机械毕业设计英文外文翻译55采用遗传算法优化加工夹具定位和加紧位置.docx

1、机械毕业设计英文外文翻译55采用遗传算法优化加工夹具定位和加紧位置附录Machining fixture locating and clamping position optimization using genetic algorithmsFixtures are used to locate and constrain a workpiece during a machining operation, minimizing workpiece and fixture tooling deflections due to clamping and cutting forces are cri

2、tical to ensuring accuracy of the machining operation. Traditionally, machining fixtures are designed and manufactured through trial-and-error, which prove to be both expensive and time-consuming to the manufacturing process. To ensure a workpiece is manufactured according to specified dimensions an

3、d tolerances, it must be appropriately located and clamped, making it imperative to develop tools that will eliminate costly and time-consuming trial-and-error designs. Proper workpiece location and fixture design are crucial to product quality in terms of precision, accuracy and finish of the machi

4、ned part. Theoretically, the 3-2-1 locating principle can satisfactorily locate all prismatic shaped workpieces. This method provides the maximum rigidity with the minimum number of fixture elements. To position a part from a kinematic point of view means constraining the six degrees of freedom of a

5、 free moving body (three translations and three rotations). Three supports are positioned below the part to establish the location of the workpiece on its vertical axis. Locators are placed on two peripheral edges and intended to establish the location of the workpiece on the x and y horizontal axes

6、. Properly locating the workpiece in the fixture is vital to the overall accuracy and repeatability of the manufacturing process. Locators should be positioned as far apart as possible and should be placed on machined surfaces wherever possible. Supports are usually placed to encompass the center of

7、 gravity of a workpiece and positioned as far apart as possible to maintain its stability. The primary responsibility of a clamp in fixture is to secure the part against the locators and supports. Clamps should not be expected to resist the cutting forces generated in the machining operation. For a

8、given number of fixture elements, the machining fixture synthesis problem is the finding optimal layout or positions of the fixture elements around the workpiece. In this paper, a method for fixture layout optimization using genetic algorithms is presented. The optimization objective is to search fo

9、r a 2D fixture layout that minimizes the maximum elastic deformation at different locations of the workpiece. ANSYS program has been used for calculating the deflection of the part under clamping and cutting forces. Two case studies are given to illustrate the proposed approach.Fixture design has re

10、ceived considerable attention in recent years. However, little attention has been focused on the optimum fixture layout design. Menassa and DeVries1used FEA for calculating deflections using the minimization of the workpiece deflection at selected points as the design criterion. The design problem w

11、as to determine the position of supports. Meyer and Liou2 presented an approach that uses linear programming technique to synthesize fixtures for dynamic machining conditions. Solution for the minimum clamping forces and locator forces is given. Li and Melkote3used a nonlinear programming method to

12、solve the layout optimization problem. The method minimizes workpiece location errors due to localized elastic deformation of the workpiece. Roy andLiao4developed a heuristic method to plan for the best supporting and clamping positions. Tao et al.5presented a geometrical reasoning methodology for d

13、etermining the optimal clamping points and clamping sequence for arbitrarily shaped workpieces. Liao and Hu6presented a system for fixture configuration analysis based on a dynamic model which analyses the fixtureworkpiece system subject to time-varying machining loads. The influence of clamping pla

14、cement is also investigated. Li and Melkote7presented a fixture layout and clamping force optimal synthesis approach that accounts for workpiece dynamics during machining. A combined fixture layout and clamping force optimization procedure presented.They used the contact elasticity modeling method t

15、hat accounts for the influence of workpiece rigid body dynamics during machining. Amaral et al. 8 used ANSYS to verify fixture design integrity. They employed 3-2-1 method. The optimization analysis is performed in ANSYS. Tan et al. 9 described the modeling, analysis and verification of optimal fixt

16、uring configurations by the methods of force closure, optimization and finite element modeling. Most of the above studies use linear or nonlinear programming methods which often do not give global optimum solution. All of the fixture layout optimization procedures start with an initial feasible layo

