机械外文文献及翻译.doc

内蒙古工业大学本科毕业设计外文翻译与机械相关的外文及翻译Multidisciplinary Design Optimization of Modular Industrial Robots by Utilizing High Level CAD Templates1、IntroductionIn the design of complex and tightly integrated engineering products, it is essential to be able to handle interactions between different subsystems of multidisciplinary nature 1. To achieve an optimal design, a product must be treated as a complete system instead of developing subsystems independently 2. MDO has been established as a convincing concurrent design optimization technique in development of such complex products 3,4.Furthermore, it has been pointed out that, regardless of discipline, basically all analyses require information that has to be extracted from a geometry model 5. Hence, according to Bow-cutt 1, in order to enable integrated design analysis and optimization it is of vital importance to be able to integrate an automated parametric geometry generation system into the design framework. The automated geometry generation is a key enabler for so-called geometry-in-the-loop6 multidisciplinary design frameworks, where the CAD geometries can serve as framework integrators for other engineering tools.To eliminate noncreative work, methods for creation and automatic generation of HLCt have been suggested by Tarkian 7.The principle of high HLCts is similar to high level primitives(HLP) suggested by La Rocca and van Tooren 8, with the exception that HLCts are created and utilized in a CAD environment.Otherwise, the basics of both HLP and HLCt can, as suggested byLa Rocca, be compared to parametric LEGOV Rblocks containing a set of design and analysis parameters. These are produced and stored in libraries, giving engineers or a computer agent the possibility to first topologically select the templates and then modify the morphology, meaning theshape,of each template parametrically.2、Multidisciplinary Design FrameworkMDO is a “systematic approach to design space exploration”17, the implementation of which allows the designer to map the interdisciplinary relations that exist in a system. In this work, the MDO framework consists of a geometry model, a finite element(FE) model, a dynamic model and a basic cost model. The geometry model provides the analysis tools with geometric input. The dynamic model requires mass properties such as mass, center of gravity, and inertia. The FE model needs the meshed geometry of the robot as well as the force and torque interactions based on results of dynamic simulations.High fidelity models require an extensive evaluation time which has be taken into account. This shortcoming is addressed by applying surrogate models for the FE and the CAD models. The models are briefly presented below.2.1 High Level CAD TemplateGeometry ModelTraditionally, parametric CAD is mainly focused on morphological modifications of the geometry. However, there is a limit to morphological parameterization as follows:The geometries cannot be radically modified.Increased geometric complexity greatly increases parameterization complexity.The geometry model of the robot is generated with presaved HLCts, created in CATIA V5. These are topologically instantiated with unique internal design variables. Topological parameterization allows deletion, modification, and addition of geometricelements which leads to a much greater design space captured.Three types of HLCts are used to define the industrial robot topologically; Datum HLCt which includes wireframe references required for placement for the Actuator HLCTs and Structure HLCts, as seen Fig.2.Fig. 2 An industrial robot (left) and a modular industrial robot(right)The names of the references that must be provided for each HLCt instantiation are stored in the knowledge base (see Appen-dix A.4), which is searched through by the inference engine. In Appendix A, pseudocode examples describes how the references are retrieved and how they are stored in the knowledge base.The process starts by the user defining the number of degrees of freedom (DOF) of the robot (see Fig. 3) and is repeated until the number of axis (i) is equal to the user defined DOF.In order to instantiate the first Structure HLCt, two Datum and two actuator instances are needed. References from the two Datum instances help orienting the structure in space, while the geometries of the actuator instances, at both ends of the link, are used to construct the actuator attachments, as seen in Figs. 2 and 3. For the remaining links, only one new instance of both datum and actuator HLCts are required, since the datum and actuator instances from adjacent links are already available.Appendix A.2 shows a pseudocode example of an instantiation function. The first instantiated datum HLCt is defined with reference to the absolute coordinate system. The remaining datum HLCt instances are placed in a sequential order, where the coordinate system of previous instances is used as reference for defining the position in space according to user inputs (see also AppendixA.3). Furthermore, the type of each actuator and structure instance is user defined.Fig. 3 The high level CAD template instantiation processSince it is possible to create new HLCts in the utilized CAD tool, the users are not forced to merely choose from the templates available. New HLCts can be created, placed in the database and parametrically inserted into the models.2.2 Dynamic Model The objective of performing dynamic simulation of a robot is to evaluate system performance, such