Gear tooth profile modification method, computer equipment and computer readable storage medium

Abstract

The invention provides a gear tooth profile modification method, computer equipment and a computer readable storage medium. The method comprises the following steps: creating a finite element model and a meshing stiffness model of a gear; according to the finite element model and the meshing stiffness model, determining a first parameter value of the first target function and a second parameter value of the second target function, and obtaining the first target function representing the dynamic load coefficient and the second target function representing the dynamic transfer error; establishing a tooth profile modification optimization model according to the first objective function and the second objective function, and determining a first optimal modification parameter value and a second optimal modification parameter value by adopting a genetic algorithm according to the tooth profile modification optimization model; and according to the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first target function and the weight of the second target function, a fuzzy comprehensive decision algorithm is adopted to determine a target optimal modification parameter value, and gear tooth profile modification is carried out according to the target optimal modification parameter value, so that the dynamic performance of gear transmission is improved.

Description

Technical Field

[0001] The present application relates to the field of mechanical transmission technology, and in particular to a gear tooth profile modification method, a computer device, and a computer-readable storage medium. Background Art

[0002] Gears are one of the most commonly used transmission components in mechanical systems, and their performance directly affects the efficiency and life of the entire mechanical system. Gear transmission, as a widely used transmission method in mechanical systems, often causes meshing impact, speed mutations, and load fluctuations, resulting in vibration and noise.

[0003] At present, traditional gear design mainly focuses on the gear tooth surface contact strength, bending strength and anti-adhesion ability, but often ignores the influence of meshing efficiency and dynamic performance, resulting in poor quality of gear manufacturing and affecting transmission performance.

[0004] Therefore, the gear design in the prior art has certain limitations. Summary of the invention

[0005] The purpose of this application is to provide a gear tooth profile modification method, a computer device and a computer-readable storage medium to address the practical needs of the gear design in the prior art, in view of the above-mentioned deficiencies in the prior art.

[0006] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0007] In a first aspect, an embodiment of the present application provides a gear tooth profile modification method, the method comprising:

[0008] Creating a finite element model of a gear and a meshing stiffness model, wherein the gear includes a driving wheel and a driven wheel;

[0009] Determine at least one first parameter value of a first objective function and at least one second parameter value of a second objective function according to the finite element model and the meshing stiffness model, to obtain the first objective function and the second objective function, wherein the first objective function is used to characterize a dynamic load coefficient, and the second objective function is used to characterize a dynamic transmission error;

[0010] Establishing a tooth profile modification optimization model according to the first objective function and the second objective function, and determining a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function by using a genetic algorithm according to the tooth profile modification optimization model;

[0011] According to the first optimal shaping parameter value, the second optimal shaping parameter value, the weight of the first objective function and the weight of the second objective function, a fuzzy comprehensive decision algorithm is used to determine the target optimal shaping parameter value of the gear tooth profile modification, and the gear tooth profile is modified according to the target optimal shaping parameter value.

[0012] As an optional implementation, determining at least one first parameter value of the first objective function and at least one second parameter value of the second objective function according to the finite element model and the meshing stiffness model includes:

[0013] According to the finite element model and the meshing stiffness model, the maximum value of the dynamic meshing force of the gear, the static meshing force of the gear under the action of the average torque, and the displacement of the gear on the meshing line are simulated;

[0014] Taking the maximum value of the dynamic meshing force and the static meshing force as a first parameter value respectively;

[0015] The displacement of the gear on the meshing line is taken as a second parameter value.

[0016] As an optional implementation manner, establishing the tooth profile modification optimization model according to the first objective function and the second objective function includes:

[0017] Determining constraint information of the tooth profile modification optimization model according to the first objective function and the second objective function, wherein the constraint information includes boundary constraint information and performance constraint information;

[0018] The tooth profile modification optimization model is established according to the modification parameter set, the first objective function, the second objective function and the constraint information.

[0019] As an optional implementation, the method of using a genetic algorithm to determine a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function according to the tooth profile modification optimization model includes:

[0020] According to the first objective function and the modification parameter set in the tooth profile modification optimization model, the genetic algorithm is used to determine a first optimal modification parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing;

[0021] According to the second objective function and the shaping parameter set in the tooth profile shaping optimization model, the genetic algorithm is used to determine the second optimal shaping parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing.

[0022] As an optional implementation, the using the genetic algorithm to determine the first optimal shaping parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing includes:

[0023] According to the first objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded by using binary coding to determine a first initialization population corresponding to the first objective function, wherein the first initialization population includes a plurality of first value sets, and each first value set is a group of first values ​​of the shaping parameter set;

[0024] Determining the fitness value of each first chromosome according to the first initialized population;

[0025] According to the fitness values ​​of the first chromosomes, a selection operator, a crossover operator and a mutation operator are used to perform selection, crossover and mutation processing on the first chromosomes, and determine the fitness value of the first initialized population;

[0026] According to the fitness value of the first initialization population, a first numerical value set when the first initialization population converges is used as a first optimal shaping parameter value of the first objective function.

[0027] As an optional implementation, the step of using the genetic algorithm to determine the second optimal shaping parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing includes:

[0028] According to the second objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded by using binary coding to determine a second initialization population corresponding to the second objective function, wherein the second initialization population includes a plurality of second value sets, and each second value set is a group of second values ​​of the shaping parameter set;

[0029] Determining the fitness value of each second chromosome according to the second initialized population;

[0030] According to the fitness values ​​of the second chromosomes, a selection operator, a crossover operator and a mutation operator are used to select, crossover and mutate the second chromosomes, and the fitness value of the second initialized population is determined;

[0031] According to the fitness value of the second initialization population, a second numerical value set when the second initialization population converges is used as a second optimal shaping parameter value of the second objective function.

[0032] As an optional implementation, the method of determining the target optimal modification parameter value of the gear tooth profile modification by using a fuzzy comprehensive decision algorithm according to the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first objective function, and the weight of the second objective function includes:

[0033] Determining an alternative set of the fuzzy comprehensive decision algorithm according to the first optimal shaping parameter value and the second optimal shaping parameter value;

[0034] Determining a factor set and a weight set of the fuzzy comprehensive decision-making algorithm according to the weight of the first objective function and the weight of the second objective function;

[0035] According to the factor set and the alternative set, a fuzzy relation matrix of the fuzzy comprehensive decision algorithm is established;

[0036] Determining a fuzzy comprehensive decision vector of the fuzzy comprehensive decision algorithm according to the weight set and the fuzzy relationship matrix;

[0037] According to the fuzzy comprehensive decision vector and the alternative set, the target optimal modification parameter value of the gear tooth profile modification is determined.

[0038] As an optional implementation, the factor set for determining the fuzzy comprehensive decision algorithm includes:

[0039] The dynamic load coefficient represented by the first objective function is used as a first factor of the factor set, and the dynamic transmission error represented by the second objective function is used as a second factor of the factor set.

[0040] In a second aspect, an embodiment of the present application provides a gear tooth profile modification device, the device comprising:

[0041] A creation module, used to create a finite element model and a meshing stiffness model of a gear, wherein the gear includes a driving wheel and a driven wheel;

[0042] a determination module, configured to determine at least one first parameter value of a first objective function and at least one second parameter value of a second objective function according to the finite element model and the meshing stiffness model, to obtain the first objective function and the second objective function, wherein the first objective function is used to characterize a dynamic load coefficient, and the second objective function is used to characterize a dynamic transmission error;

[0043] The determination module is further used to establish a tooth profile modification optimization model according to the first objective function and the second objective function, and to determine a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function by using a genetic algorithm according to the tooth profile modification optimization model;

[0044] The determination module is also used to determine the target optimal shaping parameter value of the gear tooth profile modification based on the first optimal shaping parameter value, the second optimal shaping parameter value, the weight of the first objective function and the weight of the second objective function by using a fuzzy comprehensive decision-making algorithm, and to modify the gear tooth profile according to the target optimal shaping parameter value.

