Slotted contact separating force calculation method adopting rapid calculation model

Abstract

The invention discloses a slotted contact separating force calculation method adopting a rapid calculation model, and relates to the technical field of electric connector mechanical property calculation. Input parameters and given tolerances of the parameters are determined; randomly generating a parameter combination in a given tolerance range through a Monte Carlo algorithm; establishing a finite element model of the slotted contact, and calculating an output separating force value; using a support vector regression algorithm to compile a rapid calculation model; a training sample is imported for solving, and a trained rapid calculation model is obtained; and importing a verification sample, comparing the verification sample with a finite element simulation value to carry out precision verification, if a requirement is met, carrying out file packaging, otherwise, supplementing the number of parameter combinations. After data expansion is carried out on input parameters, a separating force result of finite element simulation is used as a reference, a rapid calculation model is compiled based on a support vector regression algorithm, training and verification are carried out, the separating force can be rapidly obtained for different slotted contacts of electric connectors of the same batch, and the calculation efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of mechanical property calculation of electric connectors, in particular to a method for calculating the separation force of a slotted contact piece using a fast calculation model. Background Art

[0002] Slotted contacts are mainly composed of slotted holes, pins, sheaths, etc., and are generally designed as circular slotted contacts. In order to ensure reliable contact between the slotted holes and the pins, in the current production and assembly process, a closing process is required to make the front end of the slotted hole produce a certain amount of plastic deformation inward. When the pin is inserted into the slotted hole, a contact normal force and multi-point contact are formed between the pin holes, which has the characteristics of reliable contact and relatively simple processing and assembly.

[0003] The electrical contact of the electrical connector is achieved by the contact pair (pin and socket), which are installed in the plug and socket of the electrical connector respectively. The contact pair is the key to complete the reception and transmission of the device electrical signal and network signal. Its mechanical properties play an important role in whether the signal can be received stably and reliably. When the pin and the socket are plugged in, the reed is forced to undergo elastic deformation, generating a certain intensity of contact pressure. This action is also called the plug-in process. The separation force is usually used to indirectly reflect whether the electrical connector is in reliable contact. Therefore, the separation force of the contact is one of the important factors affecting the performance of the electrical connector, which is directly related to the performance and use of the electrical connector.

[0004] There are many factors that affect the separation force of slotted contacts, such as Young's modulus and yield strength of the contact material, inner and outer diameters of the socket, depth and width of the slot, and other parameters. The traditional separation force calculation method mainly uses finite element software simulation method, which cannot quickly calculate the separation force of the contact, resulting in a slow calculation speed of the separation force of the electrical connector contact. Therefore, how to directly calculate the separation force between the pin and the socket through the relevant parameters of the contact has become an urgent problem to be solved.

[0005] In view of this, the present invention takes into account the influence of material parameters, dimensional parameters and process parameters on the separation force of electrical connector contacts, and establishes a rapid calculation model of the separation force between contact pairs of electrical connectors under different parameters through finite element simulation and support vector regression machine learning algorithm, thereby realizing the rapid calculation of the separation force of electrical connector contacts. Summary of the invention

[0006] In order to address the shortcomings of the background technology, the present invention provides a method for calculating the separation force of slotted contacts using a fast calculation model. After data expansion for the input parameters, the separation force results of finite element simulation are used as a reference, and a fast calculation model is written based on a support vector regression algorithm and trained and verified. The separation force of different slotted contacts of the same batch of electrical connectors can be quickly obtained, thereby improving the calculation efficiency.

[0007] To achieve the above object, the present invention adopts the following technical solution: a method for calculating the separation force of a slotted contact using a fast calculation model, comprising the following steps:

[0008] Step 1: According to the process flow and design drawings of the electrical connector, determine the material parameters, dimensional parameters and process parameters of the slotted contact as input parameters, and determine the given tolerances of each parameter. The material parameters include Young's modulus, yield strength, tensile strength and elongation. The dimensional parameters include the inner diameter of the jack, the outer diameter of the jack, the slot length and the slot width. The process parameters include the fixture stroke and placement height, and the separation force value is used as the output parameter.

