An assembly process parameter optimization method

Abstract

The application discloses an assembly process parameter optimization method, comprising the following steps: determining assembly process parameters based on a target function method, wherein the target function comprises an assembly contact surface stress distribution uniformity optimization function and an assembly contact surface geometric error distribution uniformity optimization function; changing the assembly process parameters, respectively calculating corresponding target function values, and determining an optimal target function value based on a constraint condition of the target function, wherein the assembly process parameters corresponding to the optimal target function value are the optimized assembly process parameters. The application accurately and quantitatively analyzes the assembly process parameters based on the uniformity of stress distribution and the uniformity of the shape error of the assembly surface and the size of the average stress generated by the assembly, so that the optimization of the assembly process parameters is realized, and a reference is provided for actual assembly.

Description

Technical Field

[0001] This invention belongs to the field of manufacturing quality prediction and control technology, and specifically relates to a method for optimizing assembly process parameters. Background Technology

[0002] Traditional assembly process parameter optimization mainly focuses on deviation optimization. For mechanical systems with low precision requirements, the impact of deviation optimization on assembly accuracy is not significant. However, for precision mechanical systems, deviation optimization can have a significant impact on assembly accuracy.

[0003] See appendix Figure 1 The three parts P1 have the same deviation, but when they are assembled as assembly reference parts with the same ideal planar part P2, the different shape error distributions of the three assembly surfaces A1 cause different positional and directional errors in the assembled part P2 after assembly. Figure 1 (a) The assembled part P2 is tilted, representing the worst assembly condition. Although from the perspective of geometric error analysis, Figure 1 (c) and Figure 1 (b) The assembled part P2 is in the same state, but, Figure 1 (c) The number of contact points between the assembled part P2 and the assembly surface of the assembly reference part is relatively small. Under the action of assembly force, the contact areas D1 and D2 near the contact points will produce large elastic-plastic deformation, accompanied by large local contact stress. This causes P2 to produce additional deformation error due to deformation, and non-uniform contact leads to the formation of a non-uniform stress field inside the structure.

[0004] Therefore, in precision mechanical systems, the deviations obtained by the traditional evaluation method of shape error are the same, but different shape error distributions will lead to different contact states between mating surfaces. As a result, due to the transmission and accumulation of shape error, different assembly errors will occur. At the same time, the non-uniform contact caused by the shape error distribution usually causes the parts to generate a non-uniform stress field. With the changes of time, temperature and mechanical environment, the energy of the non-uniform stress field will be released, causing the assembly accuracy to change further.

[0005] The paper "An entropy-based method to evaluate plane form error for precision assembly" proposes an evaluation method based on geometric entropy index, which can evaluate the uniformity of the distribution of shape error. Patent application number CN201610779936.8 proposes an assembly contact stress distribution evaluation method based on entropy theory, which can evaluate the uniformity of the assembly stress distribution on the surface of the part. However, neither of them has been applied in the optimization of assembly process parameters.

[0006] In summary, traditional assembly process optimization methods cannot reveal the impact of non-uniform stress fields on assembly accuracy. They can only perform rough quantitative analysis of assembly process parameters and consider fewer factors, namely, only deviations, without considering the distribution of shape errors and the distribution of non-uniform stress fields caused by the shape error distribution. Summary of the Invention

[0007] In view of this, the present invention provides an assembly process optimization method, which, based on the uniformity of stress distribution and the uniformity of shape error of assembly surfaces, and combined with the magnitude of average stress generated during assembly, accurately and quantitatively analyzes the assembly process parameters, thereby optimizing the assembly process parameters and providing a reference for actual assembly.

[0008] This invention is achieved through the following technical solution:

[0009] An assembly process parameter optimization method includes determining assembly process parameters based on an objective function method, wherein the objective function includes an optimization function for the uniformity of stress distribution on the assembly contact surface and an optimization function for the uniformity of geometric error distribution on the assembly contact surface.

