A method, device and equipment for optimizing assembly sealability of a ball-cone joint

Abstract

The application provides a ball head-conical surface joint assembly sealing optimization method, device and equipment, and relates to the technical field of static sealing structure. The method comprises the following steps: constructing a target finite element model for calculating the sealing performance of the ball head-conical surface joint assembly; calculating the actual contact area of the ball head-conical surface joint for representing the sealing performance of the ball head-conical surface joint assembly according to the target finite element model; constructing a response surface model according to the actual contact area; determining the corresponding relationship between the sealing performance of the ball head-conical surface joint assembly and the optimization variable according to the response surface model, and optimizing the sealing performance of the ball head-conical surface joint assembly according to the corresponding relationship. The problem that the research on sealing does not consider the influence law of the uncertainty in the machining or assembly process on the sealing performance, resulting in low calculation accuracy of the sealing performance of the ball head-conical surface joint assembly is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of static sealing structure, and in particular relates to a ball head-conical surface joint assembly sealing optimization method, device and equipment. BACKGROUND

[0002] In the manufacturing process of the ball head-conical surface structure, the part processing and product assembly have typical uncertainty (for example, when the ball head and the conical surface are processed according to the design tolerance, the size and shape error of the ball head and the conical surface are uncertain; although the surface roughness is specified in the processing process, the microscopic features of the surface topography are uncertain; in the assembly process, the assembly deflection angle is uncertain, and a certain torque is applied to the nut, and due to the torque coefficient change and assembly misalignment, the pre-tightening force is uncertain), which will inevitably lead to the typical uncertainty of the sealing performance of the ball head-conical surface structure. The existing technology does not consider the influence of the uncertainty in the processing or assembly process on the sealing performance, resulting in low calculation accuracy of the ball head-conical surface joint assembly sealing. SUMMARY

[0003] The embodiments of the present application provide a ball head-conical surface joint assembly sealing optimization method, device and equipment, to solve the problem that the existing technology does not consider the influence of the uncertainty in the processing or assembly process on the sealing performance, resulting in low calculation accuracy of the ball head-conical surface joint assembly sealing.

[0004] In order to solve the above technical problems, the present application adopts the following technical solutions:

[0005] The embodiments of the present application provide a ball head-conical surface joint assembly sealing optimization method, device and equipment, to solve the problem that the existing technology does not consider the influence of the uncertainty in the processing or assembly process on the sealing performance, resulting in low calculation accuracy of the ball head-conical surface joint assembly sealing.

[0006] A target finite element model for calculating the sealing performance of the ball head-conical surface joint assembly is constructed, and the target finite element model is related to the roughness of the inner surface of the conical surface joint and the uncertain variables;

[0007] According to the target finite element model, the actual contact area of the ball head-conical surface joint assembly is calculated to represent the sealing performance of the ball head-conical surface joint assembly;

[0008] According to the actual contact area, a response surface model is constructed, and the response surface model is related to the uncertain variables and the optimization variables;

[0009] According to the response surface model, the corresponding relationship between the ball head-conical surface joint assembly sealing performance and the optimization variables is determined, and the sealing performance of the ball head-conical surface joint assembly is optimized according to the corresponding relationship;

[0010] The uncertain variables include: size error of the ball head, size error of the inner surface of the taper joint, deflection angle of the ball head and the taper joint assembly, and axial pre-tightening force of the ball head; and the optimization variables include: radius of the ball head, and / or inclination angle of the inner taper surface of the taper joint.

[0011] Further, the roughness of the inner surface of the taper joint is calculated by the following formula:

[0012]

[0013] wherein R(τ x ,τ y ) is a correlation function of the micro-topography height value of the inner surface of the taper joint, and R(τ x ,τ y ) = E{z(x,y)z(x+τ x ,y+τ y )}; E{·} represents mathematical expectation of a random variable; τ x ,τ y represent relative positions with respect to coordinates (x,y); and σ is surface roughness; β x ,β y are autocorrelation lengths in x and y directions respectively.

