制造业中的机械CAD数据解释外文文献翻译、中英文翻译.doc
制造业中的机械CAD数据解释外文文献翻译、中英文翻译
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制造业中的机械CAD数据解释在CAD/CAM、计算机显示和智能制造业中，一个零件形状的描述对CAD数据库来说是一个重要问题。它可用于在CAD / CAM的设计评价，机构的计算机描述和机器的识别中，并在智能，自动化，集成制造中起连接设计和制造的作用。70年代后期以来，这主题一直在积极研究，相当数量的计算方法已经被提出，以确定零件有工程意义的几何要素（这里称为 “特征” ）。然而，每一个被提议的机械装置只能够解决的有限几何域的零件（如多面体组成的零件） ，或只能应用于零件，而在相互信息交流的方面受到限制。本文的目的是审查和总结发展研究CAD数据的机器特征识别，以讨论的优势和潜在的问题，每一种处理办法，指出一些今后调查可能需要的有用的指导。由于大部分在这一领域的工作集中在加工的特点，文章的主要内容包括这些特征与制造领域。为了更好地理解技术发展水平，特征自动识别的方被分为以下类别：图表基础，结构调整模式识别，规则基础，和体积。每一类我们研究的问题，如特征的定义，机制承认特点，应用范围，作出的假设。此外，该问题是解决信息的输入的透视图，边界代表性的优点和缺点。它对机械的立体几何（ CSG ），和2D绘图方面有重要的作用。重点放在尖端的机制相关问题互动信息交流特征。1 .导言设计是一组重要的过程，发生在不同的产品生产周期阶段。计算机辅助设计（ CAD ） ，一般来说，是指利用计算机，以协助设计过程中的各职能。工程师考虑将用CAD数据代表了产品或零件的要素：在机械部件领域，这些通常是一组工程图纸或一个零件的实体模型。虽然CAD被用于协助各种各样的设计任务， CAPP (计算机辅助工艺过程规划)通常被用于转换零件设计成制造业指示描述如何导致零件或如何建立汇编满足设计规格活动的汇集。通常被用于零件转换设计到制造的的描述，描述如何零件的生产或如何建立装配以满足设计规范。机械零部件的领域，工艺规划涉及零件加工顺序过程的确定（如铣，磨，钻等） ，夹具配置建立在零件加工的每一个进程和使用的工具，及每道工艺的顺序。基于加工过程和工具的性能，为了实现一个组成部分的任务，过程设计规划者需要解释数据（形状，表面光洁度，公差等）。 计算机集成制造（CIM）系统尝试将设计，工艺规划，和其他功能（物料装卸，工厂管理等）统一在一个生产系统中，共享全部的信息。然而，不成熟的真实集成制造系统已被证明是一个不平凡的事业。一个重要原因是，包括附注的工程图或固体模型的零件的CAD数据不是制造业特征表示和一般代表的低层次的描述，如零件的边，顶点，以及面，而进程的计划者规划如插槽和洞的形状制作（与性能等原始尺寸和表面处理）的流程和所需工具。为了克服一体化设计与工艺规划之间的障碍，这项工作以前依靠手工解释过程的工程师，提出了几个有建树的成就，所有使用的特征概念。最新的解释，该战略已被纳入CAD数据在设计过程中的一体化特征，或提取的CAD数据特征或两者兼而有之。在下一节中，我们首先考虑的特征和他们所提到本文的其它数据。1.1特征对特征的定义，还没有被普遍接受。事实上，在这一领域，这一直是研究人员面临的困难。然而，最近的两个图书Shah和Mantyla 1995 ; Shah等 1994 所描述的特征是零件拓扑实体的集合，在生产上有重要的语义意义，还需要参照验证。显然，这说明意味着特征的定义是依赖领域和以应用为导向的。对于不同的应用，如设计，制造，应力分析等，零件因此被用来检验在不同的特征系统 Mantyla等。 1996 。从进程规划的角度来看，特征设置可以显示所组成的形状和有关制造过程和工具的技术属性 Shah1991 。例如，穴，槽，孔，和步骤的例子共同加工的特点，如图1 （ a ）显示。穴及插槽可能由铣削和磨削加工产生的，而洞可由钻实现。虽然一些研究人员已经扩大的特征概念，包括等实体公差的特征，表面光洁度的特征，材料特点等，在本文中，有限地讨论“形”的特征shah和Mantyla 1995 或实体的几何与拓扑集合，一个组成部分的形状和有关制造过程和工具的技术属性。从今以后，我们专注于共同的加工特征，如穴，插槽，轮毂，洞等，在主要著作中这些特征已经被告研究：从现在开始，以后的特征是指他们，除非另有说明。为了发展系统的处理功能，现有的研究已处理不同特征的定义。有些工作已经把特征认作是（封闭的）大量的特性（矩形块，有两个相反的两端，等等） ，而另一些研究人员已经把他们作为一个拓扑实体的集合（面，边，等，不一定关闭的体积）满足某些地理数据关系（ 4个面，一双平行线等）。实例研究每一个范畴，包括Sakurai和 Gossard 1990 ，他们其中一项特征是指收集的表面的特点，和Vandenbrande和Requicha 1990 ， 1993 ，他们定义了体积特征。图1（ a ）表示了同样的一套共同的体积特征或一组的拓扑实体。重要的是要了解特征，它可以有不同的定义，因为拟议实体模型的特征识别算法不能在互换办法之间的替代使用。特征识别算法可以只涉及一个具体的定义的特征。虽然有无限可能形成形状特征的定义，它仍然有可能是组合或阶层的归类。这种分类将是有益于的特征维护，制订一个标准术语和数据交换。例如，特征可能会被列为多面体或非多面体。可能也被列为棱柱或旋转体。属性与特征可能包括尺寸，方向，公差，空间关系，拓扑组件。图1 . （一）几种常见棱柱制造业的特点，可以界定为空间或几何特征; （二）零件的互动特征。1.2几何模型特征积极的方面，在整合CAD与CAPP方面，已获得很大的重视。已发展了CAD的数据智能解释（几何模型）以获取特征。这样的解释，将有助于把低级别的实体（表面）的几何模型制作的一个CAD系统纳入了一套适合于制造功能特征自动识别过程（AFR）的方式，以确定从一个特征到现有的CAD生产的一个零件的几何模型，如边界表示法（B- REP ）的，建设性的立体几何表示法（ CSG ）的，工程图纸，等等。在下一节，我们讨论零件的几何模型。图2 .零件模块的特征自动识别。图2显示了零件模块的特征自动识别shah 1991 构建特征构成了的高级别原语的语义包含用于制造业信息化进程的规划或组装。一旦特征有假设发现，他们必须核实它的正确性。这必须要通过任何的规则，以确定任何特定特征是否无效的，或者通过批量检查方法，确保每个特征产生的内体积不与被原料加工背离。反馈回路如图所示到位的情况下，验证失败，在这种情况下，特征识别过程必须反复寻找一套不同的特征和新特征必须经过核实。我们注意到，有许多系统，任务特征识别的部分或全部包含职责范围内（计算机辅助）工艺规划。例如，特征验证，甚至特征识别本身可能被视为一种责任制进程规划。为明确起见，我们将特征识别任务从其他工艺规划任务（例如，工具选择）分开讨论，只注重它。在过去十年里，特征自动识别的多种技术已经提出，但也出现了困难，缺乏相关的标准定义。多种功能的定义，有时有助于为同一形状的文献分类。例如，图3已把为一个插槽分类 Marefat和Kashyap1990 ; Joshi 和 Chang 1988年 ，为穴 Gupta等人1994 ，或俯角分类弗洛利亚尼1989 。另一个困难，研究和提出的特征识别技术已经在处理鲁棒性的相互作用中广泛运用。当两个或两个以上的特征几何交叉，研究一个对比另一个，依此类推，这一般称为产品的交互式特征。交互式特征的定义，取决于所采取的做法在确定功能。正如在上一节中，一些工作把特征视为（封闭）容积的某些特性，而其他研究者把它们作为拓扑实体的集合（不一定容积）满足一定的几何关系。容积特征的定义，交互式的特征相当于一个十字路口的容积的两个（或两个以上）的特点。 以拓扑为基础的特征定义，交互作用应符合拓扑原理和关系之间的修改，确定每个特征涉及互动关系。例如，图1 （ b ）为同一个零件之间的相互作用及其两个特点，就是两个插槽。无论特征被视为量或特定集合的拓扑元素，交互式特征的已经被提议的技术难点在特征的几何交互方面往往产生不同的定义，新实例的特点，不同于以往代表性特征，使用的技术来确定特定特征的类别。新的定义可能有不同数量的面，不同数量的边界，不同的几何约束（垂直度等） ，相邻的面，原材料库存量图3 一个简单的插槽零件。的范围不符合一类特征预期的容积，等等。由于这些潜在的差异，特征识别技术，因此，不能期望在一个特征和实例特征的所有特点之间直接匹配。这导致非唯一性的特点在确定特征定义需要的能力或智能，以对付非唯一性和完成任务。 另一种自然结果，交互式特征必然有的其他方式根据其特点来描述一个零件，无论是通过零件的几何模型和特征的表示法之间相匹配，如果没有一套完整的匹配，就会导致替代推理路径替代零件匹配。例如，根据特征如图1所示（ a ），零件在图1 （ b ）可以被解释为通过两个插槽的相互作用，但同样有效的解释（基于几何实例显示特征）将有四个暗插槽和中穴。能够产生系统候补解释是非常有用的，因为它允许制造过程规划，有益于信息和过程计划的生成是根据成本，质量，或两者兼而有之。提供所有较少方向的特征解释可能会产生更好的加工过程计划，因为据估计，多达80 的生产时间都花在建立不同的计划中。 在本文中，我们第一次审查计划通过代表零件的CAD几何模型，然后讨论了解决自动特征识别这个问题的各种办法。我们学习每一个解决方案的优点和缺点，以及适用范围。基于全部途径，机制的自动识别特征分为几类的方法：句法模式识别，基于图形，基于规则，基于体积和单元，并基于证据的推理。这种非正式分组是有益的，以便更好地了解，涉及CAD几何模型的特征识别的最先进艺术级的技术。把各相关技术联系起来对特征识别来说是重要的，包括如何描述特征，特征识别的算法开发，提出的方法的应用范围，所依据的假设，以及原型系统的开发。本文中的一个重点是对的机制攻击与交互式特征的联系起来的问题，因为正如我们已经指出，在特征识别方面，他们带来了困难。2 . CAD和几何造型设计是一个反复的过程，包括提出一个方案的设计，测试和评价解决方案的设计，修改建议的解决办法，并最终优化方案。在计算机辅助设计，计算机的图形处理能力将取代一直用铅笔和纸的传统工作。此外，计算机的模拟能力帮助设计者测试和评估拟议的设计方案。计算机辅助设计可以减少设计周期，提高设计精度，以及把工人从繁琐的重复性劳动解放出来。与快速发展的资料库技术、仿真和人工智能技术，计算机辅助设计的功能已经从简单的计算机图形技术到电脑辅助绘图和起草，以先进的三维图形表示，分析和模拟。当前CAD系统允许用户设计一个三维的零件，通过模拟，可以研究了零件的机械作用，并自动产生零件的工程图纸。采用有限元分析技术，用户还可以分析零件的应力和变形。通用CAD系统功能可能包括几何造型，工程分析，自动起草，以及运动学分析。从本条的目的看，我们关心的是CAD系统方面的几何建模。这种几何模型都是通过自动搜索特征识别技术，或增加与信息交流功能的特征设计。因此，重要的是适用于几何造型的基本方面2.1几何建模和图形表示几何模型被使用线框来描述，线框模型描述了零件形状相互关联边缘要素，或利用三维实体模型，用物理设计的形状来模拟封闭的容积。由于实体模型比线框模型支持更多的信息，大部分特征的研究使用了实体模型来输入。在一个典型的固体建模系统中，用户构造一个模型，它实体形状的基本成分称为元。用户可能会建立或修改模型的大小，添加或减少从原几何体。该基础的组成部分通常代表一个实体的矩形块称为基元。一系列商业实体模型和设计软件包都可以使用，但重要的是必须区分它们。在实体建模软件包的中心是一个内核，它支持实体模型的操作，如交叉线，差异性，大规模计算中心，等等。设计包（或标准的CAD工具）一般允许建造设计模型（这可能包括或不包括实体模型） ，但不得允许任何上述实体建模操作。有软件包结合了实体造型内核和设计工具。ACIS（空间技术）、商业固体建模和Pro / ENGINEER （参数技术公司）就是一个例子，一个商业的CAD软件包和设计工具的结合。实体建模的图形表示定义是映射S绘制有形物体从M模型到模型R的表示;也就是说， S：M R。图形表示就是，每个表示法在模型空间对应一个领域的一个物理对象。图形表示法应确保每个物理对象只能承认（可映射到）一个正确的代表性。六大技术（图解）用来描绘和维护一个CAD实体建模系统的三维模型的是：纯基本的实例（PPI） ，空间占用枚举（SOE） ， 单元分解（CD） ， 扫描（ s ）立体几何学（ CSG ）边界表示法（B- REP ）纯基本实例包括重新使用固体描述，如块，支架等，并改变实例的某些参数，以适应新的对象。尽管最初的描述被称为通用元，个人创建的对象，通过这种转变被称为基本实例。纯基本实例是一个独特的代表性计划，但由于它缺乏限制的通用函数库，它并不一定是明确的。纯基本实例没有规定创建对象实例的结构，描绘新的和更复杂的对象。空间占用枚举的三维空间细分成小体积称为体素（简称为体积元素） 。代表一个对象，它将体积作为空白或包含固体。在三维空间中，这是一个数据结构的变更。它被递归分为八分圆或归类为完整的，空的，或部分完整。 （Juan-Arinyo and Sole 1995 1995 提出了空间占用枚举转换为一个数据结构的变种方法。 ）空间占用枚举是独一无二的和毫不含糊的，但缺点是过于复杂，因为它列举性质。单元分解（CD）类似空间占用枚举，但它分解的对象不是三维空间。单元分解法细分对象成基本组件，要么不交，要么相交于一个共同的面，边，或顶点。这对象为这些基本部件粘在一起提供思维方法。图4 （a）所示，一个简单的例子对象的插槽。由于工程的对象可能以不同的方式分类构成部分，单元分解是一般而言的，并不是独一无二的。扫描法是非常接近于广义圆柱体的概念。它是基于移动的二维系统的概念（横截面）沿三维空间曲线（轴）扫描了固体的体积（图4 （ b ） ） 。扫描一般都可以用W描述W（r,f） ，其中r(s) x(s), y(s), z(s)是一个向量函数代表轴参数的弧长变量。