Evaluation of automobile parts design scheme for intelligent manufacturing

Abstract

Aiming at the selection of modern auto parts design schemes, an evaluation method of automobile parts design scheme for intelligent manufacturing is proposed. Considered the environment-friendly factors and traditional manufacturing factors, the evaluation index system of automobile parts design scheme was established, which taken technology, environment, economy and quality reliability as the first level evaluation index, and 12 second level indexes under the first level index. The game theory method was used to comprehensively consider the proportion of subjective and objective weights to determine the composite weight of each index. Aimed at the certainty and uncertainty of the evaluation index, the set pair analysis relative closeness method was used to calculate the composite weight and established the model. Three sets of automobile panel design schemes of a company were selected, and the mathematical model was used to calculate them. With the help of C language program, the advantages and disadvantages of the three design schemes were evaluated, and the optimal scheme was obtained. The calculation example proves that the method is true and effective, conforms to the background of modern intelligent manufacturing, and the calculation is simple, and has good feasibility and practicability.

Keywords

Automobile parts program evaluation intelligent manufacturing relative closeness game theory C language

1. Introduction

With the proposal of “made in China 2025” plan [1], in response to the development requirements of the times, automobile enterprises are gradually developing towards intelligent and green [2], and enterprises pay more and more attention to the selection of automobile parts design scheme [3]. In the early stage, Wu Zhenya [4, 5] and others used single weight and set pair analysis method, fuzzy evaluation method to evaluate water quality assurance and engine reliability; Fu[6] and others used combination weighting, TOPSIS method and relative closeness method to evaluate relay protection status and collision risk situation, and the evaluation method showed a trend of facing multiple fields and gradually maturing; Up to now, evaluation research in various fields has gradually developed to modern intelligence, such as Yuan Lixiang’sevaluation research on tire production line design based on intelligent system [7], Zhang evaluation research on intelligent manufacturing green for mechanical and electrical products [8], etc. At the same time, the evaluation research on the design scheme of specific auto parts is also gradually developing. For example, Zhang et al. [9] proposed the evaluation research on the design scheme of auto parts for traditional manufacturing considering the factors of cost, quality and technology; And the evaluation research of automobile parts design scheme based on TOPSIS considering environmental factors [10]. As far as the evaluation research of automobile design scheme is concerned, up to now, some researches have taken traditional manufacturing factors and environment-friendly factors into consideration. However, the evaluation research of automobile parts design scheme considering intelligent manufacturing factors and facing modern production is still lacking.

In order to make the design scheme of automobile parts better applied to the intelligent manufacturing of modern enterprises, the following evaluation ideas of automobile parts design scheme are put forward: the evaluation index system of automobile parts design scheme including intelligent automatic production and environment-friendly factors is constructed, and the game theory is selected to obtain the optimal proportion of the winner and objective weight, so as to obtain the composite weight.Considering the traditional evaluation methods [11] such as fuzzy evaluation method will lose some data, TOPSIS method is relatively complex, set pair analysis connection degree method [12] is cumbersome and so on, this paper proposes to use set pair analysis relative closeness degree sorting method to evaluate the design scheme and get the optimal design scheme, so as to achieve the purpose of multi-faceted consideration and efficient evaluation.

2. Construction of evaluation index system of intelligent production scheme

Auto parts are the general name of various large and small assemblies and parts of the automobile. The processing and production of auto parts is directly related to the quality of the automobile. Applying the idea of intelligent manufacturing to the production process of auto parts can effectively improve the manufacturing cost, production technology, product quality and production environment. Intelligent manufacturing production involves the setting of intelligent automatic production workshop [13], the equipment cost will increase correspondingly, and the corresponding labor cost, production efficiency and environmental pollution will be greatly improved compared with the traditional production workshop [14]. The traditional evaluation index system of automobile parts design scheme will no longer be applicable to the modern intelligent production workshop. Therefore, considering the factors of intelligent automatic production, environment and related labor costs, the evaluation index system of intelligent production scheme of auto parts is constructed, which includes economy, quality reliability, technology and environment. The evaluation of the design scheme is carried out from 12 specific aspects under the above four aspects. After obtaining the index data of each influencing factor, the evaluation calculation can be further carried out. The evaluation index system of intelligent production scheme for auto parts is shown in Fig. 1.

