机械毕业设计英文外文翻译55采用遗传算法优化加工夹具定位和加紧位置

机械毕业设计英文外文翻译55采用遗传算法优化加工夹具定位和加紧位置
机械毕业设计英文外文翻译55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 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 DeVries[1]used 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 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 workpiece location errors due to localized elastic deformation of the workpiece. 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–workpiece system subject to time-varying 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

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 [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 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 chromosome’s 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 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:

(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 chromosome’s 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.

采用遗传算法优化加工夹具定位和加紧位置

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,夹具原则对于定位所有的轴类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。

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

表明连续遗传算法提出了最优质的解决方案。Li和Shiu使用遗传算法确定了夹具设计最优配置的金属片。MSC/NASTRAN 已经用于适应度值评价。Liao提出自动选择最佳夹子和夹钳的数目以及它们在金属片整合的夹具中的最优位置。Krishnakumar和Melkote开发了一种夹具布局优化技术,它是利用遗传算法找到了夹具布局,由于整个刀具路径中的夹紧力和加工力使加工表面变形量最小化。通过节点编号使定位器和夹具位置特殊化。一个内置的有限元求解器研制成功。

一些研究没考虑到整个刀具路径的优化布局以及磨屑清除。一些研究采用节点编号作为设计参数。

在本研究中,开发GA工具用于寻找在二维工件中的最优定位器和夹紧位置。使用参考边缘的距离作为设计参数而不是用FEA节点编号。真正编码遗传算法的染色体的健康指数是从FEA结果中获得的。ANSSYS用于FEA计算。用染色体文库的方法是为了减少解决问题的时间。用两个问题测试已开发的遗传算法工具。给出的两个实例说明了这个开发的方法。本论文的主要贡献可以概括为以下几个方面:

(1)开发了遗传算法编码结合商业有限元素求解;

(2)遗传算法采用染色体文库以降低计算时间;

(3)使用真正的设计参数,而不是有限元节点数字;

(4)当工具在工件中移动时考虑磨屑清除工具。

遗传算法最初由John Holland开发。Goldberg出版了一本书,解释了这个理论和遗传算法应用实例的详细说明。遗传算法是一种随机搜索方法,它模拟一些自然演化的机制。该算法用于种群设计。种群从一代到另一代演化,通过自然选择逐渐提高了适应环境的能力,更健康的个体有更好的机会,将他们的特征传给后代。

该算法中,要基于为每个设计计算适合性,所以人工选择取代自然环境选择。适应度值这个词用来指明染色体生存几率,

它在本质上是该优化问题的目标函数。生物定义的特征染色体用代表设计变量的字符串中的数值代替。

被公认的遗传算法与传统的梯度基础优化技术的不同主要有如下四种方式:

(1)遗传算法和问题中的一种编码的设计变量和参数一起工作而不是实际参数本身。

(2)遗传算法使用种群—类型研究。评价在每个重复中的许多不同的设计要点而不是一个点顺序移动到下一个。(3)遗传算法仅仅需要一个适当的或目标函数值。没有衍生品或梯度是必要的。

(4)遗传算法以用概率转换规则来发现新设计为探索点而不是利用基于梯度信息的确定性规则来找到这些新观点。

加工过程中,用夹具来保持工件处于一个稳定的操作位置。对于夹具最重要的标准是工件位置精确度和工件变形。一个良好的夹具设计使工件几何和加工精度误差最小化。另一个夹具设计的要求是夹具必须限制工件的变形。考虑切削力以及夹紧力是很重要的。没有足够的夹具支撑,加工操作就不符合设计公差。有限元分析在解决这其中的一些问题时是一种很有力的工具。

轴类零件常见的定位方法是3-2-1方法。该方法具有最大刚体度以及最小夹具元件数。在三维中一个工件可能会通过六自由度定位方法快速定位为了限制工件的九个自由度。其他的三个自由度通过夹具元件消除了。

定位面得数量不得超过两个避免冗余的位置。基于3-2-1的夹具设计原则有两种精确的定位平面包含于两个或一个定位器。因此,在两边有最大的夹紧力抵抗每个定位平面。夹紧力总是指向定位器为了推动工件接触到所有的定位器。定位点对面应定位夹紧点防止工件由于夹紧力而扭曲。因为加工力沿着加工面,所以有必要确保定位器的反应力在所有时间内是正的。任何负面的反应力表示工件从夹具元件中脱