[0014] Further, the actual contact area of the ball head-taper joint assembly for characterizing the sealing performance of the ball head-taper joint is calculated according to the target finite element model, and the calculation includes:

[0015] fixed constraints are set to the taper joint of the target finite element model, and axial pressure constraints are set to the ball head of the target finite element model;

[0016] the target finite element model after setting the constraints is simulated to obtain the actual contact area.

[0017] Further, the response surface model is constructed according to the actual contact area, and the construction includes:

[0018] uniform experimental design is performed on the actual contact area to obtain sample data;

[0019] the response surface model is constructed according to the sample data.

[0020] Further, the response surface model is a second-order response surface model, and the second-order response surface model is:

[0021]

[0022] wherein, is an approximate value; x is the optimization variable, n is the number of the optimization variable; a is a pending coefficient containing only x, and the pending coefficient is (n+1)(n+2) / 2; ε is a random error; δ is the uncertain variable, m is the number of the uncertain variable; β is a cross term pending coefficient, and is a pending coefficient containing only δ.

[0023] Further, the determining the corresponding relationship between the optimization variable and the sealing performance of the ball-conical joint assembly according to the response surface model comprises:

[0024] calculating the fitting accuracy of the response surface model;

[0025] in the case that the fitting accuracy of the response surface model reaches a preset requirement, determining the corresponding relationship between the radius of the ball head and the inclination angle of the inner conical surface of the conical joint and the sealing performance of the ball-conical joint assembly according to the response surface model.

[0026] Further, the fitting accuracy of the response surface model is calculated by:

[0027]

[0028] wherein, R 2 is a determination coefficient, used for representing the fitting accuracy of the response surface model; k is the number of uniform test design; y i is a calculated value calculated by the finite element model; is a calculated value calculated by the response surface model; is the average value of y i .

[0029] Further, the determining the corresponding relationship between the optimization variable and the sealing performance of the ball-conical joint assembly according to the response surface model comprises:

[0030] calculating the corresponding relationship between the optimization variable and the sealing performance of the ball-conical joint assembly by the response surface model and using a double-layer nested particle swarm optimization algorithm.

[0031] The embodiment of the application further provides a ball-conical joint assembly sealing performance optimization device, comprising:

[0032] a first construction module, used for constructing a target finite element model used for calculating the sealing performance of the ball-conical joint assembly, the target finite element model being related to the roughness of the inner surface of the conical joint and an uncertain variable;

[0033] a calculation module, used for calculating the actual contact area between the ball head and the conical joint for representing the sealing performance of the ball-conical joint assembly according to the target finite element model.

[0034] a second constructing module, configured to construct a response surface model according to the actual contact area, the response surface model being related to the uncertain variables and the optimization variables;

[0035] a determining module, configured to determine a corresponding relationship between the sealing performance of the ball-cone joint assembly and the optimization variables according to the response surface model, and optimize the sealing performance of the ball-cone joint assembly according to the corresponding relationship;

[0036] wherein the uncertain variables include a size error of the ball head, a size error of an inner surface of the cone joint, a deflection angle of the ball head and the cone joint assembly, and an axial pre-tightening force of the ball head; and the optimization variables include a radius of the ball head and / or an inclination angle of the inner cone surface of the cone joint.

[0037] The embodiment of the present application further provides a ball-cone joint assembly sealing performance optimization device, which comprises a transceiver, a processor, a memory, and a program or instructions stored in the memory and executable on the processor; and the processor implements the ball-cone joint assembly sealing performance optimization method as described above when executing the program or instructions.