1)
Economy. Economic indicators determine the cost of parts production and processing, and directly affect the production efficiency of enterprises, including production line setting cost, material cost and labor cost. The cost of configuring intelligent manufacturing workshop for auto parts is high, accounting for the main part of the production cost; Material cost involves suppliers; Generally speaking, intelligent manufacturing workshop has higher automation and requires less production personnel, so the labor cost is greatly reduced [15]. Economic indicators are all cost indicators, so the lower the cost, the better. The index data can be obtained through statistics.
2)
Quality reliability. Quality reliability affects production costs and benefits, involving the quality of production materials and products, including material quality, material utilization rate and parts qualified rate. Material quality is related to finished product quality; The utilization rate of materials is related to the production cost. The intelligent production workshop uses all kinds of intelligent equipment for production and processing, and the utilization rate of materials is high; The whole process of intelligent manufacturing is closely monitored by computer, so the qualified rate of parts is high [16]. The quality reliability index is benefit index, the larger the value is, the better. The index data can be obtained through statistics.
3)
Technical. Technology involves the intelligent manufacturing level of production workshop, which embodies the advanced and technical nature of workshop production. Equipment automation can reduce production personnel and improve production efficiency; Intelligent production is convenient for production control and can improve product quality to a great extent; The production benefit is related to the profit of the enterprise, and is the direct embodiment of the technology and profit of the production workshop. Because equipment automation, intelligent production and production benefit can only be expressed qualitatively, in order to facilitate calculation, these indicators are processed quantitatively [11]. That is to say, the above indexes are quantified according to different segment values. Technical index is benefit index, and the larger the value, the better. Datum of these indicators can be obtained by expert scoring method. The quantification of technical indicators is shown in Table 1.

Table 1
Quantitative table of technical indicators

Segment value Technical index grade

2 Lower

4 Low

6 Middle

8 High

10 Higher

Figure 1.
Evaluation structure of intelligent manufacturing design scheme for auto parts.

4)
Environmental. Environmental quality is related to production environment and environmental protection. Whether the environmental quality meets the standard directly affects whether the workshop can be put into production, including production safety, production environment and waste pollution. Production safety is related to personnel safety; The production environment affects the product quality to a certain extent; The degree of waste pollution is related to the national production discharge standard, which is directly related to whether the workshop can be put into production. Production safety, production environment and waste pollution can only be expressed qualitatively, so these indicators are treated quantitatively. The environmental index is a reverse index [10], and the smaller the value, the better. Datum of these indicators can be obtained by expert scoring method. The quantification of environmental indicators is shown in Table 2.

Table 2
Quantitative table of environmental indicators

Segment value Technical index grade

10 Worst

8 Poor

6 Middle

4 Excellent

2 Best

3. Determination of index weight

Segment value	Technical index grade
2	Lower
4	Low
6	Middle
8	High
10	Higher

Segment value	Technical index grade
10	Worst
8	Poor
6	Middle
4	Excellent
2	Best

For the evaluation of automobile parts design scheme, determining the weight is a very important link. Whether the evaluation result of the scheme is scientific and reasonable has a great relationship with whether the index weight is reasonable or not. Considering the inaccuracy of the traditional dynamic weight determination method, the game theory [17] method is used to calculate the optimal composite weight. Among them, the subjective weight is calculated by analytic hierarchy process, and the objective weight is calculated by coefficient of variation method.

3.1 Determination of subjective weight

Analytic hierarchy process (AHP) [16] is to divide the evaluation indexes, compare the indexes of each layer, construct a comparison matrix, and determine the weight of each index of this layer by solving the eigenvector corresponding to its maximum eigenvalue. Analytic hierarchy process (AHP) can fully consider the mutual influence factors of each index, and it is more appropriate to use this method to solve the subjective weight for the evaluation of automobile parts design scheme for intelligent manufacturing. The specific steps of solving subjective weight by AHP are as follows:

1)
Solving characteristic equation.

According to the evaluation index system established in Fig. 1, the judgment matrix $A=\left({A_{ij}}\right)_{n\times m}$ is established by pair wise comparison between the indexes of the same layer and the upper layer, and the characteristic Eq. (1) is obtained by calculation.