通常情况下，f表示的边界断面曲线，也就是函数f (x(u, s), y(u, s) ，虽然f还可以设置成员函数，描述截面的内部点，在任何一点都沿着轴线。依赖截面的弧长允许的轴截面缩小，扩大或改变其他时装，因为它是体积的沿轴线。虽然扫描是直观的吸引力，就像单元分解，它不是一个唯一的代表性计划。在过去两年固体建模技术（CSG和B - REP ）的已被认为是应用最广泛的，因此被认为是最重要的代表性计划。构造实体几何（ CSG ）是一个表示体积图解法，在固体可派代表作为成分，通过（系统化）设置布尔运算，基础形状实体在空间位置适当通过刚体运动操作。基本形状实体用于CSG通常参数块，缸，筒，和球类，以及布尔运算包括系统化连结差异和相同点 Requicha 1980年 。CSG的表示和维护对象，并保持物体的零件，零件是基本的实体参与建设的目标和内部节点是布尔运算和运动变化（图4 （ c ）项） 。在“ * ”中的数字指系统化布尔行动，以防止棱边和面。 基本的容积概念的补充和减去，这就是所谓的基础概念，在一个抽象的水平可以比喻为设计过程中的操纵特征（类似的特征基础的设计） 。CSG也可以转化为机械操作，其中提供了进一步的最初动机为实施该计划。例如，钻孔，可以解释为从圆柱减去一部分的。CSG表示方法的一个缺点是，一般来说，它不是唯一的。边界表示法（B-Reps）模型对象的边界分层存储。对象边界分为一套不相重叠的面。每个面对所指定的表面描述，它是内嵌于和其包围边缘。每边是反过来，所表示的曲线在于和任何相关的顶点。图4 .实体模型的表示图解，例如插槽零件： （a）单元分解的细分成（胶合一起）基本成分，以满足面的精确性；（b）扫描表示了对象的横截面（f） ，和一个旋转轴（R（ s ） ）; （c）CSG表示对象作为树，它的节点是基本体积，布尔运算，和严格的议案; （ B-REP计划所描述的对象所拥有的优势，和在一个层次结构上顶点形成的边界。顶点是简单的三维坐标点。例如，在图的4（ d ）显示了边界代表组成的一个简单的插槽。早些时候，一个有影响力的系统通过这种办法 Baumgart 1972 提出 “翼边”数据结构来表示这方面的资料。边界表示方法是唯一 Requicha 1980年 。当该对象的边界分割成最大和连接的面时，他们是唯一。这一方面是可取的，因为它使边界表示，不同于CSG，不依赖于操作（或其顺序）构建模型。CSG转换的表示法和边界表示法是比较完整 Requicha和Voelcker 1985年 。然而，逆问题一直没有得到很好的解决，直到最近夏皮罗和福斯勒尔1993 。由于CSG的不确定性质，边界表示是常用的几何建模和特征识别，但更重要的是，几乎所有的自动特征识别机制依赖边界表示或CSG的投入。许多这些几何造型系统使用的程序在殴拉定理基础上，测试并帮助确保有效的模式。不论固体建模方案使用的输出电流固态建模系统去文士部分拓扑和几何方面的低层次的表面实体，如面，边和顶点，可能顺着信息，如表面光洁度，尺寸，密度，公差等。先前讨论的方案是“多方面的”模式与严格的规则下的拓扑的正确性。然而，有些应用需要“ 单方面”的模式和某些规则拓扑正确性不需要严格。读者可参考选择几何物（SGC）模型 Rossignac和奥康纳1990 为进一步的讨论。2.2步骤：国际标准的产品模型数据交换最后，应该指出的是，有一个发达国家的标准交换实体模型数据，但迄今它是有限的商业用途。步骤（国际标准的产品模型数据交换）是由国际标准化组织（ ISO ） （步骤它自身的ISO标准10303 ） 。步骤应用协议203（ AP203 ）是交换标准机械零件和组装数据。在美国，PDES是一个负责测试和支持的步骤的组织。步骤包括存储和传输几何原始数据（实体模型）使用Express语言。这些序列文件格式的定义不允许转让的产品数据之间的计算机辅助设计（或实体造型）系统。该格式的目的是独立的方式，创建的信息和存储在任何特定的CAD系统。所载的资料这种格式可能包括几何与拓扑信息，如顶点的边缘，边缘的面，和一个物体的面，以及一些制造业的信息，如密度和层面。3 机制特征识别的非正式分类所有非正式算法包括两个重要组成部分：特征定义和特征识别机制。各种方法已经开发出来，用于自动特征识别机制，可在正式分为两类：结构模式识别， 图形为基础的方法， 规则为基础的方法， 容体法（包括“单元为基础的”技术） ，以及证据推理方法。结构模式识别的总体特点是形状的某些基元的合成物。特征识别收益的解析输入结构表达的一部分，利用语法规则来确定结构模式表示特征。图中的特征识别，拓扑形状的部分代表作为图形（通常与节点图相应面临的对象和弧的图对应的边缘对象。然而，其他图形表示，例如，Chuang 和 Henderson 1990 ，代表对象图形的节点是顶点的对象，其弧线符合其边缘） 。该图的代表性，然后寻找某些特性来识别功能嵌入部分。在以规则为基础的方法，规则，试图指定了一套必要和足够的先决条件的模式中发现的一个特点。识别是通过推理控制机制，确定如何适用这些规则输入数据。这包括正向推理，反向链接，或概率输入。在体积的方法，成品材料为代表的组合一套卷。如果CSG代表的部分是输入， 非唯一的代表权是一个障碍，有许多方法来界定同一特征的不同组合的布尔运算的几何基元。因此， 非唯一代表必须简化才能认识到，通过模式匹配。凸壳的方法，零件（或容积不一样的零件）是分割基本体积。凸壳算法计算对象之间的差异及其凸包递归直至差异是一个空集。凸壳重新安排，以获得加工余量。单元为基础的技术也分解零件（或容积不一样的零件）到基本体积（单元） 。这些单元，然后重组，以支持视频功能，原来的一部分。在证据为基础的推理方法不仅表示或证明，而不是全面的功能，首先生成。然后阐述的模式，通过暗示重组或证据积累。最后，有几个方法，并不完全属于这些类别的任何一种，我们已将其作为“其他”由于其独特的或混合性质。 （请注意，许多机密技术仍然使用几种不同的特征识别战略。从这个意义上讲，甚至是机密的技术可称为混合型。 ）4 .机械特征鉴定和识别技术在本节中，我们调查的各种非正式的技术和总结每种技术的方法特征定义，机制，制定特征识别，应用范围，假设，和局限性。非正式技术分为3组，根据其输入的信息：边界表示，立体几何法，及二维模型。4.1非正式的边界代表大多数提出非正式的技术研究人员使用边界表示其输入信息。这些方法包括结构，图形为基础，以规则为基础，单元，并以证据为基础的推理方法，以及一些混合的方法。4.1.1结构模式识别。结构模式的特征识别方法，从边界交涉备受关注的80年代初FU 1982年 。在此之前，它已成功地应用于二维识别计算机显示中。结构模式识别是一个正式的技术 对表示复杂图案简单而言，是子与子之间的关系。某一模式分解成简单的递归子称为元。正如一个字母在一个正式的语言可能会合并成单词和句子，序列基元可结合起来，形成一个代表表达的复杂模式的特征。可能的组合序列，可组织根据语法规则的语言一样，语法的正式语言。由此产生的语言（或表达）被称为模式描述语言。规则的定义，确定有效成分的基本模式到指定的语法的模式描述语言。原理所定义的命令元组（V，V，P，S） ，其中V是一套终端符号，V是一套非末端符号， P是一套制作，和S是开始标志语言。正确的判决是在这个语言建造的顺序使用作品从P开始后编号S到一个终端符号达成。识别过程的收益，首先构建了表达方式或对象的基础上界定元和语法规则的。这一表述，包括字符串函数库，然后解析使用一套制作在P ，以确定它的功能格局。在大多数应用中，模式指的轮廓截面的一部分和原始通常线段和曲线段（如边缘）这种形式的轮廓。雅库博夫斯基 1982 使用结构方法自动解释回转体零件部件，有一个轴对称和多面体零件。这种做法，机械零件等描述作为原始轮廓线段（如边缘） ，曲线段，表面部分，加上了一套操作，如旋转和扫描，描述操作生成三维部分其二维截面。图5显示一个例子在此基础上的方法。图5 （ a ）显示的一些基本几何元。群体的类似部分来描述在例示一个通用描述某些参数（例如，部分图5 （ d ）是代表（图5 （ b ）的实例化的若干个S1-S4基本元） 。零件描述了组织原为表达根据语法规则。两个语法规则已应用于构建表达式的一部分，并制定自己的解析器：延长上下文无关文法和定期权的一部分语法。最重要的任务的语法规则是要区分的重复部分的轮廓从不重复的雅库博夫斯基1982年 。图5 （ c ）和（ d ）显示正则表示的一类旋转零件，以及所代表的轮廓表达和部分例如有这样的轮廓。以确定所代表的特征表示，解析器建造使用相同的语法规则，并随后适用于表示的零件的输入。图6 .立方体及其面临邻接超代表性。类似的技术已经开发出来，用于更具体的零件类别。例如，李 1986 使用了类似的，但更简单的办法来确定转向特征从截面轮换的零件。崔 1982 极限的办法确定孔几何形状。斯特利等。 1983 开发了一种方法用于原始凹陷或突起的横截面包括完全的选择一套二维原始形态，但不能延伸到的一个特点，而横截面不沿不同路段的清扫轴。这是一个共同的所有这些方法，因为他们所有的3D部分解释其横截面，他们失败截面不同。所有这些方法都使用类似的原始的部分截面，如果模式不匹配的符号在特定语法的方法行不通。认识到特征在一般的3D一部分，奇普里亚诺 1980年提议最早的方法，直接用于结构模式识别加上零件的图表示法。在此方法中，在零件的边界表示法是第一次转换成面对面边缘节点图的图面和弧表示零件的边缘。标记的弧线的边缘凹度。特征所产生剖析面先进的图形。这项工作的意义是，表面和边缘是基础，而不是原始模式线和曲线段。因此，它是有可能的三维特征识别首次直接转换为一个二维的表示。这三个基本结构元用于构建功能语法凸环，凹环，和平坦的。循环是一种面有序的组合，这样，第一个和最后一个面，每对相邻的面，每一的边缘。循环是指凸如果连续面临相隔只有凸边缘。这是凹的，如果它至少包含一个凹边缘平滑，如果它包含边缘上没有任何变化表面正常相邻之间的面。基于这些基本的，一个凸边的确定面的内循环，同样，一个突出的凹边确定面的内循环。它是可能确定特征的不同类型，如插槽，口袋里，简单的或嵌套凹陷，突起等，使用这种技术。承认包括几个步骤。首先，所有零件图的边缘和循环的审查和边缘分为凹，凸，或平滑。然后列出的特点是重视创建。最后，所有的面属于特征确定和公认的特点是分为零件的凹或凸出。由于特征的结构模式定义可能无法确定关闭数量，公认的特点，然后完成增加虚拟实体，如边缘，面，和顶点，形成封闭固体积。Choi等人。 1984 描述了类似的做法，但他们不保留零件表信息。因此，这种办法只能承认棱柱特征的凹陷。最近， Falcidieno和Giannini 1989 在奇普里亚诺 1980年 延长工作。解析结构面临邻接超图（ SFAHs ） ，而不是简单的面对面的边缘图。面A邻接图（FAH）是第一次为3G的定义（N，A，H） ，其中n是一套节点图对象相应面，A是建立在所有弧图表相应对象的边缘，H是一套超弧的图表，每个顶点的对象，连接所有节点对应的事件面临的顶点。图6显示一个立方体及其发代表性。 所公认的特征剖析发图是一个层次图， SFAH 叙述了通过相邻等级之间的关系的提取特征。也就是说， SFAH是一个图，其节点的零件特征，并代表邻接弧（或控制）的特征。这样一个层次的说明中提取的特征的优势是，它提供了一个更具全球性的观点。，这种方法的局限是，该系统只能识别特征，确定循环对象的边界上，也就是说，棱柱椎间盘突出和抑郁症的特点。因此，它并不适用于功能实体，如斜面 ，倒角，和步骤（功能范畴） ，因为循环的特征，例如确定对象的边界上配合面的外部回路。总而言之，结构模式的方法已被成功应用于二维棱柱体零件，零件的车削旋转功能和轴对称。然而，成功非轴对称三维零件或转动部件旋转的功能是有限的。这可能是三维物体缺乏合适的语言。另一个局限是含糊不清的结构模式Wang 1992 。在基本参与结构方法通常不能代表一定的几何性质，如大小，基本的，相对的方向，边凹等，这是非常重要的区分特征。这一方面可能导致一个结构表达相应的几个不同的特点，因此，导致无效的形状构造待确定。随后的有效规则在许多情况下可能很难获得，而这些规则可能不足以过滤掉这些杂散结构。由于使用二维模式结构模式识别能力的严重局限性，语言处理与世界的CAD零件（一般三维） ，有可能写的规则，一般的推理规则。然而，这一技术将使方法更像是一个以规则为基础的方法比结构模式识别，因此，在很大程度上解决结构分析的目的。大多数结构模式识别技术开发于近10年前，目前很少有特征识别计划采用这一战略。最近在普拉巴卡尔和恒基兆业 1992 ，采用了神经网络攻击特征识别的难题，神经网络在本研究执行模式匹配修改了边界表示输入识别功能。 （虽然这个模式匹配也可视为一种规则发射，这是合理的，在这里，讨论这一做法。 ）采取这种做法的零件的边界表示法代理模型，将被转换为一个邻接矩阵描述邻接各零件的面。矩阵解析为模式（即模式匹配）用不同的神经网络为每个特征定义，如果神经网络对某一功能找到匹配的邻接矩阵，那么这是公认的特殊特征。