机械模具类外文翻译

外文原文 A Discussion on Modern Design Optimization The integration of optimization techniques with Finite Element Analysis(FEA) and CAD is having pronounced effects on the product design process.This integration has the power to reduce design costs by shifting the burden from the engineer to the computer.Furthermore,the mathematical rigor of a properly implemented optimization tool can add confidence to the design process.Generally,an optimization method controls a series of applications,including CAD software as well as FEA automatic solid meshers and analysis processors.This combination allows for shape optimizations on CAD parts or assemblies under a wide range of physical scenarios including mechanical and thermal effects. Modern optimization methods perform shape optimizations on components generated within a choice of CAD packages.Ideally,there is seamless data exchange via direct memory transfer between the CAD and FEA applications without the need for file translation.Furthermore,if associativity between the CAD and FEA software exists,any changes made in the CAD geometry are immediately reflected in the FEA model.In the approach taken by ALGOR,the design optimization process begins before the FEA model is generated.The user simply selects which dimension in the CAD model needs to be optimized and the design criterion,which may include maximum stresses,temperatures or frequencies.The analysis process appropriate for the design criterion,and,if necessary without any humen intervention,the CAD geometry is updated.Care is taken such that the FEA model is also updated using the principle of associativity,which implies that constraints and loads are preserved from the prior analysis.The new FEA model,including a new high-quality solid mesh,is now analyzed,and the results are again compared with the design criterion.This process is repeated until the design criterion is satisfied.Fig.7.1 shows the procedure of shape optimization. Introduction The typical design process involves iterations during which the geometry of the part(s) is altered.In general,each iteration also involves some from of analysis in order to obtain viable engineering results.Optimal designs may require a large number of such iterations,each of which is costly,especially if one considers the value of an engineer’s time.The principle behind design optimization applications is to relieve the engineer of the laborious task by automatically conducting these iterations.At first glance,it may appear that design optimization is a means to replace the engineer and his or her expertise from the design loop. Fig.7.1 Procedure of shape Optimization This is certainly not the case because any design optimization application cannot infer what should be optimized,and what are the design variables,the quantities or parameters that can be changed in order to achieve an optimum design.Thus,design optimization applications are simply another tool available to the engineer.The usefulness of this tool is gauged by its ability to efficiently identify the optimum. Design optimization applications tend to be numerically because they must still perform the geometrical and analysis iterations.Fortunately,most design optimization problems can be cast as a mathematical optimization problem for which there exist many efficient solution methods.The drawback to having many methods is that