$\displaystyle\textit{AZ}=\lambda_{\text{max}}Z$ (1)
2)
Solving subjective weight vector.

The subjective weight vector can be solved by Eq. (1), and the characteristic equation of $\lambda_{\textit{max}}$ can be obtained after normalization. The formula of subjective weight vector is as follows:

$\displaystyle\omega_{i}=\left({\frac{z_{1}}{\mathop{\sum}\nolimits_{i=1}^{m}z_% {i}},\frac{z_{2}}{\mathop{\sum}\nolimits_{i=1}^{m}z_{i}},\ldots,\frac{z_{n}}{% \mathop{\sum}\nolimits_{i=1}^{m}z_{i}}}\right)$ (2)
3)
Solving subjective weight set.

In order to facilitate the subsequent composite weight calculation, the subjective weights calculated by each layer are put into the same set, the subjective weight set $W_{1}=\left\{{\omega_{1},\omega_{2},\ldots,\omega_{i}}\right\}(j=1,2,$ $\ldots,n)$ of design scheme evaluation index is obtained

3.2 Determination of objective weight

The objective weight of design scheme evaluation index is calculated by coefficient of variation method. Coefficient of variation method is a method to represent the difference between the indicators by the weight coefficient of variation. The larger the value is, the larger the proportion of the corresponding index weight is. Coefficient of variation method is a relatively objective weight calculation method, which can objectively reflect the change information and importance of each index data, and can effectively obtain the objective weight of the design scheme evaluation system. The specific solving steps of coefficient of variation method are as follows:

1)
Calculate the mean $\bar{F}_{i}$ and variance $G_{i}$ of the index.

If there are $y$ schemes to be evaluated and $i$ indexes $f_{i}\left({i=1,2,\ldots,n}\right)$ , the mean value solving equation and variance solving equation of the indexes are as follows:

$\displaystyle\bar{F}_{i}=\frac{1}{y}\sum^{n}_{i=1}f_{i}$ (3) $\displaystyle G_{i}=\sqrt{\frac{\mathop{\sum}\nolimits_{i=1}^{n}(f_{i}-\bar{F}% _{i})^{2}}{y}}$ (4)
2)
Calculate the coefficient of variation $v_{i}$ .

The importance of each index in the evaluation index system of the overall design scheme is expressed by the variation coefficient of the same index. Coefficient of variation:

$\displaystyle v_{i}=\frac{G_{i}}{\bar{F}_{i}}$ (5)
3)
Solving objective weight vector.

The weight vector of each index calculated by the coefficient of variation method can be obtained by the coefficient of variation. Objective weight vector:

$\displaystyle\omega^{\prime}_{i}=\frac{v_{i}}{\mathop{\sum}\nolimits_{i=1}^{n}% v_{i}}$ (6)
4)
Solving objective weight set.

In order to facilitate the subsequent composite weight solution, the weight vectors of each index calculated by Eqs (3)–(6) are stored in the same set, the objective weight set $W_{2}=\left\{\omega^{\prime}_{1},\omega^{\prime}_{2},\ldots,\omega^{\prime}_{i% }\right\}\left({i=1,2,\ldots,n}\right)$ is obtained

3.3 Determination of compound weight

Subjective weight can consider subjective bias, but lack of data support; Objective weight has scientific basis, but subjective bias is not considered. In order to merge the advantages of the two, a combination weighting method is selected to combine the two to obtain the combination weights with both subjective and objective characteristics. Here, the game theory combination weighting method [17] is used to solve the combination weight. Game theory is a decision-making means to maximize the interests by coordinating the behaviors of competitors through game. Game theory calculation refers to the conflict between subjective and objective weights, takes Nash equilibrium as the coordination goal, uses the idea of minimizing deviation and scientific mathematical calculation to make the subjective and objective weights play a game, so as to obtain the comprehensive optimal proportion of subjective and objective weights. Through the calculation of game theory, we can comprehensively consider the subjective and objective influencing factors, that is, we consider the subjective artificial bias, and make the composite weight objective through scientific calculation. Through the game, we get that the proportion of subjective and objective weight is the comprehensive optimal proportion. The combination weight of game theory is calculated as follows:

1)
The linear combination of subjective and objective weight vectors.