14 Machine Interpretation of CAD Data for ManufacturingApplicationsMachine interpretation of the shape of a component from CAD databases is an important problem in CAD/CAM, computer vision, and intelligent manufacturing. It can be used in CAD/CAM for evaluation of designs, in computer vision for machine recognition and machine inspection of objects, and in intelligent manufacturing for automating and integrating the link between design and manufacturing. This topic has been an active area of research since the late 70s, and a significant number of computational methods have been proposed to identify portions of the geometry of a part having engineering significance (here called “features”). However, each proposed mechanism has been able to solve the problem only for components within a restricted geometric domain (such as polyhedral components), or only for components whose features interact with each other in a restricted manner. The purposes of this article are to review and summarize the development of research on machine recognition of features from CAD data, to discuss the advantages and potential problems of each approach, and to point out some of the promising directions future investigations may take. Since most work in this field has focused on machining features, the article primarily covers those features associated with the manufacturing domain. In order to better understand the state of the art, methods of automated feature recognition are divided into the following categories of methods based on their approach: graph-based, syntactic pattern recognition, rule-based, and volumetric. Within each category we have studied issues such as the definition of features, mechanisms developed for recognition of features, the application scope, and the assumptions made. In addition, the problem is addressed from the perspective of information input requirements and the advantages and disadvantages of boundary representation, constructive solid geometry (CSG), and 2D drawings with respect to machine recognition of features are examined. Emphasis is placed on the mechanisms for attacking problems associated with interacting features.1. INTRODUCTIONDesign is a set of important processes that occur at different life-cycle stages of a product. Computer-aided design (CAD), in general, refers to using computers to assist with the various functions in the design process. Engineers consider CAD data to be the data that represent a product or component: in the domain of mechanical components these are often represented as a set of engineering drawings or a solid model of a component.Although CAD has been used to assist with various design tasks, CAPP (computer-aided process planning) has usually referred to the collection of activities that convert a part design into manufacturing instructions that de-scribe how to produce the part or how to build an assembly to satisfy the design specifications. In the domain of machined components, process planning involves finding the sequence of processes with which parts are to be machined (such as milling, grinding, drilling, etc.), the fixturing configuration to set up the part for each process to be carried out, and the tools to be used to carry out each operation in the sequence. In order to achieve this task for a component, process planners interpret the design data (the shape, surface finish, tolerances, etc.) based on process and tool capabilities.Computer-integrated manufacturing (CIM) systems attempt to integrate design, process planning, and other functions (material handling, factory management, etc.) in a production environment. However, developing truly integrated manufacturing systems has proved not to be a trivial undertaking. One important reason has been that CAD data consisting of annotated engineering drawings or the solid model of a component are not manufacturing-specific and generally represent geometry by a low-level description of edges, vertices, and faces of a component, whereas process planners work with primitives such as slots and holes (and properties of the primitives such as dimensions and surface finish) that are shapes produced by processes and tools. In order to overcome the integration barrier between design and process planning, a task which previously relied upon a manual interpretation process by an engineer, several conscious efforts have been made, all using the concept of features. As explained later, the strategy has been either to incorporate features in the CAD data during the design process or to extract the features from CAD data, or a combination of both. In the next section, we first consider features and what they refer to in the remainder of this article.1.1 FeaturesThere is no universally accepted definition of features. In fact, this has been one of the difficulties researchers have faced in this area. However, two recent books Shah and Mantyla 1995; Shah et al. 1994 have described features as groupings of topological entities from a component that are semantically significant in its production and thus need to be referenced together. Clearly this description implies that feature definitions are domain-dependent and application-oriented. For varied applications such as design, manufacturing, stress analysis, and the like, the part is therefore to be viewed in terms of different sets of features Mantyla et al. 1996.From the point of view of process planning, a feature set can be visualized as consisting of shapes and technological attributes associated with manufacturing operations and tools Shah 1991. For example, pockets, slots, holes, and steps are examples of common machining features, instances of which are shown in Figure 1(a). Pockets and slots may be produced by milling and grinding process operations, and holes may be achieved by drilling processes. Although some researchers have broadened the notion of features to include such entities as tolerance features, surface finish features, material features, and the like, in this article the term is restricted to “shape” features Shah and Mantyla 1995 or groupings of geometric and topological entities from a component that correspond to primitive shapes produced by given manufacturing operations and tools. Henceforth, we concentrate on common machining features such as pockets, slots, bosses, holes, and so on because these have been the predominant features discussed in the literature: from here on, the term