Pressing rivet quality optimization method based on entropy weight-grey relational degree analysis

Abstract

The invention relates to the technical field of pressing rivet optimization, in particular to a pressing rivet quality optimization method based on entropy weight-grey correlation analysis. By means of the method, the optimal parameter set can be rapidly evaluated from numerous parameters to optimize the pressing rivet quality, the problem that in the prior art, when the pressing rivet is optimized, the parameters are not screened, and the parameters are directly evaluated through an algorithm is solved, and the pressing rivet quality is improved.

Description

Technical Field

[0001] The invention relates to the technical field of press riveting optimization, and in particular to a press riveting quality optimization method based on entropy weight-grey correlation analysis. Background Art

[0002] Compression riveting is a riveting method. Specifically, it refers to the process of using external pressure to squeeze the compression riveted parts (such as rivets, nuts or studs, etc.) into the body material, causing the body material to undergo plastic deformation, so that they enter the special prefabricated grooves in the riveted screw and nut structure, thereby achieving a reliable connection between two or more parts.

[0003] The important indicators for measuring riveting quality mainly include push-out force and twist-out force. Optimizing the riveting quality can ensure a stronger connection between riveted parts and reduce problems such as loosening and falling off caused by substandard strength. The methods for optimizing the riveting quality in the prior art mainly use single-factor optimization methods such as the Taguchi method to optimize the riveting quality. This method may rely on traditional experimental design, statistical analysis, or empirical rules during the optimization process. These methods do not consider the degree of influence of parameters on the optimization target during optimization, so all parameters need to be comprehensively evaluated. When solving complex optimization problems, the amount of calculation is large and the optimization efficiency is low. At the same time, during the optimization process, it is often necessary to try different parameter combinations one by one in the solution space, which is not targeted at important influencing parameters and is easy to fall into local optimal solutions.

[0004] In view of the above problems, the present invention provides a press riveting quality optimization method based on entropy weight-grey correlation analysis, which can quickly and accurately find the optimal press riveting quality optimization solution, thereby significantly improving the press riveting quality. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for optimizing riveting quality based on entropy weight-grey correlation analysis.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for optimizing riveting quality based on entropy weight-grey correlation analysis comprises the following steps: S1. Taguchi experiment: Determine the initial process parameter domain of grinding, construct a riveting quality test model with busbar design parameters and automatic riveting machine control parameters as design optimization variables, and take push-out force and twist-out force as responses respectively, and construct a basic experimental plan by changing different influencing factors; S2. Normalization of original data: normalize the original data of the two target values ​​of push-out force and twist-out force to eliminate the dimension and normalize the data; S3. Use the normalized data as a comparison sequence to calculate the grey correlation coefficient ; S4. Calculate weights using entropy weight method ; S5. Calculate the grey correlation degree , sort the grey relational ranking and determine the optimal parameters of the actual experimental scheme; S6. Calculate the grey correlation degree of the theoretical optimal variable parameters and compare it with the grey correlation degree of the actual optimal parameters obtained in step 5 to verify the rationality of the method.

[0007] Furthermore, the variables in S1 include pore size, maximum pressure value and pressing speed, and each variable includes experimental schemes at different levels.

[0008] Furthermore, the specific operation of S2 is as follows: The positive indicator formula is used for data processing. In the formula, represents the maximum value of all solutions under the jth index, It represents the minimum value of all solutions under the jth indicator.

[0009] Furthermore, the calculation formula of the grey correlation coefficient in S3 is as follows: In the formula To respond to the reference sequence; is the resolution coefficient, .

[0010] Furthermore, the specific operation of S4 is as follows: (1) Construct a decision matrix with two target test values, namely, push-out force and twist-out force, as elements: In the formula, m is the number of experiments; n is the number of target indicators; (2) Calculate the degree of variation of each indicator, measured by the proportion of the j-th indicator in the i-th scheme to the sum of all test values ​​of the j-th indicator, (3) Calculate the information entropy value of the j-th indicator as In the formula, k is a constant, ; (4) Calculate the information entropy redundancy as The weight of each indicator is calculated as .

[0011] Furthermore, the calculation formula of the S5 grey relational degree is as follows: .

[0012] Furthermore, the specific operations of step S6 are as follows: (1) Calculate the average value of grey correlation degree at different levels of each variable to obtain the mean response table; the formula is as follows: The calculation formula of the average value of grey relational degree at different levels of each variable is as follows: represents the average grey relational degree, T represents the sum of grey relational degrees corresponding to the variables at a single level, and m represents the number of experimental schemes in a single level of each variable; (2) The grey relational degree at the optimal level of each variable and the overall average grey relational degree are used to predict the grey relational degree at the optimal level. The prediction formula is as follows: Where: ——Overall average grey relational degree; ——The average value of grey relational degree under the optimal level of each variable; q – the number of variables; (3) Compare the grey correlation predicted value with the grey correlation of the optimal solution obtained in step 5 to determine the rationality of this method.