there usually exists an optimum mathematical optimization method for a given problem.This complexity should be remedied by the design optimization application by giving the engineer not only a choice of methods,but also a suggestion as to which appropriate for his or her design problem. In this paper,we focus on the design optimization of mechanical parts or assemblies.In this case,a typical optimized quantity is the maximum stress experienced.Typical design variables include geometric quantities,such as the thickness of a particular part.The design of the part or assembly is initiated within a CAD software application.If the component warrants an engineering analysis,the engineer will generally opt to apply finite element analysis(FEA) in order to model or simulate its mechanical behavior.The FEA results,such as the maximum stress,can be used to ascertain the validity of the design.During the design process,the engineer may alter parameters or characteristics of the CAD and／or FEA models,including some of the physical dimensions,the material or how the part or assembly is loaded or constrained.Associativity between the CAD and FEA software should allow the engineer to alter the model in either application,and have the other automatically reflect these changes.For example,if the thickness of a part is changed or a hole is added in the CAD software,the FEA model’s mesh should automatically reflect those changes.Under most circumstances,engineers will employ linear static FEA to obtain the stresses.This analysis approach has the benefit of yielding a solution for FEA models with many elements in relatively little time.Obviously,linear static FEA has drawbacks as well.For example,significant engineering expertise may be required when estimating the magnitude and direction of loads that are a consequence of motion. Background and Theoy In this section,wo focus on the theory underlying some of the machematical methods employed by design optimization procedures.But,first we describe how the optimization problem arises.Consider a three-step process: （1）Generation of geometry of part or assembly in CAD; （2）Creation of FEA model of part or assembly; （3）Evaluation of results of FEA models. For now,we limit ourselves to the case of linear static FEA.Therefore,the results are comprised of deflections and stresses at one instance.The manual design process involves all three steps,with the results being used to evaluate whether the design is appropriate.If the design is found inadequate,changes are made to steps (1) or (2) or both.It is clear from this description that the output of the FEA results is what should be optimized,and that any input to the CAD or FEA models can be viewed as a design variable.A design optimization algorithm conducts manual FEA runs,each one with a different set of values for the design parameters.Before the manual design approach can be transformed into a design optimization algorithm,there must be associativity between the CAD and FEA applications.The rational behind this requirement is best explained using an example.Consider the initial design stage when the engineer applies constraints on a particular surface of the FEA model;it can be safely assumed that this surface coincides with a surface in the CAD model.Now,if the design optimization algorithm decides to alter the geometry of the CAD surface,then the FEA model must