features refer to them unless otherwise stated.In order to develop systems-handling features, existing research has approached the definition of features differently. Some work has regarded features as (closed) volumes with certain characteristics (rectangular block, with two opposite ends open, etc.), whereas other researchers have regarded them as a group of topologic entities (faces, edges, etc. that are not necessarily closed volumes) satisfying certain geo metric relationships (four faces, pair wise parallel, etc.). Examples of research in each category include Sakurai and Gossard 1990, where a feature is defined as a collection of faces, and Vandenbrande and Requicha 1990, 1993, who define a feature as a volume. Figure 1(a) shows the same set of common features regarded as volumes or as a group of topologic entities. It is important to recognize how features can be defined differently because the proposed algorithms for feature recognition from solid models cannot be used interchangeably between the alternate approaches. Algorithms for feature recognition can deal only with a specific definition of features. Although there are infinite possible shape patterns for features, it may still be possible to categorize them into groups or classes. Such a classification would be useful for feature support, for developing a standard terminology, and for data exchange. For example, features may be classified as polyhedral or nonpolyhedral. Features may also be classified as prismatic or rotational. The attributes associated with features may include dimension, orientation, tolerance, spatial relationship, and topologic components.Figure 1. (a) Several common prismatic manufacturing features which can be defined volumetrically or geometrically; (b) part with interacting features.1.2 Interpreting Geometric Models to Obtain Features An active area that has received much attention in integrating CAD and CAPP has been the development of an intelligent interpreter of CAD data (geometric models) to obtain features. Such an interpreter would serve to translate the low-level entities (faces) in the geometric models produced by a CAD system into a set of features suitable for manufacturing by means of an automatic feature recognition process (AFR) that would determine the features from an existing CAD-produced geometric model of a component such as a boundary representation (B-Rep), a constructive solid geometry representation (CSG), engineering drawings, and so on. We discuss geometric models of components in the next section.Figure 2. Component modules in automatic feature recognition.Figure 2 shows the component modules of automatic feature recognition Shah 1991. The constructed features constitute the high-level primitives that contain the semantic manufacturing information used for process planning or assembly. Once features are hypothetically found, they must be verified for correctness. This is generally done through either a set of rules, which determine if any given feature is not valid, or through volume-checking methods, which ensure that each feature generated is within the volume to be removed from the machined raw stock. The feed-back loop shown in the figure is in place in case the verification fails, in which case the feature recognition process must be repeated to search for a different set of features and the new features must be verified in turn. We note that for a number of systems, some or all of the tasks of feature recognition are included within the duties of (computer-aided) process planning. For example, feature verification or even feature recognition itself may be considered a duty of the process planner. For clarity, we separate the feature recognition task from other process planning tasks (e.g., tool selection), and focus only on it.A number of techniques for automatic feature recognition have been proposed in the past decade, but there have been difficulties associated with a lack of standard definitions. The multiplicity of feature definitions has sometimes contributed to different classifications for the same shape within the literature.For example, Figure 3 has been classified as a slot Marefat and Kashyap1990; Joshi and Chang 1988, a pocket Gupta et al 1994, or a depression De Floriani 1989. Another difficulty in studying and proposing feature-recognition techniques has been robustness in handling feature interactions. When two or more features intersect geometrically, open into one another, and so on, this produces what is generally termed a feature interactions. The definition of feature interaction depends on the approach taken in defining features. As mentioned in the previous section, some work has regarded features as (closed) volumes with certain characteristics, and other studies have regarded them as groups of topological entities (not necessarily volumes) satisfying certain geometric relationships. For volumetric feature definitions, a feature interaction corresponds to an intersection of the volumes of two (or more) features. For topology-based feature definitions, an interaction corresponds to modification of the topological elements and the relationships between the elements that define each feature involved in the interaction. For example, Figure 1(b) shows a component with interaction between its two features, that is, two slots.Regardless of whether features are regarded as volumes or as particular groups of topological elements, the difficulty of feature interactions for proposed techniques has been that the geometric interaction of features often produces a different version of a feature, and the characteristics of this new instance are different from the representational characteristics used by the technique to define the given class of Figure 3. Example of simple part with slot.features. The new version might have a different number of faces, a different number of edges, different geometric constraints (perpendicularity, etc.) between adjacent faces, a raw stock bounded volume that does not correspond to the expected volume for that class of feature, and so on. Because of these potential differences, feature-recognition techniques cannot therefore expect direct matches between the characteristics expected to represent a feature and the characteristics of all instances of that feature. This leads to non-uniqueness in characteristics defining in stances of a feature and a need for capability and/or intelligence to cope with non-uniqueness and perform robustly in spite of it.Another natural outcome when features interact is that there are inevitably alternative ways to describe a component according to its features, either by having alternative sets of matches among the geometric model of the component and the representations for features or, if there is no set of complete matches, by having alternative reasoning paths leading to alternative partial matches. For example, according to the features shown in Figure 1(a), the component in Figure 1(b) can be interpreted as having two interacting through slots, but an equally valid interpretation(based on geometric instances of shown features) would be four blind slots and a pocket in the middle. Being able to generate alternate interpretations systematically is very useful, because it allows manufacturing process planners to benefit from the information and generate process plans that are superior in terms of cost, quality, or both. Interpretations that provide access to all features from fewer access directions may produce better machining process plans, because it is estimated that as much as 80% of production time is spent in establishing different setups.In the remainder of this article, we first review schemes for representing components via CAD geometric models and then discuss a variety of solutions to the problem of automatic feature recognition. We study advantages and disadvantages, as well as the application scope of each solution. The mechanisms for automatic feature recognition are divided into several categories of methods based on the overall approach: syntactic pattern recognition, graph-based, rule-based, volume- and cell-based, and evidence-based reasoning. Such an informal grouping is useful to better understand the state-of-the-art technology related to feature recognition from CAD geometric models. Associated with each technique are important issues related to feature recognition including how features are represented, algorithms developed for feature recognition, application scope of the proposed technique, underlying assumptions, and prototype systems developed. One emphasis of the article is on mechanisms for attacking problems associated with interacting features since, as we have noted, they have posed difficulties in feature recognition.2. CAD AND GEOMETRIC MODELINGDesign is an iterative process that involves proposing a design solution, testing and evaluating the design solution, modifying the proposed solution, and finally optimizing the solution. Within CAD, the graphics capabilities of a computer are substituted for the work that traditionally would have been done with pencil and paper. Furthermore, the simulation capabilities of the computer help the designer test and evaluate a proposed design solution. CAD can reduce the design cycle, increase design accuracy, and free workers from tedious and repetitive work.With the rapid development in data-base, simulation, and artificial intelligence technology, CADs functions have evolved from simple computer graphics and computer-aided drawing and drafting to advanced 3D graphical representation, analysis, and simulation. Current CAD systems allow a user to design a 3D part, study the mechanical action of the part through simulation, and automatically produce engineering drawings of the part. The user can also analyze stresses and deflection of the part using finite element analysis techniques. The generic functions of a CAD system may include geometric modeling, engineering analysis, and automated drafting, as well as kinematics analysis. For the purposes of this article, we are concerned with the geometric modeling aspect of CAD systems. Such geometric models are either searched via automatic feature-recognition techniques or are augmented with feature information in feature-based design. Therefore, it is important to cover the basic aspects of geometric modeling.2.1 Geometric Modeling and Representation SchemesGeometric models are represented using wireframes, which represent the part shape with interconnected edge segments, or by using 3D solid models, which model the volume enclosed by the shape of the physical design. Since solid models carry more information than wireframe representations, most research on features has used solid models as input. In a typical solid-modeling system, the user constructs a model with building blocks of elementary solid shapes called primitives. The user may generate and/or modify a model by sizing, adding, and subtracting geometric solid primitives from a base component. The base component is typically a solid rectangular block called the stock. A range of commercial solid modelers and design packages are available, but an important distinction must be made between solid-modeling packages and design packages. At the heart of a solid modeling package is a kernel that sup-ports solid-modeling operations such as intersections, differences, center of mass calculations, and the like. A design package (or standard CAD tool) generally allows the construction of design models (which may or may not include solid models), but may not allow access to any of the preceding solid-modeling operations. There are packages that combine a solid-modeling kernel and a design tool as well. ACIS (Spatial Technologies) is an example of a commercial solid modeler and Pro/ENGINEER (Parametric Technology Corporation) is