[0045] As an optional implementation manner, the determining module is specifically used for:

[0046] According to the finite element model and the meshing stiffness model, the maximum value of the dynamic meshing force of the gear, the static meshing force of the gear under the action of the average torque, and the displacement of the gear on the meshing line are simulated;

[0047] Taking the maximum value of the dynamic meshing force and the static meshing force as a first parameter value respectively;

[0048] The displacement of the gear on the meshing line is taken as a second parameter value.

[0049] As an optional implementation manner, the determining module is specifically used for:

[0050] Determining constraint information of the tooth profile modification optimization model according to the first objective function and the second objective function, wherein the constraint information includes boundary constraint information and performance constraint information;

[0051] The tooth profile modification optimization model is established according to the modification parameter set, the first objective function, the second objective function and the constraint information.

[0052] As an optional implementation manner, the determining module is specifically used for:

[0053] According to the first objective function and the modification parameter set in the tooth profile modification optimization model, the genetic algorithm is used to determine a first optimal modification parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing;

[0054] According to the second objective function and the shaping parameter set in the tooth profile shaping optimization model, the genetic algorithm is used to determine the second optimal shaping parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing.

[0055] As an optional implementation manner, the determining module is specifically used for:

[0056] According to the first objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded by using binary coding to determine a first initialization population corresponding to the first objective function, wherein the first initialization population includes a plurality of first value sets, and each first value set is a group of first values ​​of the shaping parameter set;

[0057] Determining the fitness value of each first chromosome according to the first initialized population;

[0058] According to the fitness values ​​of the first chromosomes, a selection operator, a crossover operator and a mutation operator are used to perform selection, crossover and mutation processing on the first chromosomes, and determine the fitness value of the first initialized population;

[0059] According to the fitness value of the first initialization population, a first numerical value set when the first initialization population converges is used as a first optimal shaping parameter value of the first objective function.

[0060] As an optional implementation manner, the determining module is specifically used for:

[0061] According to the second objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded by using binary coding to determine a second initialization population corresponding to the second objective function, wherein the second initialization population includes a plurality of second value sets, and each second value set is a group of second values ​​of the shaping parameter set;

[0062] Determining the fitness value of each second chromosome according to the second initialized population;

[0063] According to the fitness values ​​of the second chromosomes, a selection operator, a crossover operator and a mutation operator are used to select, crossover and mutate the second chromosomes, and the fitness value of the second initialized population is determined;

[0064] According to the fitness value of the second initialization population, a second numerical value set when the second initialization population converges is used as a second optimal shaping parameter value of the second objective function.

[0065] As an optional implementation manner, the determining module is specifically used for:

[0066] Determining an alternative set of the fuzzy comprehensive decision algorithm according to the first optimal shaping parameter value and the second optimal shaping parameter value;

[0067] Determining a factor set and a weight set of the fuzzy comprehensive decision-making algorithm according to the weight of the first objective function and the weight of the second objective function;

[0068] According to the factor set and the alternative set, a fuzzy relation matrix of the fuzzy comprehensive decision algorithm is established;

[0069] Determining a fuzzy comprehensive decision vector of the fuzzy comprehensive decision algorithm according to the weight set and the fuzzy relationship matrix;

[0070] According to the fuzzy comprehensive decision vector and the alternative set, the target optimal modification parameter value of the gear tooth profile modification is determined.

[0071] As an optional implementation manner, the determining module is specifically used for:

[0072] The dynamic load coefficient represented by the first objective function is used as a first factor of the factor set, and the dynamic transmission error represented by the second objective function is used as a second factor of the factor set.

[0073] In a third aspect, an embodiment of the present application provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the gear tooth profile modification method as described in the first aspect above.

[0074] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the gear tooth profile modification method as described in the first aspect above are executed.

[0075] The beneficial effects of this application are:

[0076] The present application provides a gear tooth profile modification method, a computer device and a computer-readable storage medium, which create a finite element model and a meshing stiffness model of a gear, and determine the first objective function characterizing the dynamic load coefficient and the second objective function characterizing the dynamic transmission error through model simulation and analytical methods according to the finite element model and the meshing stiffness model. According to the first objective function characterizing the dynamic load coefficient and the second objective function characterizing the dynamic transmission error, a tooth profile modification optimization model based on the dynamic characteristics of the gear transmission is established, and the minimization of the dynamic load coefficient and the minimization of the dynamic transmission error are respectively used as single optimization objectives, and the genetic algorithm is used to determine the first optimal modification parameter value corresponding to the first objective function and the second optimal modification parameter value corresponding to the second objective function, respectively, to achieve the minimization of the dynamic load coefficient and the minimization of the dynamic transmission error. Taking into account the influence of the dynamic load coefficient and the dynamic transmission error on the gear tooth profile modification, a fuzzy comprehensive decision algorithm is used to perform fuzzy control comprehensive decision of multi-objective optimization, and the target optimal modification parameter value of the gear tooth profile modification is determined according to the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first objective function and the weight of the second objective function, and the gear tooth profile is modified according to the target optimal modification parameter value. By combining the single-objective optimization of genetic algorithm and the multi-objective optimization of fuzzy comprehensive decision-making algorithm, the gear tooth profile is modified based on the target optimal modification parameter value, which reduces the dynamic load coefficient and dynamic transmission error of the gear at the same time, thereby improving the stability of the gear transmission, slowing down the wear and aging of the gear, and improving the dynamic performance of the gear transmission, achieving the best vibration and noise reduction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0078] Figure 1 A schematic diagram of a process flow of a gear tooth profile modification method provided in an embodiment of the present application;

[0079] Figure 2 A schematic diagram of gear tooth profile modification provided in an embodiment of the present application;

[0080] Figure 3 A comparison diagram of the dynamic transmission error of the gear before and after the tooth profile modification provided in the embodiment of the present application;

[0081] Figure 4 A schematic diagram of a process for determining a first parameter value and a second parameter value of a gear tooth profile modification method provided in an embodiment of the present application;

[0082] Figure 5 A schematic diagram of a process of establishing a gear profile modification optimization model for a gear profile modification method provided in an embodiment of the present application;

[0083] Figure 6 A schematic diagram of a process for determining a first optimal modification parameter value and a second optimal modification parameter value of a gear tooth profile modification method provided in an embodiment of the present application;

[0084] Figure 7 A schematic diagram of a process for determining a first optimal modification parameter value of a gear tooth profile modification method provided in an embodiment of the present application;

[0085] Figure 8 A schematic diagram of a process for determining a second optimal modification parameter value of a gear tooth profile modification method provided in an embodiment of the present application;

[0086] Fig. 9 A schematic diagram of a process for determining target optimal modification parameter values ​​for gear tooth profile modification in a gear tooth profile modification method provided in an embodiment of the present application;

[0087] Fig.10 A module structure diagram of a gear tooth profile modification device provided in an embodiment of the present application;

[0088] Fig.11 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0089] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0090] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0091] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0092] In mechanical systems, gear transmission often encounters meshing impact, speed mutation and load fluctuation, which generates vibration and noise. At present, gear design usually focuses on the gear tooth surface contact strength, bending strength and anti-adhesion ability, but ignores the influence of meshing efficiency and dynamic performance, resulting in poor quality of gear manufacturing and affecting gear transmission performance.

[0093] Based on the above problems, an embodiment of the present application proposes a gear tooth profile modification method, which takes minimizing the dynamic load coefficient and minimizing the dynamic transmission error as optimization goals, and adopts a multi-objective optimization method that combines a genetic algorithm with a fuzzy comprehensive decision-making algorithm to achieve the optimization of the gear tooth profile modification parameter set, and obtain the target optimal modification parameter values, so that the gear tooth profile can be modified according to the target optimal modification parameter values, so that the dynamic load coefficient and dynamic transmission error of the gear are effectively optimized, thereby improving the dynamic performance of the gear transmission and reducing the failure rate of the gear transmission.

[0094] Figure 1 The present invention provides a flow chart of a gear tooth profile modification method provided in an embodiment of the present invention. The execution subject of the method can be any computer device with computing processing capabilities. Figure 1 As shown, the method includes:

[0095] S101. Create a finite element model and a meshing stiffness model of a gear, where the gear includes a driving wheel and a driven wheel.