Constructing weight vector set $W=\left\{{W_{1},W_{2}}\right\}$ , $W_{1}$ is the weight set calculated by analytic hierarchy process, and $W_{2}$ is the weight set calculated by coefficient of variation method. Then the linear combination of subjective and objective weight vectors is as follows:

$\displaystyle W=\alpha_{1}W_{1}^{T}+\alpha_{2}W_{2}^{T}$ (7)
2)
Determine the objective function.

By using the idea of minimizing the deviation to optimize $\alpha_{1}$ and $\alpha_{2}$ , the optimal combination weight can be obtained, and the objective function can be determined:

$\displaystyle\text{min}||W-W_{k}||_{2},k=1,2$ (8)
3)
The objective function is transformed into a system of linear equations.

The linear equations can be obtained by mathematical transformation of Eq. (8):

$\displaystyle\left[\begin{array}[]{cc}{W_{1}W_{1}^{T}}&{W_{1}W_{2}^{T}}\\ {W_{2}W_{1}^{T}}&{W_{2}W_{2}^{T}}\\ \end{array}\right]\left[\begin{array}[]{c}{\alpha_{1}}\\ {\alpha_{2}}\\ \end{array}\right]=\left[\begin{array}[]{c}{W_{1}W_{1}^{T}}\\ {W_{2}W_{2}^{T}}\\ \end{array}\right]$ (9)
4)
Solving compound weight.

By normalizing $\alpha_{1}$ and $\alpha_{2}$ , we can get the combination weight $W$ with both subjective and objective characteristics:

$\displaystyle W=\alpha_{1}^{\ast}W_{1}^{T}+\alpha_{2}^{\ast}W_{2}^{T}$ (10)

In Eq. (10):

$\displaystyle\alpha_{1}^{\ast}=\frac{\alpha_{1}}{\alpha_{1}+\alpha_{2}};\alpha% _{2}^{\ast}=\frac{\alpha_{2}}{\alpha_{1}+\alpha_{2}}.$

4. Construction of evaluation structure model based on relative closeness of set pair analysis

The composite weight can reflect the importance of each evaluation index relative to the overall evaluation, and can be used to judge the bias of the evaluation scheme, such as economy, technology and so on. In addition, in the evaluation stage of enterprise production design scheme, it is necessary to select the comprehensive optimal design scheme for actual production. In order to achieve the purpose of intelligent manufacturing oriented automobile parts design scheme optimization, the set pair analysis relative closeness ranking evaluation method is proposed to evaluate the design scheme. The set pair analysis relative closeness ranking method is to calculate the relative closeness of an automobile parts design scheme to be evaluated by calculating the connection degree between various indexes. By comparing the relative closeness value of each scheme to be evaluated, the comprehensive optimal design scheme can be obtained, that is, the scheme with the largest relative closeness value. The relative closeness ranking method considers the relative relationship between various indexes. The comprehensive optimal design scheme can be optimized through simple mathematical calculation, and the calculation is effective and simple.

4.1 Determine comparative space

The set pair analysis relative closeness ranking method [18] is used to evaluate the design scheme. Before calculating the relative closeness value, the evaluation space should be determined: The index of comparative space is recorded as $H=(S,E,W,X)$ , $S=\{s_{1},s_{2},\ldots,s_{m}\}$ is the set of automobile parts design schemes to be evaluated; $s_{k}$ is the k-th auto parts design scheme, $k\in\left[{1,m}\right]$ ; $E=\left\{{e_{1},_{2},\ldots,e_{n}}\right\}$ is the index set of evaluation system, $e_{r}$ is the r-th evaluation index, $r\in\left[{1,n}\right]$ , the benefit index is $E_{1}$ (the bigger the better), and the cost index is $E_{2}$ (the smaller the better); $W=\left\{{\omega_{1},\omega_{2},\ldots,\omega_{n}}\right\}$ is the composite weight set of evaluation index, $\omega_{r}$ is the composite weight corresponding to the r-th evaluation index, $\sum_{r=1}^{n}\omega_{r}=1$ , $\omega_{r}>0$ ; $X=\left({x_{kr}}\right)_{m\times n}$ is the decision matrix, and $x_{kr}$ is the index value of the r-th index of the $k$ -th scheme to be evaluated.