an example of a commercial CAD package or design tool.A representation scheme for solid modeling is defined as a mapping S that maps physical objects from a domain M into representations in a model space R; that is, S: MR. In unambiguous representation schemes, each representation in the model space corresponds to one physical object in the domain. Unique representation schemes ensure that each physical object admits (can be mapped to) only one syntactically correct representation. Six major techniques (schemes) used to represent and maintain a 3D model by a CAD solid-modeling system are:pure primitive instancing (PPI),spatial occupancy enumeration (SOE),cell decomposition (CD),sweeping (S),constructive solid geometry (CSG),andboundary representation (B-Rep).PPI involves reusing already stored descriptions of solids, such as blocks, brackets, and the like, and applying a transformation to them by instantiating certain parameters, to generate new objects. Although the original descriptions are referred to as generic primitives, the individual objects created through this transformation are called primitive instances. PPI is a unique representation scheme, but because it lacks restrictions on the generic primitives, it is not necessarily unambiguous. PPI provides no way to combine object instances to create structures that represent new and more complex objects.SOE subdivides 3D space into small volumes called voxels (an abbreviation for volume elements). To represent an object, it classifies these volumes as either empty or containing a solid. This is a variation of octrees in which 3D space is recursively subdivided into octants and classified as full, empty, or partially full. (Juan-Arinyo and Sole 1995 present a conversion from SOE to a variant of octrees.) SOE is unique and unambiguous but has the drawback of being potentially verbose, due to itsenumerative nature.Cell decomposition (CD) is similar to SOE, but it starts the decomposition with the object, not with 3D space. CD subdivides the object into primitive components that are either disjoint or meet precisely at a common face, edge, or vertex. The object is then thought of as being these primitive components glued together. Figure 4(a) shows the CD representation for a simple example object with a slot. Since engineering objects may be decomposed into constituting components in different ways, CD is, in general, not unique.Sweeping is very closely related to the concept of a generalized cylinder. It is based on the notion of moving a two-dimensional set (a cross-section) along a three-dimensional space curve (axis) to sweep out a solid volume (Figure 4(b). A sweep can generally be described by W W(r, f), where r(s) x(s), y(s), z(s) is a vector function representing the axis parametrically in terms of the arc length variable s. Normally, f represents the boundary of the cross-section curve; that is, f (x(u, s), y(u, s), although f may also be a set member-ship function describing the interior points of the cross-section at any point along the axis. The dependence of the cross-section on the arc length of the axis allows the cross-section to shrink, expand, or change in other fashions as it is swept along the axis. Although sweeping is intuitively appealing, like cell decomposition, it is not a unique representation scheme.The last two solid-modeling techniques (CSG and B-Rep) have received the most attention and therefore are considered the most significant representation schemes. Constructive solid geometry (CSG) is a volumetric representation scheme in which solids may be represented as compositions, via (regularized) set Boolean operations, of primitive shape entities positioned properly in space via rigid motion operations. The primitive shape entities used in CSG are typically parameterized blocks, cylinders, cones, and spheres, and the Boolean operations include regularized union, difference, and intersection Requicha 1980. CSG rep-resents and maintains objects as trees, the leaves of which are the primitive entities involved in constructing the object and the interior nodes of which are Boolean operations and motion transformations (Figure 4(c). The “*” in the figure refers to regularized Boolean operations so as to prevent dangling edges and faces.The concepts of adding and subtracting elementary volumes, called primitives, at an abstract level can be likened to manipulating features during the design process (similar to feature-based design). These CSG primitives may also translate into machining operations, which originally provided further motivation for the scheme. For example, drilling a hole can be interpreted as subtracting a cylinder from a base part. One drawback of the CSG representation scheme is that, in general, it is not unique.Boundary representations (B-Reps) model objects by hierarchically storing their boundaries. The object boundary is segmented into a set of nonoverlapping faces. Each face is specified by describing the surface it is embedded on and its bounding edges. Each edge is, in turn,Figure 4. Solid model representation schemes for example object with a slot: (a) cell decomposition subdivides the object into (glued-together) primitive components that meet precisely at a face; (b) sweeping represents the object by a cross-section (f), and a sweep axis (r(s); (c) CSG represents the object as a tree whose nodes are primitive volumes, Boolean operations, and rigid motions; (d) B-Rep scheme represents the object by describing faces, edges, and vertices forming its boundary in a hierarchical structure.represented by the curve it lies on and any associated vertices. Vertices are simple three-dimensional coordinate points. The example in Figure 4(d) shows the boundary representation for a simple component with a slot. One of the earlier influential systems adopting this approach Baumgart 1972 pro-posed “winged-edge” data structures for representing this