[0096] Optionally, in the three-dimensional modeling software, the gear tooth surface equation and the tooth root transition curve equation are obtained, and a parameterized tooth profile curve generation program is written according to the tooth surface equation and the tooth root transition curve equation to generate a parameterized tooth profile curve. Among them, the three-dimensional modeling software can be any software with modeling and finite element analysis functions in Computer-Aided Engineering (CAE). The gear tooth surface equation is used to describe the geometric shape of the gear tooth profile, including parameters such as the gear tooth top height, module, and pressure angle. The tooth root transition curve equation can be determined by the motion trajectory of the tool center. According to the parameterized tooth profile curve and the preset gear geometric parameters, a finite element model of the gear is accurately created, wherein the gear includes a pair of driving wheels and driven wheels that mesh with each other.

[0097] The meshing position is determined by the meshing point pressure angle of the driving wheel and the driven wheel, the influence of the gear fluctuation speed and lateral vibration displacement on the meshing point is analyzed, and the dynamic boundary conditions of the single and double tooth meshing and modification areas are determined. The meshing stiffness is calculated by analytical method, and then the meshing stiffness model of the gear is created. Among them, the meshing point is the point where the tooth surface of the driving wheel contacts the tooth surface of the driven wheel, the meshing point pressure angle is the angle between the tangent line of the meshing point and the radial line at the meshing point, and the meshing stiffness is the degree of rigidity of the gear pair when in contact. Increasing the gear fluctuation speed will reduce the meshing force, and the lateral vibration displacement generated when the gear meshes will cause the position of the meshing point to change. The larger the lateral vibration displacement, the greater the position fluctuation of the meshing point.

[0098] S102. Determine at least one first parameter value of a first objective function and at least one second parameter value of a second objective function according to a finite element model and a meshing stiffness model, and obtain a first objective function and a second objective function, wherein the first objective function is used to characterize a dynamic load coefficient, and the second objective function is used to characterize a dynamic transmission error.

[0099] Optionally, based on the finite element model and meshing stiffness model of the gear, at least one first parameter value of the first objective function and at least one second parameter value of the second objective function are determined by simulation, and based on the first parameter value and the second parameter value, an analytical method is used to obtain the first objective function characterizing the dynamic load coefficient and the second objective function characterizing the dynamic transmission error, respectively.

[0100] Among them, dynamic load is the load caused by vibration and impact during the operation of the gear. The size of the dynamic load is related to the design, manufacturing accuracy and operating status of the gear. Excessive dynamic load will cause gear wear and loosening. Dynamic transmission error is the position error between the rotation axis of the driving wheel and the driven wheel during the gear transmission process. Excessive dynamic transmission error will cause obvious gear collision and noise.

[0101] Considering the impact of gear meshing and disengagement and the influence of dynamic load coefficient and dynamic transmission error caused by load distribution on the dynamic performance of gear transmission during gear transmission, through the first objective function characterizing the dynamic load coefficient and the second objective function characterizing the dynamic transmission error, the minimization of dynamic load coefficient and dynamic transmission error can be used as the optimization goals of gear tooth profile modification to reduce the dynamic load coefficient and dynamic transmission error and improve the dynamic performance of gear transmission.

[0102] S103, establishing a tooth profile modification optimization model according to the first objective function and the second objective function, and using a genetic algorithm to determine a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function according to the tooth profile modification optimization model.

[0103] Optionally, a mathematical relationship between the first objective function, the second objective function and the modification parameter set is established, and a tooth profile modification optimization model based on the dynamic characteristics of the gear transmission is established according to the mathematical relationship, the first objective function and the second objective function.

[0104] With minimizing the dynamic load coefficient as a single optimization goal, a genetic algorithm is used to solve the tooth profile modification optimization model to obtain the first optimal modification parameter value corresponding to the first objective function; correspondingly, with minimizing the dynamic transmission error as a single optimization goal, a genetic algorithm is used to solve the tooth profile modification optimization model to obtain the second optimal modification parameter value corresponding to the second objective function. Among them, the genetic algorithm is an adaptive global optimization probability search algorithm. In the process of optimizing the gear tooth profile modification parameters, two sets of optimal modification parameter values ​​are obtained according to the dynamic load coefficient and dynamic transmission error of the gear, thereby respectively achieving the minimization of the dynamic load coefficient and the minimization of the dynamic transmission error.

[0105] S104. According to the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first objective function and the weight of the second objective function, a fuzzy comprehensive decision algorithm is used to determine the target optimal modification parameter value of the gear tooth profile modification, and the gear tooth profile is modified according to the target optimal modification parameter value.

[0106] Optionally, after obtaining two sets of optimal shaping parameter values ​​with the dynamic load coefficient and dynamic transmission error of the gear as single optimization objectives, the dynamic load coefficient and the dynamic transmission error are comprehensively considered as two optimization objectives, and a fuzzy comprehensive decision algorithm is used to discretize the points of the first optimal shaping parameter value and the second optimal shaping parameter value. According to the preset weight of the first objective function, the preset weight of the second objective function and the discretization point selection results, a fuzzy control comprehensive decision of multi-objective optimization is performed to determine the target optimal shaping parameter values ​​of the gear tooth profile modification, and the tooth profiles of the driving wheel and the driven wheel are modified according to the target optimal shaping parameter values.

[0107] Among them, the target optimal modification parameter value is obtained by multi-objective optimization that comprehensively considers the minimization of dynamic load coefficient and dynamic transmission error, taking into account the influence of dynamic load coefficient and dynamic transmission error on gear tooth profile modification. Figure 2 The gear tooth profile modification principle diagram provided in the embodiment of the present application is as follows: Figure 3 The comparison diagram of the dynamic transmission error of the gear tooth profile before and after modification provided in the embodiment of the present application is shown in FIG. Figure 2 and Figure 3 After the gear tooth profile is modified according to the target optimal modification parameter value, the dynamic load coefficient of the gear is reduced, the dynamic transmission error of the gear is reduced, the stability of the gear transmission is improved, and the wear and aging of the gear are slowed down.

[0108] In this embodiment, a finite element model and a meshing stiffness model of the gear are created, and based on the finite element model and the meshing stiffness model, a first objective function characterizing the dynamic load coefficient and a second objective function characterizing the dynamic transmission error are determined by model simulation and analytical methods. According to the first objective function characterizing the dynamic load coefficient and the second objective function characterizing the dynamic transmission error, a tooth profile modification optimization model based on the dynamic characteristics of the gear transmission is established, and the minimization of the dynamic load coefficient and the minimization of the dynamic transmission error are respectively used as single optimization objectives, and a genetic algorithm is used to respectively determine the first optimal modification parameter value corresponding to the first objective function and the second optimal modification parameter value corresponding to the second objective function, so as to achieve the minimization of the dynamic load coefficient and the minimization of the dynamic transmission error. Taking into account the influence of the dynamic load coefficient and the dynamic transmission error on the gear tooth profile modification, a fuzzy comprehensive decision algorithm is used to perform fuzzy control comprehensive decision of multi-objective optimization, and the target optimal modification parameter value of the gear tooth profile modification is determined according to the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first objective function and the weight of the second objective function, and the gear tooth profile is modified according to the target optimal modification parameter value. By combining the single-objective optimization of genetic algorithm and the multi-objective optimization of fuzzy comprehensive decision-making algorithm, the gear tooth profile is modified based on the target optimal modification parameter value, which reduces the dynamic load coefficient and dynamic transmission error of the gear at the same time, thereby improving the stability of the gear transmission, slowing down the wear and aging of the gear, and improving the dynamic performance of the gear transmission, achieving the best vibration and noise reduction effect.

[0109] Hereinafter, a process of determining at least one first parameter value of the first objective function and at least one second parameter value of the second objective function according to the finite element model and the meshing stiffness model will be described in detail.