[0038] The present application has the following advantages:

[0039] The ball-cone joint assembly sealing performance optimization method provided by the embodiment of the present application can consider the influence of uncertainty in the process of machining or assembly on the sealing performance by constructing a target finite element model related to the roughness of the inner surface of the cone joint and the uncertain variables, and can calculate the actual contact area of the ball head and the cone joint for representing the sealing performance of the ball-cone joint assembly; and by constructing the response surface model, the corresponding relationship between the optimization variables including the radius of the ball head and / or the inclination angle of the inner cone surface of the cone joint and the sealing performance of the ball-cone joint assembly can be determined, and the optimization variables can be optimized. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 a schematic diagram showing the steps of the ball-cone joint assembly sealing performance optimization method of the embodiment of the present application;

[0041] Figure 2 a schematic diagram showing the process of superimposing the rough surface and the ideal cone surface of the embodiment of the present application;

[0042] Figure 3 a schematic diagram showing the boundary conditions of the ball-cone structure finite element model of the embodiment of the present application;

[0043] Figure 4 a flowchart showing the double-layer nested particle swarm optimization algorithm of the embodiment of the present application.

[0044] Figure 5 Figure 1 shows a schematic diagram of a structure of a ball-cone joint assembly sealing performance optimization device according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the technical problems to be solved by the present application, technical solutions and advantages clearer, specific embodiments will be described in detail below with reference to the drawings. In the following description, specific details such as specific configurations and components are provided only to help a comprehensive understanding of embodiments of the present application. Therefore, it should be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, descriptions of known functions and configurations are omitted for clarity and conciseness.

[0046] It should be understood that the term "one embodiment" or "an embodiment" as referred to throughout the specification means that a specific feature, structure, or characteristic relating to an embodiment is included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0047] As shown in Figure 1 Figure 1, an embodiment of the present application provides a ball-cone joint assembly sealing performance optimization method, comprising the following steps:

[0048] Step 101, constructing a target finite element model for calculating the sealing performance of a ball-cone joint assembly, the target finite element model being related to the roughness of the inner surface of the cone joint and uncertain variables;

[0049] Step 102, calculating the actual contact area of the ball-cone joint assembly according to the target finite element model, the actual contact area being used to characterize the sealing performance of the ball-cone joint assembly;

[0050] Step 103, constructing a response surface model according to the actual contact area, the response surface model being related to the uncertain variables and optimization variables;

[0051] Step 104, determining the corresponding relationship between the sealing performance of the ball-cone joint assembly and the optimization variables according to the response surface model, and optimizing the sealing performance of the ball-cone joint assembly according to the corresponding relationship;

[0052] The uncertain variables include: size error of the ball head, size error of the inner surface of the conical joint, deflection angle of the ball head and the conical joint assembly, and axial pre-tightening force of the ball head; and the optimization variables include: radius of the ball head, and / or inclination angle of the inner conical surface of the conical joint.

[0053] Optionally, the conjugate gradient method is used to obtain the surface micro-topography, and the surface topography information is superimposed on the smooth conical surface considering the size error, so as to obtain the rough conical surface containing the size error and the surface topography.

[0054] In an embodiment of the present application, the process of obtaining the surface micro-topography by the conjugate gradient method is a simulation based on statistics, and the essence is to generate a surface height matrix with a specified statistical distribution rule. The autocorrelation function is used to represent the statistical distribution rule, and provides the spatial geometric information of the data and the correlation information between the data. The autocorrelation function of the surface micro-topography height value can be expressed as:

[0055] R(τ x ,τ y )=E{z(x,y)z(x+τ x ,y+τ y )}

[0056] Wherein, E{·} represents the mathematical expectation of the random variable; τ x ,τ y represent the relative position to the coordinates (x, y), and when x = 0, y = 0, R(0, 0) = σ 2 , and σ is the surface roughness.

[0057] In the actual calculation process, the surface micro-topography is represented by discrete data points, and the discrete form of the autocorrelation function can be expressed as:

[0058]

[0059] In the formula, n and m respectively represent the maximum value of the autocorrelation length in the x and y directions.

