Feature Recognition
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The term "feature" does not imply the same meaning in different engineering disciplines. This has resulted in several ambiguous definitions for feature. A feature, in Computer-Aided Design (CAD) software, can be called a region of a part with some interesting geometric or topological patterns (Pratt and Wilson, 1985). This meaning can refer to all sorts of information, such as for example, shape, functional or manufacturing information (Regli 1995). Although many types of features have been investigated (Shah and Mäntylä, 1995), the most common type of feature is the form feature, which contains both shape information and parametric information. Examples of form features which are common in many shape models are round holes, slots, bosses, and pockets.
Features can also be used to represent manufacturing information of the part. Different manufacturing domains require different feature representations. Some of the properties that need to be encoded by features are assembly method, manufacturing process and tolerances. A manufacturing feature can be defined as a form feature, but not necessarily vice versa (Regli, 1995). Among manufacturing features, the ones received extensive attention are the machining features. A machining feature can be regarded as the volume swept by a cutting tool (Chang, 1990). In this sense, it is always a negative (subtracted) volume in contrast with form features which are sometimes positive (added) volumes.
Feature data in a CAD model can be represented either as a collection of surfaces or volumetrically. Surface features are naturally used for example to describe manufacturing tolerances or locating surfaces in fixture design. volumetric features on the other hand, are used in process planning since manufacturing information (particularly in machining) is better portrayed volumetrically (Pratt, 1985).
The first published work on features was for the original boundary representation modelling system, BUILD, and was performed by Lyc Kyprianou (Kyprianou, 1980). Soon other work followed based on different solid representations. Overviews on the work on features can be found in Shah et al, 2001; Subrahmanyam and Wozny, 1995; Salomons et al., 1993. Work on features (generally called feature technology)can be divided into two rough categories: Design-by-features and Feature recognition. In design-by-features, also known as feature-based design (FBD), feature structures are introduced directly into a model using particular operations or by sewing in shapes. By using features to build up shape models, the design process is made more efficient, because the shape of features can be pre-defined. Features in FBD can be directly associated to manufacturing information (Shah and Rogers, 1988) so that these informations can be retrieved in downstream applications. In this way, and overall CAD/CAM system can be fully automated, however, the idea of using manufacturing features to design a part has its own shortcomings (Regli, 1995): The features used to design the part do not necessarily represent the best way to manufacture it. It would be therefore the responsibility of the designer to evaluate all the methods that can produce the part. Furthermore, manufacturing features are not the most natural way of designing a part.
The goal of feature recognition (FR) is to algorithmically extract higher level entities (e.g. manufacturing features) from lower level elements (e.g. surfaces, edges, etc) of a CAD model. The classical Kyprianou's method was aimed to encode parts for group technology (GT). The purpose of GT is to systematically classify objects based on their manufacturing method. Kyprianou's work involved classifying faces into primary and secondary groups and then identifying features according to patterns of these primary or secondary faces. A primary face is the one in which there are multiple boundaries (also called "hole-loops") or mixed concave and convex boundaries. A concave boundary is a set of concave edges, where the solid angle over the edge is more than 180. Secondary faces are all other faces. Kyprianou's work was continued and extended by Jared et al. to cover a number of important special cases where features interacted.
Automatic Feature Recognition (AFR) is regarded as an ideal solution to automate design and manufacturing processes. Successful automation of CAD and CAM systems is a vital connection in building Computer-Integrated Manufacturing (CIM) systems (Scholenius,1992). This is the part of the FR research that has attracted much of the attention. Another important application of AFR is for manufacturability evaluation (Gupta and Nau, 1995). The AFR system should be able to interpret the design differently based on alternative features and feed back the manufacturability and cost of those interpretations to the designer.