[0110] Figure 4 A schematic diagram of a process for determining a first parameter value and a second parameter value of a gear tooth profile modification method provided in an embodiment of the present application, such as Figure 4As shown, the step of determining at least one first parameter value of the first objective function and at least one second parameter value of the second objective function according to the finite element model and the meshing stiffness model in the above step S102 includes:

[0111] S401. According to the finite element model and the meshing stiffness model, the maximum value of the dynamic meshing force of the gear, the static meshing force of the gear under the action of the average torque, and the displacement of the gear on the meshing line are simulated.

[0112] Optionally, the finite element analysis module in the three-dimensional modeling software is used to perform dynamic simulation processing and static simulation processing of the finite element analysis according to the finite element model and meshing stiffness model of the gear, and the maximum value F of the dynamic meshing force of the gear is obtained respectively. max , Static meshing force F of gears under average torque S And the displacement TE of the gear on the meshing line.

[0113] Among them, the angle is converted into displacement, and the displacement TE of the gear on the meshing line is the gear angular error TE A The product of the base circle radius R, that is, TE = TE A ×R. Gear angular error TE A is the difference between the theoretical rotation angle θ′ and the actual rotation angle θ of the gear, that is, TE A =θ′-θ.

[0114] S402: Taking the maximum value of the dynamic meshing force and the static meshing force as a first parameter value respectively.

[0115] Optionally, the maximum value of the dynamic meshing force F of the gear max And the static meshing force F of the gear under the average torque S They are respectively used as the first parameter value of a first objective function, and the first objective function representing the dynamic load coefficient is determined by an analytical method based on the following formula:

[0116]

[0117] Among them, f(X 1 ) is the first objective function, X 1 Can express the dynamic load factor, F max is the maximum value of the dynamic meshing force of the gear, F S is the static meshing force.

[0118] S403: Taking the displacement of the gear on the meshing line as a second parameter value.

[0119] Optionally, the displacement TE of the gear on the meshing line is used as the second parameter value of the second objective function. According to the displacement TE of the gear on the meshing line, the dynamic transmission error TE at any meshing point d is determined based on the following formula: d Dynamic transmission error TE with maximum meshing point position max Fluctuation ΔTE d :

[0120] ΔTE d =TE max -TE d

[0121] Among them, ΔTE d To dynamically transmit error fluctuations, TE max is the dynamic transmission error of the maximum meshing point position, TE d is the dynamic transmission error of any meshing point position d.

[0122] When the gear tooth profile is modified by taking the increase of the normal pitch as the positive direction, the sum of the modification amounts of the driving wheel and the driven wheel at the meshing point d is calculated. d , the comprehensive deformation value δ of the gear at the meshing point d d , dynamic transmission error TE at the maximum meshing point position max and the dynamic transmission error fluctuation ΔTE d , the second objective function characterizing the dynamic transmission error is determined by analytical method based on the following formula:

[0123] f(X 2 )=ΔTE d =TE max -(e d -δ d )

[0124] Among them, f(X 2 ) is the second objective function, X 2 Can represent the dynamic transmission error, ΔTE d To dynamically transmit error fluctuations, TE max is the dynamic transmission error of the maximum meshing point position, e d is the sum of the modification amounts of the driving wheel and the driven wheel at the meshing point d, δ d is the comprehensive deformation value of the gear at the meshing point d.

[0125] In this embodiment, based on the finite element model and meshing stiffness model of the gear, the maximum value of the dynamic meshing force of the gear, the static meshing force of the gear under the average torque, and the displacement of the gear on the meshing line are obtained through simulation processing. The maximum value of the dynamic meshing force of the gear and the static meshing force of the gear under the average torque are respectively used as the first parameter value of a first objective function, and the first objective function is determined by an analytical method based on the first parameter value. The displacement of the gear on the meshing line is used as the second parameter value of a second objective function, and the second objective function is determined by an analytical method based on the second parameter value. By combining simulation processing and analytical methods, the first parameter value and the second parameter value are accurately determined, so that the first objective function characterizing the dynamic load coefficient and the second objective function characterizing the dynamic transmission error are respectively determined based on the first parameter value and the second parameter value.

[0126] The process of establishing the tooth profile modification optimization model according to the first objective function and the second objective function is described in detail below.

[0127] Figure 5 A schematic diagram of a process for establishing a gear profile modification optimization model for a gear profile modification method provided in an embodiment of the present application, such as Figure 5 As shown, the step of establishing the tooth profile modification optimization model according to the first objective function and the second objective function in the above step S103 includes:

[0128] S501. Determine constraint information of a tooth profile modification optimization model according to a first objective function and a second objective function, where the constraint information includes boundary constraint information and performance constraint information.

[0129] Optionally, when performing gear profile modification, the top straight line modification is performed on both the driving wheel and the driven wheel, the modification length of the gear is converted into a modification angle, and the maximum modification amount S of the driving wheel is converted into a modification angle. p , the modification angle α of the driving wheel p , the maximum modification amount S of the driven wheel g and the modified angle α of the driven wheel g As each shaping parameter in the shaping parameter set. That is, the shaping parameter set X = [S p ,S g ,α p ,α g ].

[0130] Based on the different modification requirements of the modification parameter set X by the dynamic load coefficient and the dynamic transmission error, the boundary constraint information and performance constraint information of the tooth profile modification optimization model are determined according to the first objective function characterizing the dynamic load coefficient and the second objective function characterizing the dynamic transmission error. Specifically, the boundary constraint information is the boundary condition of the modification parameter set X, that is, X min ≤X≤X max , where Xmin is the set of minimum values ​​of each shaping parameter in the shaping parameter set X, X max is the set of maximum values ​​of each shaping parameter in the shaping parameter set X. The minimum value of each shaping parameter is the intersection of the minimum values ​​of the two factors of dynamic load coefficient and dynamic transmission error, and the maximum value of each shaping parameter is the intersection of the maximum values ​​of the two factors of dynamic load coefficient and dynamic transmission error.

[0131] The performance constraint information includes the bending fatigue strength constraint information and the contact fatigue strength constraint information of the gear. Specifically, and Among them, σ w is the bending fatigue strength of the gear, is the intersection of the maximum bending fatigue strength considering the dynamic load coefficient and dynamic transmission error, σ c is the contact fatigue strength, It is the intersection of the maximum contact fatigue strength considering the dynamic load coefficient and dynamic transmission error respectively.

[0132] S502: Establish a tooth profile modification optimization model according to the modification parameter set, the first objective function, the second objective function and the constraint information.

[0133] Optionally, combining the shaping parameter set X, the first objective function f(X 1 ), the second objective function f(X 2 ), boundary constraint information and performance constraint information, and establish the tooth profile modification optimization model as minf(X), where X min ≤X≤X max , and In order to use the genetic algorithm to solve the tooth profile modification optimization model minf(X), obtain the first optimal modification parameter value corresponding to the first objective function and the second optimal modification parameter value corresponding to the second objective function, and achieve the optimization goals of minimizing the dynamic load coefficient and minimizing the dynamic transmission error, thereby reducing the dynamic load coefficient and dynamic transmission error in the gear transmission process.

[0134] In this embodiment, the boundary constraint information and performance constraint information of the tooth profile modification optimization model are determined according to the first objective function representing the dynamic load coefficient and the second objective function representing the dynamic transmission error. The tooth profile modification optimization model is established according to the modification parameter set, the first objective function, the second objective function, the boundary constraint information and the performance constraint information, so as to solve the target optimal modification parameter value of the tooth profile modification based on the tooth profile modification optimization model.

[0135] The following is a detailed description of a process of determining a first optimal modification parameter value corresponding to a first objective function and a second optimal modification parameter value corresponding to a second objective function using a genetic algorithm according to a tooth profile modification optimization model.

[0136] Figure 6 A schematic diagram of a process for determining a first optimal modification parameter value and a second optimal modification parameter value of a gear tooth profile modification method provided in an embodiment of the present application, such as Figure 6 As shown, the step of using a genetic algorithm to determine a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function according to the tooth profile modification optimization model in step S103 includes:

[0137] S601. According to a first objective function and a modification parameter set in a tooth profile modification optimization model, a genetic algorithm is used to determine a first optimal modification parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing.

