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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 2005AbstractDeformation 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 workpiece 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 batch 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 of 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 algorithms; Optimization4 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 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 and 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 ofprecision, accuracy and finish of the machined 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 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. 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 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 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 for 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.5 Review of related worksFixture design has received 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 was 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 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 forthe best supporting and clamping positions. Tao et al.5presented a geometrical reasoning methodology for determining 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 fixture workpiece system subject to time-varying machining loads. The influence of clamping placement 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 that 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 fixturing 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 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 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 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 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 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 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 into account while tool forces moving on the workpiece.6 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 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 chances 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 the 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 four major ways 10:6.1.GAs work with a coding of the design variables and parameters in the problem, rather than with the actual parameters themselves.6.2. GAs makes use of population-type search. Many different design points are evaluated during each iteration instead of sequentially moving from one point to the next.6.3.GAs needs only a fitness or objective function value. No derivatives or gradients are necessary.6.4. 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.7 Approach1. Fixture positioning principlesIn machining 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 requirement 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 these problems 22.Common locating method for prismatic parts is 3-2-1 method. This method provides the maximum rigidity with the minimum number of fixture elements. A workpiece in 3D may be positively located by means of six points positioned so that they restrict nine degrees of freedom of the workpiece. The other three degrees of freedom are removed by clamp elements. An example layout for 2D workpiece based 3-2-1 locating principle is shown in Fig. 4.Fig. 4. 3-2-1 locating layout for 2D prismatic workpieceThe number of locating faces must not exceed two so as to avoid a redundant location. Based on the 3-2-1 fixturing principle there are two locating planes for accurate location containing two and one locators. Therefore, there are maximum of two side clampings against each locating plane. Clamping forces are always directed towards the locators in order to force the workpiece to contact all locators. The clamping point should be positioned opposite the positioning points to prevent the workpiece from being distorted by the clamping force.Since the machining forces travel along the machining area, it is necessary to ensure that the reaction forces at locators are positive for all the time. Any negative reaction force indicates that the workpiece is free from fixture elements. In other words, loss of contact or the separation between the workpiece and fixture element might happen when the reaction force is negative. Positive reaction forces at the locators ensure that the workpiece maintains contact with all the locators from the beginning of the cut to the end. The clamping forces should be just sufficient to constrain and locate the workpiece without causing distortion or damage to the workpiece. Clamping force optimization is not considered in this paper.2. Genetic algorithm based fixture layout optimization approachIn real design problems, the number of design parameters can be very large and their influence on the objective function can be very complicated. The objective function must be smooth and a procedure is needed to compute gradients. Genetic algorithms strongly differ in conception from other search methods, including traditional optimization methods and other stochastic methods 23. By applying GAs to fixture layout optimization, an optimal or group of sub-optimal solutions can be obtained.In this study, optimum locator and clamp positions are determined using genetic algorithms. They are ideally suited for the fixture layout optimization problem since no direct analytical relationship exists between the machining error and the fixture layout. Since the GA deals with only the design variables and objective function value for a particular fixture layout, no gradient or auxiliary information is