automatically reflect these changes,and apply the constraints on the new representation of this surface.Thus,associativity is required in order to achieve this automatic communication between the CAD and FEA models.Having defined the design optimization problem for mechanical systems,we now describe the mathematics used to solve these problems. Most optimization problems are made up of three basic components. （1）An objective function which we want to minimize(or maximize).For instance,in designing an automobile panel,we might want to minimize the stress in a particular region. （2）A set of design variables that affect the value of the objective funtion.In the automobile panel design problem,the variables used define the geometry and material of the panel. （3）A set of constrains that allow the design variables to have certain values but exclude others.In the automobile panel design problem,we would probably want to limit its weight. It is possible to develop an optimization problem without constraints.Some may argue that almost all problems have some form of constraints.For instance,the thickness of the automotive panel cannot be negative.Although in practice,answers that make good sense in terms of the underlying physics,such as a positive thickness,can often be obtained without enforcing constraints on the design variables. Benefits and Drawbanks The elimination or reduction of repetitive manual tasks has been the impetus behind many software applications.Automatic design optimization is one of the latest applications used to reduce man-hours at the expense of possibly increasing the computational effort.It is even possible that an automatic design optimization scheme may actually require less computational effort than a manual approach.This is because the mathematical rigor on which these schemes are based may be more efficient than a human-based solution.Of course,these schemes do not replace human intuition,which can occasionally significantly shorten the design cycle.One definite advantage of automated methods over manual approaches is that software applications,if implemented correctly,should consider all viable possiblilities.That is,no variable combination of the design parameters is left unconsidered.Thus,designs obtained using design optimization software should be accurate to within the resolution of the overall method. Mould Design and Manufacturing CAD and CAM are widely applied in mould design and mould making.CAD allows you to draw a model on screen,then view it from every angel using 3-D animation and,finally,to test it by introducing various parameters into the digital simulation models(pressure,temperature,impact,etc.).CAM,on the other hand ,allows you to control the manufacturing quality.The advantages of these computer technologies are legion:shorter design times(modifications can be made at the speed of the computer),lower cost,faster manufacturing,etc.This new approach also allows shorter production runs,and to make last-minute changes to the mould for a particular part.Finally,also,these new processes can be used to make complex parts. （外文来源：朱林，杨春杰．机电 工程 路基工程安全技术交底工程项目施工成本控制工程量增项单年度零星工程技术标正投影法基本原理 专业英语（第2版）．北京大学出版社，2010） 外文翻译 现在设计优化方法基础 如今，综合运用机械设计方法、有限元 分析 定性数据统计分析pdf销售业绩分析模板建筑结构震害分析销售进度分析表京东商城竞争战略分析 方法和计算机辅助设计技术进行产品设计过程产生了深远的影响。这种综合运用的手段将工程师身上的设计重担交由计算机完成，因而降低了产品的设计成本。此外，正确运用优化设计中严谨的数学推理也可以提高产品的设计的可靠性。