[0138] Optionally, according to the first objective function f(X 1 ) and a modification parameter set X, with minimizing the dynamic load coefficient as a single optimization goal, a genetic algorithm is used to encode, select, crossover and mutate the modification parameter set X, solve the tooth profile modification optimization model, and obtain the first optimal modification parameter value corresponding to the first objective function. Among them, the genetic algorithm has a strong global search capability, and by increasing the crossover probability and mutation probability, as many individuals as possible are generated, thereby expanding the global search range.

[0139] If the gear tooth profile is modified based on the first optimal modification parameter value, the dynamic load coefficient of the gear is minimized, which can reduce the impact and vibration loads borne by the gear during transmission and improve the stability of the gear transmission.

[0140] S602: According to the second objective function and the modification parameter set in the tooth profile modification optimization model, a genetic algorithm is used to determine a second optimal modification parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing.

[0141] Optionally, according to the second objective function f(X 2 ) and a shaping parameter set X, with minimizing the dynamic transmission error as a single optimization goal, a genetic algorithm is used to encode, select, crossover and mutate the shaping parameter set X to solve the tooth profile modification optimization model and obtain the second optimal shaping parameter value corresponding to the second objective function.

[0142] If the gear tooth profile is modified based on the second optimal modification parameter value, the dynamic transmission error of the gear is minimized, the vibration and noise during the gear transmission process can be reduced, the wear and aging of the gear can be slowed down, and the service life of the gear can be extended.

[0143] In this embodiment, according to the first objective function and the shaping parameter set X in the tooth profile modification optimization model, minimizing the dynamic load coefficient is taken as a single optimization goal, and a genetic algorithm is used to encode, select, cross and mutate the shaping parameter set to solve the tooth profile modification optimization model, and obtain the first optimal shaping parameter value corresponding to the first objective function, so that the dynamic load coefficient of the gear is minimized. According to the second objective function and the shaping parameter set in the tooth profile modification optimization model, minimizing the dynamic transmission error is taken as a single optimization goal, and a genetic algorithm is used to encode, select, cross and mutate the shaping parameter set to solve the tooth profile modification optimization model, and obtain the second optimal shaping parameter value corresponding to the second objective function, so that the dynamic transmission error of the gear is minimized. Through the global search capability of the genetic algorithm, the first optimal shaping parameter value that minimizes the dynamic load coefficient and the second optimal shaping parameter value that minimizes the dynamic transmission error are obtained respectively.

[0144] The following describes in detail the process of determining the first optimal shaping parameter value corresponding to the first objective function by using a genetic algorithm through encoding, selection, crossover and mutation processing.

[0145] Figure 7 A schematic diagram of a process for determining a first optimal modification parameter value for a gear tooth profile modification method provided in an embodiment of the present application, such as Figure 7 As shown, the step of using a genetic algorithm in the above step S601 to determine the first optimal shaping parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing includes:

[0146] S701. According to the first objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded using binary coding to determine a first initialization population corresponding to the first objective function, wherein the first initialization population includes a plurality of first value sets, and each first value set is a group of first values ​​of the shaping parameter set.

[0147] Optionally, according to the shaping parameter set X, the maximum shaping amount S of the driving wheel in the shaping parameter set X is coded by binary coding. p , the modification angle α of the driving wheel p , the maximum modification amount S of the driven wheel g and the modified angle α of the driven wheel g Gene encoding is performed, where each modification parameter S p , α p , S g and αg All are 16-bit binary codes, resulting in a binary genetic code with 64 bits.

[0148] Taking the minimization of dynamic load coefficient as the optimization goal, according to the first objective function f(X 1 ), determine the first value range of each shaping parameter value in the shaping parameter set based on the dynamic load coefficient, take the first value range as the first boundary condition, randomly generate n first value sets that meet the first boundary condition as first chromosome code strings, and form the first objective function f(X 1 ) corresponding to the first initialization group. Wherein, each first value set is a set of first values ​​of the shaping parameter set X, such as each shaping parameter S p , α p , S g and α g The corresponding first value in the first value set.

[0149] Exemplarily, the size of the first initialization population, that is, the value n, can be 50. If the value n is too small, the first initialization population will converge too early and the global optimal solution cannot be obtained. If the value n is too large, the calculation time will be too long, affecting the efficiency of obtaining the global optimal solution.

[0150] S702: Determine the fitness value of each first chromosome according to the first initialized population.

[0151] Optionally, the first chromosome code strings in the first initialization group are decoded in binary coding to obtain the maximum modification amount S of n groups of driving wheels. p , the modification angle α of the driving wheel p , the maximum modification amount S of the driven wheel g and the modified angle α of the driven wheel g As n first chromosomes, according to the decoded n first chromosomes, the maximum value of the first objective function maxf(X 1 ) and the minimum value minf(X 1 ). The maximum value of the first objective function maxf(X 1 ) and the minimum value minf(X 1 ) is used as the fitness value to evaluate the first initialization population. Specifically, the fitness value of each first chromosome is determined based on the following formula:

[0152]

[0153] Among them, maxf(X 1 ) is the maximum value of the first objective function, minf(X 1 ) is the minimum value of the first objective function, is the fitness value of each first chromosome, d is the number of each first chromosome, and n is the size of the first initialized population.

[0154] S703 . According to the fitness value of each first chromosome, use a selection operator, a crossover operator and a mutation operator to perform selection, crossover and mutation processing on each first chromosome, and determine the fitness value of the first initialized population.

[0155] Optionally, the fitness values ​​of the first chromosomes are sorted in order from large to small or from small to large, and according to the fitness values ​​of the first chromosomes, a selection operator is used to determine the first chromosomes inherited to the next generation to form a new first initialization population. Specifically, a roulette wheel selection operator (Roulette Wheel Selection), i.e., a proportional selection operator, is used to select according to the fitness value ratio of each first chromosome, so that the probability of each first chromosome being selected is related to the ratio of its corresponding fitness value to the average fitness value of the first initialization population, and the probability of each first chromosome being selected in the first initialization population is determined based on the following formula p(S d ):

[0156]

[0157] Among them, p(S d ) is the probability of each first chromosome being selected, is the fitness value of each first chromosome, d is the number of each first chromosome, and n is the size of the first initialized population.

[0158] A crossover operator is used to select a pair of first chromosomes to be mated through multi-point crossover. For example, four crossover points are randomly selected to perform partial gene exchange in an interval exchange manner to generate two new offspring as new first chromosomes. More new first chromosomes are generated by increasing the crossover probability. A mutation operator is used to perform a mutation on each first chromosome in the first initialization population with a preset first mutation probability p. b1 The genes at each first chromosome locus are replaced with other alleles to generate multiple new first chromosomes, where the first mutation probability p b1 Affects the diversity of the new first initialization population. For example, the preset first mutation probability p b1 It can be 0.01, and the mutation operator is used according to the preset first mutation probability p b1 Randomly reverse the binary characters of a certain allele to perform mutation processing, so that each selected first chromosome has a preset first mutation probability p b1 The mutation is performed and inserted into the next generation to obtain a new first initialized population of each generation, and the fitness value of the new first initialized population of each generation is calculated.

[0159] S704 . According to the fitness value of the first initialization group, a first value set when the first initialization group converges is used as a first optimal shaping parameter value of the first objective function.

[0160] Optionally, according to the fitness values ​​of the first initialized populations of each generation, it is determined whether the first initialized population of the current generation converges. If so, a first numerical value set when the first initialized population of the current generation converges is used as the first objective function f(X 1 ) is the first optimal shaping parameter value. If the fitness value of the first initialized population of the current generation has no significant change compared with the fitness value of the first initialized population of the previous generation, it is determined that the first initialized population of the current generation converges.