as predicting acceleration and time performance, but it also yields loads on each actuated axis, needed for actuator lifetime calculations and subsequent stress analysis based on FE calculations. The dynamic model in the outlined framework is developed in Modelica using Dymola, and it constitutes a seven-axis robot arm based on the Modelica Standard library 18. The dynamic model receives input from the geometry model,as well as providing output to the FE model, which is further described in Sec. 2.3. However, to better understand the couplings between the models, the NewtonEuler formulation will be briefly discussed. In this formulation, the link velocities and acceleration are iteratively computed, forward recursively When the kinematic properties are computed, the force and torque interactions between the links are computed backward recursively from the last to the first link2.3 FE Surrogate Model To compute the structural strength of the robot, FE models for each robot link is created utilizing CATIA V5, see Fig. 4. For each HLCt, mesh and boundary conditions are manually preprocessed in order to allow for subsequent automation for FE-model creation. The time spent on preprocessing each FE-model is thus extensive. Nonetheless, the obtained parametric FE-model paves way for automated evaluation of a wide span of concepts. Each robot link is evaluated separately with the load conditions extracted from the dynamic model. The force (fi-11and fi) and torque (i-1and ti) are applied on the surfaces where the actuators are attached.2.4 Geometric Surrogate Models. Surrogate models are numerically efficient models to determine the relation between inputs and outputs of a model 19. The input variables for the proposed application are the morphological variables thickness and link height as well as a topological variable actuator type. The outputs of the surrogate models are mass m, Inertia I, and center of gravity ri,ci. To identify the most suitable type of surrogate model for the outlined problem, a range of surrogate models types are created and evaluated using 50 samples. The precision of each surrogate model is compared with the values of the original model with 20 new samples. The comparison is made using the relative average absolute error (RAAE) and relative maximum absolute error (RMAE) as specified by Shan et al. 20, as well as the normalized root mean square error (NRMSE), calculated as seen in Eq. (3). All precision metrics are desired to be as low as possible, since low values mean that the surrogate model is accurate The resulting precision metrics can be seen in Appendix B and the general conclusion is that anisotropic kriging 21, neural networks 22, and radial basis functions 23 are the most promising surrogate models. To investigate the impact of increasing number of samples, additional surrogate models of those three are fitted using 100 samples, and the results compiled in Appendix B. The resulting NRMSEs for 50 and 100 samples for anistotropic kriging, neural networks, and radial basis functions can be seen in Fig.5. The figures inside the parentheses indicate the number of samples used to fit the surrogate models.Fig. 5 Graph of the NRMSEs for different surrogate models, fitted using 50 and 100 samplesAccording to Fig. 5, anisotropic kriging outperforms the other surrogate models and the doubling of the number of samples usedfor fitting the surrogate model increases the precision dramatically.2.5 FE Surrogate Models For generating FE surrogate models, the anisotropic kriging was also proven to be the most accurate compared to the methods evaluated in Sec. 2.4. Here, one surrogate model is created for each link. Inputs are thickness, actuators, force (fi-11and fi) and torque (i-1and ti). The output for each surrogate model is maximum stress (MS).A mean error of approximately 9% is reached when running 1400 samples for each link. The reason for the vast number of samples, compared to geometry surrogate models, has to do with a much larger design space.利用高水平CAD模板进行模块化工业机器人的多学科设计优化1 介绍指出,除了规则,基本上所有的分析都需要信息,而这些信息需要从一个几何模型中提取。因此,根据Bowcutt1中,为了使综合设计分析和优化，最重要的是能够将在设计的复杂和紧密集成的工程产品的过程中,必须要有能力处理不同的子系统的多学科性质之间的相互作用。达到一个最优的设计,一个产品必须被视为一个完整的系统,而不是正在开发子的独立系统 。此外,已经一个自动化的参数化几何生成系统融入到设计框架中。自动化的几何生成对于所谓几何循环多学科设计框架是一个关键驱动因素，在这个框架中CAD几何图形可以作为框架连接者来连接其他工程工具。消除没有创新的工作，Tarkian已经提出了创造和生成HLCt的方法 。高HLCts的原理类似于La Rocca 和van Tooren提出的高水平的原始(HLP)，例外的是，HLCts是在CAD的环境中被创造和应用的。此外，HLP 和 HLCt的基础被比作参数LEGOVR块包含一个组设计和分析参数。这些都是生产和存储在库中,使工程师或计算机代理可能首先选择模板,然后修改形态、意义的形状,每个模板参数化。2 多学科设计框架 MDO“是一个用来设计空间探索的系统化的方法”，是允许设计师来映射存在于系统中的跨学科关系的执行。在这个过程中,MDO框架由一个几何模型、有限元(FE)模型、动态模型和一个基本的成本模型构成。这个几何模型提供了有几何输入的分析工具。这个动态模型需要诸如质量,中心的重力和惯性等大量的属性。有限元模型需要网状的几何机器人以及基于动态模型结果的力与力矩的相互作用。高保真模型需要一个广泛的、已经被考虑的评估时间。这个缺点是通过在有限元与CAD模型中运用代理模型来解决的，下面简要介绍的模型。2.1高水平的CAD模板几何模型传统上,参数化CAD主要集中在几何形态的变化。然而,有限度的形态学参数如下: 这个几何图形不能彻底修改。 增加几何复杂性大大增加参数的复杂性。几何模型的机器人与presaved生成HLCts,创建在CATIA V5。这些是拓扑实例化与独特的内部设计变量。拓扑参数化允许删除,修改和添加导致获得一个更大的设计空间的几何元素。三种类型的HLCts用于定义工业机器人拓扑; 基准HLCt包括需要的执行机构HLCTs位置和结构HLCTs的线框引用,见图。 图2致动器(左),基准(中心)和结构HLCt(右) 引用的名称,必须为每个存储在通过推理引擎才能搜索到的知识库的HLCt实例提供。在附录A,伪代码示例描述了引用如何被检索和它们是如何存储在知识库中的。 这个过程开始于由用户定义机器人的自由度（见图3)和重复直到数量的轴(I)等于用户定义的自由度。图3高水平CAD模块安装程序为了实例化第一HLCt结构,需要两个基准和两个致动器实例。引用这两个基准实例帮助定向结构在空间,而几何图形的致动器实例的两端连接,用于构建致动器附件,见图2和3。对于剩下的链接,只有一个新实例的两个基准和致动器HLCts是必需的,因为来自于相邻链接的基准面和致动器实例已经可用。附录A.2显示了一个实例化的伪代码例子，第一个实例化基准HLCt是指绝对坐标系统的引用。剩下的基准面HLCt实例被放置在一个顺序中,在这个顺序中坐标系统先前的实例作为参考来定义空间的位置，而这个位置根据用户输入。此外,每个执行机构的类型和结构实例是由用户定义的。因此在利用CAD工具创建新的HLCts是可能的,用户不是被迫仅仅选择可用模板。可以创建