[0010] By changing the assembly process parameters, the corresponding objective function values ​​are calculated respectively. Based on the constraints of the objective function, the optimal objective function value is determined. The assembly process parameters corresponding to the optimal objective function value are the optimized assembly process parameters.

[0011] Furthermore, the method is based on a working condition where several screws are used for assembly on the assembly surface, and the assembly process parameter is the preload of n screws, where the assembly process parameter X is:

[0012] X = [X1, X2, ..., X] n ] T

[0013] Furthermore, the stress distribution uniformity optimization function of the assembly contact surface is:

[0014]

[0015] Among them, H S H represents the stress entropy value of the assembly surface after the assembly process parameters have been changed, while H represents the stress entropy value of the assembly surface corresponding to the initial assembly process parameters.

[0016] Furthermore, the optimization function for the uniformity of the geometric error distribution of the assembly contact surface is:

[0017]

[0018] The assembly contact surface is divided into n regions based on the distribution of n screws, with each screw corresponding to one region; in the formula, H g (X iApply a preload X to the area corresponding to the i-th screw. i The subsequent geometric entropy value, For the region corresponding to the i-th screw, the preload X i The corresponding standard geometric entropy value, and the standard geometric entropy value will change with the preload X. i It changes with the size.

[0019] Furthermore, the constraints of the objective function are as follows:

[0020]

[0021] Where c% represents the actual optimized technical target achieved, and X i Let σ be the preload of the i-th screw, a be the minimum preload value of the screw, and b be the maximum preload value of the screw. The values ​​of a and b are obtained empirically. max σ1 represents the maximum stress after n preloads are applied to the assembly surface, and σ1 represents the micro-yield limit of the assembly surface material.

[0022] Furthermore, the objective function also includes an average stress value function, which is:

[0023]

[0024] Where i = 1, 2, ..., n, X i Let A be the preload force of the i-th screw, and A be the area of ​​each region on the assembly surface.

[0025] The constraints on the objective function also include:

[0026]

[0027] Where i = 1, 2, ..., n,

[0028] Beneficial effects:

[0029] (1) This invention determines assembly process parameters based on the objective function method. The objective functions include the stress distribution uniformity optimization function and the geometric error distribution uniformity optimization function of the assembly contact surface. Since the stress uniformity and geometric error distribution uniformity of the contact surface affect the assembly performance, this invention takes into account the distribution of non-uniform stress field and geometric error, and uses the objective function method for quantitative analysis to optimize the assembly process parameters. Furthermore, it can quantitatively predict the assembly accuracy of the assembly surface and adjust the assembly posture.

[0030] (2) The stress distribution uniformity optimization function of the assembly contact surface of the present invention is: By comparing the stress entropy value of the assembly surface after the assembly process parameters are changed with the stress entropy value of the assembly surface corresponding to the initial assembly process parameters, a quantitative analysis can be performed to optimize the uniformity of stress distribution on the assembly contact surface.

[0031] (3) The optimization function for the uniformity of geometric error distribution in this invention is: This function applies a preload X to a single area. i The resulting geometric entropy value and preload X i The corresponding standard geometric entropy values ​​are compared, and the objective function value is unified according to the sum of squares method. Therefore, the smaller the objective function value, the more preload X is applied. i The closer the geometric entropy value is to the standard value, the more uniform the geometric error distribution, thus enabling quantitative analysis of the optimization of the geometric error distribution uniformity.

[0032] (4) The constraints of the objective function of this invention are:

[0033]

[0034] This invention can flexibly apply to different assembly precision requirements by changing the optimized technical index c%, which is the percentage increase in overall stress uniformity after the assembly process parameters are optimized compared to the overall stress uniformity corresponding to the initial assembly process parameters.

[0035] The assembly process parameters of this invention must satisfy the requirement that the preload is within the range of [a, b]. The range of assembly process parameters can be roughly determined by empirically determining the values ​​of a and b, thus narrowing the range of changes in the assembly process parameters and reducing the amount of calculation. The assembly process parameters must also satisfy the requirement that the maximum stress after applying n preloads on the assembly surface is less than the micro yield limit of the assembly surface material, so as to ensure the dimensional stability of the parts.

