Multi-level fuzzy comprehensive evaluation model for machine tool equipment selection


The design and manufacture of mechanical products are inseparable from machine tools. Especially in the concurrent engineering environment, the AD/APP parallel design system will: a. search the equipment database to check the processing capability, and provide manufacturability evaluation information b. arrange the process route for the existing part design information and processing requirements. Determine the appropriate processing parameters for the process and step design. d. Check the current state of the machine tool, including faults and load conditions, and analyze its economic accuracy and processing cost to provide decision support for process optimization or multi-process options. It can be seen that device selection is an important part of the project in parallel design. However, since the interaction design between AD and APP is often based on phased part design, that is, based on incomplete part information, equipment selection based on part information must have different degrees of uncertainty, which is generally accurate. Reasoning (such as symbol matching, numerical comparison) brings difficulties. The method of fuzzy reasoning can solve such problems better. 1 The multi-level fuzzy comprehensive evaluation basic model uses fuzzy set theory to judge the decision of things. The most important thing is to determine the fuzzy relationship between the factor set U and the evaluation set V. R. In the finite field of view, R can be represented by the matrix R, and r ≤ 1 , which means that the factor u corresponds to the degree to which a thing is rated v. The importance of each factor is different for the evaluation object. The weight distribution coefficient W can be obtained by mathematical statistics or expert scoring. Therefore, there is finally Y = WR. The decision is made according to the Y value, which is called primary comprehensive evaluation.
The principle of establishing a multi-level evaluation model is that many factors to be considered are divided into several categories according to attributes. Firstly, primary comprehensive evaluation is carried out within each category, and then high-level comprehensive evaluation between categories is carried out for each type of evaluation results. Taking the second-level evaluation model as an example, the process is as follows: a. The factor set (evaluation index) U is divided into n categories according to the different angles P of the evaluation, and the second-level factor set U/P = contains k factors, U contains? The factors of k are subject to primary comprehensive evaluation. The weights of factors of U are assigned to W i , and the evaluation matrix is ​​R i , which can be obtained b. The evaluation results of the n factors of U/P are also evaluated according to the primary model. Evaluation of single factor U in U/P. Let U/P's weight coefficient assignment be W 2 and the evaluation matrix be R 2 , which can be obtained as the comprehensive evaluation result of U/P and the comprehensive evaluation result of all factors in U. In the equipment selection model, the characteristic that the operator can be used is to take both the overall situation and the key points.
The construction of the second-level fuzzy comprehensive evaluation model selects the appropriate processing machine for the parts, and should comprehensively consider the requirements of processing type, size range, accuracy level, equipment status and economy. To do this, construct the secondary synthesis model of the equipment selection by following the steps below.
Step 1 Determine the evaluation indicators at all levels, obtain data from the equipment database, and establish the secondary evaluation indicators as shown in Figure 1.
Step 2 Establish a fuzzy relationship between the first-level evaluation indicators and the candidate equipment. Let P be a fuzzy subset on the part type domain, which is described as a 1 × n order matrix, p 1, 2,..., n), reflecting the degree of likelihood that a part belongs to each type in the part type universe. E is a fuzzy subset on the device resource domain, described by a 1 × order matrix, e) indicating the possibility of selecting each device from the perspective of the appropriate part type. R is from the part type domain to the processing equipment domain. A fuzzy subset of the binary fuzzy mapping relationship, where each element r represents the possibility that the i-th part type is processed on device j, there is r). Then, the mapping to E is transformed into E = PR. With the axin operator, the fuzzy mapping relationship R (the element is r) of the processing method domain to the device domain can be defined, and the processing method domain is established on this basis. The mapping of a fuzzy subset (the membership of its elements) to a fuzzy subset E (the membership of its elements is e) on the device resource domain, and the representation is considered from the perspective of the appropriate processing method The possibility that each device is selected for use.
Equations (1) and (2) are used to judge the indicators u and u in the first-level evaluation, respectively, which can be integrated into the index U-type matching in the second-level evaluation, and assign u and weight to W to apply different memberships. The degree function definition method can give the following membership degrees and judgments: e and e respectively correspond to the indicators u and u 23 in the first-level evaluation, and they can be integrated into the second-level evaluation index U size is appropriate. The weights assigned to W and e correspond to u and u 33 in Fig. 1, respectively. By assigning W and W = 0.2, they can be combined into U precision.
Corresponding to e, u and u 42 in Fig. 1 have weights of W =0.4, which can be integrated into the U state.
And e indicates that the operating cost of the membership is reasonable and the maintenance fee is reasonable, corresponding to u and u 52, and the weight is W economically reasonable.
Step 3 Primary judgement. Based on the above results, the device j is subjected to a primary evaluation, and the evaluation result y T is obtained, wherein the step 4 is a secondary evaluation. Equation (3) actually constitutes the evaluation matrix vector R j of the second-level evaluation. At the same time, for each element in Y, that is, the weight assigned to u in Figure 1, there is W =0.1, therefore, the final evaluation result is) The device that was ultimately selected for use.
It is worth noting that in the primary evaluation, a threshold value of the evaluation index should be set (the value may be determined according to the specific situation, such as α = 0.25), in order to filter out some single factor evaluation indicators are very low. equipment. For example, the diameter dimension of an e-machined part of a device should be excluded, because the device will not be selected for actual use regardless of the overall evaluation result.

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