information. Boundary representation schemes are unambiguous Requicha 1980. They are also unique when the boundary of the object is partitioned into maximal and connected faces. This aspect is desirable, because it makes B-Rep, unlike CSG, independent of the operations (or their order) used in constructing the model. Conversion of CSG representations to boundary representations is relatively well understood Requicha and Voelcker 1985. However, the inverse problem has not been well addressed until recently Shapiro and Vossler 1993. Due to CSGs non-unique nature, B-Rep is more commonly used for geo-metric modeling and feature recognition, but more important, nearly all automatic feature recognition mechanisms rely on B-Rep or CSG for their input.Many of these geometric modeling systems use procedures based on the Euler theorem to test and help ensure validity of the models. Regardless of the solid-modeling scheme used, the output of current solid-modeling systems de-scribes part topology and geometry in terms of low-level surface entities such as faces, edges, and vertices, possibly along with such information as surface finish, dimensions, density, tolerances, and so on.The schemes previously discussed are“manifold” models with strict rules for their topological correctness. However, some applications require “non-manifold” models and some of the rules on topological correctness need to be relaxed. Readers are referred to the selective geometric complexes (SGC) model Rossignac and OConnor 1990 for a further discussion.2.2 STEP: The International Standard for the Exchange of Product Model DataFinally, it should be noted that there is a developed standard for the exchange of solid model data, although it has been limited commercial usage thus far. STEP (the international Standard for the Exchange of Product Model Data) is being developed by the International Standards Organization (ISO) (STEP it-self is ISO Standard 10303). STEP Application Protocol 203 (AP203) is the standard for the exchange of mechanical part and assembly data. PDES is the organization responsible for the testing and support of STEP within the United States. STEP consists of schemes to store and transmit geometric primitives data (solid models) using the EXPRESS language. These sequential file formats are defined to permit the transfer of product data between different CAD (or solid modeling) systems. The format is intended to be independent of the manner in which the information is created and stored within any particular CAD system. The information contained in this format may include geometric and topological information such as vertices of an edge, edges of a face, and faces for an object as well as certain manufacturing information such as density and dimension.3. AN INFORMAL CLASSIFICATION OF FEATURE RECOGNITION MECHANISMSAll AFR algorithms include two important components: the definition of the features and the feature-recognition mechanism. Various approaches have been developed for automatic feature-recognition mechanisms that can be in-formally classified into the categories:syntactic pattern recognition,graph-based methods,rule-based methods,volumetric methods (including “cell-based” techniques), andevidence-based reasoning methods.Syntactic pattern recognition characterizes the overall part shape as the com-position of certain geometric primitives. Feature recognition proceeds by parsing the input syntactic expression of a part using grammar rules to identify the syntactic patterns representing features. In graph-based feature recognition, the topological shape of a part is represented as a graph (generally with nodes of the graph corresponding to the faces of the object and the arcs of the graph corresponding to the edges of the object. However, other graph representations, for example, Chuang and Henderson 1990, represent the object with a graph whose nodes are vertices of the object and whose arcs correspond to its edges). This graph representation is then searched for certain properties to identify the features embedded in the part. In rule-based methods, rules attempt to specify a set of necessary and sufficient preconditions for the patterns found in a feature. Recognition is carried out through an inference control mechanism that determines how to apply these rules to the input data. This includes forward chaining, backward chaining, or opportunistic rule firing. In volumetric methods, the finished material is represented as a combination of a set of volumes. If a CSG representation of the part is the input, the nonuniqueness of the representation would be a hurdlethere are many ways to define the same feature by different combina-tions of Boolean operations on the CSG primitives. Thus, the nonunique representation must be simplified before it can be recognized through pattern matching. In the convex-hull method, the part (or the volume to be removed from the part) is partitioned into sub-volumes. The convex-hull algorithms compute the difference between the object and its convex hull recursively until the difference is a null set. The convex hulls are then rearranged to obtain removal volumes for machining. Cell-based techniques also decompose the part (or the volume to be removed from the part) into primitive volumes (cells). These cells are then recombined to pro-vide the features of the original part. In the evidence-based reasoning method only hints or evidence, and not full-fledged features, are generated at first. Then the patterns are elaborated through hint recombination or evidence accumulation. Finally, there are a few methods that do not entirely fall into any of these categories, and we have classified them as “other” due to their unique or hybrid nature. (Note that many of the classified techniques still utilize several different feature-recognition strategies. In this sense, even the classified techniques could be called hybrid.)4. MACHINE