[0009] Step 2: For the input parameters of step 1, randomly generate N groups of parameter combinations within a given tolerance range using the Monte Carlo algorithm and store them in an Excel file;

[0010] Step 3: Use ABAQUS software to establish a finite element model of the slotted contact, perform finite element calculation on the parameter combination obtained in step 2 through model simulation, output the separation force value corresponding to each parameter combination, and store it in an Excel file;

[0011] Step 4: Use the support vector regression algorithm to write a calculation program for the separation force of the slotted contact in MATLAB as a fast calculation model. The specific operations are as follows:

[0012] The relationship between the input parameter x and the predicted value f(x) of the fast calculation model is established as follows:

[0013] f(x)=w T x+b (1)

[0014] Where w is the normal vector of the hyperplane and b is the displacement term;

[0015] The error function is expressed as:

[0016]

[0017] Where y is the simulation value of the finite element model, and ε is the bandwidth of the insensitive band;

[0018] The generated N groups of parameter combinations are used as samples, and the samples are divided into training samples and verification samples according to requirements;

[0019] The optimization problem of the support vector regression algorithm is expressed as:

[0020]

[0021] In the formula, C is the penalty parameter, i represents the i-th sample, l represents the number of training samples, ξ iis the slack variable, Indicates the distance between the true value and the sensitive band, y i represents the simulated value of the i-th sample, x i Represents the input parameters of the i-th sample;

[0022] Introducing Lagrange multipliers, according to the Carlo-Kuhn-Tucker condition and its supplementary conditions, we get:

[0023]

[0024] The kernel function is introduced to improve the calculation efficiency. The kernel function is expressed as follows:

[0025]

[0026] Therefore, formula (5) can be rewritten as:

[0027]

[0028] In the formula, a i is the Lagrange multiplier, for a i The complex conjugate of It is the mapping relationship from low dimension to high dimension;

[0029] Step 5: Import the training samples into MATLAB and solve them using the support vector regression algorithm to obtain a trained fast calculation model;

[0030] Step 6: Import the verification sample into the trained fast calculation model for accuracy verification, and compare the output prediction value with the simulation value of the corresponding finite element model. If the overall average error is less than the set threshold, the fast calculation model is packaged into a file. If the overall average error is not less than the set threshold, return to step 2 to supplement the number of parameter combinations until the requirements are met.

[0031] Furthermore, in the step 2, the number of parameter combinations generated for the first time is N≥400.

[0032] Furthermore, in step 4, a kernel function is introduced to select a Gaussian kernel function, and the expression is as follows:

[0033]

[0034] Where σ>0 is the bandwidth of the Gaussian kernel function.

[0035] Furthermore, in step six, the threshold is set to 10%.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention uses the Monte Carlo algorithm to expand the data of the input parameters, and uses ABAQUS to perform finite element simulation analysis to calculate the corresponding separation force value, and at the same time, writes a fast calculation model in MATLAB, and obtains the predicted value of the separation force of the slotted contact of the electrical connector under given material parameters, dimensional parameters and process parameters according to the support vector regression algorithm, and performs training and verification in combination with the parameter combination after the input parameter expansion, so that the subsequent calculation of the separation force of the slotted contacts of the same specification does not require simulation analysis, and can be directly obtained through the fast calculation model, which solves the problem that the traditional finite element software simulation method is difficult to quickly calculate the separation force of the contact, and effectively improves the calculation efficiency of the separation force. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is an implementation flow chart of the calculation method of the present invention;

[0038] Figure 2 is a schematic diagram of the error function of the support vector regression algorithm in the calculation method of the present invention;

[0039] Figure 3 It is a partial parameter setting diagram of the parameter combination selected in the embodiment during the finite element calculation process. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0041] like Figure 1-2 As shown, a method for calculating the separation force of a slotted contact using a fast calculation model is implemented by combining Figure 1 As shown, the following steps are included:

[0042] Step 1: According to the process flow and design drawings of the electrical connector, determine the material parameters, dimensional parameters and process parameters of the slotted contact as input parameters, and determine the given tolerances of each parameter. The material parameters include Young's modulus, yield strength, tensile strength and elongation. The dimensional parameters include the inner diameter of the jack, the outer diameter of the jack, the slot length and the slot width. The process parameters include the fixture stroke and placement height, and the separation force value is used as the output parameter.

