Journal of Control Science and Engineering

Volume 2019, Article ID 9272383, 8 pages

https://doi.org/10.1155/2019/9272383

## Design of Winding Parameters Based on Multiobjective Decision-Making and Fuzzy Optimization Theory

Shanghai Institute of Technology, School of Electrical and Electronic Engineering, Shanghai 201418, China

Correspondence should be addressed to Wenping Jiang; moc.qq@40105892

Received 31 January 2019; Revised 5 May 2019; Accepted 7 May 2019; Published 2 June 2019

Academic Editor: Sing Kiong Nguang

Copyright © 2019 Wenping Jiang and Jun Min. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The design tries to solve the problem of low pass rate of platinum wire production and the waste of platinum in company. The paper uses multiobjective decision system fuzzy optimization theory to analyze five parameters, which are tensile strength, ductility, fracture load, filling aperture, and resistance. Besides, MATLAB software is used to write programs and calculate. To sum up the above analysis, the weight vector of five parameters is obtained and that can be used to determine which parameter has the greatest influence on the pass rate of the wire winding process.

#### 1. Foreword

The diameter of Pt wire, which is the raw material of the winding process, is 12.7 *μ*m. The process is through hand and machine coordination. The final platinum wire is a spiral product with uniform spacing, no rebound and no deformation. The length of the spiral platinum wire after winding is required to be in the range of 305-356 *μ*m. At the same time, each lap must be evenly spaced at 33 *μ*m. Any rebound or uneven spacing will be considered nonconforming. Since wire winding is a standard machine process, the only difference is the characteristic of Pt which is used in the winding process. Therefore, if we test the characteristic parameters of platinum wire (tensile strength, extension value, fracture load, perfusion aperture, and resistance), we will find out the certain parameter which has the greatest influence on the pass rate. After that, the factory can control the parameters to improve the winding yield.

In the paper, we use multiobjective decision-making system fuzzy optimization theory and combine fuzzy optimal decision theory with dynamic programming optimization theory to solve the problem of complexity system, whose classic optimization technique cannot be done. On the one hand, we use the concept of relative continuous system of numbers to describe the relative membership degree of fuzzy phenomena, events and concept [1–4]. On the other hand, we establish a set of engineering fuzzy theory based on the concept of relative membership of dynamic changes. Finally, the theory of variable fuzzy sets is proposed again.

#### 2. Multiobjective Fuzzy Decision Cycle Iterative Model

We suppose that the set of n samples that remain to clustering is , clustering samples with m indicator eigenvalue vectors , and obtained indicator eigenvalue matrix , where is the eigenvalue of sample and index , . Since the physical dimensions of the m clustering indicator feature values are different, this requires us to normalize the index feature. That is to say, the index eigenvalue should be transformed into the relative membership degree of the index of clustering sample about fuzzy concept A [5–7]. There are usually three types of indicators in fuzzy clustering. The first is the positive indicator, which just means the lager the indicator feature value is, the higher the rank of the clustering category will be. Its normalization formula is shown in The second is negative indicator, which just means the smaller the indicator feature value is, the higher the rank of the clustering category will be [8]. Its normalization formula is shown as The last one is intermediate indicator, which just means the rank of the clustering will be higher when the indicator feature value is certain. Its normalization formula is shown as We transform the index eigenvalue matrix into the relative membership matrix of the index pair fuzzy concept A and then obtain the index eigenvalue normalization matrix R, . N samples have been normalized according to m index feature values and cluster according to c levels. Its fuzzy clustering matrix is , where is the relative membership of the sample , . At the same time, the conditions need to be met, formula (4) [9–11]. The m norm eigenvalue normalization numbers of level h represent the h-level clustering features, which are often called the cluster center in fuzzy clustering. Then c-level clustering features can be represented by m×c-order fuzzy clustering feature matrix, just , in which is the clustering feature normalization number of the h-level indicator, and . The difference between sample j and category is shown as formula (5).In formula (5), p is a variable distance parameter, which can be taken as the Hamming distance: p = 1 and Euclidean distance: p = 2. We introduce the index weight vector since different indicators have different influences on clustering. . At the same time, formula (6) needs to be met.The difference between the sample j and the h-level can be described by the generalized index weight distance (7).In order to obtain the optimal relative membership degree, the optimal clustering feature , and optimal weight vector , we use weighted generalized index weight distance with relative membership degree uhj as weight. Finally, we get the weighted generalized distance , where is the distance concept; it contains the variables u, s, w. An objective function is created which is shown in formula (8) [12–14].The formula satisfies the following constraints:In (8), a is a variable optimization criterion parameter; when a = 1, the meaning of the objective function (8) is the cluster sample set n’s first-order power sum minimum to . When a = 2, the meaning of the objective function (8) is the cluster sample set n’s second power sum minimum to . It is significant for the variable fuzzy sets theory to extend the least-absolute criteria and the least-squares criteria to ’s sum minimum and ’s sum of square minimum in the classic mathematics. Besides, it is also the basis of variable fuzzy clustering, pattern recognition, and optimum decision-making and evaluation model. In order to extend conditional extreme value problem to unconditional extreme value problem, we construct Lagrangian function, as shown in formula (10),In formula (10), , , , , , , , , . During this way it can get formulae (11), (12), and (13).As for the aspect of being purely mathematical, formula (11), formula (12), and formula (13) are too complicated to be solved with gradient descent method. However, the index weight vector w is a variable parameter according to the practical problems in the variable fuzzy set theory.

#### 3. Multiobjective Fuzzy Decision Optimization Theory

The flow chart of multiobjective fuzzy decision method is shown in Figure 1. We choose 25 suitable schemes in multiobjective decision-making system and judge them by five target eigenvalues; the formula is shown as (14). In formula (14), is the special value for the target i and the scheme j, . In this scheme, five target eigenvalues are divided into 5 levels according to superiority down to inferiority.Obviously, the relative superiority can be specified as 1, and the inferior relative superiority can be set to zero, the superiority is 1-level, and the inferior level is c-level for any target parameter. During the fuzzy theory, superiority shows a gradual change in the intermediate transition period; therefore, it can be considered that the process of relative superiority from 1-level to c-level is equivalent to a linear increment process of 0 to 1. It can be concluded that the relative superiority decrement difference of two adjacent levels should meet ; for any target parameter, the relative superiority standard value vector for each level from 1-level to c-level should be shown as formula (15).In formula (15), . The target parameter of multiobjective decision system is composed of five parameters: tensile strength, extension value, breaking load, perfusion aperture, and resistance, which can be divided into three types, just the positive indicator, negative indicator, and intermediate indicator. We select the relative membership degree of objectives according to the different conditions of target eigenvalue and the formulas shown as (16), (17), and (18). If the system is good because the feature value is small, we use formula (16). If the system is good because the feature value is bigger, we use formula (17). If the system is good because the feature value is in the middle, we use formula (18).In a word, is the relative superiority for the scheme j and target i, max is the upper bound of the target eigenvalue I, and min is the lower bound. is the intermediate optimal value of target i. By analyzing the data given in Table 1, we can get some conclusions just as shown in Table 2. As shown in Table 1, T-V represents tensile value, E-V represents elongation value, B-L represents breaking load, C-D represents cast diameter, and R represents resistance.