[0013] The beneficial technical effect of the present invention is that the multi-factor and multi-objective optimization of the riveting process is carried out with the push-out force and the twist-out force as the dual objectives, which is more accurate than the previous multi-factor single-objective optimization. The method of the present invention can quickly evaluate the optimal parameter group from a large number of parameters to optimize the riveting quality, which solves the problem caused by the failure to screen the parameters when optimizing the riveting in the traditional technology and directly evaluating the parameters through the algorithm, thereby improving the quality of the riveting. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the present invention; Figure 2 2 is the main effect diagram of the mean of the present invention. DETAILED DESCRIPTION

[0015] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0016] A method for optimizing the quality of press riveting based on entropy weight-grey correlation analysis, the specific steps are as follows: S1. Taguchi experiment: Determine the initial process parameter domain of grinding, construct a riveting quality test model with busbar design parameters and automatic riveting machine control parameters as design optimization variables, and take push-out force and twist-out force as responses respectively, and construct a basic experimental plan by changing different influencing factors; In this embodiment, the experimental scheme is shown in the following table: Table 1 Taguchi experimental orthogonal design scheme and experimental results S2. Normalization of original data: normalize the original data of the two target values ​​of push-out force and twist-out force to eliminate the dimension and normalize the data; In the riveting process, the larger the push-out force and the twist-out force, the better. The original data of the two target values ​​of the push-out force and the twist-out force are standardized to eliminate the dimension. The data are normalized as a comparison sequence. The larger the two target values ​​of the push-out force and the twist-out force, the better the processing quality. Therefore, the positive indicator formula is used for data processing, and there is Where, i=1,2,…,13;j=1,2; represents the maximum value of all solutions under the jth index, It represents the minimum value of all solutions under the jth indicator.

[0017] In this embodiment, the normalized data matrix obtained is , a total of 13 rows and 2 columns, each element of the matrix is ​​located at The interval is as follows: S3. Use the normalized data as a comparison sequence to calculate the grey correlation coefficient ; The grey correlation coefficient is used to measure the correlation between the comparison sequence and the reference sequence in each scheme. It is the core of the grey correlation analysis method. In the formula To respond to the reference sequence, usually ; is the resolution coefficient, , often taken .

[0018] In this embodiment, the matrix composed of the gray correlation coefficients is calculated. .

[0019] S4. Calculate weights using entropy weight method ; Calculation of weights by entropy weight method: Entropy weight method is a comprehensive evaluation method suitable for multiple targets. It can determine the weight coefficient of the indicator according to the degree of variation of each target value. It has the advantages of high accuracy and strong objectivity, and can largely avoid the interference of human subjective factors. The greater the degree of variation of the target, the smaller the entropy value, the greater the amount of information reflected, and the greater its weight. Construct a decision matrix with two target test values ​​of push-out force and twist-out force as elements, Where m is the number of trials and n is the number of target indicators.

[0020] In this embodiment, m=13, n=2 is taken, and the obtained matrix p is as follows Calculate the information entropy value of the jth indicator as Calculated In the formula, k is a constant, .

[0021] Calculate the information entropy redundancy as Calculated The weight of each indicator is calculated as Calculated The weight calculation results are shown in Table 2: Table 2. Weight calculation results (%) S5. After obtaining the weight coefficient of each indicator, calculate the grey correlation degree , sort the grey relational ranking and determine the optimal parameters of the actual experimental scheme; The formula is as follows: In this embodiment, the grey correlation degree of each scheme is shown in Table 3, and the best experimental scheme obtained is Group 2.

[0022] Table 3. Grey correlation data table of each experimental scheme S6. Calculate the grey correlation degree of the theoretical optimal variable parameters and compare it with the grey correlation degree of the actual optimal parameters obtained in step 5 to verify the rationality of the method.

[0023] (1) Calculate the average value of grey correlation degree at different levels of each variable to obtain the mean response table; the formula is as follows: The calculation formula of the average value of grey relational degree at different levels of each variable is as follows: represents the average grey relational degree, T represents the sum of grey relational degrees corresponding to the variables at a single level, and m represents the number of experimental schemes in a single level of each variable; In this embodiment, the mean response table obtained is shown in Table 4, and the main effect diagram of the mean is shown in Figure 2 .

[0024] Table 4. Average values ​​of grey relational degree at different levels of each variable From Table 4 and Figure 2 From the analysis results, it can be seen that within the selected process range, the influence of each variable parameter on the grey correlation degree is in the following order: aperture>pressing speed>maximum pressure value.

