finally, create a rigid connection, edit the length of the rigid connection of the differential case, and complete the creation of the rigid connection model.

[0113] 7) Assemble the above-established model according to the positioning requirements of the layout drawing: assemble the front and rear bearings of the input shaft to the input shaft, assemble the first-stage driving gear to the fixed position of the input shaft, and complete the assembly of the input shaft assembly; assemble the front and rear bearings of the intermediate shaft to the intermediate shaft, assemble the second-stage driving gear to the fixed position of the intermediate shaft, assemble the first-stage driven gear to the gear-first-stage driven spoke, assemble the intermediate shaft and the gear-first-stage driven spoke together through a rigid connection, and complete the assembly of the intermediate shaft assembly; assemble the front and rear bearings of the differential to the differential case, assemble the second-stage driven gear to the main reduction driven gear spoke, assemble the differential case and the main reduction driven gear spoke together through a rigid connection, and complete the assembly of the differential case assembly.

[0114] 8) Adjust the detailed assembly form of the bearings, and determine the fixing method of the inner and outer walls of the front and rear bearings of the input shaft, the fixing method of the inner and outer rings of the intermediate shaft and differential bearings, and the size of the preload according to the requirements of the layout drawing.

[0115] 9) Adjust the spatial layout of the input shaft, intermediate shaft, and differential center axis, and complete the spatial layout of the three axes through center distance and layout angle.

[0116] 10) Set the load spectrum, add power input and output nodes on the input shaft and differential housing, and edit and input each working condition in the form of (name, time, speed, torque) according to the requirements of the reducer load spectrum to complete the simulation input of the load spectrum.

[0117] 11) Run the power flow to verify the model. If there is no error message, it means that the simulation model is automatically generated accurately. Save the reducer simulation model.

[0118] 4. Based on the above technical solution, after completing the automatic establishment of the reducer simulation model, an embodiment of automatic optimization of gear parameters is provided. The specific method flow is as follows:

[0119] 1) The gear parameters preliminarily determined are shown in Table 1. Based on the gear design experience, the optimization items and ranges of the gear parameters are given on the basis of the preliminarily set gear parameters as shown in Table 2, including: module, pressure angle, helix angle, number of teeth, modification coefficient, tooth top height coefficient, and tooth top and root coefficient difference.

[0120] Table 1

[0121]

[0122]

[0123] Table 2

[0124]

[0125] 2) Multi-objective genetic algorithm flow chart, such as Figure 4 To generate the initial population, the following parameters need to be determined according to the actual situation: the number of individuals in the population, the number of chromosomes (the number of gear parameters that need to be optimized), the number of iterations (the number of generations of genetic crossover), the mutation probability, the crossover probability, and the value range of each optimization parameter.

[0126] 3) Determine the fitness calculation method. The fitness function is mainly determined by the following gear simulation results and basic gear parameters. F1 is the gear overlap fitness, which is weightedly calculated by the end face overlap, longitudinal face overlap, and total overlap of the first-level gear pair and the second-level gear pair. F2 is the gear strength fitness, which is weightedly calculated by the contact fatigue safety factor, bending fatigue safety factor, contact static safety factor, bending static safety factor, and tooth top thickness of the first-level master-slave gear and the second-level master-slave gear. Different weights should be selected according to the NVH and strength requirements of different reducers, and can also be adjusted according to the actual optimization results.

[0127] According to design experience, each parameter must meet the following conditions, as shown in the following formula:

[0128]

[0129] In the formula, gear s e is the tooth top thickness, ε α is the end face overlap, ε β is the longitudinal surface overlap, ε γ is the total overlap, S FH is the contact fatigue safety factor, S FF is the bending fatigue safety factor, S SH is the static contact safety factor, S SF is the static safety factor of bending, ds is the effective involute starting circle diameter, df is the root circle diameter, db is the base circle diameter, S1 and S2 refer to the safety factors of the primary gear and the secondary gear respectively.

[0130] 4) Calculate the fitness, as shown in the attached Figure 2 As shown, the gear parameters generated by the first generation are written into the MASTA reducer simulation model through APl, the reducer model is simulated through the code, the simulation results are read out through APl, and the fitness values ​​of each generation of gear parameters are solved through the gear optimization fitness calculation method in the previous step.