[0043] Step 2: Since it is difficult to keep all parameters of all slotted contacts unchanged in actual production, for the input parameters of step 1, N groups of parameter combinations are randomly generated within a given tolerance range through the Monte Carlo algorithm. The number of generated parameters N is ≥ 400 for the first time, which is used to simulate the material parameters, size parameters and process parameters of different slotted contacts in actual production, and store them in an Excel file;

[0044] Step 3: Use ABAQUS software to establish a finite element model of the slotted contact, perform finite element calculation on the parameter combination obtained in step 2 through model simulation, output the separation force value corresponding to each parameter combination, and store it in an Excel file;

[0045] Step 4: Use the support vector regression algorithm (SVR) to write a calculation program for the separation force of the slotted contact in MATLAB as a fast calculation model. The program provides input windows and output windows corresponding to the input parameters and separation force values. The specific operations are as follows:

[0046] The prediction of separation force belongs to solving regression problems. The relationship between the n-dimensional (a total of 10 input parameters, n = 10) input parameter x and the predicted value f(x) of the fast calculation model is established, which is expressed as follows:

[0047] f(x)=w T x+b (1)

[0048] Where w is the normal vector of the hyperplane and b is the displacement term (bias).

[0049] The error function is expressed as:

[0050]

[0051] Combination Figure 2 As shown in FIG. 1 , formula (2) is represented in the figure as the area of ​​the near-sighted orbit, which is called the insensitive zone. No penalty is imposed on the data in this area. In the formula, y is the simulation value of the finite element model, and ε is the bandwidth of the insensitive zone.

[0052] The generated N groups of parameter combinations are used as samples, and the samples are divided into training samples and verification samples according to requirements.

[0053] The optimization problem of the support vector regression algorithm can be expressed as:

[0054]

[0055] In the formula, C is the penalty parameter, i represents the i-th sample, l represents the number of training samples, ξ i is the slack variable, Indicates the distance between the true value and the sensitive band, reducing the requirements for the hyperplane, y i represents the simulated value of the i-th sample, x i Represents the input parameter of the i-th sample.

[0056] Introducing the Lagrange multiplier, according to the Carlo-Kuhn-Tucker (KKT) condition and its supplementary conditions, we can get:

[0057]

[0058] In the formula, a i is the Lagrange multiplier, for a i The complex conjugate of It is a mapping relationship from low dimension to high dimension.

[0059] In order to linearize the nonlinear feature variables, the kernel function is introduced to project the nonlinear sample space into the high-dimensional linear feature space to reduce the difficulty of nonlinear calculation and thus improve the calculation efficiency. The kernel function is expressed as follows:

[0060]

[0061] Therefore, formula (5) can be rewritten as:

[0062]

[0063] Common kernel functions include linear kernel function, polynomial kernel function, Gaussian kernel function, Laplace kernel function and Sigmoid kernel function, among which the convergence domain and learning accuracy kernel calculation performance of Gaussian kernel function are better than those of other kernel functions. Therefore, Gaussian kernel function is preferred as the kernel function of this solution, and the expression is as follows:

[0064]

[0065] Where, σ>0, is the bandwidth of the Gaussian kernel function;

[0066] Step 5: Import the training samples into MATLAB and solve them using the support vector regression algorithm to obtain a trained fast calculation model;

[0067] Step 6: Import the verification sample into the trained fast calculation model for accuracy verification, and compare the output prediction value with the simulation value of the corresponding finite element model. If the overall average error is less than the set threshold (which can be set to 10%), the fast calculation model is packaged as a file for subsequent calculation of the separation force value of slotted contacts of the same model but different specifications. If the overall average error is not less than the set threshold, return to step 2 to supplement the number of parameter combinations until the requirements are met.

[0068] Example

[0069] (1) According to the process flow and design drawings of the electrical connector, taking the circular slotted contact as an example, Young's modulus, yield strength, tensile strength, elongation, jack inner diameter, jack outer diameter, slot length, slot width, fixture stroke, and placement height are determined as input parameters, and the given tolerances of each parameter are determined. The data are shown in Table 1:

[0070] Table 1 Center value and tolerance of input parameters

[0071]

[0072] (2) For the input parameters, 450 sets of parameter combinations are randomly generated within a given tolerance range through the Monte Carlo algorithm to simulate the Young's modulus, yield strength, tensile strength, elongation, socket inner diameter, socket outer diameter, slot length, slot width, fixture stroke and placement height of different circular slotted contacts in actual production, and the parameters are stored in an Excel file;

[0073] (3) The finite element model of the circular slotted contact is established using ABAQUS software. The parameter combination obtained in step 2 is subjected to finite element calculation through model simulation. 450 sets of separation force values ​​are output and stored in an Excel file. One set of parameter combinations is shown in Table 2:

[0074] Table 2 Parameter combinations

[0075]

[0076] In the process of finite element calculation with this parameter combination, some parameter settings are combined with Figure 3 As shown, part a) shows the Young's modulus setting, part b) shows the fixture stroke setting, and part c) shows the size parameter setting, including the inner diameter of the socket, the outer diameter of the socket, the slot length, and the slot width;

[0077] (4) Using the support vector regression algorithm (SVR), a calculation program for the separation force of the slotted contact was written in MATLAB as a fast calculation model. The program provided an input window and an output window corresponding to the input parameters and the separation force value. The generated 450 sets of parameter combinations were divided into 440 groups as training samples and 10 groups as verification samples.