[0060] The autocorrelation function of the surface micro-topography can be approximately described by the form of the exponential function, and the autocorrelation function of the present embodiment is used to represent the roughness of the inner surface of the conical joint:

[0061]

[0062] Wherein, R(τ x ,τ y ) is the autocorrelation function of the micro-topography height value of the inner surface of the conical joint, and R(τ x ,τ y ) = E{z(x,y)z(x+τx y + τ y ); E{·} denotes the mathematical expectation of random variable; τ x , τ y denotes the relative position with coordinate (x, y); σ is the surface roughness; β x , β y are the autocorrelation lengths in x, y directions respectively.

[0063] Optionally, the autocorrelation length of the isotropic surface is equal in any direction, i.e. β x = β y .

[0064] In the cylindrical coordinate system, the expression of the ideal cone surface can be written as follows:

[0065]

[0066] In the formula, and represent the coordinate values of a point on the cone surface; represents the size of the cone angle, and is the cone inclination angle. If the angle dimension error of the cone inclination angle is considered, the expression of the cone surface is transformed into:

[0067]

[0068] In the formula, β' represents the size of the cone angle considering the dimension error, and Δα is the angle dimension error of the cone inclination angle.

[0069] As shown in the formula, the superposition process of the rough surface and the ideal cone surface can be represented as: Figure 2

[0070]

[0071]

[0072] θ″ i,j = θ′ i,j = θ i,j

[0073] In the formula, r i,j , z i,j , θ i,j are the coordinate values of a point on the ideal cone surface; r′ i,j , z′ i,j , θ′ i,j are the coordinate values of the point considering the dimension error; r″ i,j , z″ i,j , θ″ i,j are the coordinate values of the point adding the surface topography information; and h i,j is the surface topography height value corresponding to the point.

[0074] Optionally, the response surface model is a second-order response surface model.​

[0075] In an embodiment of the present application, the response surface method is used to construct the agent model, which mainly includes the following steps: test design, formulating the test scheme for constructing the response surface model; model establishment, fitting the response surface model according to the obtained test data; model evaluation, evaluating the fitting accuracy of the response surface model.

[0076] Optionally, the constructing the response surface model according to the actual contact area comprises:

[0077] Performing uniform test design on the actual contact area to obtain sample data;

[0078] Constructing the response surface model according to the sample data.

[0079] Considering the uncertain factors in the machining and assembly process, the actual contact area is selected to represent the sealing performance of the ball head-conical surface structure, the ball head radius R and the conical surface inclination angle a are selected as the optimization parameters, and a second-order response surface model reflecting the sealing performance of the ball head-conical surface structure is established. Since the surface topography cannot be reflected in the response surface model, only the size error, the assembly deflection angle and the axial pre-tightening force are selected as the uncertain variables. Therefore, the established second-order response surface model contains 2 optimization variables and 3 uncertain variables.

[0080] Optionally, the determining the corresponding relationship between the optimization variable and the sealing performance of the ball head-conical joint assembly according to the response surface model comprises:

[0081] The corresponding relationship between the optimization variable and the sealing performance of the ball head-conical joint assembly is calculated by using a double-layer nested particle swarm optimization algorithm through the response surface model.

[0082] The general form of the second-order response surface model can be expressed as:

[0083]

[0084] wherein, is an approximate value; x is an optimization variable, and n is the number of optimization variables; a is a to-be-determined coefficient, the number of to-be-determined coefficients that can be obtained is (n+1)(n+2) / 2; and ε is a random error. When the uncertain factors are considered, it can be converted into the following form, that is, the second-order response surface model (the corresponding relationship between the optimization variable and the sealing performance of the ball head-conical joint assembly) is:

[0085]

[0086] wherein, is an approximate value; x is the optimization variable, n is the number of the optimization variable; alpha is a pending coefficient containing only x, and the pending coefficient is (n+1)(n+2) / 2; epsilon is a random error; delta is the uncertain variable, m is the number of the uncertain variable; beta is a cross term pending coefficient, and is a pending coefficient containing only delta term.

