采用遺傳算法優(yōu)化加工夾具定位和加緊位置外文文獻(xiàn)翻譯@工裝夾具類外文翻譯@中英文版翻譯

附錄 Machining fixture locating and clamping position optimization using genetic algorithms Fixtures 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 of precision, 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. Fixture 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 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 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 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. 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 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: 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 point 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 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 workpiece The 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. In 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 var iables 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: (1) Locator and clamp positions are extracted from binary string as real parameters. (2) These parameters and ANSYS input batch file (modeling, solution and post processing commands) are sent to ANSYS using WinExec function. (3) Displacement values are written to a text file after solution. (4) 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. 采用遺傳算法優(yōu)化加工夾具定位和加緊位置 夾具用來定位和束縛機(jī)械操作中的工件，減少由于對(duì)確保機(jī)械操作準(zhǔn)確性的夾緊方案和切削力造成的工件和夾具的變形。傳統(tǒng)上，加工夾具是通過反復(fù)試驗(yàn)法來設(shè)計(jì)和制造的，這是一個(gè)既造價(jià)高又耗時(shí)的制造過程。為確保工件按規(guī)定尺寸和公差來制造，工件必須給予適當(dāng)?shù)亩ㄎ缓蛫A緊以確保有必要開發(fā)工具來消除高造價(jià)和耗時(shí)的反復(fù)試驗(yàn)設(shè)計(jì)方法。適當(dāng)?shù)墓ぜㄎ缓蛫A具設(shè)計(jì)對(duì)于產(chǎn)品質(zhì)量的精密度、準(zhǔn)確度和機(jī)制件的完飾是至關(guān)重要的。 從理論上說，夾具原則對(duì)于定位所有的軸類零件是很令人滿意的。該方法具有最大的剛性 與最少量的夾具元件。從動(dòng)力學(xué)觀點(diǎn)來看定位零件意味著限制了自由移動(dòng)物體的六自由度（三個(gè)平動(dòng)自由度和三個(gè)旋轉(zhuǎn)自由度）。在零件下部設(shè)置三個(gè)支撐來建立工件在垂直軸方向的定位。在兩個(gè)外圍邊緣放置定位器旨在建立工件在水平 x 軸和y 軸的定位。正確定位夾具的工件對(duì)于制造過程的全面準(zhǔn)確性和重復(fù)性是至關(guān)重要的。定位器應(yīng)該盡可能的遠(yuǎn)距離的分開放置并且應(yīng)該放在任何可能的加工面上。放置的支撐器通常用來包圍工件的重力中心并且盡可能的將其分開放置以維持其穩(wěn)定性。夾具夾子的首要任務(wù)是固定夾具以抵抗定位器和支撐器。不應(yīng)該要求夾子反抗加工操作中的切削力。 對(duì)于給定數(shù)量的夾具元件，加工夾具合成的問題是尋找夾具優(yōu)化布局或工件周圍夾具元件的位置。本篇文章提出一種優(yōu)化夾具布局遺傳算法。優(yōu)化目標(biāo)是研究一個(gè)二維夾具布局使工件不同位置上最大的彈性變形最小化。 ANSYS 程序以用于計(jì)算工件變形情況下夾緊力和切削力。