[0131] 5) Iterate through the multi-objective genetic algorithm. When the preset number of iterations is reached, the fitness of each generation calculated is sorted, and the gear parameter groups with the highest priority are selected as the result parameters of this optimization.

[0132] 6) Input the optimization result parameters into the MASTA simulation model through APl, save the reducer simulation model, and complete the gear parameter optimization work.

[0133] 5. Based on the reducer simulation model maintained according to step 4, the specific simulation projects can be confirmed in the MASTA simulation software according to the normal reducer development process, and the feasibility of the above-mentioned automatic modeling and gear parameter optimization solutions can be verified. When all simulation calculations are verified, the reducer parameter design and simulation work is completed.

[0134] For the method steps disclosed in the above embodiments, the method steps are described as a series of action combinations for the purpose of simple description, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0135] Based on the same inventive concept, the embodiment of the present application also provides a reducer gear parameter optimization system based on MASTA simulation for implementing the above-mentioned reducer gear parameter optimization method based on MASTA simulation. The implementation solution provided by the system to solve the problem is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations in one or more embodiments of the reducer gear parameter optimization system based on MASTA simulation provided below can refer to the limitations of the above-mentioned reducer gear parameter optimization method based on MASTA simulation, which will not be repeated here. It can be understood that the above description of its method is also applicable to the description of the system.

[0136] In one embodiment, a reducer gear parameter optimization system based on MASTA simulation is also provided. The embodiments of the present invention are described below in conjunction with the accompanying drawings. Figure 5 As shown, the system specifically includes:

[0137] A construction module 210 is used to construct a reducer simulation model based on predetermined initial gear parameters and positioning requirements of reducer shaft gear parts;

[0138] The determination module 220 is used to define the number of iterations and the range of optimization variables to generate an initial population; the gear parameters generated by the initial population are used as inputs of the reducer simulation model, and the gear optimization fitness is calculated based on the gear performance parameters output by the reducer simulation model;

[0139] Iterative calculation module 230, for selecting individuals in each sub-group with higher fitness to form a new sub-group based on a multi-objective genetic optimization algorithm; iteratively calculating the fitness value of the gear parameter of each sub-group that meets the gear parameter constraint condition, and updating the previous generation gear parameter;

[0140] The optimization module 240 is used to determine whether a preset termination condition is met, and when the preset termination condition is met, obtain the optimal gear parameters.

[0141] It is worth noting that although only some basic functional modules are disclosed in the embodiments of the present invention, it does not mean that the composition of the present system is limited to the above basic functional modules. On the contrary, what the present embodiment wants to express is that on the basis of the above basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with the prior art to form an infinite number of embodiments or technical solutions. In other words, the present system is open rather than closed. Just because the present embodiment only discloses individual basic functional modules, it cannot be considered that the protection scope of the claims of the present invention is limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above devices are described in terms of functions and are described separately in various units and modules. Of course, when implementing the present invention, the functions of each unit and module can be implemented in the same or one or more software and / or hardware.

[0142] like Figure 6 As shown, the present invention also provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the reducer gear parameter optimization method based on M ASTA simulation.

[0143] Figure 6 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 6 The electronic device provided in the embodiment of the present invention includes: one or more processors 310 and a storage device 320; the processor 310 in the electronic device may be one or more, Figure 6 A processor 310 is taken as an example; the storage device 320 is used to store one or more programs; the one or more programs are executed by the one or more processors 310, so that the one or more processors 310 implement the method for optimizing reducer gear parameters based on MASTA simulation as described in any of the embodiments of the present invention.

[0144] The electronic device may further include: an input device 330 and an output device 340 .

[0145] The processor 310, storage device 320, input device 330 and output device 340 in the electronic device can be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0146] The storage device 320 in the electronic device is a computer-readable storage medium that can be used to store one or more programs, which can be software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the reducer gear parameter optimization method based on MASTA simulation provided in the embodiment of the present invention. The processor 310 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the storage device 320, that is, the method for optimizing the reducer gear parameters based on MASTA simulation in the above method embodiment is implemented.

