夹具设计外文翻译采用遗传算法优化加工夹具定位和加紧位置.docx

1、夹具设计外文翻译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 algorithmsNecmettin Kaya*Department of Mechanical Engineering, Uludag University, Gorukle, Bursa 16059, Turkey Received 8 July 2004; accepted 26 May 2005Available online 6 September 2005Abstract

2、Deformation of the workpiece may cause dimensional problems in machining. Supports and locators are used in order to reduce the error caused by elastic deformation of the workpiece. The optimization of support, locator and clamp locations is a critical problem to minimize the geometric error in work

3、piece machining. In this paper, the application of genetic algorithms (GAs) to the fixture layout optimization is presented to handle fixture layout optimization problem. A genetic algorithm based approach is developed to optimise fixture layout through integrating a finite element code running in b

4、atch mode to compute the objective function values for each generation. Case studies are given to illustrate the application of proposed approach. Chromosome library approach is used to decrease the total solution time. Developed GA keeps track of previously analyzed designs; therefore the numbers o

5、f function evaluations are decreased about 93%. The results of this approach show that the fixture layout optimization problems are multi-modal problems. Optimized designs do not have any apparent similarities although they provide very similar performances.Keywords: Fixture design; Genetic algorith

6、ms; Optimization1. IntroductionFixtures 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 critical to ensuring accuracy of the machining operation. Traditionally, machining fixtures a

7、re 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 and tolerances, it must be appropriately located and clamped, making it imperative to develo

8、p 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 machined part. Theoretically, the 3-2-1 locating principle can satisfactorily locate all prisma

9、tic 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 free moving body (three translations and three rotations). Three supports are positioned

10、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. Properly locating the workpiece in the fixture is vital to the overall accuracy and repe

11、atability 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 gravity of a workpiece and positioned as far apart as possible to maintain its stability.

12、 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 given number of fixture elements, the machining fixture synthesis problem is the finding o

13、ptimal 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 for a 2D fixture layout that minimizes the maximum elastic deformation at different location

14、s 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.2. Review of related worksFixture design has received considerable attention in recent years. However, little a

15、ttention 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 was to determine the position of supports. Meyer and Liou2 presen

16、ted 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 solve the layout optimization problem. The method minimizes work

17、piece 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 determining the optimal clamping points and clamping sequence for

18、 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 placement is also investigated. Li and Melkote7presented a fixture

19、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 that accounts for the influence of workpiece rigid body dynamics

20、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 fixturing configurations by the methods of force closure, optimizati

21、on 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 layout. Solutions from these methods are depending on the initial fi

22、xture 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 solution space and requires a search tool to find the best design. Few

23、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. Vallapuzha et al. 15 presented GA based optimization method that uses sp

24、atial 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. 16 presented results of an extensive investigation into the rela

25、tive 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 been used for fitness evaluation. Liao 18 presented a method to

26、 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 the fixture layout that minimizes the deformation of the machin

27、ed 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 of the layout for entire tool path and chip removal is not taken

28、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 parameters are used rather than FEA node numbers. Fitness values of real

29、 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 case studies are given to illustrate the developed approach. Mai

30、n 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 are used rather than FEA node numbers;(4) chip removal is taken in

31、to account while tool forces moving on the workpiece.3. Genetic algorithm conceptsGenetic 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 algorithm is a random search technique tha

32、t 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 chances of transmitting their character

33、istics 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 the objective function of the optimization pr