17、ut. Solutions from these methods are depending on the initial fixture layout. They do not consider the fixture layout optimization on overall workpiece deformation. The GAs has been proven to be useful technique in solving optimization problems in engineering 1012. Fixture design has a large solutio

18、n space and requires a search tool to find the best design. Few researchers have used the GAs for fixture design and fixture layout problems. Kumar et al. 13 have applied both GAs and neural networks for designing a fixture. Marcelin 14 has used GAs to the optimization of support positions. Vallapuz

19、ha et al. 15 presented GA based optimization method that uses spatial coordinates to represent the locations of fixture elements. Fixture layout optimization procedure was implemented using MATLAB and the genetic algorithm toolbox. HYPERMESH and MSC/NASTRAN were used for FE model. Vallapuzha et al.

20、16 presented results of an extensive investigation into the relative effectiveness of various optimization methods. They showed that continuous GA yielded the best quality solutions. Li and Shiu 17 determined the optimal fixture configuration design for sheet metal assembly using GA. MSC/NASTRAN has

21、 been used for fitness evaluation. Liao 18 presented a method to automatically select the optimal numbers of locators and clamps as well as their optimal positions in sheet metal assembly fixtures. Krishnakumar and Melkote 19 developed a fixture layout optimization technique that uses the GA to find

22、 the fixture layout that minimizes the deformation of the machined surface due to clamping and machining forces over the entire tool path. Locator and clamp positions are specified by node numbers. A built-in finite element solver was developed. Some of the studies do not consider the optimization o

23、f the layout for entire tool path and chip removal is not taken into account. Some of the studies used node numbers as design parameters. In this study, a GA tool has been developed to find the optimal locator and clamp positions in 2D workpiece. Distances from the reference edges as design paramete

24、rs are used rather than FEA node numbers. Fitness values of real encoded GA chromosomes are obtained from the results of FEA. ANSYS has been used for FEA calculations. A chromosome library approach is used in order to decrease the solution time. Developed GA tool is tested on two test problems. Two

25、case studies are given to illustrate the developed approach. Main contributions of this paper can be summarized as follows:(1) developed a GA code integrated with a commercial finite element solver;(2) GA uses chromosome library in order to decrease the computation time;(3) real design parameters ar

26、e used rather than FEA node numbers;(4) chip removal is taken into account while tool forces moving on the workpiece.Genetic algorithms were first developed by John Holland. Goldberg 10 published a book explaining the theory and application examples of genetic algorithm in details. A genetic algorit

27、hm is a random search technique that mimics some mechanisms of natural evolution. The algorithm works on a population of designs. The population evolves from generation to generation, gradually improving its adaptation to the environment through natural selection; fitter individuals have better chan

28、ces of transmitting their characteristics to later generations.In the algorithm, the selection of the natural environment is replaced by artificial selection based on a computed fitness for each design. The term fitness is used to designate the chromosomes chances of survival and it is essentially t

29、he objective function of the optimization problem. The chromosomes that define characteristics of biological beings are replaced by strings of numerical values representing the design variables.GA is recognized to be different than traditional gradient based optimization techniques in the following

30、four major ways 10:1. GAs work with a coding of the design variables and parameters in the problem, rather than with the actual parameters themselves.2. GAs makes use of population-type search. Many different design points are evaluated during each iteration instead of sequentially moving from one p

31、oint to the next.3. GAs needs only a fitness or objective function value. No derivatives or gradients are necessary.4. GAs use probabilistic transition rules to find new design points for exploration rather than using deterministic rules based on gradient information to find these new points.In mach

32、ining process, fixtures are used to keep workpieces in a desirable position for operations. The most important criteria for fixturing are workpiece position accuracy and workpiece deformation. A good fixture design minimizes workpiece geometric and machining accuracy errors. Another fixturing requir

33、ement is that the fixture must limit deformation of the workpiece. It is important to consider the cutting forces as well as the clamping forces. Without adequate fixture support, machining operations do not conform to designed tolerances. Finite element analysis is a powerful tool in the resolution of some of the