[0147] The storage device 320 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the storage device 320 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the storage device 320 may further include a memory remotely arranged relative to the processor 310, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0148] The input device 330 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. The output device 340 may include a display device such as a display screen.

[0149] The present invention also provides a computer-readable storage medium, which stores a computer program that can be executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the reducer gear parameter optimization method based on MASTA simulation.

[0150] Specifically, the computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A reducer gear parameter optimization method based on MASTA simulation, characterized in that: The method comprises: Based on the predetermined initial gear parameters and the positioning requirements of the reducer shaft gear parts, a reducer simulation model is constructed; The number of iterations and the range of optimization variables are defined to generate an initial population; the gear parameters generated by the initial population are used as inputs of a reducer simulation model, and the gear optimization fitness is calculated based on the gear performance parameters output by the reducer simulation model; Based on the multi-objective genetic optimization algorithm, individuals with higher fitness are selected from each sub-group to form a new sub-group; the fitness value of the gear parameters of each sub-group that meets the gear parameter constraints is iteratively calculated to update the previous generation gear parameters; It is determined whether the preset termination condition is met, and when the preset termination condition is met, the optimal gear parameters are obtained.

2. The method according to claim 1, characterized in that The construction of the reducer simulation model includes: According to the requirements of reducer automatic simulation modeling and gear parameter optimization, build MASTA secondary development environment, write the corresponding function codes of automatic simulation modeling and gear parameter optimization on MATLAB, and define the model structure tree; According to the reducer layout diagram, considering the transmission ratio and center distance requirements, the gear parameters are preliminarily confirmed to obtain the initial gear parameters; Create a reducer shaft gear parts assembly under a predefined model structure tree; wherein the reducer shaft gear parts include: input shaft, intermediate shaft, gear and differential; the gear includes: gear-primary driven gear, main reducer driven gear; The created input shaft assembly, gear assembly, intermediate shaft assembly and differential assembly are assembled respectively according to the initial gear parameters and the positioning requirements of the reducer shaft gear parts; and the MATLAB secondary development environment is used to use the parameters of the assembled reducer shaft gear parts as the input of the reducer simulation model, and the power flow is run to verify the model. After verifying that the reducer simulation model is correct, the MASTA reducer simulation model is saved and output.

3. The method according to claim 2, characterized in that The step of creating a reducer shaft gear parts assembly under a predefined model structure tree includes: According to the reducer layout diagram, determine the overall outline size of the reducer shaft gear parts, and separate the outline nodes based on the outline size; According to the distribution pattern of the contour nodes, the contour node coordinates of the input shaft, the intermediate shaft, the gear and the differential housing are edited, and in combination with the layout requirements of the reducer, the input shaft assembly, the gear assembly, the intermediate shaft assembly and the differential assembly are created under the predefined model structure tree.

4. The method according to claim 3, characterized in that The creating of the input shaft assembly comprises: Edit the outer diameter contour node and inner diameter contour node of the input shaft in sequence to create the input shaft; According to the layout requirements of the reducer, the manufacturer information and model size of the front and rear bearings of the input shaft are edited in sequence to create the front and rear bearings of the input shaft; and an input shaft assembly consisting of the input shaft and the front and rear bearings of the input shaft is obtained; The creating of the intermediate shaft assembly comprises: Edit the outer diameter contour nodes and inner diameter contour nodes of the intermediate shaft in sequence to establish the front and rear bearings of the intermediate shaft; and, Edit the outer diameter contour node and the inner diameter contour node of the first-level driven spoke in sequence to establish the first-level driven spoke; create a rigid connector, edit the length of the rigid connection, and establish a spoke rigid connection; An intermediate shaft assembly consisting of the front and rear intermediate shaft bearings, the primary driven wheel spokes and the spokes rigidly connected is obtained.

5. The method according to claim 3, characterized in that The creating of the gear assembly comprises: Edit and input the gear parameters of the primary driving gear, the secondary driving gear, and the secondary driving gear in sequence, establish the primary driving gear, the secondary driving gear, and the secondary driving gear; obtain the gear assembly consisting of the primary driving gear, the secondary driving gear, and the secondary driving gear; wherein the gear parameters include: center distance, module, pressure angle, helix angle, number of teeth, displacement coefficient, tooth width, tooth addendum coefficient, tooth root height coefficient, and hand direction.