needed 19.The flowchart of the proposed approach is given in Fig. 5.Fixture layout optimization is implemented using developed software written in Delphi language named GenFix. Displacement values are calculated in ANSYS software 24. The execution of ANSYS in GenFix is simply done by WinExec function in Delphi. The interaction between GenFix and ANSYS is implemented in four steps: Locator and clamp positions are extracted from binary string as real parameters. These parameters and ANSYS input batch file (modeling, solution and post processing commands) are sent to ANSYS using WinExec function. Displacement values are written to a text file after solution. GenFix reads this file and computes fitness value for current locator and clamp positions.In order to reduce the computation time, chromosomes and fitness values are stored in a library for further evaluation. GenFix first checks if current chromosomes fitness value has been calculated before. If not, locator positions are sent to ANSYS, otherwise fitness values are taken from the library. During generating of the initial population, every chromosome is checked whether it is feasible or not. If the constraint is violated, it is eliminated and new chromosome is created. This process creates entirely feasible initial population. This ensures that workpiece is stable under the action of clamping and cutting forces for every chromosome in the initial population.The written GA program was validated using two test cases. The first test case uses Himmelblau function 21. In the second test case, the GA program was used to optimise the support positions of a beam under uniform loading.8 Fixture layout optimization case studiesThe fixture layout optimization problem is defined as: finding the positions of the locators and clamps, so that workpiece deformation at specific region is minimized. Note that number of locators and clamps are not design parameter, since they are known and fixed for the 3-2-1 locating scheme. Hence, the design parameters areselected as locator and clamp positions. Friction is not considered in this paper. Two case studies are given to illustrate the proposed approach.9 ConclusionIn this paper, an evolutionary optimization technique of fixture layout optimization is presented. ANSYS has been used for FE calculation of fitness values. It is seen that the combined genetic algorithm and FE method approach seems to be a powerful approach for present type problems. GA approach is particularly suited for problems where there does not exist a well-defined mathematical relationship between the objective function and the design variables. The results prove the success of the application of GAs for the fixture layout optimization problems.In this study, the major obstacle for GA application in fixture layout optimization is the high computation cost. Re-meshing of the workpiece is required for every chromosome in the population. But, usages of chromosome library, the number of FE evaluations are decreased from 6000 to 415. This results in a tremendous gain in computational efficiency. The other way to decrease the solution time is to use distributed computation in a local area network.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. It is shown that fixture layout problems are multi-modal therefore heuristic rules for fixture design should be used in GA to select best design among others.Fig. 5. The flowchart of the proposed methodology and ANSYS interface.采用遗传算法优化加工夹具定位和加紧位置Necmettin Kaya*Department of Mechanical Engineering, Uludag University, Gorukle, Bursa 16059, Turkey Received 8 July 2004; accepted 26 May 2005Available online 6 September 2005摘 要工件变形的问题可能导致机械加工中的空间问题。支撑和定位器是用于减少工件弹性变形引起的误差。支撑、定位器的优化和夹具定位是最大限度的减少几何在工件加工中的误差的一个关键问题。本文应用夹具布局优化遗传算法（GAs ）来处理夹具布局优化问题。遗传算法的方法是基于一种通过整合有限的运行于批处理模式的每一代的目标函数值的元素代码的方法，用于来优化夹具布局。给出的个案研究说明已开发的方法的应用。采用染色体文库方法减少整体解决问题的时间。已开发的遗传算法保持跟踪先前的分析设计，因此先前的分析功能评价的数量降低大约93%。结果表明，该方法的夹具布局优化问题是多模式的问题。优化设计之间没有任何明显的相似之处，虽然它们提供非常相似的表现。关键词：夹具设计；遗传算法；优化1. 引言夹具用来定位和束缚机械操作中的工件，减少由于对确保机械操作准确性的夹紧方案和切削力造成的工件和夹具的变形。传统上，加工夹具是通过反复试验法来设计和制造的，这是一个既造价高又耗时的制造过程。为确保工件按规定尺寸和公差来制造，工件必须给予适当的定位和夹紧以确保有必要开发工具来消除高造价和耗时的反复试验设计方法。适当的工件定位和夹具设计对于产品质量的精密度、准确度和机制件的完饰是至关重要的。从理论上说，3-2-1 定位原则对于定位所有的棱柱形零件是很令人满意的。该方法具有最大的刚性与最少量的夹具元件。从动力学观点来看定位零件意味着限制了自由移动物体的六自由度（三个平动自由度和三个旋转自由度）。在零件下部设置三个支撑来建立工件在垂直轴方向的定位。在两个外围边缘放置定位器旨在建立工件在水平x轴和y轴的定位。正确定位夹具的工件对于制造过程的全面准确性和重复性是至关重要的。定位器应该尽可能的远距离的分开放置并且应该放在任何可能的加工面上。放置的支撑器通常用来包围工件的重力中心并且尽可能的将其分开放置以维持其稳定性。夹具夹子的首要任务是固定夹具以抵抗定位器和支撑器。不应该要求夹子反抗加工操作中的切削力。对于给定数量的夹具元件，加工夹具合成的问题是寻找夹具优化布局或工件周围夹具元件的位置。本篇文章提出一种优化夹具布局遗传算法。优化目标是研究一个二维夹具布局使工件不同位置上最大的弹性变形最小化。ANSYS程序以用于计算工件变形情况下夹紧力和切削力。本文给出两个实例来说明给出的方法。2. 回顾相关工程结构最近几年夹具设计问题受到越来越多的重视。然而，很少有注意力集中于优化夹具布局设计。Menassa 和Devries用FEA计算变形量使设计准则要求的位点的工件变形最小化。设计问题是确定支撑器位置。Meyer和Liou提出一个方法就是使用线性编程技术合成动态编程条件中的夹具。给出了使夹紧力和定位力最小化的解决方案。Li和Melkote 用非线性规划方法解决布局优化问题。这个方法使工件位置误差最小化归于工件的局部弹性变形。Roy和Liao 开发出一种启发式方法来计划最好的支撑和夹紧位置。Tao 等人提出一个几何推理的方法来确定最优夹紧点和任意形状工件的夹紧顺序。Liao 和Hu提出一种夹具结构分析系统这个系统基于动态模型分析受限于时变加工负载的夹具工件系统。本文也调查了夹紧位置的影响。Li和Melkote提出夹具布局和夹紧力最优合成方法帮我们解释加工过程中的工件动力学。本文提出一个夹具布局和夹紧力优化结合的程序。他们用接触弹性建模方法解释工件刚体动力学在加工期间的影响。Amaral等人用ANSYS验证夹具设计的完整性。他们用3-2-1 方法。 ANSYS提出优化分析。 Tan等人通过力锁合、优化与有限建模方法描述了建模、优化夹具的分析与验证。以上大部分的研究使用线性和非线性编程方式这通常不会给出全局最优解决方案。所有的夹具布局优化程序开始于一个初始可行布局。这些方法给出的解决方案在很大程度上取决于初始夹具布局。他们没有考虑到工件夹具布局优化对整体的变形。GAs已被证明在解决工程中优化问题是有用的。夹具设计具有巨大的解决空间并需要搜索工具找到最好的设计。一些研究人员曾使用GAs解决夹具设计及夹具布局问题。Kumar等人用 GAs和神经网络设计夹具。Marcelin 已经将GAs用于支撑位置的优化。Vallapuzha 等人提出基于优化方法的GA，它采用空间坐标来表示夹具元件的位置。夹具布局优化程序设计的实现是使用MATLAB 和遗传算法工具箱。HYPERMESH和MSC / NASTRAN用于FE模型。Vallapuzha等人提出一些结果关于一个广泛调查不同优化方法的相对有效性。他们的研究表明连续遗传
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