[0161] In this embodiment, binary coding is used to genetically encode each shaping parameter in the shaping parameter set, and the minimization of the dynamic load coefficient is used as the optimization goal. According to the first objective function in the tooth profile shaping optimization model, the first initialization population corresponding to the first objective function is determined, and according to the first initialization population, the fitness value of each first chromosome in the first initialization population is determined. According to the fitness value of each first chromosome, the selection operator, crossover operator and mutation operator are used to select, crossover and mutate each first chromosome to obtain a new first initialization population of each generation, and the fitness value of each new first initialization population of each generation is calculated. According to the fitness value of each new first initialization population, a first numerical set when the new first initialization population converges is used as the first optimal shaping parameter value of the first objective function. The global search range is expanded by the genetic algorithm, the accuracy of the first optimal shaping parameter value is improved, and the minimization of the dynamic load coefficient is achieved.

[0162] The following describes in detail the process of determining the second optimal shaping parameter value corresponding to the second objective function by using a genetic algorithm through encoding, selection, crossover and mutation processing.

[0163] Figure 8 A schematic diagram of a process for determining a second optimal modification parameter value for a gear tooth profile modification method provided in an embodiment of the present application, such as Figure 8 As shown, the step of using a genetic algorithm in the above step S602 to determine the second optimal shaping parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing includes:

[0164] S801. According to the second objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded using binary coding to determine a second initialization population corresponding to the second objective function, wherein the second initialization population includes a plurality of second numerical value sets, and each second numerical value set is a group of second numerical values ​​of the shaping parameter set.

[0165] Optionally, according to the shaping parameter set X, the maximum shaping amount S of the driving wheel in the shaping parameter set X is coded by binary coding. p , the modification angle α of the driving wheel p , the maximum modification amount S of the driven wheel g and the modified angle α of the driven wheel g Gene encoding is performed, where each modification parameter S p , α p , S g and α g All are 16-bit binary codes, resulting in a binary genetic code with 64 bits.

[0166] Taking the minimization of dynamic transmission error as the optimization goal, according to the second objective function f(X 2 ), determine the second value range of each shaping parameter value in the shaping parameter set based on the dynamic transmission error, take the second value range as the second boundary condition, randomly generate z second value sets that meet the second boundary condition as the second chromosome code strings, and form the second objective function f(X 2 ) corresponding to the second initialization group. Wherein, each second value set is a set of second values ​​of the shaping parameter set X, such as each shaping parameter S p , α p , S g and α g The corresponding second value in the second value set.

[0167] Exemplarily, the size of the second initialization population can be the same as that of the first initialization population, that is, the z value can be 50. A z value that is too small will cause the second initialization population to converge prematurely and fail to obtain the global optimal solution. A z value that is too large will result in a long calculation time, affecting the efficiency of obtaining the global optimal solution.

[0168] S802: Determine the fitness value of each second chromosome according to the second initialized population.

[0169] Optionally, the second chromosome code strings in the second initialization population are decoded in binary coding to obtain the maximum modification amount S of n groups of driving wheels. p , the modification angle α of the driving wheel p , the maximum modification amount S of the driven wheel g and the modified angle α of the driven wheel g As z second chromosomes, according to the decoded z second chromosomes, the maximum value of the second objective function maxf(X 2 ) and the minimum value minf(X 2 ). The maximum value of the second objective function maxf(X 2 ) and the minimum value minf(X 2) is used as the fitness value to evaluate the second initialization population. Specifically, the fitness value of each second chromosome is determined based on the following formula:

[0170]

[0171] Among them, maxf(X 2 ) is the maximum value of the second objective function, minf(X 2 ) is the minimum value of the second objective function, is the fitness value of each second chromosome, k is the number of each second chromosome, and z is the size of the second initialized population.

[0172] S803. According to the fitness value of each second chromosome, use a selection operator, a crossover operator and a mutation operator to perform selection, crossover and mutation processing on each second chromosome, and determine the fitness value of the second initialized population.

[0173] Optionally, the fitness values ​​of the second chromosomes are sorted in order from large to small or from small to large, and according to the fitness values ​​of the second chromosomes, a selection operator is used to determine the second chromosomes inherited to the next generation to form a new second initialization population. Specifically, a roulette wheel selection operator, i.e., a proportional selection operator, is used to select according to the fitness value ratio of each second chromosome, so that the probability of each second chromosome being selected is related to the ratio of its corresponding fitness value to the average fitness value of the second initialization population, and the probability p(S) of each second chromosome being selected in the second initialization population is determined based on the following formula k ):

[0174]

[0175] Among them, p(S k ) is the probability of each second chromosome being selected, is the fitness value of each second chromosome, k is the number of each second chromosome, and z is the size of the second initialized population.

[0176] A crossover operator is used to select a pair of second chromosomes to be mated through multi-point crossover. For example, four crossover points are randomly selected to perform partial gene exchange in an interval exchange manner to generate two new offspring as new second chromosomes. By increasing the crossover probability, more new second chromosomes are generated. A mutation operator is used to perform a second mutation on each second chromosome in the second initialization population at a preset second mutation probability p. b2 The genes at each second chromosome locus are replaced with other alleles to generate multiple new second chromosomes, where the second mutation probability p b2 Affects the diversity of the new second initialization population. For example, the preset second mutation probability p b2It can also be 0.01, and the mutation operator is used according to the preset second mutation probability p b2 Randomly reverse the binary characters of a certain allele to perform mutation processing, so that each selected second chromosome has a preset second mutation probability p b2 The mutation is performed and inserted into the next generation to obtain a new second initialized population of each generation, and the fitness value of the new second initialized population of each generation is calculated.

[0177] S804 . According to the fitness value of the second initialization population, a second value set when the second initialization population converges is used as a second optimal shaping parameter value of the second objective function.

[0178] Optionally, according to the fitness values ​​of the new second initialized populations of each generation, it is determined whether the new second initialized population of the current generation converges. If so, a second numerical value set when the new second initialized population of the current generation converges is used as the second objective function f(X 2 ) is the second optimal shaping parameter value. If the fitness value of the new second initialization population of the current generation has no significant change compared with the fitness value of the new second initialization population of the previous generation, it is determined that the new second initialization population of the current generation converges.

[0179] In this embodiment, binary coding is used to genetically encode each shaping parameter in the shaping parameter set, and the minimization of dynamic transmission error is taken as the optimization goal. According to the second objective function in the tooth profile shaping optimization model, the second initialization population corresponding to the second objective function is determined, and according to the second initialization population, the fitness value of each second chromosome in the second initialization population is determined. According to the fitness value of each second chromosome, the selection operator, crossover operator and mutation operator are used to select, crossover and mutate each second chromosome to obtain a new second initialization population of each generation, and the fitness value of each new second initialization population of each generation is calculated. According to the fitness value of each new second initialization population, a second numerical set when the new second initialization population converges is used as the second optimal shaping parameter value of the second objective function. The global search range is expanded by the genetic algorithm, the accuracy of the second optimal shaping parameter value is improved, and the minimization of the dynamic transmission error is achieved.

[0180] The following describes in detail the process of determining the target optimal modification parameter value of the gear tooth profile modification using a fuzzy comprehensive decision algorithm based on the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first objective function, and the weight of the second objective function.

[0181] Fig. 9 A schematic diagram of a process for determining the target optimal modification parameter value of the gear tooth profile modification of the gear tooth profile modification method provided in an embodiment of the present application, such as Fig. 9As shown, the step of determining the target optimal modification parameter value of the gear tooth profile modification by using a fuzzy comprehensive decision algorithm according to the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first objective function and the weight of the second objective function in the above step S104 includes:

[0182] S901. Determine an alternative set of a fuzzy comprehensive decision algorithm according to a first optimal shaping parameter value and a second optimal shaping parameter value.

[0183] Optionally, the first optimal shaping parameter value and the second optimal shaping parameter value are discretized to determine the alternative set B of the fuzzy comprehensive decision algorithm. Exemplarily, six equal parts are taken between the first optimal shaping parameter value and the second optimal shaping parameter value to form seven groups of discrete optimal shaping parameter values ​​as the alternative set B, that is, B=(x 1 ,x 2 ,x 3 ,x 4 ,x 5 ,x 6 ,x 7 ), where x j =[S,α] T ,1≤j≤7. S is the optimal modification amount, including the optimal modification amount of the driving wheel and the optimal modification amount of the driven wheel, and α is the optimal modification angle, including the optimal modification angle of the driving wheel and the optimal modification angle of the driven wheel.