[0036] (5) The objective function of the present invention also includes the average stress value function. Since the average stress in a single area will change after the preload is applied, the present invention will also take into account the average stress value that affects the assembly performance. According to the output average stress value, it can be verified that the preload applied by the screw obtained by the stress distribution uniformity optimization function and the geometric error distribution uniformity optimization function based on the assembly contact surface does not exceed the yield limit of the assembly surface material itself. Attached Figure Description

[0037] Figure 1 This is an assembly drawing of three parts, P2 and P2, with the same flatness as the assembly surface. (a) Assembly Figure I (b) Assembly Figure II (c) Assembly Figure III ;

[0038] Figure 2This is a schematic diagram of the assembly process optimization method;

[0039] Figure 3 This is a schematic diagram of a flange with 12 screws. Detailed Implementation

[0040] This embodiment provides a method for optimizing assembly process parameters. The method determines the assembly process parameters based on the objective function method. The objective function includes an optimization function for the uniformity of stress distribution on the assembly contact surface and an optimization function for the uniformity of geometric error distribution on the assembly contact surface. By changing the assembly process parameters, the corresponding objective function values ​​are calculated respectively. Based on the constraints of the objective function, the optimal objective function value is determined. The assembly process parameters corresponding to the optimal objective function value are the optimized assembly process parameters.

[0041] In this embodiment, when optimizing assembly process parameters, the uniformity of stress on the contact surface and the uniformity of geometric error distribution affect assembly performance. Therefore, the distribution of non-uniform stress field and geometric error are taken into account, and the objective function method is used for quantitative analysis to optimize the assembly process parameters.

[0042] Furthermore, the above method is based on the condition of assembling with several screws on the assembly surface, where the assembly process parameter is the preload of n screws, and the assembly process parameter X is:

[0043] X = [X1, X2, ..., X] n ] T Formula (1)

[0044] Furthermore, the optimization function for the stress distribution uniformity of the assembly contact surface is:

[0045]

[0046] Among them, H S H represents the stress entropy value of the assembly surface after the assembly process parameters have been changed, while H represents the stress entropy value of the assembly surface corresponding to the initial assembly process parameters.

[0047] Furthermore, the optimization function for the uniformity of geometric error distribution on the assembly contact surface is:

[0048]

[0049] The assembly contact surface is divided into n regions based on the distribution of n screws, with each screw corresponding to one region; in the formula, H g (X i Apply a preload X to the area corresponding to the i-th screw. i The subsequent geometric entropy value, For the region corresponding to the i-th screw, the preload X iThe corresponding standard geometric entropy value, and the standard geometric entropy value will change with the preload X. i It changes with the size.

[0050] Furthermore, the constraints on the objective function are:

[0051]

[0052] Where c% represents the actual optimized technical target achieved, and X i Let σ be the preload of the i-th screw, a be the minimum preload value of the screw, and b be the maximum preload value of the screw. The values ​​of a and b are obtained empirically. max σ1 represents the maximum stress after n preloads are applied to the assembly surface, and σ1 represents the micro-yield limit of the assembly surface material.

[0053] Example 2:

[0054] In another embodiment of this application, an assembly process parameter optimization method is provided, wherein the objective function further includes an average stress value function:

[0055]

[0056] Where i = 1, 2, ..., n, X i Let A be the preload force of the i-th screw, and A be the area of ​​each region on the assembly surface.

[0057] Then according to X i The constraints include a fixed area for each region, and the objective function also has the following constraints:

[0058]

[0059] Where i = 1, 2, ..., n,

[0060] Since the average stress in a single area will also change after the preload is applied, this embodiment also uses the average stress value as the objective function. Based on the final output average stress value, it can be verified that the average stress value obtained in Embodiment 1 does not exceed the yield limit of the assembly surface material itself.