离。换句话说,当反应力是负的时候,工件和夹具元件之间接触或分离的损失可能发生。定位器内正的反应力确保工件从切削开始到结束都能接触到所有的定位器。夹紧力应该充分束缚和定位工件且不导致工件的变形或损坏。本文不考虑夹紧力的优化。

在实际设计问题中,设计参数的数量可能很大并且它们对目标函数的影响会是非常复杂的。目标函数曲线必须是光滑的并且需要一个程序计算梯度。遗传算法在理念上远不同于其他的探究方法,它们包括传统的优化方法和其他随机方法。通过运用遗传算法来对夹具优化布局,可以获得一个或一组最优的解决方案。

本项研究中,最优定位器和夹具定位使用遗传算法确定。它们是理想的适合夹具布局优化问题的方法因为没有直接分析的关系存在于加工误差和夹具布局中。因为遗传算法仅仅为一个特别的夹具布局处理设计变量和目标函数值,所以不需要梯度或辅助信息。

使用开发的命名为GenFix的Delphi语言软件来实现夹具布局优化。位移量用ANSYS软件计算。通过WinExec功能在GenFix中运行ANSYS很简单。GenFix和ANSYS之间相互作用通过四部实现:

(1)定位器和夹具位置从二进制代码字符串中提取作为真正的参数。

(2)这些参数和ANSYS输入批处理文件(建模、解决方案和后置处理)用WinExec功能传给ANSYS。

(3)解决后将位移值写成一个文本文件。

(4)GenFix读这个文件并为当前定位器和夹紧位置计算适应度值。

为了减少计算量,染色体与适应度值储存在一个文库里以备进一步评估。GenFix首先检查是否当前的染色体的适应度值已经在之前被计算过。如果没有,定位器位置被送到ANSYS,

否则从文库中取走适应度值。在初始种群产生过程中,检查每一个染色体可行与否。如果违反了这个原则,它就会出局然后新的染色体就产生了。这个程序创造了可行的初始种群。这保证了初始种群的每个染色体在夹紧力和切削力作用下工件的稳定性。用两个测试用例来验证提到的遗传算法计划。第一个实例是使用Himmelblau功能。在第二个测试用例中,遗传算法计划用来优化均布载荷作用下梁的支撑位置。

本科毕业设计文献综述范例(1)

###大学 本科毕业设计(论文)文献综述 课题名称: 学院(系): 年级专业: 学生姓名: 指导教师: 完成日期:

燕山大学本科生毕业设计(论文) 一、课题国内外现状 中厚板轧机是用于轧制中厚度钢板的轧钢设备。在国民经济的各个部门中广泛的采用中板。它主要用于制造交通运输工具(如汽车、拖拉机、传播、铁路车辆及航空机械等)、钢机构件(如各种贮存容器、锅炉、桥梁及其他工业结构件)、焊管及一般机械制品等[1~3]。 1 世界中厚板轧机的发展概况 19世纪五十年代,美国用采用二辊可逆式轧机生产中板。轧机前后设置传动滚道,用机械化操作实现来回轧制,而且辊身长度已增加到2m以上,轧机是靠蒸汽机传动的。1864年美国创建了世界上第一套三辊劳特式中板轧机,当时盛行一时,推广于世界。1918年卢肯斯钢铁公司科茨维尔厂为了满足军舰用板的需求,建成了一套5230mm四辊式轧机,这是世界上第一套5m以上的轧机。1907年美国钢铁公司南厂为了轧边,首次创建了万能式厚板轧机,于1931年又建成了世界上第一套连续式中厚板轧机。欧洲国家中厚板生产也是较早的。1910年,捷克斯洛伐克投产了一套4500mm二辊式厚板轧机。1940年,德国建成了一套5000mm四辊式厚板轧机。1937年,英国投产了一套3810mm中厚板轧机。1939年,法国建成了一套4700mm 四辊式厚板轧机。这些轧机都是用于生产机器和兵器用的钢板,多数是为了二次世界大战备战的需要。1941年日本投产了一套5280mm四辊式厚板轧机,主要用于满足海军用板的需要。20世纪50年代,掌握了中厚板生产的计算机控制。20世纪80年代,由于中厚板的使用部门萧条,许多主要产钢国家的中厚板产量都有所下降,西欧国家、日本和美国关闭了一批中厚板轧机(宽度一般在3、4米以下)。国外除了大的厚板轧机以外,其他大型的轧机已很少再建。1984年底,法国东北方钢铁联营敦刻尔克厂在4300mm轧机后面增加一架5000mm宽厚板轧机,增加了产量,且扩大了品种。1984年底,苏联伊尔诺斯克厂新建了一套5000mm宽厚板轧机,年产量达100万t。1985年初,德国迪林冶金公司迪林根厂将4320mm轧机换成4800mm 轧机,并在前面增加一架特宽得5500mm轧机。1985年12月日本钢管公司福山厂新型制造了一套4700mmHCW型轧机,替换下原有得轧机,更有效地控制板形,以提高钢板的质量。 - 2 -