[0087] After the second-order response surface model is arranged in a matrix form, the pending coefficients thereof can be solved by a least square method, and the matrix form is as follows:

[0088]

[0089]

[0090] wherein,

[0091] k is the number of design test; X (k) is the sample value of the kth test, is the response value of the kth test; epsilon (k) represents a random error.

[0092] when k >= (n+1)(n+2) / 2, X T X is non-singular, and the least square method can be known as follows:

[0093]

[0094]

[0095] A=(X T X) -1 X T Y

[0096] The coefficient matrix A is brought into the general form of the second-order response surface model, and the second-order response surface model can be obtained.

[0097] At present, the swarm intelligence optimization algorithm has been proved to be able to effectively solve the bi-level programming problem, and the bi-level nested particle swarm optimization algorithm becomes an ideal algorithm for solving the problem due to the outstanding characteristics such as simple structure, easy programming and strong search ability.

[0098] The bi-level nested particle swarm optimization algorithm of the embodiment of the application is divided into two optimization layers of inside and outside, and the particle swarm optimization algorithm is used for optimization. The outer optimization layer optimizes two optimization variables of the ball head radius and the conical surface inclination angle, the inner optimization layer optimizes three uncertain variables of the size error, the assembly deflection angle and the axial pre-tightening force under the constraint of the outer optimization layer, obtains the maximum value and the minimum value of the optimization target under a certain design variable, and further calculates the central value and the interval value of the optimization target.

[0099] As Figure 4 The double-layer nested particle swarm optimization algorithm procedure is as follows:

[0100] The basic parameters of the inner and outer layer particle swarm algorithm are determined, such as the iteration number, population size, variable number, optimization range, speed range, and the values of other parameters.

[0101] The initial position and speed of the outer layer particle are randomly generated within the feasible region range.

[0102] The initial position and speed of the inner layer particle are randomly generated within the feasible region range.

[0103] The individual optimal value, individual optimal position, global optimal value and global optimal position of the inner layer particle are updated, wherein the 'optimal' in the inner loop includes two concepts of'maximum' and'minimum'.

[0104] The position and speed of the inner layer particle are updated, and the boundary condition is processed.

[0105] It is judged whether the inner layer optimization result meets the requirement, if not, return to step 3 to enter the inner loop, otherwise end the inner loop and enter the next step.

[0106] The individual optimal value, individual optimal position, global optimal value and global optimal position of the outer layer particle are updated.

[0107] The position and speed of the outer layer particle are updated, and the boundary condition is processed.

[0108] It is judged whether the outer layer optimization result meets the requirement, if not, return to step 2 to enter the outer loop, otherwise end the outer loop and enter the next step.

[0109] The fitness evolution curve is generated, and the global optimal position of the particle in the outer loop is output, that is, the optimal solution.

[0110] The ball-cone joint assembly sealing optimization method of the embodiment can consider the influence of uncertainty on sealing performance in the machining or assembly process by constructing a target finite element model related to the roughness of the inner surface of the cone joint and the uncertain variables, calculating the actual contact area of the ball-cone joint assembly for representing the sealing performance of the ball-cone joint assembly.

[0111] Optionally, the actual contact area of the ball-cone joint assembly for representing the sealing performance of the ball-cone joint assembly is calculated according to the target finite element model, including:

[0112] A fixed constraint is set for the conical joint of the target finite element model, and an axial pressure constraint is set for the spherical head of the target finite element model;

[0113] The target finite element model after setting the constraint is simulated to obtain the actual contact area.