The optimal evaluation set $U=\left\{{u_{1},u_{2},\ldots,u_{n}}\right\}$ and the worst evaluation set $V=\left\{{v_{1},v_{2},\ldots,v_{n}}\right\}$ , where $u_{r}$ and $v_{r}$ respectively represent the optimal and inferior index values $\left({\max x_{kr}/\min x_{kr}}\right)$ of each index set $K$ group to be evaluated. If $e_{r}\in E_{1}$ , the comparison interval is $\left[{v_{r},u_{r}}\right]$ ; If $e_{r}\in E_{2}$ , then the comparison interval is $\left[{u_{r},v_{r}}\right]$ . The best and worst evaluation sets $U$ and $V$ constitute the comparison space $\left[{V,U}\right]$ .

4.2 Determine the degree of connection

After the comparative space of evaluation is determined, the degree of identity, degree of opposition and degree of difference of each index can be calculated through statistical data. The calculation steps are as follows:

1)
For the benefit index, in the comparison interval $[{v_{r},u_{r}}]$ , the same degree $a_{kr}=\frac{x_{kr}}{u_{r}+v_{r}}$ , the opposite degree $c_{kr}=\frac{u_{r}v_{r}}{\left({u_{r}+v_{r}}\right)x_{kr}}$ , and the difference degree $b_{kr}=\frac{\left({u_{r}-x_{kr}}\right)\left({x_{kr}-v_{r}}\right)}{\left({u_% {r}+v_{r}}\right)x_{kr}}$ can be used to calculate the same degree, opposite degree, and difference degree of each index, the expression of connection degree of each index is as follows:

$\displaystyle\mu\left\{{x_{kr},u_{r}}\right\}=\frac{x_{kr}}{u_{r}+v_{r}}+\frac% {\left({u_{r}-x_{kr}}\right)\left({x_{kr}-v_{r}}\right)}{\left({u_{r}+v_{r}}% \right)x_{kr}}i+\frac{u_{r}v_{r}}{\left({u_{r}+v_{r}}\right)x_{kr}}j$ (11)
2)
For the cost index, in the comparison interval $\left[{u_{r},v_{r}}\right]$ , the degree of identity, opposition and difference are $a_{kr}=\frac{u_{r}v_{r}}{\left({u_{r}+v_{r}}\right)x_{kr}}$ , $b_{kr}=\frac{\left({u_{r}-x_{kr}}\right)\left({x_{kr}-v_{r}}\right)}{\left({u_% {r}+v_{r}}\right)x_{kr}}$ and $c_{kr}=\frac{x_{kr}}{u_{r}+v_{r}}$ respectively, the connection degree of each index is expressed as follows:

$\displaystyle\mu\left\{{x_{kr},u_{r}}\right\}=\frac{u_{r}v_{r}}{\left({u_{r}+v% _{r}}\right)x_{kr}}+\frac{\left({u_{r}-x_{kr}}\right)\left({x_{kr}-v_{r}}% \right)}{\left({u_{r}+v_{r}}\right)x_{kr}}i+\frac{x_{kr}}{u_{r}+v_{r}}j$ (12)

4.3 Determine the comprehensive connection degree

The correlation degree $u_{x_{kr}}$ can only indicate the degree of superiority and inferiority of the evaluation system for the k-th group of the r-th evaluation index. Due to the different importance of each index in the design scheme evaluation system relative to the overall evaluation system, in order to calculate the proportion of the importance of each index of $n$ indexes of m groups of schemes to be evaluated relative to the overall evaluation, it is necessary to calculate the comprehensive connection degree. The comprehensive degree of connection can be calculated by the comprehensive degree of identity $a_{k}=\sum_{r=1}^{n}\omega_{r}a_{kr}$ , the comprehensive degree of difference $b_{k}=\sum_{r=1}^{n}\omega_{r}b_{kr}$ and the comprehensive degree of opposition $c_{k}=\sum_{r=1}^{n}\omega_{r}c_{kr}$ , the expression of comprehensive connection degree is as follows:

$\displaystyle\mu_{k}=a_{k}+b_{k}i+c_{k}j$ (13)