[0061] Example 3:

[0062] A specific embodiment of this application: an assembly process parameter optimization method based on the optimization of a flange with 12 screws, see attached document. Figure 2 and 3Both surfaces of the flange are machined. The actual dimensions are an outer diameter of 110mm, an inner diameter of 60mm, a screw diameter of 5mm, and the material is 45 steel with a micro-yield strength σ1 of 355MPa. Based on experience, the minimum preload of the screw is a = 1000N, and the maximum preload is b = 4500N. The actual specified optimization technical indicator is 20%, meaning that the overall stress uniformity after the assembly process parameters are optimized should be improved by 20% compared to the overall stress uniformity corresponding to the initial assembly process parameters.

[0063] The method for optimizing assembly process parameters is as follows:

[0064] Determine an initial assembly process parameter X0 according to formulas (1) and (2), and let X i =2000N, i=1,2,…,n, then we have

[0065] X0=[2000N,2000N,…,2000N] T

[0066] At this point, the stress entropy value H of the assembly surface corresponding to the initial assembly process parameters is calculated to be M according to the entropy model. The assembly contact surface of the flange is divided into 12 annular regions based on the distribution of the 12 screws, with each screw corresponding to one annular region. The area of ​​a single region is S. The preload X within a single annular region can then be calculated using the entropy model. i The corresponding standard geometric entropy value is For example, the standard geometric entropy value corresponding to a preload of 2000N is The standard geometric entropy value corresponding to a preload of 3000N is

[0067] The objective function is then:

[0068]

[0069] Where i = 1, 2, ..., 12;

[0070] The constraints are:

[0071]

[0072] Where i = 1, 2, ..., 12;

[0073] Within the constraints, the assembly process parameters are changed multiple times, and the corresponding objective function values ​​are calculated for each change. Then, based on the constraints of the objective function, the optimal objective function value is determined. The assembly process parameters corresponding to the optimal objective function value are the optimized assembly process parameters.

[0074] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing assembly process parameters, characterized in that, This includes determining assembly process parameters based on the objective function method, where the objective functions include the stress distribution uniformity optimization function of the assembly contact surface and the geometric error distribution uniformity optimization function of the assembly contact surface. The stress distribution uniformity optimization function of the assembly contact surface is: ， in, This represents the stress entropy value of the assembly surface after the assembly process parameters have been changed. This represents the stress entropy value of the assembly surface corresponding to the initial assembly process parameters. The optimization function for the uniformity of geometric error distribution on the assembly contact surface is: ， The assembly contact surface is divided into n regions based on the distribution of n screws, with each screw corresponding to one region; in the formula, For the first Apply preload to the area corresponding to each screw. The subsequent geometric entropy value, For the first Preload within the area corresponding to each screw The corresponding standard geometric entropy value, and the standard geometric entropy value will change with the preload. It changes with the size; By changing the assembly process parameters, the corresponding objective function values ​​are calculated respectively. Based on the constraints of the objective function, the optimal objective function value is determined. The assembly process parameters corresponding to the optimal objective function value are the optimized assembly process parameters.

2. The assembly process parameter optimization method as described in claim 1, characterized in that, Based on the working condition of using several screws for assembly on the assembly surface, the assembly process parameters are the preload of n screws. for: 。 3. The assembly process parameter optimization method as described in claim 1, characterized in that, The constraints of the objective function are: ， in, To specify the optimized technical indicators to be achieved in practice, For the first The preload of each screw, This is the minimum preload value for the screw. The maximum preload value of the screw, and The values ​​are all obtained from experience. Apply to the assembly surface The maximum stress after preload The micro-yield limit of the assembly surface material.

4. The assembly process parameter optimization method as described in claim 3, characterized in that, The objective function also includes an average stress value function, which is: ， in, , For the first The preload of each screw, The area of ​​each region on the assembly surface; The constraints on the objective function also include: ， in, , , .

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