【正文】 n problem was to determine the position of supports. Meyer and Liou[2] 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 Melkote[3]used a nonlinear programming method to solve the layout optimization problem. The method minimizes work piece location errors due to localized elastic deformation of the work piece. Roy andLiao[4]developed a heuristic method to plan for the best supporting and clamping positions. Tao et al.[5]presented a geometrical reasoning methodology for determining the optimal clamping points and clamping sequence for arbitrarily shaped workpieces. Liao and Hu[6]presented a system for fixture configuration analysis based on a dynamic model which analyses the fixture–work piece system subject to timevarying machining loads. The influence of clamping placement is also investigated. Li and Melkote[7]presented a fixture layout and clamping force optimal synthesis approach that accounts for work piece dynamics during machining. A bined fixture layout and clamping force optimization procedure presented. They used the contact elasticity modeling method that accounts for the influence of work piece rigid body dynamics during machining. Admiral et al. [8] used ANSYS to verify fixture design integrity. They employed 321 method. The optimization analysis is performed in ANSYS. Tan et al. [9] described the modeling, analysis and verification of optimal featuring 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 layout. Solutions from these methods are depending on the initial fixture layout. They do not consider the fixture layout optimization on overall work piece deformation. The GAs has been proven to be useful technique in solving optimization problems in engineering [10–12]. Fixture design has a large solution 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. Vallapuzha 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. [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 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 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 builtin 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 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 work piece. Distances from the reference edges as design parameters 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 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 mercial finite element solver。 therefore the numbers of function evaluations are decreased about 93%. The results of this approach show that the fixture layout optimization problems are multimodal problems. Optimized designs do not have any apparent similarities although they provide very similar performances.Keywords: Fixture design。Robotics and CIMS, Vol. 1, No. 2, 1984, pp. 167172.附錄A 外文文獻(xiàn)及譯文Machining fixture locating and clamping position optimization using genetic algorithmsNecmettin KayakDepartment of Mechanical Engineering, Uludag University, Go ru , Bursa 16059, Turkey Received 8 July 2004。 由于我的學(xué)術(shù)水平有限，所寫論文難免有不足之處，懇請各位老師和學(xué)友批評和指正！作者簽名： 日期： 年 月 日參考文獻(xiàn)[1] （第二版）[M].北京：機(jī)械工業(yè)出版社，[2] [M].武漢；華中科技大學(xué)出版社，[3] [M].北京：冶金工業(yè)出版社，[4] [M].北京：北京出版社，[5] 王健石. 機(jī)械加工常用刀具手冊[M].北京：機(jī)械工業(yè)出版社，[6] 艾興，[M].北京：機(jī)械工業(yè)出版社，[7] 益民機(jī). 機(jī)械制造工藝設(shè)計(jì)簡明手冊[M].北京：機(jī)械工業(yè)出版社，[8] 王凡，宋建新. 實(shí)用機(jī)械制造工藝設(shè)計(jì)手冊[M].北京：機(jī)械工業(yè)出版社，[9] 趙雪松，任小中，[M].武漢：華中科技大學(xué)出版社，[10] 張秀珍，[M].北京：北京大學(xué)出版社，[11] [M].北京：化學(xué)工業(yè)出版社，[12] 王光斗，[M].上海：上?？茖W(xué)技術(shù)出版社，[13] 游輝. 機(jī)床夾具的夾緊誤差分析[J].成都：成都科技大學(xué)出版社，.[14] . Barry, Application of CAD/CAM to fixture design, Int. Machine Tool Technology Conj, 1982, .[15] X. Li, Interactive Computeraided Modular Fixture Design based on Locating Method Analysis, M. S. Thesis, Southern Illinois University, 1996..[16] Y. Rang and Y. Bai, Automated generation of modular fixture configuration design, ASME Design Automation Conference, 1995, pp. 681688.[17] Y. H Fur, A. Y C. Nee, A. Menthol Kumar, and S. Toe, IFDA: an interactiv