4.4 Calculation of relative closeness

For the defined design scheme evaluation index system, the relative closeness value of the k-th group of design schemes to be evaluated can be calculated by the comprehensive degree of identity and comprehensive degree of opposition of n indexes. Relative closeness value:

$\displaystyle\gamma_{k}=\frac{a_{k}}{\left({a_{k}+c_{k}}\right)}$ (14)

In Eq. (14), the comprehensive degree of identity $a_{k}$ and comprehensive degree of opposition $c_{k}$ are relatively determined. The value of relative closeness $\gamma_{k}$ represents the degree that the scheme to be evaluated is close to the optimal evaluation set. The larger the value is, the better the corresponding design scheme is.

5. C language program implementation of evaluation model

Considering that an enterprise may evaluate multiple design schemes at the same time, it is necessary to calculate the subjective and objective weights of 12 evaluation indexes of n design schemes, as well as the relative closeness of n schemes, so the amount of calculation is large. The method of programming the established model into C language is put forward, and the optimal design scheme is obtained by computer direct calculation. On the visual c++ software platform, the C language program of the above evaluation method is compiled through the C language programming rules, and the computer design scheme evaluation is realized by running the program. The C language program implementation process of the evaluation model is shown in Fig. 2.

Figure 2.

C program implementation process.

6. Case analysis

Automobile panel involves automobile appearance and driving performance. Its volume is relatively large, and it is not only an important part of automobile, but also a relatively representative automobile part; In addition, the design and production of automobile panels need to consider product raw materials, die making, stamping equipment allocation and production efficiency, that is, economy, quality reliability, technology and environment need to be considered, which is consistent with the index system established in this paper. Therefore, automobile panels are taken as a calculation example.

Three sets of automobile panel design schemes 1, 2 and 3 of a company are selected for comprehensive evaluation, and the optimal design scheme is obtained.According to the 12 indexes in Fig. 1, the composite weight is calculated, and the relative closeness of each scheme is calculated by using the composite weight, and the three design schemes are sorted according to their size.

6.1 Weight of secondary indicators

Table 3
Economic statistics

	Design scheme 1	Design scheme 2	Design scheme 3
$H_{11}$	100	80	75
$H_{12}$	47.21	53.08	54.47
$H_{13}$	8	10.67	8

The statistical data of three secondary indicators under economic indicators are obtained by reference [19, 20]. Material cost, labor cost and production line setting cost are all cost indicators, and the smaller the value, the better. As shown in Table 3.

According to the statistical data of three secondary indexes under the quality reliability index obtained from reference [21, 22], material quality, material utilization rate and qualified rate of parts are all benefit indexes, and the larger the value is, the better. As shown in Table 4.

Table 4

Quality reliability statistics

	Design scheme 1	Design scheme 2	Design scheme 3
$H_{21}$	78.92	72.44	71.83
$H_{22}$	55	58	67
$H_{23}$	99.3	99.3	99.1

According to Table 1, the three secondary indicators under the technical indicators in each scheme are quantified. The three indicators are all positive indicators, and the larger the value, the better. As shown in Table 5.

Table 5

Technical statistics

	Design scheme 1	Design scheme 2	Design scheme 3
$H_{31}$	4	6	6
$H_{32}$	6	4	6
$H_{33}$	8	6	8

According to Table 2, the three secondary indicators under the technical indicators in each scheme are quantified. The values of the three indicators are all reverse indicators, and the smaller the better. As shown in Table 6.

Table 6

Environmental statistics

	Design scheme 1	Design scheme 2	Design scheme 3
$H_{41}$	6	8	8
$H_{42}$	6	6	6
$H_{43}$	8	6	6

The subjective weight is calculated from Eq. (1) to Eq. (2):

$\displaystyle W_{1}=\{\omega_{1},\omega_{2},\ldots,\omega_{12}\}=\{0.104,0.099% ,0.082,0.076,0.106,0.092,0.075,0.040,0.151,0.060,0.054,0.061\}$

The objective weight is calculated from Eq. (3) to Eq. (6):

$\displaystyle W_{2}=\left\{{\omega^{\prime}_{1},\omega^{\prime}_{2},\ldots,% \omega^{\prime}_{12}}\right\}=\{0.105,0.050,0.117,0.036,0.070,0.001,0.146,0.14% 6,0.106,0.106,0.003,0.114\}$