本文給出兩個(gè)實(shí)例來說明給出的方法。 最近幾年夾具設(shè)計(jì)問題受到越來越多的重視。然而，很少有注意力集中于優(yōu)化夾具布局設(shè)計(jì)。 Menassa 和 Devries 用 FEA計(jì)算變形量使設(shè)計(jì)準(zhǔn)則要求的位點(diǎn)的工件變形最小化。設(shè)計(jì)問題是確定支撐器位置。 Meyer 和 Liou 提出 一個(gè)方法就是使用線性編程技術(shù)合成動(dòng)態(tài)編程條件中的夾具。給出了使夾緊力和定位力最小化的解決方案。 Li和 Melkote 用非線性規(guī)劃方法解決布局優(yōu)化問題。這個(gè)方法使工件位置誤差最小化歸于工件的局部彈性變形。 Roy 和 Liao 開發(fā)出一種啟發(fā)式方法來計(jì)劃最好的支撐和夾緊位置。 Tao 等人提出一個(gè)幾何推理的方法來確定最優(yōu)夾緊點(diǎn)和任意形狀工件的夾緊順序。 Liao 和 Hu 提出一種夾具結(jié)構(gòu)分析系統(tǒng)這個(gè)系統(tǒng)基于動(dòng)態(tài)模型分析受限于時(shí)變加工負(fù)載的夾具 工件系統(tǒng)。本文也調(diào)查了夾緊位置的影響。 Li 和 Melkote 提出夾具布局和夾緊力最優(yōu)合成 方法幫我們解釋加工過程中的工件動(dòng)力學(xué)。本文提出一個(gè)夾具布局和夾緊力優(yōu)化結(jié)合的程序。他們用接觸彈性建模方法解釋工件剛體動(dòng)力學(xué)在加工期間的影響。 Amaral等人用 ANSYS 驗(yàn)證夾具設(shè)計(jì)的完整性。他們用 3-2-1 方法。 ANSYS提出優(yōu)化分析。 Tan 等人通過力鎖合、優(yōu)化與有限建模方法描述了建模、優(yōu)化夾具的分析與驗(yàn)證。 以上大部分的研究使用線性和非線性編程方式這通常不會(huì)給出全局最優(yōu)解決方案。所有的夾具布局優(yōu)化程序開始于一個(gè)初始可行布局。這些方法給出的解決方案在很大程度上取決于初始夾具布局。他們沒有考慮到工件夾具布局優(yōu) 化對(duì)整體的變形。 GAs 已被證明在解決工程中優(yōu)化問題是有用的。夾具設(shè)計(jì)具有巨大的解決空間并需要搜索工具找到最好的設(shè)計(jì)。一些研究人員曾使用 GAs 解決夾具設(shè)計(jì)及夾具布局問題。Kumar 等人用 GAs 和神經(jīng)網(wǎng)絡(luò)設(shè)計(jì)夾具。 Marcelin 已經(jīng)將 GAs 用于支撐位置的優(yōu)化。Vallapuzha 等人提出基于優(yōu)化方法的 GA，它采用空間坐標(biāo)來表示夾具元件的位置。夾具布局優(yōu)化程序設(shè)計(jì)的實(shí)現(xiàn)是使用 MATLAB和遺傳算法工具箱。 HYPERMESH和MSC / NASTRAN 用于 FE模型。 Vallapuzha 等人提出一些結(jié)果關(guān)于一個(gè)廣 泛調(diào)查不同優(yōu)化方法的相對(duì)有效性。他們的研究表明連續(xù)遺傳算法提出了最優(yōu)質(zhì)的解決方案。 Li和 Shiu 使用遺傳算法確定了夾具設(shè)計(jì)最優(yōu)配置的金屬片。 MSC/NASTRAN 已經(jīng)用于適應(yīng)度值評(píng)價(jià)。 Liao 提出自動(dòng)選擇最佳夾子和夾鉗的數(shù)目以及它們?cè)诮饘倨系膴A具中的最優(yōu)位置。 Krishnakumar 和 Melkote 開發(fā)了一種夾具布局優(yōu)化技術(shù)，它是利用遺傳算法找到了夾具布局，由于整個(gè)刀具路徑中的夾緊力和加工力使加工表面變形量最小化。通過節(jié)點(diǎn)編號(hào)使定位器和夾具位置特殊化。一個(gè)內(nèi)置的有限元求解器研制成功。 一些研究沒考慮到整 個(gè)刀具路徑的優(yōu)化布局以及磨屑清除。一些研究采用節(jié)點(diǎn)編號(hào)作為設(shè)計(jì)參數(shù)。 在本研究中，開發(fā) GA工具用于尋找在二維工件中的最優(yōu)定位器和夾緊位置。使用參考邊緣的距離作為設(shè)計(jì)參數(shù)而不是用 FEA節(jié)點(diǎn)編號(hào)。真正編碼遺傳算法的染色體的健康指數(shù)是從 FEA結(jié)果中獲得的。 ANSSYS 用于 FEA計(jì)算。用染色體文庫的方法是為了減少解決問題的時(shí)間。用兩個(gè)問題測(cè)試已開發(fā)的遺傳算法工具。給出的兩個(gè)實(shí)例說明了這個(gè)開發(fā)的方法。本論文的主要貢獻(xiàn)可以概括為以下幾個(gè)方面： （ 1） 開發(fā)了遺傳算法編碼結(jié)合商業(yè)有限元素求解； （ 2） 遺傳算法采用染色體 文庫以降低計(jì)算時(shí)間； （ 3） 使用真正的設(shè)計(jì)參數(shù)，而不是有限元節(jié)點(diǎn)數(shù)字； （ 4） 當(dāng)工具在工件中移動(dòng)時(shí)考慮磨屑清除工具。 遺傳算法最初由 Jo