1、专业英语,姓名： 陈永朋 学号：212016080200018 学院： 机械工程学院 专业： 机械工程 老师： 卢劲竹,Self-Introduction,My name is Chenyongpeng, 24. I come from Shangqiu, Henan. I graduated from the mechanical and electrical engineering of Yangtze Normal University which is located in Fuling, Chongqing. In July 2016, I was admitted to Xihua
2、 University as a graduate student. My tutor is Zhangjunfu. His research field is uncertain and mechatronics. At present, he takes over a project of pipeline robot. I think I will be a member of the teamwork, but this is not the case. Last week, he arranged for me to assist another teacher in the stu
3、dy of AGV. Thus, I turned to the direction of control. This is a big change for me. No matter what, I will brave to face.,I have a happiness family of four members, my parents, my sister and me. My father is a harder worker. He is busy and fulfilling every day. His favorite way to relax is reading.
4、My mother is kind and generous. What is interesting is that she can always see what I am thinking. It was their encouragement and help that led me to this day . I hope I will be repay them in the future.,optimal of design conic-cylindrical gear reduction unit using fuzzy physical programming 基于模糊物理规
5、划的圆锥齿轮减速器优化设计,abstract,Conic-cylindrical gear reduction unit as a high-performance power transmission device is widely used to build various machineries. There are lots of fuzzy factors in its manufacturing process and operation environment, which should be taken into consideration in the design pro
6、cess.Fuzzy physical programming is an effective multiobjective optimization method which incorporates fuzziness in its problem formulation. The fuzzy physical programming model for the optimal design of two-stage conic-cylindrical gear reduction unit is developed in this paper, and genetic algorithm
7、 is used to solve the model. An example is given to illustrate that fuzzy physical programmingcan consider the fuzziness of conic-cylindrical gear reduction unit substantially, and conforms more perfectly to the engineering realities.,Key words: conic-cylindrical gear reduction unit, multiobjective
8、optimization, physical programming, fuzzy physical programming,圆锥齿轮减速器是一种高性能的传动装置，广泛应用于各种传动机械。由于其在制造过程和工作环境中存在很多模糊因素，因此在设计过程中就应该对他们加以考虑。本文采用模糊物理规划法对其进行多目标（独立模糊因素）优化设计。首先建立了模糊物理规划模型，然后应用遗传算法进行求取获得最优解，最后举例验证优化结果的可行性。,1. Introduction,Generally, mechanical design has been taken as the multiobjective pro
9、blem and the design process is actually an optimizing process, considering multi-restricted conditions. Multiobjective optimization has been applied widely in the field of mechanical design. In recent years, some new algorithms for the multiobjective optimization appear, such as collaborative optimi
10、zation(协同优化设计), interactive multi-objective optimization（交互式多目标优化设计）, physical programming （物理规划）and VEGA (Vector Evaluated Genetic Algorithm 向量评估遗传算法). These algorithms have characteristics of their own, and have found applications in various engineering practical problems. 机械设计由传统设计发展到优化设计，由对单一目标的
11、优化发展到多目标优化。,There are lots of fuzzy factors in manufacture process and operationenvironment. The key sources of fuzziness are as follows: the complexity of systems structure and mechanism; the limitation of test conditions; the limitation and subjectivity （主观性）of human being. Because of the existenc
12、e of fuzziness, the product performances (weight, price, volume, etc.) are thus fuzzy sets. Fuzzy physical programming is a new efficient multiobjective optimization method, which inherits the advantages of physical programming and considers the fuzziness of multiobjective systems. Fuzzy physical pr
13、ogramming can solve fuzzy multiobjective design problems and get the design results considering fuzzy factors by incorporating (包含)fuzziness in design variables, objective functions and constraints.,Conic-cylindrical gear reduction unit as a high-performance power transmission system is widely used
14、to make various machineries, and its structure and performance has distinct influence on robust(稳健性), noise level（噪声水平）,bearing capacity（承载能力） and service life of the whole machinery（整机寿命）. Currently, there are rare researches in this area. Regarding geometry structure and bearing capacity are as ma
15、in optimization objectives, and considering the fuzziness of the design variables and objectives substantially, this paper develops the fuzzy physical programming model for optimal design of conic-cylindricalgear reduction unit. Genetic Algorithms is applied to solve the formulated fuzzy physical pr