[0025] (2) The grey relational degree at the optimal level of each variable and the overall average grey relational degree are used to predict the grey relational degree at the optimal level. The prediction formula is as follows: Where: ——Overall average grey relational degree; ——The average value of grey relational degree under the optimal level of each variable; q – the number of variables, here the value is 3; According to the above formula, the predicted value of grey correlation degree under the optimal factor level combination is 1.12.

[0026] (3) Compare the grey correlation predicted value with the grey correlation of the optimal solution obtained in step 5 to determine the rationality of this method.

[0027] The predicted value of the grey correlation degree is 1.12, which is close to the experimental value of 0.931. In order to verify the effectiveness of the Taguchi-grey correlation analysis method in the multi-objective optimization of riveting process parameters, the initial process and the optimized process are compared, as shown in Table 5.

[0028] Table 5. Comparison between initial process and optimized process Through calculation, the grey relational degree of the optimized grinding process parameters is 0.360 higher than that of the initial grinding process parameters.

[0029] The grey correlation degree of the initial process parameter combination is 0.571, and the grey correlation degree after process optimization is 0.931, which is an increase of 63.05%. The push-out force is increased from 3.151N to 4.473N, and the twist-out force is increased from 25.63N to 28.13N. The experimental results show that both the twist-out force and the push-out force are greatly improved, which proves the effectiveness of the method.

Claims

1. A method for optimizing riveting quality based on entropy weight-grey correlation analysis, characterized in that: The steps include: S1. Taguchi experiment: Determine the initial process parameter domain of grinding, construct a riveting quality test model with busbar design parameters and automatic riveting machine control parameters as design optimization variables, and take push-out force and twist-out force as responses respectively, and construct a basic experimental plan by changing different influencing factors; S2. Normalization of original data: normalize the original data of the two target values ​​of push-out force and twist-out force to eliminate the dimension and normalize the data; S3, taking the normalized data as a comparison sequence and calculating the grey correlation coefficient; S4. Calculate weights using entropy weight method ; S5. Calculate the grey correlation degree , sort the grey relational ranking and determine the optimal parameters of the actual experimental scheme; S6. Calculate the grey correlation degree of the theoretical optimal variable parameters and compare it with the grey correlation degree of the actual optimal parameters obtained in step 5 to verify the rationality of the method.

2. The method for optimizing the press riveting quality based on entropy weight-grey correlation analysis according to claim 1 is characterized in that: The specific operation of S2 is as follows: The positive indicator formula is used for data processing. In the formula, represents the maximum value of all solutions under the jth index, It represents the minimum value of all solutions under the jth indicator.

3. The method for optimizing the press riveting quality based on entropy weight-grey correlation analysis according to claim 1 is characterized in that: The calculation formula of the grey correlation coefficient in S3 is as follows: In the formula To respond to the reference sequence; is the resolution coefficient, .

4. The method for optimizing the press riveting quality based on entropy weight-grey correlation analysis according to claim 1 is characterized in that: The specific operation of S4 is as follows: (1) Construct a decision matrix with two target test values, namely, push-out force and twist-out force, as elements: In the formula, m is the number of experiments; n is the number of target indicators; (2) Calculate the degree of variation of each indicator, measured by the proportion of the j-th indicator in the i-th scheme to the sum of all test values ​​of the j-th indicator, (3) Calculate the information entropy value of the j-th indicator as In the formula, k is a constant, ; (4) Calculate the information entropy redundancy as The weight of each indicator is calculated as 。 5. The method for optimizing the press riveting quality based on entropy weight-grey correlation analysis according to claim 4 is characterized in that: The calculation formula of the S5 grey relational degree is as follows: 。 6. The method for optimizing the press riveting quality based on entropy weight-grey correlation analysis according to claim 1 is characterized in that: The specific operation of step S6 is as follows: (1) Calculate the average value of grey correlation degree at different levels of each variable to obtain the mean response table; the formula is as follows: The calculation formula of the average value of grey relational degree at different levels of each variable is as follows: represents the average grey relational degree, T represents the sum of grey relational degrees corresponding to the variables at a single level, and m represents the number of experimental schemes in a single level of each variable; (2) The grey relational degree at the optimal level of each variable and the overall average grey relational degree are used to predict the grey relational degree at the optimal level. The prediction formula is as follows: Where: ——Overall average grey relational degree; ——The average value of grey relational degree under the optimal level of each variable; q – the number of variables; (3) Compare the grey correlation predicted value with the grey correlation of the optimal solution obtained in step 5 to determine the rationality of this method.

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