[0184] S902: Determine a factor set and a weight set of a fuzzy comprehensive decision-making algorithm according to the weight of the first objective function and the weight of the second objective function.

[0185] Optionally, the influence of the dynamic load coefficient and the dynamic transmission error on the dynamic performance of the gear transmission is comprehensively considered, and according to the first objective function f(X 1 ) and the second objective function f(X 2 ) to characterize the dynamic transmission error and determine the factor set A of the fuzzy comprehensive decision-making algorithm.

[0186] Preset the first objective function f(X 1 ) is weighted by β and the second objective function f(X 2 ) has a weight of 1-β, according to the preset first objective function f(X 1 ) and the second objective function f(X 2 ) is weighted 1-β to determine the weight set a of the fuzzy comprehensive decision-making algorithm, that is, a=(β,1-β),β∈(0,1). Among them, the weight set a is used to characterize the importance of each factor in the factor set A in the fuzzy control comprehensive decision-making.

[0187] S903. Establish a fuzzy relationship matrix of the fuzzy comprehensive decision-making algorithm based on the factor set and the alternative set.

[0188] Optionally, a fuzzy relationship matrix R of the fuzzy comprehensive decision algorithm is established based on the factor set A and the alternative set B. Specifically, there is a fuzzy satisfaction relationship between the factor set A and the alternative set B. Any element B in the alternative set B j , for factor A in factor set A i The satisfaction level is R ij , then the fuzzy relationship matrix R is expressed as follows, where 1≤j≤7, 1≤i≤2:

[0189]

[0190] S904: Determine a fuzzy comprehensive decision vector of the fuzzy comprehensive decision algorithm according to the weight set and the fuzzy relationship matrix.

[0191] Optionally, according to the fuzzy transformation principle, the fuzzy comprehensive decision vector C of the fuzzy comprehensive decision algorithm is determined according to the weight set a and the fuzzy relationship matrix R. Specifically, the product of the weight set a and the fuzzy relationship matrix R is used as the fuzzy comprehensive decision vector C, that is, C=a×R.

[0192] S905. Determine the target optimal modification parameter value of the gear tooth profile modification according to the fuzzy comprehensive decision vector and the alternative set.

[0193] Optionally, a synthesis operator is used to perform fuzzy synthesis according to the fuzzy comprehensive decision vector C and the alternative set B, wherein the synthesis operator can be a Zadeh operator. The Zadeh operator is used to highlight the determination of the target optimal modification parameter value B of the gear tooth profile modification through a synthesis method determined by the main factors. * The main reason for gear tooth profile modification is determined by weighted average. * Specifically, the target optimal shaping parameter value B is determined based on the following formula: * :

[0194]

[0195] Among them, B * is the target optimal shaping parameter value, C j is the fuzzy comprehensive decision vector of the jth group of optimal shaping parameter values, B j is the optimal shaping parameter value of the jth group.

[0196] In this embodiment, the first optimal modification parameter value and the second optimal modification parameter value are discretized to determine the alternative set of the fuzzy comprehensive decision algorithm. According to the dynamic load coefficient characterized by the first objective function and the dynamic transmission error characterized by the second objective function, the factor set of the fuzzy comprehensive decision algorithm is determined. According to the preset weight of the first objective function and the weight of the second objective function, the weight set of the fuzzy comprehensive decision algorithm is determined to characterize the importance of each factor in the factor set in the fuzzy control comprehensive decision. Since there is a fuzzy satisfaction relationship between the factor set and the alternative set, the fuzzy relationship matrix of the fuzzy comprehensive decision algorithm is established according to the factor set and the alternative set, and the product of the weight set and the fuzzy relationship matrix is ​​used as the fuzzy comprehensive decision vector. Fuzzy synthesis is performed according to the fuzzy comprehensive decision vector and the alternative set, and the Zade operator is used to highlight the main cause. The target optimal modification parameter value of the gear tooth profile modification is determined by weighted average. Through the multi-objective processing of fuzzy control comprehensive decision, the target optimal modification parameter value of the gear tooth profile modification is accurately determined.

[0197] As an optional implementation, the step of determining the factor set of the fuzzy comprehensive decision algorithm in the above step S902 includes:

[0198] The dynamic load coefficient represented by the first objective function is used as the first factor of the factor set, and the dynamic transmission error represented by the second objective function is used as the second factor of the factor set.

[0199] Optionally, the evaluation factors of the optimal modification parameters are mainly the dynamic load coefficient and dynamic transmission error of the gear. 1 ) is used as the first factor of factor set A, and the second objective function f(X 2 ) is taken as the second factor of factor set A, then factor set A = (dynamic load coefficient, dynamic transmission error).

[0200] In this embodiment, the influence of the dynamic load coefficient and dynamic transmission error of the gear on the dynamic performance of gear transmission is comprehensively considered, the dynamic load coefficient represented by the first objective function is used as the first factor of the factor set, and the dynamic transmission error represented by the second objective function is used as the second factor of the factor set, and the factor set of the fuzzy comprehensive decision-making algorithm is determined, so as to determine the target optimal shaping parameter value based on the two factors of dynamic load coefficient and dynamic transmission error.

[0201] Based on the same inventive concept, a gear tooth profile shaping device corresponding to the gear tooth profile shaping method is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned gear tooth profile shaping method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0202] Fig.10The module structure diagram of the gear tooth profile modification device provided in the embodiment of the present application is as follows: Fig.10 As shown, the device comprises:

[0203] The creation module 1001 is used to create a finite element model and a meshing stiffness model of a gear, wherein the gear includes a driving wheel and a driven wheel.

[0204] Determination module 1002 is used to determine at least one first parameter value of the first objective function and at least one second parameter value of the second objective function according to the finite element model and the meshing stiffness model, so as to obtain the first objective function and the second objective function, wherein the first objective function is used to characterize the dynamic load coefficient and the second objective function is used to characterize the dynamic transmission error.

[0205] The determination module 1002 is also used to establish a tooth profile modification optimization model according to the first objective function and the second objective function, and to determine a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function using a genetic algorithm according to the tooth profile modification optimization model.

[0206] The determination module 1002 is also used to determine the target optimal shaping parameter value of the gear tooth profile modification based on the first optimal shaping parameter value, the second optimal shaping parameter value, the weight of the first objective function and the weight of the second objective function by using a fuzzy comprehensive decision-making algorithm, and to modify the gear tooth profile according to the target optimal shaping parameter value.

[0207] As an optional implementation, the determination module 1002 is specifically configured to:

[0208] According to the finite element model and the meshing stiffness model, the maximum value of the dynamic meshing force of the gear, the static meshing force of the gear under the average torque and the displacement of the gear on the meshing line are simulated.

[0209] The maximum value of the dynamic meshing force and the static meshing force are respectively taken as a first parameter value.

[0210] The displacement of the gear on the meshing line is taken as a second parameter value.

[0211] As an optional implementation, the determination module 1002 is specifically configured to:

[0212] According to the first objective function and the second objective function, constraint information of the tooth profile modification optimization model is determined, and the constraint information includes boundary constraint information and performance constraint information.

[0213] A tooth profile modification optimization model is established according to the modification parameter set, the first objective function, the second objective function and the constraint information.

[0214] As an optional implementation, the determination module 1002 is specifically configured to:

[0215] According to the first objective function and the modification parameter set in the tooth profile modification optimization model, a genetic algorithm is used to determine the first optimal modification parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing.

[0216] According to the second objective function and the modification parameter set in the tooth profile modification optimization model, a genetic algorithm is used to determine the second optimal modification parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing.

[0217] As an optional implementation, the determination module 1002 is specifically configured to:

[0218] According to the first objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded using binary coding to determine a first initialization group corresponding to the first objective function. The first initialization group includes multiple first numerical value sets, and each first numerical value set is a group of first numerical values ​​of the shaping parameter set.

[0219] According to the first initialized population, the fitness value of each first chromosome is determined.