[0114] In the assembly process of the spherical head-conical structure, the pre-tightening torque is applied to the nut to realize the tightening. In order to simplify the assembly simulation process, the pre-tightening torque can be converted into an axial pre-tightening force according to the torsion-tension relationship. Under the action of the axial pre-tightening force, the contact surface of the spherical head-conical structure is extruded to deform and gradually form a ring-shaped sealing interface to play a sealing role. In order to simulate this process, a fixed constraint is applied to the lower end surface of the inner conical structure finite element model, and an axial pressure F is applied to the upper end surface of the spherical head finite element model. The assembly deflection between the spherical head and the inner conical structure is realized in the process of finite element grid division. The boundary conditions of the spherical head-conical structure finite element model are as shown in Figure 3 .

[0115] This assembly simulation process is a classic nonlinear contact process, and the nonlinearity mainly exists in two aspects: one is that the relative position and contact form of the contact area cannot be determined, and the contact area is constantly changing with the change of the applied load; the other is that the constraint condition has high nonlinearity, such as the friction of the contact surface, the non-intrusion condition, etc. In order to accurately simulate the actual contact situation, the spherical surface is taken as the target surface to establish the TARGET 170 unit, the rough conical surface is taken as the contact surface to establish the CONTACT 173 unit, and the friction coefficient of the contact surface is set to 0.15. On this basis, nonlinear solving is carried out to analyze the contact characteristics of the sealing interface of the spherical head-conical structure.

[0116] Under the action of the axial pre-tightening force, the spherical surface and the conical surface are extruded to each other, which causes the deformation of part of the micro-convex body structure on the sealing interface, but this cannot completely eliminate the contact gap of the sealing interface, that is, the actual contact area is always smaller than the nominal contact area.

[0117] The percolation theory in the sealing field can be expressed as follows: in the process of contact of the sealing interface, when the actual contact area increases to a certain value, the probability of the existence of the leakage channel sharply decreases, that is, the sealing structure has a great probability to achieve the sealing effect of zero leakage. Based on this theory, it can be considered that the larger the actual contact area is, the lower the possibility of the existence of the leakage channel is, and the better the sealing performance is. Therefore, the actual contact area is taken as an evaluation index of the sealing performance, so as to facilitate the uncertain optimization design of the spherical head-conical structure.

[0118] The actual contact area is taken as the sealing performance evaluation index, and has the following advantages: first, the greater the actual contact area, the lower the probability of the existence of the sealing leakage channel, that is, the better the sealing performance; second, the contact state data of the contact surface can be easily obtained through the finite element simulation method; and third, the subsequent data processing is simple, and the actual contact area can be calculated by simply processing the obtained surface contact state data.

[0119] In an embodiment of the application, the contact state data obtained through simulation is converted into a contact state matrix containing only 0 and 1, wherein 1 represents contact and 0 represents non-contact. For subsequent calculation, the conical contact surface is expanded into a rectangular contact surface through MATLAB software processing, and this process does not change the size of the actual contact area.

[0120] Optionally, the determining, according to the response surface model, of the corresponding relationship between the optimization variable and the sealing performance of the ball-head-taper joint assembly comprises:

[0121] calculating the fitting accuracy of the response surface model;

[0122] in a case where the fitting accuracy of the response surface model reaches a preset requirement, determining, according to the response surface model, of the corresponding relationship between the radius of the ball head and the inclination angle of the inner taper surface of the taper joint and the sealing performance of the ball-head-taper joint assembly.

[0123] In an embodiment of the application, in order to ensure that the response surface model can accurately reflect the response of the system and make the deviation between the response surface model and the actual model small enough, the determination coefficient is commonly used to represent the fitting accuracy of the second-order response surface model, so as to determine whether the model meets the requirements. The determination coefficient can be understood as the correlation coefficient of all variables, and can reflect the numerical characteristics between a random variable and multiple random variables, and is used to reflect the reliability of the fitted model. In a case where the fitting accuracy requirement is not high, the term containing the second-order uncertain variable can be ignored.