$\alpha_{1}^{\ast}=$ 0.2029, $\alpha_{2}^{\ast}=$ 0.7971 are calculated from Eqs (7)–(9). Therefore, the composite weight can be calculated from Eq. (10):

$\displaystyle W=\left\{{0.105,0.060,0.110,0.044,0.077,0.019,0.132,0.124,0.115,% 0.097,0.011,0.105}\right\}$

6.2 Construction of decision matrix

The decision matrix $X$ is obtained from Table 2:

$\displaystyle X=\left[{{\begin{array}[]{ccc}{x_{11}}&\ldots&{x_{1n}}\\ \vdots&\ddots&\vdots\\ {x_{m1}}&\cdots&{x_{mn}}\\ \end{array}}}\right]=\left[\begin{array}[]{cccccccccccc}{100}&{47.21}&8&{78.92% 4}&{55}&{99.3}&4&6&8&6&6&8\\ {80}&{53.08}&{10.67}&{72.44}&{58}&{99.3}&6&4&6&8&6&6\\ {75}&{54.67}&8&{71.828}&{67}&{99.1}&6&6&8&8&6&6\\ \end{array}\right]$

From the formula:

$\displaystyle x_{kr}=\frac{x_{kr}}{\mathop{\sum}\nolimits_{k=1}^{m}x_{kr}}$ (15)

The decision matrix $X$ is normalized and the optimal and worst index values $u_{r}$ and $v_{r}$ of each index are found. The results are shown in Table 7.

Table 7

Index normalization data and optimal and inferior solutions

Normalized data	Design scheme 1	Design scheme 2	Design scheme 3	Optimal value $u_{r}$	Worst value $v_{r}$
$H_{11}$	0.392	0.314	0.294	0.294	0.392
$H_{12}$	0.305	0.343	0.352	0.305	0.352
$H_{13}$	0.300	0.400	0.300	0.3	0.4
$H_{21}$	0.354	0.325	0.322	0.354	0.322
$H_{22}$	0.306	0.322	0.372	0.372	0.306
$H_{23}$	0.334	0.334	0.333	0.334	0.333
$H_{31}$	0.250	0.375	0.375	0.375	0.25
$H_{32}$	0.375	0.250	0.375	0.375	0.25
$H_{33}$	0.364	0.273	0.364	0.364	0.273
$H_{41}$	0.273	0.364	0.364	0.364	0.273
$H_{42}$	0.333	0.333	0.333	0.333	0.333
$H_{43}$	0.400	0.300	0.300	0.4	0.3

For design scheme 1, from Eq. (11) to Eq. (13), it is calculated that:

Comprehensive identity $a_{1}=\sum_{r=1}^{n}\omega_{r}a_{kr}=$ 0.507 Comprehensive opposition $c_{1}=\sum_{r=1}^{n}\omega_{r}c_{kr}=$ 0.492 From Eq. (14), it can be calculated that: Relative closeness $\gamma_{1}=\frac{a_{1}}{\left({a_{1}+c_{1}}\right)}=\frac{0.507}{\left({0.507+% 0.492}\right)}=$ 0.500 Similarly, $\gamma_{2}=$ 0.490, $\gamma_{3}=$ 0.549.

6.3 Analysis and discussion of evaluation results

According to the program implementation steps mentioned above, after programming and debugging in the visual c++ environment, input the data of 12 indicators corresponding to the three groups of design schemes, and click enter to get the evaluation results of the three groups of schemes, as shown in Fig. 3.

Figure 3.

C language program calculation results.

Based on the analysis of the evaluation results of the relative closeness degree of automobile panel design scheme for modern intelligent manufacturing, the following conclusions can be drawn:

The $W_{1}$ calculated from Eq. (1) to Eq. (10) shows that the weight proportion of subjective economic index is the highest of 0.285; According to $W_{2}$ , in the objective weight of the indicators, the proportion of technical indicators is the highest, accounting for 0.398; The composite weight is calculated by game theory, and the highest proportion of technical is 0.371, followed by economic is 0.275. The subjective weight proportion was scored by man-made, and the subjective was strong, and its linear combination coefficient was relatively small 0.2; Theobjective weight is obtained by scientific calculation of specific statistical data, and its linear combination coefficient is relatively large, which is 0.8. The game theory is to solve the optimal subjective and objective composite coefficient with the goal of minimizing the range. It can make the value of the composite weight calculated more reasonable and effective, this method is suitable for the evaluation of automobile parts design scheme for intelligent manufacturing.