优化方法决定了产品设计过程中的精度问题，包括CAD软件建模的准确度，有限元分析中网格划分的正确度以及分析处理器的计算精度等，这种方法能够在考虑机械，热等许多实际情况的影响下，对CAD系统构建的零部件、装配体的结构进行优化。 现代优化设计技术能够对CAD软件构建的零件结构进行优化。从优化设计理论的角度上说，CAD格式的文件和FEA格式的文件之间不需要任何的格式转换，就可以实现数据的无缝交换。这时这两个文件之间存在关联性，对CAD文件所做的任何修改在相应的FEA文件中都能反映出来。例如，在使用有限元分析软件ALGOR对某零部件或装配体计算时，根本不需要建立其有限元模型就可以进行优化设计。用户只要挑选出零部件或装配体CAD模型中需要优化的几何尺寸，确定相应的设计准则（如最大应力、最高温度和最大频率），然后运行相应的分析计算过程，该软件通过计算、比较，就可以完成CAD模型的结构优化，并且整个过程通常不需要使用者参与。需要注意的是，CAD与FEA格式文件之间的关联性使得FEA模型更新了，但约束和施加的载荷保持不变，因此需要对更新过后的有限元模型进行计算，对整个过程不断重复迭代，直到最终的计算结果满足设计 要求 对教师党员的评价套管和固井爆破片与爆破装置仓库管理基本要求三甲医院都需要复审吗 为止。图7.1所示的是零部件形状优化 流程 快递问题件怎么处理流程河南自建厂房流程下载关于规范招聘需求审批流程制作流程表下载邮件下载流程设计 图。 引言 零部件结构的优化过程往往需要近视迭代计算，在整个计算过程中，零部件的几何外形不断变化，优化。在每一步迭代计算中要进行一定的分析，以便得到与工程实际较为相符的设计结果。优化设计一般需要很多部这样的迭代计算，每一步计算都较为费时。所以，在进行机械结构优化设计过程中使用优化设计软件的主要目的是自动运行上述的迭代计算，减少工程师的工作负担。咋看上去，优化设计技术是一种能够替代工程师进 行工程设计的工具，但事实上不是这样，因为任何优化设计软件都不能确定应该优化什么对象，哪些是设计变量，需要改变哪些量或参数，所以，优化设计软件只是工程师进行设计的一种工具，其用途由其优化计算的能力来决定。 优化设计软件常常要进行零部件几何外形的优化计算，一般具有较强的数值计算能 图7.1 零部件形状优化设计过程示意 力。庆幸的是，大多数零部件结构优化设计的问题都可以看成是数学中的极值问题。求极值的有效方法很多，但方法太多也不好，因为对于一个待定的问题，其最佳的解法只有一种。利用优化设计软件可以很好地解决这个问题，因为优化设计软件不仅可以帮助工程师选择解决问题的方法，而且还能够帮助工程师找到最佳解决方法。 本文重点阐述机械零部件或装配体结构的优化设计。我们经常需要优化零部件或装配体在实际工作过程中承受的最大应力，所涉及的设计变量一般是零部件或装配体的几何尺寸，比如一个指定零件的厚度。我们一般先用CAD软件构建零部件或装配体的几何外形，如果设计结构正确，那么工程师会选择相应的有限元分析软件，对上述结构的机械性能进行数值模拟；然后根据计算结果，比如最大应力的分布状况，来判断设计是否有效。在设计过程中，工程师可能需要改变CAD或FEA模型的一些参数或特征属性，如零部件或装配体的几何尺寸、材料参数以及约束和加载状况。CAD和FEA软件之间的关联性使得工程师只需要修改其中任何一个模型即可，例如，在CAD软件中改变了某个零件的厚度或增加了一个孔，它的有限元模型会也自动做相应修改。大多数情况下，工程师采用线性静力学的方法来分析应力状况，这种方法的优点在于能用较少的耗时，较多的有限元分析单元得到需要的有限元分析结果。但该方法也存在缺点，例如，在估算处于运动状态的零部件或装配体的载荷大小或方向时，往往需要较丰富的专业知识（这种方法无法满足要求）。 基础知识和理论 本部分着重讨论优化设计的一些数学理论方法，首先介绍利用有限元方法进行优化设计的过程，该过程一般有3个步骤： （1）在CAD软件中构造出某一零部件或装配体的几何模型； （2）建立相应的有限元分析模型； （3）对有限元分析的计算结果进行分析和判断。 现在只讨论线性静力学有限元分析方法，需要计算的是零部件/装配体在外载荷作用下的应变和应力分布状况。一般人工优化设计过程都要涉及上述3个步骤，也需要根据计算结果来判断设计的合理性；如果设计结果不合理，就要对步骤（1）和（2）做修改，也可能（1）、（2）都要改。可以清楚的看出，有限元分析的结果就是优化的结果，由于每个输入到CAD或FEA模型中的参数或特征属性都可以看成是设计变量。优化设计算法对许多有限元分析都要指导作用，它的每一种算法对不同的设计变量会产生不同的数据组，所以，CAD软件和FEA软件之间必须具有关联性，才能将人工设计方法转化为优化设计算法。可以通过例子来说明上述问题，例如，在刚开始对某一零部件或装配体进行有限元分析时，工程师一般要对其有限元分析模型上的某一平面施加约束。假定这个平面就是零部件或装配体CAD模型上的平面。现在需要优化这个平面的结构，由于CAD软件和FEA软件之间的关联性，其FEA模型上的平面会随着CAD模型的变化而变化，这样，上述约束就会施加在改变了的平面模型上。为了实现CAD模型与FEA模型之间的自动数据交换，CAD软件和FEA软件之间必须具备这种关联性。下面要讨论的问题是使用什么样的数学方法来解决这些问题。 大多数优化设计都要涉及以下3个基本问题： （1）目标函数的最小值（或最大值）：例如，在设计汽车的仪表板时，往往需要它在某一指定区域上受到应力最小。 （2）影响目标函数值的设定变量组：例如，在汽车仪表板设计中用来确定仪表板几何外形和材料的变量。 （3）约束条件：这些约束条件使得优化设计中的变量只能在某一范围内取值。例如，在设计汽车的仪表板时，常常需要限制它的质量。 实际上，建立一个无约束的优化问题也是非常可能的。也许有人会认为几乎所有的问题都应具有一定的约束条件。例如，汽车仪表板的厚度不能为负值，不过实际上无须对一些设计变量施加约束条件，常常也可以获得与基本常识相符的结果，如上述的仪表板厚度为正值的问题即是如此。 优化设计的优点和缺点 目前许多应用软件都以解除或减少人的重复工作为目的。基于计算机的优化设计技术属于一种最新的应用设计技术，其目的计算增加计算机的计算量，减少人的工作时间。实际上在进行优化设计计算时，使用计算机需要的计算甚至比人工设计方法还要少，这是因为优化设计技术采用了严谨的数学计算方法，所以它的设计效率要比人工设计方法高。当然基于计算机的优化设计技术取代不了人的思维，因为人的思想有时可以大大缩短设计过程。基于计算机的优化设计方法与人工设计方法相比，其明显的优点是，如果优化设计软件使用正确，它能够考虑到所有的设计 方案 气瓶 现场处置方案 .pdf气瓶 现场处置方案 .doc见习基地管理方案.doc关于群访事件的化解方案建筑工地扬尘治理专项方案下载 ，也就是说，会考虑到各种可行的设计参数，因此利用优化设计软件进行计算的结果应该是最精确的。 模具设计与制造 目前CAD和CAM技术已经广泛地应用早模具的设计和制造中。例如，首先利用CAD软件在计算机上构建出模具的模型，然后采用三维动画的方法从各个角度察看模具的结构，最后将模具的各种参数（压力、温度、冲力等）导入到数字模型中进行模拟试验与分析。另外，CAM能够控制模具的制造质量。采用上述计算机技术对模具进行设计和制造有很多优点：如较短的设计时间（该时间可随着计算机的运行速度而变化）、较低的制造成本和较高的制造效率等。这种新的设计、制造方法可以进行小批量的模具生产，可以在最后时刻对某个特定的模具零件进行修改。此外，这些新的工艺过程还可以用来制造复杂的模具零件。 最优设计 优化模块 有限元求解程序 原始设计 预处理 有限元前处理程序 优化前处理程序 Optimization design Optimization Module FE Solver Optimization Loop Optimization Preprocessor FE Preprocessor Preprocessing Initial Design PAGE 5