[0078] (5) Import 440 sets of training samples into MATLAB, some of which are shown in Table 3:

[0079] Table 3 Training samples (part)

[0080]

[0081] Solve through support vector regression algorithm to obtain the trained fast calculation model;

[0082] (6) The 10 groups of verification samples were imported into the trained fast calculation model for accuracy verification. The output prediction values ​​were compared with the simulation values ​​of the corresponding finite element model. The verification results are shown in Table 4:

[0083] Table 3 Verification results

[0084]

[0085] It can be seen that the calculated values ​​obtained by the trained fast calculation model have an overall average error smaller than the simulation values ​​of the corresponding finite element model (about 4.36%, less than 10%). It is believed that the accuracy of the fast calculation model meets the requirements, and the number of parameter combinations is no longer supplemented. The app is established and the file is packaged. The separation force values ​​of slotted contacts of the same model and different specifications can be quickly obtained by importing the input parameters into the fast calculation model.

[0086] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other forms of assembly without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and range of equivalents of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0087] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for calculating the separation force of a slotted contact using a fast calculation model, characterized in that: The following steps are involved: Step 1: According to the process flow and design drawings of the electrical connector, determine the material parameters, dimensional parameters and process parameters of the slotted contact as input parameters, and determine the given tolerances of each parameter. The material parameters include Young's modulus, yield strength, tensile strength and elongation. The dimensional parameters include the inner diameter of the jack, the outer diameter of the jack, the slot length and the slot width. The process parameters include the fixture stroke and placement height, and the separation force value is used as the output parameter. Step 2: For the input parameters of step 1, randomly generate N groups of parameter combinations within a given tolerance range using the Monte Carlo algorithm and store them in an Excel file; Step 3: Use ABAQUS software to establish a finite element model of the slotted contact, perform finite element calculation on the parameter combination obtained in step 2 through model simulation, output the separation force value corresponding to each parameter combination, and store it in an Excel file; Step 4: Use the support vector regression algorithm to write a calculation program for the separation force of the slotted contact in MATLAB as a fast calculation model. The specific operations are as follows: The relationship between the input parameter x and the predicted value f(x) of the fast calculation model is established as follows: f(x)=w T x+b (1) Where w is the normal vector of the hyperplane and b is the displacement term; The error function is expressed as: Where y is the simulation value of the finite element model, and ε is the bandwidth of the insensitive band; The generated N groups of parameter combinations are used as samples, and the samples are divided into training samples and verification samples according to requirements; The optimization problem of the support vector regression algorithm is expressed as: In the formula, C is the penalty parameter, i represents the i-th sample, l represents the number of training samples, ξ i is the slack variable, Indicates the distance between the true value and the sensitive band, y i represents the simulated value of the i-th sample, x i Represents the input parameters of the i-th sample; Introducing Lagrange multipliers, according to the Carlo-Kuhn-Tucker condition and its supplementary conditions, we get: The kernel function is introduced to improve the calculation efficiency. The kernel function is expressed as follows: Therefore, formula (5) can be rewritten as: In the formula, a i is the Lagrange multiplier, for a i The complex conjugate of It is the mapping relationship from low dimension to high dimension; Step 5: Import the training samples into MATLAB and solve them using the support vector regression algorithm to obtain a trained fast calculation model; Step 6: Import the verification sample into the trained fast calculation model for accuracy verification, and compare the output prediction value with the simulation value of the corresponding finite element model. If the overall average error is less than the set threshold, the fast calculation model is packaged into a file. If the overall average error is not less than the set threshold, return to step 2 to supplement the number of parameter combinations until the requirements are met.

2. The method for calculating the separation force of a slotted contact using a fast calculation model according to claim 1, characterized in that: In the step 2, the number N of parameter combinations generated for the first time is ≥400.

3. The method for calculating the separation force of a slotted contact using a fast calculation model according to claim 1, characterized in that: In step 4, a kernel function is introduced to select a Gaussian kernel function, and the expression is as follows: Where σ>0 is the bandwidth of the Gaussian kernel function.

4. The method for calculating the separation force of a slotted contact using a fast calculation model according to claim 1, characterized in that: In step six, the threshold is set to 10%.

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