[0124] Optionally, the fitting accuracy of the response surface model is calculated by the following method:

[0125]

[0126] wherein, R 2 is the determination coefficient, used to represent the fitting accuracy of the response surface model; k is the number of uniform test design; y i is the calculated value calculated by the finite element model; is the calculated value calculated by the response surface model; is the average value of y i .

[0127] The ball-conical joint assembly sealing optimization method provided by the embodiment of the application adopts a second-order response surface model to shield noise data, reduce numerical fluctuation of a calculation result, and make a fitting result more stable and reliable.

[0128] As shown in Figure 5 The embodiment of the application also provides a ball-conical joint assembly sealing optimization device 500, which comprises:

[0129] A first construction module 501 is configured to construct a target finite element model used for calculating sealing of a ball-conical joint assembly, wherein the target finite element model is related to roughness of an inner surface of the conical joint and uncertain variables;

[0130] A calculation module 502 is configured to calculate, according to the target finite element model, an actual contact area of the ball-conical joint assembly used for characterizing sealing of the ball-conical joint assembly.

[0131] A second construction module 503 is configured to construct, according to the actual contact area, a response surface model, wherein the response surface model is related to the uncertain variables and optimization variables.

[0132] A determination module 504 is configured to determine, according to the response surface model, a corresponding relationship between the ball-conical joint assembly sealing and the optimization variables, and optimize the sealing of the ball-conical joint assembly according to the corresponding relationship.

[0133] The uncertain variables include size error of the ball, size error of the inner surface of the conical joint, deflection angle of the ball and the conical joint assembly, and axial pre-tightening force of the ball; and the optimization variables include radius of the ball and / or inclination angle of the inner conical surface of the conical joint.

[0134] The embodiment of the application also provides a ball-conical joint assembly sealing optimization device, which comprises a transceiver, a processor, a memory, and a program or instructions stored in the memory and executable on the processor; and the processor implements the ball-conical joint assembly sealing optimization method as described above when executing the program or instructions.

[0135] The above is the preferred embodiment of the application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the application, and these improvements and refinements are also within the protection scope of the application.

Claims

1. A method of optimizing the assembly tightness of a ball- cone joint, characterized in that, The method comprises the following steps: constructing a target finite element model for calculating the sealing performance of a ball-conical joint assembly, the target finite element model being related to the roughness of the inner surface of the conical joint and uncertain variables; calculating, according to the target finite element model, the actual contact area of the ball-conical joint for representing the sealing performance of the ball-conical joint assembly; constructing, according to the actual contact area, a response surface model, the response surface model being related to the uncertain variables and optimization variables; determining, according to the response surface model, the corresponding relationship between the sealing performance of the ball-conical joint assembly and the optimization variables, and optimizing the sealing performance of the ball-conical joint assembly according to the corresponding relationship; wherein the uncertain variables include the size error of the ball, the size error of the inner surface of the conical joint, the deflection angle of the ball and the conical joint assembly, and the axial pre-tightening force of the ball; the optimization variables include the radius of the ball and / or the inclination angle of the inner conical surface of the conical joint; the response surface model is a second-order response surface model, and the second-order response surface model is: wherein, is an approximation; x is the optimization variable, n is the number of optimization variables; a is a pending coefficient containing only x, and the pending coefficient is (n+1)(n+2) / 2; e is a random error; d is the uncertain variable, m is the number of uncertain variables; b is a pending coefficient of cross terms, and is a pending coefficient containing only d terms; the determining, according to the response surface model, of the corresponding relationship between the optimization variables and the sealing performance of the ball-conical joint assembly comprises: calculating the fitting accuracy of the response surface model; in the case that the fitting accuracy of the response surface model reaches a preset requirement, determining, according to the response surface model, the corresponding relationship between the radius of the ball and the inclination angle of the inner conical surface of the conical joint and the sealing performance of the ball-conical joint assembly.

