1 Web-based machine tool energy evaluation architecture can be seen from the machine energy evaluation system structure is based on the B / S mode of the three-tier system of the client / server distributed structure f12 The first layer is the customer browser, the customer design machine tool through the Internet The second layer of the energy evaluation system is the Web server layer for the operation of the Web application. The layer becomes the S service to interpret the asp page, use the component to issue data requests, and generate dynamic web pages. The third layer is the database server layer for storage. The data and knowledge of the machine tool energy evaluation system are mainly derived from the contents contained in the database established by the machine tool energy evaluation system.
1 The influence of energy (power of the motor: The motor capacity should select the motor with smaller rated power Pr as much as possible to ensure sufficient cutting power, which can reduce the electric loss Pl6 and directly improve the energy utilization rate of the machining process.
Prototype system for machine tool energy evaluation system b. No-load rate, short shift time, improved fixture, reduced tool change and less time for workpiece clamping, helping to reduce energy consumption.
* The energy loss of the thermal motor during operation almost becomes thermal energy. In addition to being lost in the surrounding medium, the rest is converted into vibration, noise, friction energy, etc., which shortens the life of the machine tool and affects human health.
Therefore, it is necessary to increase the cutting load and reduce the energy loss.
1.2 Influence of fixtures The higher the hardness and strength of the workpiece material, the greater the cutting force. The higher the cutting temperature, the less energy-intensive materials should be selected.
Tool geometry parameters: front angle, back angle, lead angle, sub-lead angle and tool inclination.
Workpiece clamping usually does not pay much attention to the workpiece clamping, and there is no guarantee that there is sufficient surface contact between the workpiece and the clamping element to cause the cutting process to falsify.
1.3 Influence of machining process Cutting depth The depth of cut has the least impact on tool durability, and a larger cutting depth is preferred.
Feed rate: From the point of view of load and energy consumption on the cutting tool, it is more advantageous to use a larger feed rate than to use a large depth of cut in the case of the same metal removal rate.
Cutting speed After the cutting depth and feed rate are selected, a larger cutting speed should be selected to improve the cutting performance of the machine.
2 Web-based evaluation method 2.1 The fuzzy mathematical model of comprehensive decision making F=(/%, /2,..., person) is the set of alternatives initially determined by the decision preparation work for each plan/; Constraints such as resources in the Z=(''...,) decision can be translated into evaluation indicators to reflect the extent to which the program satisfies the constraint. The membership function and membership degree of the clear indicator in the evaluation index set Z can be obtained by realistic constraints, prediction techniques or by statistical data. The fuzzy indicator can invite multiple experts to form the decision group P=(9,9...also), and each member has nine pairs of evaluations on each fuzzy indicator in the evaluation index set Z. The language result is very good for £2, which means that a better £3 means that a general £4 means that a poorer £5 means poor. For the five possible outcomes, use the triangular fuzzy number 玟 = () to indicate. They are the lower and upper limits of the triangular fuzzy number. The membership of the variable for 玟 is therefore determined by the decision maker A of the five triangular fuzzy numbers 2 fuzzy indicator membership on the available field of the natural comment set. The comment value for the fuzzy evaluation factor is r%=(r/) For convenience, it is assumed that the weights of each decision maker are the same and the single factor fuzzy evaluation of the decision group to the scheme/the fuzzy evaluation factor can be taken as / 23 The improvement of the calculation method of the comprehensive evaluation result vector The weight vector of the index set Z is obtained by the AHP method. And the fuzzy evaluation algorithm needs to be improved. Firstly, the vectors of each row of the obtained fuzzy relation matrix are normalized, and the 1T= formula is used to characterize the relative contribution rate of different schemes to the same evaluation index.
According to the division operation of triangular fuzzy numbers, the membership function formula of fuzzy numbers in fuzzy matrix can be obtained. It can be known from the principle of ADAMO fuzzy number ordering method that only the high-order membership function of each fuzzy number is calculated, and the comprehensive evaluation result vector is calculated as 2.4. Evaluation result processing and decision-making scheme selection M2. For the obtained comprehensive evaluation vector B such as bl>b, the scheme z is superior to the scheme in this way, and thus the pros and cons of n schemes are obtained, thereby providing an important basis for decision-making.
3 Web-based evaluation example calculation Web-based evaluation results are shown in 圄 2. The user clicks on the five command buttons on the browser to call the database resource through the web application and display the evaluation result on the browser.
Initially, four options for machining a workpiece are proposed. At represents the motor power. A2 indicates that the no-load rate Z3 indicates energy consumption. A4 indicates that the workpiece material A5 indicates that the tool geometry A6 indicates the workpiece clamping A, indicating that the cutting depth A8 indicates Feed rate, A9 indicates the cutting speed.
The membership function is expressed as U2)=1-圄2 Web-based evaluation method calculation 圄b. Determine the membership degree of the fuzzy index. Experts 20 people are composed of decision-making methods and ADAMO methods for calculation and normalization for 1T.
The group report type I1 Hunan type I2) respectively obtained the evaluation results of the four indicators of A3 people according to the formula Jiang Pingyu. Research on Wb process design system based on ASP concept 3J system 0 manufacturing automation 200I Ni Zhonghua Yi Hong. Wb-based CAPP generalization process decision J. System 0 manufacturing automation 20 (08) 24? 28. Lu Xiaowei Wang Longde. The evaluation method of fuzzy evaluation method in local public utility decision-making J. Journal of Shanghai University of Technology 200I2) 22-25. Liu Feizong Zong Jun Dan Bin et al. Energy characteristics of mechanical processing systems and their application 甩M. Beijing Mechanical Industry Press, 1995.
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