机械毕业设计英文外文翻译460数字控制 (2)

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机械毕业设计英文外文翻译204机电一体化

附录 INTEGRATION OF MACHINERY (From ELECTRICAL AND MACHINERY INDUSTRY)ABSTRACT Machinery was the modern science and technology development inevitable result, this article has summarized the integration of machinery technology basic outline and the development background .Summarized the domestic and foreign integration of machinery technology present situation, has analyzed the integration of machinery technology trend of development. Key word:integration of machinery ,technology,present situation ,product t,echnique of manufacture ,trend of development 0. Introduction modern science and technology unceasing development, impelled different discipline intersecting enormously with the seepage, has caused the project domain technological revolution and the transformation .In mechanical engineering domain, because the microelectronic technology and the computer technology rapid development and forms to the mechanical industry seepage the integration of machinery, caused the mechanical industry the technical structure, the product organization, the function and the constitution, the production method and the management system has had the huge change, caused the industrial production to enter into “the integration of machinery” by “the machinery electrification” for the characteristic development phase. 1. Integration of machinery outline integration of machinery is refers in the organization new owner function, the power function, in the information processing function and the control function introduces the electronic technology, unifies the system the mechanism and the computerization design and the software which constitutes always to call. The integration of machinery development also has become one to have until now own system new discipline, not only develops along with the science and technology, but also entrusts with the new content .But its basic characteristic may summarize is: The integration of machinery is embarks from the system viewpoint, synthesis community technologies and so on utilization mechanical technology, microelectronic technology, automatic control technology,

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本科毕业论文(设计) 外文翻译 学院:机电工程学院 专业:机械工程及自动化 姓名:高峰 指导教师:李延胜 2011年05 月10日 教育部办公厅 Failure Analysis,Dimensional Determination And

Analysis,Applications Of Cams INTRODUCTION It is absolutely essential that a design engineer know how and why parts fail so that reliable machines that require minimum maintenance can be designed.Sometimes a failure can be serious,such as when a tire blows out on an automobile traveling at high speed.On the other hand,a failure may be no more than a nuisance.An example is the loosening of the radiator hose in an automobile cooling system.The consequence of this latter failure is usually the loss of some radiator coolant,a condition that is readily detected and corrected.The type of load a part absorbs is just as significant as the magnitude.Generally speaking,dynamic loads with direction reversals cause greater difficulty than static loads,and therefore,fatigue strength must be considered.Another concern is whether the material is ductile or brittle.For example,brittle materials are considered to be unacceptable where fatigue is involved. Many people mistakingly interpret the word failure to mean the actual breakage of a part.However,a design engineer must consider a broader understanding of what appreciable deformation occurs.A ductile material,however will deform a large amount prior to rupture.Excessive deformation,without fracture,may cause a machine to fail because the deformed part interferes with a moving second part.Therefore,a part fails(even if it has not physically broken)whenever it no longer fulfills its required function.Sometimes failure may be due to abnormal friction or vibration between two mating parts.Failure also may be due to a phenomenon called creep,which is the plastic flow of a material under load at elevated temperatures.In addition,the actual shape of a part may be responsible for failure.For example,stress concentrations due to sudden changes in contour must be taken into account.Evaluation of stress considerations is especially important when there are dynamic loads with direction reversals and the material is not very ductile. In general,the design engineer must consider all possible modes of failure,which include the following. ——Stress ——Deformation ——Wear ——Corrosion ——Vibration ——Environmental damage ——Loosening of fastening devices

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外文翻译 专业机械设计制造及其自动化学生姓名刘链柱 班级机制111 学号1110101102 指导教师葛友华

外文资料名称: Design and performance evaluation of vacuum cleaners using cyclone technology 外文资料出处:Korean J. Chem. Eng., 23(6), (用外文写) 925-930 (2006) 附件: 1.外文资料翻译译文 2.外文原文

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