2. The method of ball-on-cone joint assembly sealability optimization of claim 1, wherein, The roughness of the inner surface of the conical joint is calculated by the following formula: Where R(τ) x , τ y ) is the correlation function of the microstructure height value of the inner surface of the conical joint, and R(τ) is the coefficient of the microstructure height value of the inner surface of the conical joint. x ,τ y )=E{z(x,y)z(x+τ x ,y+τ y E{·} represents the expected value of a random variable; τ x ,τ y Represents the relative position with respect to coordinates (x, y); σ is the surface roughness; β x ,β y These are the autocorrelation lengths in the x and y directions, respectively.

3. The method of ball-on-cone joint assembly sealability optimization of claim 1, wherein, the calculating, according to the target finite element model, of the actual contact area of the ball-conical joint for representing the sealing performance of the ball-conical joint assembly comprises: setting a fixed constraint on the conical joint of the target finite element model and setting an axial pressure constraint on the ball of the target finite element model; performing simulation calculation on the target finite element model after setting the constraints to obtain the actual contact area.

4. The method of ball-on-cone joint assembly sealability optimization of claim 1, wherein, the constructing, according to the actual contact area, of a response surface model comprises: performing uniform experimental design on the actual contact area to obtain sample data; constructing the response surface model according to the sample data.

5. The method of ball-on-cone joint fitment sealability optimization of claim 1, wherein, the calculating the fitting accuracy of the response surface model comprises: wherein R 2 is the coefficient of determination, used to characterize the fitting accuracy of the response surface model; k is the number of uniform experimental design; y i is the calculated value obtained by the finite element model; is the calculated value obtained by the response surface model; is the average value of y i .

6. The method of ball-on-cone joint fitment sealability optimization of claim 1, wherein, the determining, according to the response surface model, of the corresponding relationship between the optimization variables and the sealing performance of the ball-conical joint assembly comprises: calculating the corresponding relationship between the optimization variables and the sealing performance of the ball-conical joint assembly by using a double-layer nested particle swarm optimization algorithm through the response surface model.

7. A ball-on-cone joint assembly sealability optimization device, comprising: The method comprises the following steps: a first construction module for constructing a target finite element model for calculating the sealing performance of a ball-conical joint assembly, the target finite element model being related to the roughness of the inner surface of the conical joint and uncertain variables; a calculation module for calculating, according to the target finite element model, the actual contact area of the ball-conical joint for representing the sealing performance of the ball-conical joint assembly; a second constructing module configured to construct a response surface model according to the actual contact area, the response surface model being related to the uncertain variables and the optimization variables; a determining module configured to determine a corresponding relationship between the sealing performance of the ball-cone joint assembly and the optimization variables according to the response surface model, and optimize the sealing performance of the ball-cone joint assembly according to the corresponding relationship; wherein the uncertain variables include a size error of the ball head, a size error of an inner surface of the cone joint, a deflection angle of the ball head and the cone joint assembly, and an axial pre-tightening force of the ball head; the optimization variables include a radius of the ball head and / or an inclination angle of the inner cone surface of the cone joint; the response surface model is a second-order response surface model, and the second-order response surface model is: wherein, is an approximation; x is the optimization variable, n is the number of optimization variables; a is a pending coefficient containing only x, and the pending coefficient is (n+1)(n+2) / 2; e is a random error; d is the uncertain variable, m is the number of uncertain variables; b is a pending coefficient of cross terms, and is a pending coefficient containing only d terms; the determining module is specifically configured to calculate a fitting accuracy of the response surface model; and in a case where the fitting accuracy of the response surface model reaches a preset requirement, determine the corresponding relationship between the radius of the ball head and the inclination angle of the inner cone surface of the cone joint and the sealing performance of the ball-cone joint assembly according to the response surface model.

8. A ball-on-cone joint assembly sealability optimization apparatus comprising: a transceiver, a processor, a memory, and a program or instructions stored on the memory and executable on the processor; and the processor implements the ball-cone joint assembly sealing performance optimization method according to any one of claims 1-6 when executing the program or instructions.

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