6. The method according to claim 3, characterized in that The step of creating a differential assembly comprises: Edit the outer diameter contour nodes and inner diameter contour nodes of the differential case in sequence to establish the differential case; and edit the outer diameter contour nodes and inner diameter contour nodes of the main reducer and driven gear spokes in sequence to establish the main reducer and driven gear spokes; and according to the layout requirements of the reducer, select the manufacturer and model of the bearing, edit the manufacturer information, model, and size of the front and rear bearings of the differential case, and establish the front and rear bearings of the differential; create a rigid connector, edit the length of the rigid connection of the differential case, and establish the rigid connection of the differential case; A differential assembly is obtained which is composed of a differential case, a main reduction driven gear wheel spoke and a differential case which are rigidly connected.

7. The method according to claim 1, characterized in that The gear performance parameters outputted by the reducer simulation model are used to calculate the gear optimization fitness, which includes: According to the actual working conditions, define the optimization items and ranges of gear parameters; including: module, pressure angle, helix angle, number of teeth, modification coefficient, tooth top height coefficient, and tooth top and root coefficient difference; Based on the multi-objective genetic algorithm, the initial population is generated, and the number of individuals in the population, the number of gear parameters to be optimized, the number of iterations, the mutation probability, the crossover probability, and the value range of each optimized gear parameter are determined according to the actual situation; The gear overlap adaptability F1 is obtained by weighted calculation of the end face overlap, longitudinal face overlap and total overlap of the first-stage gear pair and the second-stage gear pair; The gear strength adaptability F2 is obtained by weighted calculation of the contact fatigue safety factor, bending fatigue safety factor, contact static safety factor, bending static safety factor, and tooth top thickness of the primary master-slave gear and the secondary master-slave gear.

8. The method according to claim 7, characterized in that The iterative calculation of the fitness value of each subgroup gear parameter that satisfies the gear parameter constraint condition and updating the previous generation gear parameters includes: According to the requirements of different reducers for adaptability and strength, different weights are selected to adjust the gear parameter optimization results; The gear parameters generated by the initial population are written into the reducer simulation model through API, the reducer simulation model is run through the code for simulation, and the simulation results are read out through API, and the fitness value of each generation of gear parameters is solved based on the gear optimization fitness calculation method; A multi-objective genetic algorithm is used to iteratively calculate the fitness value that satisfies the gear parameter constraints. When the preset number of iterations is reached, the fitness values ​​of each generation calculated are sorted in descending order, and the gear parameter groups with the highest priority are selected as the result parameters of this optimization. The optimization result parameters are input into the MASTA reducer simulation model through API to complete the gear parameter optimization work.

9. The method according to claim 8, characterized in that The gear parameter constraints are defined by the following formula: In the formula, gear s e is the tooth top thickness, ε α is the end face overlap, ε β is the longitudinal surface overlap, ε γ is the total overlap, S FH is the contact fatigue safety factor, S FF is the bending fatigue safety factor, S SH is the static contact safety factor, S SF is the static safety factor of bending, ds is the effective involute starting circle diameter, df is the root circle diameter, db is the base circle diameter, S1 and S2 refer to the safety factors of the primary gear and the secondary gear respectively.

10. A reducer gear parameter optimization system based on MASTA simulation, characterized in that: The system comprises: A construction module, used for constructing a reducer simulation model based on predetermined initial gear parameters and positioning requirements of reducer shaft gear parts; A determination module is used to define the number of iterations and the range of optimization variables to generate an initial population; the gear parameters generated by the initial population are used as inputs of a reducer simulation model, and the gear optimization fitness is calculated based on the gear performance parameters output by the reducer simulation model; An iterative calculation module is used to select individuals in each sub-group with higher fitness to form a new sub-group based on a multi-objective genetic optimization algorithm; iteratively calculate the fitness value of the gear parameters of each sub-group that meets the gear parameter constraint conditions, and update the previous generation gear parameters; The optimization module is used to determine whether the preset termination conditions are met, and when the preset termination conditions are met, the optimal gear parameters are obtained.

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