[0220] According to the fitness value of each first chromosome, a selection operator, a crossover operator and a mutation operator are used to perform selection, crossover and mutation processing on each first chromosome, and the fitness value of the first initialized population is determined.

[0221] According to the fitness value of the first initialization population, a first value set when the first initialization population converges is used as a first optimal shaping parameter value of the first objective function.

[0222] As an optional implementation, the determination module 1002 is specifically configured to:

[0223] According to the second objective function and the shaping parameter set, binary coding is used to genetically encode each shaping parameter in the shaping parameter set to determine a second initialization group corresponding to the second objective function. The second initialization group includes multiple second numerical value sets, and each second numerical value set is a group of second numerical values ​​of the shaping parameter set.

[0224] According to the second initialization population, the fitness value of each second chromosome is determined.

[0225] According to the fitness value of each second chromosome, a selection operator, a crossover operator and a mutation operator are used to perform selection, crossover and mutation processing on each second chromosome, and the fitness value of the second initialization population is determined.

[0226] According to the fitness value of the second initialization population, a second numerical value set when the second initialization population converges is used as a second optimal shaping parameter value of the second objective function.

[0227] As an optional implementation, the determination module 1002 is specifically configured to:

[0228] According to the first optimal shaping parameter value and the second optimal shaping parameter value, an alternative set of the fuzzy comprehensive decision algorithm is determined.

[0229] According to the weight of the first objective function and the weight of the second objective function, a factor set and a weight set of the fuzzy comprehensive decision-making algorithm are determined.

[0230] According to the factor set and alternative set, the fuzzy relation matrix of the fuzzy comprehensive decision-making algorithm is established.

[0231] According to the weight set and the fuzzy relationship matrix, the fuzzy comprehensive decision vector of the fuzzy comprehensive decision algorithm is determined.

[0232] According to the fuzzy comprehensive decision vector and the alternative set, the target optimal modification parameter value of the gear tooth profile modification is determined.

[0233] As an optional implementation, the determination module 1002 is specifically configured to:

[0234] The dynamic load coefficient represented by the first objective function is used as the first factor of the factor set, and the dynamic transmission error represented by the second objective function is used as the second factor of the factor set.

[0235] The present application also provides a computer device, such as Fig.11 1 is a schematic diagram of the structure of a computer device provided in an embodiment of the present application, including: a processor 111, a memory 112 and a bus 113. The memory 112 stores machine-readable instructions executable by the processor 111 (for example, Fig.10 Module 1001 is created in the device and execution instructions corresponding to module 1002 are determined, etc.), when the computer device is running, the processor 111 communicates with the memory 112 through the bus 113, and when the machine-readable instructions are executed by the processor 111, the steps of the gear tooth profile modification method in the above embodiment are executed.

[0236] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the gear tooth profile modification method in the above embodiment are executed.

[0237] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0238] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program code.

[0239] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A gear tooth profile modification method, characterized in that: include: Creating a finite element model of a gear and a meshing stiffness model, wherein the gear includes a driving wheel and a driven wheel; Determine at least one first parameter value of a first objective function and at least one second parameter value of a second objective function according to the finite element model and the meshing stiffness model, to obtain the first objective function and the second objective function, wherein the first objective function is used to characterize a dynamic load coefficient, and the second objective function is used to characterize a dynamic transmission error; Establishing a tooth profile modification optimization model according to the first objective function and the second objective function, and determining a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function by using a genetic algorithm according to the tooth profile modification optimization model; According to the first optimal shaping parameter value, the second optimal shaping parameter value, the weight of the first objective function and the weight of the second objective function, a fuzzy comprehensive decision algorithm is used to determine the target optimal shaping parameter value of the gear tooth profile modification, and the gear tooth profile is modified according to the target optimal shaping parameter value.

2. The method according to claim 1, characterized in that Determining at least one first parameter value of a first objective function and at least one second parameter value of a second objective function according to the finite element model and the meshing stiffness model comprises: According to the finite element model and the meshing stiffness model, the maximum value of the dynamic meshing force of the gear, the static meshing force of the gear under the action of the average torque, and the displacement of the gear on the meshing line are simulated; Taking the maximum value of the dynamic meshing force and the static meshing force as a first parameter value respectively; The displacement of the gear on the meshing line is taken as a second parameter value.

3. The method according to claim 1, characterized in that The establishing of the tooth profile modification optimization model according to the first objective function and the second objective function comprises: Determining constraint information of the tooth profile modification optimization model according to the first objective function and the second objective function, wherein the constraint information includes boundary constraint information and performance constraint information; The tooth profile modification optimization model is established according to the modification parameter set, the first objective function, the second objective function and the constraint information.

4. The method according to claim 1, characterized in that: The method of using a genetic algorithm to determine a first optimal modification parameter value corresponding to the first objective function and a second optimal modification parameter value corresponding to the second objective function according to the tooth profile modification optimization model includes: According to the first objective function and the modification parameter set in the tooth profile modification optimization model, the genetic algorithm is used to determine a first optimal modification parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing; According to the second objective function and the shaping parameter set in the tooth profile shaping optimization model, the genetic algorithm is used to determine the second optimal shaping parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing.

5. The method according to claim 4, characterized in that The step of using the genetic algorithm to determine the first optimal shaping parameter value corresponding to the first objective function through encoding, selection, crossover and mutation processing includes: According to the first objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded by using binary coding to determine a first initialization population corresponding to the first objective function, wherein the first initialization population includes a plurality of first value sets, and each first value set is a group of first values ​​of the shaping parameter set; Determining the fitness value of each first chromosome according to the first initialized population; According to the fitness values ​​of the first chromosomes, a selection operator, a crossover operator and a mutation operator are used to perform selection, crossover and mutation processing on the first chromosomes, and determine the fitness value of the first initialized population; According to the fitness value of the first initialization population, a first numerical value set when the first initialization population converges is used as a first optimal shaping parameter value of the first objective function.

6. The method according to claim 4, characterized in that The step of using the genetic algorithm to determine the second optimal shaping parameter value corresponding to the second objective function through encoding, selection, crossover and mutation processing includes: According to the second objective function and the shaping parameter set, each shaping parameter in the shaping parameter set is genetically encoded by using binary coding to determine a second initialization population corresponding to the second objective function, wherein the second initialization population includes a plurality of second value sets, and each second value set is a group of second values ​​of the shaping parameter set; Determining the fitness value of each second chromosome according to the second initialized population; According to the fitness values ​​of the second chromosomes, a selection operator, a crossover operator and a mutation operator are used to select, crossover and mutate the second chromosomes, and the fitness value of the second initialized population is determined; According to the fitness value of the second initialization population, a second numerical value set when the second initialization population converges is used as a second optimal shaping parameter value of the second objective function.

7. The method according to claim 1, characterized in that The method of determining the target optimal modification parameter value of the gear tooth profile modification by using a fuzzy comprehensive decision algorithm according to the first optimal modification parameter value, the second optimal modification parameter value, the weight of the first objective function, and the weight of the second objective function comprises: Determining an alternative set of the fuzzy comprehensive decision algorithm according to the first optimal shaping parameter value and the second optimal shaping parameter value; Determining a factor set and a weight set of the fuzzy comprehensive decision-making algorithm according to the weight of the first objective function and the weight of the second objective function; According to the factor set and the alternative set, a fuzzy relation matrix of the fuzzy comprehensive decision algorithm is established; Determining a fuzzy comprehensive decision vector of the fuzzy comprehensive decision algorithm according to the weight set and the fuzzy relationship matrix; According to the fuzzy comprehensive decision vector and the alternative set, the target optimal modification parameter value of the gear tooth profile modification is determined.

8. The method according to claim 7, characterized in that The factor set for determining the fuzzy comprehensive decision-making algorithm includes: The dynamic load coefficient represented by the first objective function is used as a first factor of the factor set, and the dynamic transmission error represented by the second objective function is used as a second factor of the factor set.

9. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the gear tooth profile modification method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the gear tooth profile modification method according to any one of claims 1 to 8 are executed.

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