16、ogramming（模糊物理规划）.,2. FUZZY PHYSICAL PROGRAMMING,2.1 PHYSICAL PROGRAMMING,Physical programming is a new effective multicriteria optimization method first brought forward by Achille Messac in 1995, which reduces the computational intensity of large problems and places the design process into a more f
17、lexible and natural framework. It can capture the designers physical understanding of the desired design outcomes by forming the aggregate objective function. Designers specify ranges of different degrees of desirability (desirable, tolerance, undesirable, etc.) for each design metric. Once the desi
18、gners preferences are articulated, obtaining the corresponding optimal design is a non-iterative process. Design objectives are classified into four different categories. Each class comprises two cases, hard and soft, subject to the sharpness of preference.The qualitative （主观性）meaning of soft prefer
19、ence function is depicted in Figure 1 .The value of the objective function , , is on the horizontal axis, and thecorresponding preference function, ,is on the vertical axis. No matter which category, the smaller the value of preference functions(偏好函数) is, the better it is.,2.2 Mathematical model of
20、fuzzy physical programming,模糊综合目标函数,偏好函数,Figure 1. Preference function ranges for ith generic metric(通用度量),2.3 Computational procedure of fuzzy physical programming,The solution to the multiobjective optimization problem using the proposed fuzzy physical programming approach can be determined using
21、the following step-by step procedure. a) Determine design objectives and design variables. b) Specify the membership function for each design metric. c) Specify the class type for each design metric (class 1S-4H). d) Provide the range limits for each design metric. e) Form the fuzzy physical program
22、ming problem model. f) Solve the problem model, and obtain the optimal design. In conventional optimization methods, multiobjective is converted into single objective, which only can obtain local optimum. In this paper. Genetic Algorithms is used in solving the formulated fuzzy physical programming
23、model, and has demonstrated its ability to obtain the global optimum even if there exists local optimums.,With the fuzzy aggregate objective function described above, the fuzzy physical programming problem model takes the following form.,nsc代表软设计标量的数目,3. Example,In the section, the case of two-stage
24、 conic-cylindrical gear reduction unit, which is used in chain moving machinery, is studied using fuzzy physical programming approach. A pair of conic gears is used as high-speed gears, while a pair of cylindrical gears is used as the low-speed gears. The performance of fuzzy physical programming is
25、 compared with that of physical programming. The objective is to minimize volume and minimize difference of power delivered between high-speed gear and low-speed gear.,3.1 PROBLEM FORMUNATION(问题表述),设计变量x（独立参数）：模数，齿数，齿宽，传动比，螺旋角 目标函数1：最小体积,目标函数2：高速级和低速级最小功率差 ZE:弹性影响系数；ZH:区域系数；Z:重合度系数,s.t.,Overlap rati
26、o:重合度,3.2 Results and discussions,Subject to the foregone conditions, we can establish the fuzzy physical programming problem model as Eq, (4). Region limits and the parameter i of the design objections are show in Table 1. Genetic algorithms is used to solve the formulated model, and the optimal re
27、sults are obtained and depicted in Table 2 and 3. The deviation between the result of fuzzy physical programming and physical programming is shown in Table 4. Considering the fuzzy factors of the system, the largest deviations of design objective and design variable are 5.9% and 13.2%, respectively.
28、 It is obvious that neglecting the fuzzy factors in the system will not lead to true optimal solution.,4.conclusion,The fuzzy physical programming model for the optimal design of two stage conic-cylindrical gear reduction unit is developed in this paper, and genetic algorithm is used to solve the mo
29、del. Compared with the physical programming approach, fuzzy physical programming is a more reasonable method for complex engineering systems. Combining with genetic algorithms, fuzzy physical progranmiing approach can obtain the global optimum. The example illustrates that fuzzy physical programming
30、 can consider the fuzziness of conic-cylindrical gear reduction unit substantially, and conforms more perfectly to the engineering realities.,REFERENCE,1. Tappeta R V, Renaud J E. Multiobjective collaborative optimization. ASME, Journal of Mechanical Design, 1997, 119(9): 403-411 2. Tappeta R V, Ren
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