（节选）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

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1、PDF外文：http:/  Machining fixture locating and clamping position optimizationusing genetic algorithms Necmettin Kaya* Department of Mechanical Engineering, Uludag University, Gorukle, Bursa 16059, Turkey Received 8 July 2004； accepted 26 May 2005 Available online 6 September 2005 Abstract Deforma

2、tion of the workpiece may cause dimensional problems in machining. Supports and locators are used in order to reduce the error causedby elastic deformation of the workpiece. The optimization of support, locator and clamp locations is a critical problem to minimize the geometricerror in workpiece mac

3、hining. In this paper, the application of genetic algorithms (GAs) to the fixture layout optimization is presented to handlefixture layout optimization problem. A genetic algorithm based approach is developed to optimise fixture layout through integrating a finiteelement code running in batch mode t

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

5、aluations are decreased about 93%. The results of this approach show that the fixture layoutoptimization problems are multi-modal problems. Optimized designs do not have any apparent similarities although they provide very similarperformances. Keywords: Fixture design； Genetic algorithms； Optimizati

6、on 1. Introduction Fixtures are used to locate and constrain a workpiece duringa machining operation, minimizing workpiece and fixturetooling deflections due to clamping and cutting forces arecritical toensuring accuracy of the machining operation.Traditionally, machining fixtures are designed and m

7、anufacturedthrough trial-and-error, which prove to be both expensiveand time-consuming to the manufacturing process. To ensure aworkpiece is manufactured according to specified dimensionsand tolerances, it must be appropriately located and clamped,making it imperative to develop tools that will elim

8、inate costlyand time-consuming trial-and-error designs. Proper workpiecelocation and fixture design are crucial to product quality interms of precision, accuracy and finish of the machined part. Theoretically, the 3-2-1 locating principle can satisfactorilylocate all prismatic shaped workpieces. Thi

9、s method providesthe maximum rigidity with the minimum number of fixtureelements. To position a part from a kinematic point of viewmeans constraining the six degrees of freedom of a free movingbody (three translations and three rotations). Three supports arepositioned below the part to establish the

10、 location of theworkpiece on its vertical axis. Locators are placed on twoperipheral edges and intended to establish the location of theworkpiece on the x and y horizontal axes. Properly locating theworkpiece in the fixture is vital to the overall accuracy andrepeatability of the manufacturing proce

11、ss. Locators should bepositioned as far apart as possible and should be placed onmachined surfaces wherever possible. Supports are usuallyplaced to encompass the center of gravity of a workpiece andpositioned as far apart as possible to maintain its stability. Theprimary responsibility of a clamp in

12、 fixture is to secure the partagainst the locators and supports. Clamps should not be expectedto resist the cutting forces generated in the machining operation. For a given number of fixture elements, the machiningfixture synthesis problem is the finding optimal layout orpositions of the fixture ele

13、ments around the workpiece. In thispaper, a method for fixture layout optimization using geneticalgorithms is presented. The optimization objective is to searchfor a 2D fixture layout that minimizes the maximum elasticdeformation at different locations of the workpiece. ANSYSprogram has been used fo

14、r calculating the deflection of the partunder clamping and cutting forces. Two case studies are givento illustrate the proposed approach. 2. Review of related works Fixture design has received considerable attention in recentyears. However, little attention has been focused on theoptimum fixture lay

15、out design. Menassa and DeVries1usedFEA for calculating deflections using the minimization of theworkpiece deflection at selected points as the design criterion.The design problem was to determine the position of supports.Meyer and Liou2 presented an approach that uses linearprogramming technique to

16、 synthesize fixtures for dynamicmachining conditions. Solution for the minimum clampingforces and locator forces is given. Li and Melkote3used anonlinear programming method to solve the layout optimizationproblem. The method minimizes workpiece location errorsdue to localized elastic deformation of

17、the workpiece. RoyandLiao4developed a heuristic method to plan for the bestsupporting and clamping positions. Tao et al.5presented ageometrical reasoning methodology for determining theoptimal clamping points and clamping sequence for arbitrarilyshaped workpieces. Liao and Hu6presented a system forf

18、ixture configuration analysis based on a dynamic model whichanalyses the fixtureworkpiece system subject to time-varyingmachining loads. The influence of clamping placement is alsoinvestigated. Li and Melkote7presented a fixture layout andclamping force optimal synthesis approach that accounts forwo

19、rkpiece dynamics during machining. A combined fixturelayout and clamping force optimization procedure presented.They used the contact elasticity modeling method that accountsfor the influence of workpiece rigid body dynamics duringmachining. Amaral et al. 8 used ANSYS to verify fixturedesign integri

20、ty. They employed 3-2-1 method. The optimizationanalysis is performed in ANSYS. Tan et al. 9 describedthe modeling, analysis and verification of optimal fixturingconfigurations by the methods of force closure, optimizationand finite element modeling. Most of the above studies use linear or nonlinear

21、programming methods which often do not give global optimumsolution. All of the fixture layout optimization procedures startwith an initial feasible layout. Solutions from these methods aredepending on the initial fixture layout. They do not consider thefixture layout optimization on overall workpiec

22、e deformation. The GAs has been proven to be useful technique in solvingoptimization problems in engineering 1012. Fixture designhas a large solution space and requires a search tool to find thebest design. Few researchers have used the GAs for fixturedesign and fixture layout problems. Kumar et al.

23、 13 haveapplied both GAs and neural networks for designing a fixture.Marcelin 14 has used GAs to the optimization of supportpositions. Vallapuzha et al. 15 presented GA basedoptimization method that uses spatial coordinates to representthe locations of fixture elements. Fixture layout optimizationpr

24、ocedure was implemented using MATLAB and the geneticalgorithm toolbox. HYPERMESH and MSC/NASTRAN wereused for FE model. Vallapuzha et al. 16 presented results of anextensive investigation into the relative effectiveness of variousoptimization methods. They showed that continuous GAyielded the best quality solutions. Li and Shiu17 determinedthe optimal fixture configuration design for sheet metalassembly using GA. MSC/NASTRAN has been used forfitness evaluation. Liao 18 presented a method to automaticallyselect the optimal numbers of