FEATURE IDENTIFICATION AND RECOGNITION TECHNIQUESIn this section, we survey various AFR techniques and summarize each techniques approach to feature definition, mechanisms developed for feature recognition, application scope, assumptions made, and limitations. AFR techniques are classified into three groups based on the their input information: B-Rep, CSG, and 2D models.4.1 AFR Using B-Rep The majority of AFR techniques proposed by researchers use B-Rep as their input information. These methods include syntactic, graph-based, rule-based, cell-based, and evidence-based reasoning methods, as well as some hybrid methods.4.1.1 Syntactic Pattern Recognition. The syntactic pattern approach for feature recognition from boundary representations received much attention in the early 80s Fu 1982. Prior to that, it had been successfully applied to 2D recognition in computer vision. Syntactic pattern recognition is a formalized technique for representing complex patterns in terms of simple subpatterns and relations among subpatterns. A given pat-tern is decomposed recursively into simpler subpatterns called primitives. Just as an alphabet in a formal language may be combined into words and sentences, sequences of geometric primitives can be combined to form an expression that represents the complex patterns of features. The possible combination sequences can be organized ac-cording to syntactic language rules, just like the grammar for a formal language. The resulting language (or expression) is called a pattern description language. The rules that define valid compositions of primitives into patterns are specified by the grammar of the pattern description language. The grammar is defined by the ordered tuple (V , V , P, S), where V is the set of terminal symbols,V is the set of nonterminal symbols, P is the set of productions, and S is the start symbol of the language. Correct sentences in this language are constructed by sequentially using productions from P after the start symbol S until a terminal symbol is reached. The recognition process proceeds by first constructing an expression for a pattern or object based on the defined primitives and the grammar rules. This expression, consisting of the string of primitives, is then parsed using the set of productions in P to identify the feature pattern it represents. In most applications, patterns refer to contours of the cross-sections of a part and the primitives are usually line segments and curve segments (such as edges) that form the contours. Jakubowski 1982 uses the syntactic approach for automatically interpreting rotational partsparts that have an axis of symmetryand polyhedral parts. With this approach, machine parts are described by such contour primitives as line segments (e.g., edges), curve segments, and surface segments, coupled with a set of operators such as revolution and sweeping, to describe the operations for generating a 3D part from its 2D cross-sections. Figure 5 shows an example based on this method. Figure 5(a) shows some of the basic geometric primitives. Groups of similar parts can be described by instantiating a generic description with certain parameters (e.g., the part shown in Figure 5(d) is represented (Figure 5(b) by the instantiation of several of the s1s4 primitives). A part is de-scribed by organizing the primitives into an expression according to grammar rules. Two grammar rules have been applied to construct the expressions for a part and to develop their parsers: extended context-free grammar and regular right part grammar. The most important task of the grammar rules is to distinguish the repetitive parts of contours from the nonrepetitive ones Jakubowski 1982. Figures 5(c) and (d) show a regular expression for the cross-section of a class of rotational Figure 6. Cube and its face adjacency hypergraph representation.parts, along with the contours represented by the expression and a part instance having such contours. To identify the features represented by the expression, parsers are constructed using the same grammar rules and are subsequently applied to the input expressions for the parts. Similar techniques have been developed for more specific classes of parts. For example, Li 1986 uses a similar, but simpler, approach to identify turning features from the cross-sections of a rotational part. Choi 1982 focuses on an approach identifying hole geometries. Staley et al. 1983 developed a method designed for primitive depressions or protrusions whose cross-sections consist entirely of the chosen set of 2D primitive patterns, but cannot be extended to a feature for which the cross-section is inconsistent along different sections on the sweep axis. This is a common thread among all of these methodssince they all interpret a 3D part from its cross-section, they fail if the cross-section varies. All the methods use similar primitives from the part cross-section, and if the pattern does not match the symbols in the given grammar the methods do not work. To recognize features in a general 3D part, Kyprianou 1980 proposed one of the earliest methods, which directly used syntactic pattern recognition coupled with a graph representation of the part. In this approach, the B-Rep of a part is first converted into a face-edge graph with nodes of the graph representing faces and arcs representing edges of the part. The arcs are labeled with the edges concavity. The features are generated by parsing the face-edge graph. The significance of this work is that surfaces and edges are the basis for the pattern primitives instead of line and curve segments. Hence, it is possible to recognize 3D features directly without first converting to a 2D representation. The three basic structural primitives used in constructing the feature grammar are convex loop, concave loop, and smooth. A loop is an ordered set of faces, such that the first and las
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