From the composite weight set $W$ , we know that the weight of equipment automation and intelligent production index accounts for the highest proportion, that is, the evaluation of auto parts design scheme pays more attention to intelligent manufacturing factors, which is in line with the intelligent production bias of modern enterprises. The mathematical model of relative closeness degree of set pair analysis is used to evaluate three design schemes of automobile panel. The normalization method is used to process the data of technical, quality reliability, environmental and economic indexes. The relative closeness degree $\gamma_{i}$ of each scheme is calculated as 0.500 for design scheme 1, 0.490 for design scheme 2 and 0.549 for design scheme 3. The larger the relative closeness value is, the better the comprehensive evaluation result of the corresponding scheme is. The $\gamma_{i}$ value of design scheme 3 is the largest, so the evaluation result is the best.The evaluation of auto parts design scheme is biased to intelligent manufacturing factors, considering the influencing factors such as technology, quality and reliability, which can optimize the auto parts design scheme with high level of intelligent production and considering product quality and environment-friendly factors, and is more suitable for the production and manufacturing of auto parts in modern enterprises.

The proposed evaluation method pays more attention to intelligent automatic production factors, which can greatly improve the production efficiency and product quality, and the influence of manufacturing cost on it is relatively low. In the past, in the process of evaluating the design scheme of automobile parts, the traditional manufacturing factors such as manufacturing cost, labor quality, operation comfort and other traditional production indexes were almost considered, and the environmental factors were comprehensively considered on the basis of the traditional production evaluation [8]. However, the intelligent manufacturing production factors were less considered, and the evaluation is relatively high affected by economic indicators. The importance of economic indicators in the overall evaluation [11] indicates the advanced manufacturing level of an enterprise. The lower the importance, the higher the advanced manufacturing level of the enterprise. It can be seen that the established evaluation model, based on the traditional scheme evaluation, comprehensively considers the modern intelligent manufacturing factors, can not only reflect the advanced manufacturing production level of enterprises, but also conform to the trend of high precision, high technology and intelligent production of enterprises.

7. Conclusion

The evaluation index system for intelligent production of modern enterprises is constructed which takes four indexes of quality reliability, economy, environment and technology as the first level index, and 12 indexes of equipment automation and production environment under the first level index as the second level index. Facing the intelligent manufacturing production, considering the environment-friendly factors, combining with the production cost and product quality, the practical problems in the process of intelligent production of automobile parts are summarized relatively comprehensively.

The game theory method is used to scientifically combine the subjective weight and objective weight, and the relative closeness method is used to establish the evaluation model of automobile parts design scheme with subjectivity and objectivity, certainty and uncertainty, which is written into a set of C language program. The relative closeness of the three automobile panel design schemes are 0.508, 0.484 and 0.549 respectively. After sorting them, the evaluation results is obtained.

The example shows that the method pays more attention to the intelligent manufacturing factors than the traditional evaluation, can effectively reflect the advanced manufacturing level of enterprises, meet the trend of modern intelligent production, and can be used in the long-term production planning of modern enterprises. With the help of C language program, the evaluation of the design scheme is realized, and the calculation is efficient and accurate. The overall design scheme evaluation model provides a more comprehensive evaluation method for modern enterprises to choose the best intelligent manufacturing design scheme of auto parts, which has important reference value for intelligent manufacturing enterprises in the new era.

Footnotes

Acknowledgments

This program was supported by the 2019 Research Program of Panzhihua University, China (Grant: 2019ZD002).

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Evaluation of automobile parts design scheme for intelligent manufacturing

Abstract

Keywords

1. Introduction

2. Construction of evaluation index system of intelligent production scheme

3.1 Determination of subjective weight

4.1 Determine comparative space

4.2 Determine the degree of connection

6.1 Weight of secondary indicators

Table 3 Economic statistics

Footnotes

Acknowledgments

References

Table 3
Economic statistics