直流電動(dòng)機(jī)的單片機(jī)恒速控制系統(tǒng)設(shè)計(jì)說(shuō)明書(shū)含開(kāi)題,直流電動(dòng)機(jī),單片機(jī),控制系統(tǒng),設(shè)計(jì),說(shuō)明書(shū),仿單,開(kāi)題 Machine Interpretation of CAD Data for Manufacturing Applications Machine 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. INTRODUCTION Design 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 Features There 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 Ma¨ntyla¨ 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 MODELING Design 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, CAD’s 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 Schemes Geometric 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),and —boundary 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 represen