There is a big stockpile of different AFR techniques that has been proposed for CAD/CAM integration and process planning. Han et al provides a critical and detailed analysisa of some of the existing approaches. The most common methods according to Han et al range from graph-based algorithms to hint-based and volumetric decomposition techniques. In the graph-based feature recognition, a graph showing the topology of the part (connection of faces) is created. The graph is often attributed, for example the edges are marked as concave or convex (Joshi 1988). This graph is then analyzed to extract subsets of nodes and arcs that match with any predefined template. This is done by a variety of techniques, including graph iso-morphism algorithms (Joshi 1988, Marefat 1990). Graph based approaches have been criticized for several shortcomings. They fail to account for manufacturability of the recognized features due to their strong reliance on topological patterns rather than geometry. The intersection of features causes an explosion in the number of possible feature patterns that spoils any attempt to formulate feature patterns. To address these difficulties, Vandenbrande and Requicha (1990) proposed to search for "minimal indispensable portion of a feature's boundary", called hints, rather than complete feature patterns. For example, presence of two opposing planar faces is a hint for potential existence of a slot feature. Hints are not necessarily restricted to the part geometry. They can be extracted form tolerances and design attributes as well. For example, "a thread attribute may be taken as a hole hint" (Han et al, 2000). This approach has been more successful in recognizing intersecting features. However, the efficiency of the approach has been argued, as there could be a huge number of traces that won't lead to valid features (Han et al, 2000). Some authors have been in favor of using a hybrid of graph based and hint based FR (Gao and Shah, Rahmani and Arezoo). Other existing FR approaches are volumetric decomposition (Kim 1990, Sakurai and Chin 1993), Artificial Neural Networks (Hwang 1991), and expert systems (Henderson 1984). Babic et al (2008) briefly introduces many of them.
However, building feature recognition system that function effectively on real industrial products has been elusive.
Features are interesting and have been worked on by a number of people. However, there is also misuse. Some CAD systems have what appear to be feature operations, but are little more than window dressing. The features obtained may not correspond to the feature name of the command. Nor is the information contained in the model history trees necessarily accurate. For this reason it is not possible to build reliable applications based on such models. Another popular use of features is for manufacturing. There is software which proclaims that it recognises features and the commands to manufacture them. These systems tend to be over-simplified and insufficient for modern manufacturing. However, features are an important topic for many areas of research and work on them continues.
[edit] External Links
Feature Recognition—The Missing Link To Automated CAM
Manufacturing feature recognition towards integration with process planning
Manufacturing feature recognition: a status report
hybrid hint-based and graph-based feature recognition
[edit] See also
[edit] References
Pratt M.J. and Wilson P.R., 1985, Requirements for support of form features in a solid modeling system, CAM-I, R-85-ASPP-01
Regli W.C., 1995, Geometric algorithms for recognition of features from solid models, PhD dissertation, Univ. Maryland, College Park MD.
Chang T.C., 1990, Expert process planning for manufacturing, Addison –Welsey, New York.
Kyprianou, L., 1980, Shape classification in Computer-Aided Design, Ph.D. Dissertation, Cambridge university.
Salomons, O., van Houten, F. J., Kals, H. J., 1993, Review of Research in Feature-Based Design, Journal of Manufacturing Systems, Vol. 12, No. 2, pp. 113-132.
Shah J.J., Mäntylä M., 1995, Parametric and feature based CAD/CAM, Wiley-Interscience Publication, John Wiley Sons Inc.
Shah, J.J., Anderson, D., Kim, Y.S., Joshi, S., 2001, A discourse on geometric feature recognition from CAD models, Journal of computing and information science in engineering, Vol 1, pp. 41-51.
Subrahmanyam, S., Wozny, M., 1995, An overview of automatic feature recognition techniques for computer-aided process planning, Computers in industry, Vol. 26, pp. 1-21.
Shah J.J. and Rogers M.T., 1988, Expert form feature modeling shell, Computer Aided Design, Vol. 20, No. 9,PP. 515-524.
Scholenius G., 1992, Concurrent Engineering, keynote paper,Annals of CIRP,41(2):645-655
Han J.H., Pratt M. and Regli W.C., 2000, Manufacturing feature recognition from solid models: A status report, IEEE Trans. On Robotics and Automation, 16(6): 782-796
Babic b., Nesic, n., Miljkovic Z., 2008, A review of automated feature recognition with rule-based pattern recognition, Computers in Industries, 59(4): 321-337.
Gupta S.K. and Nau S.K.,1995, “A systematic approach for analyzing the manufacturability of machined parts”, Computer Aided Design, Vol. 27.
S. Joshi and T. C. Chang, 1988, Graph-based heuristics for recognition of machined features from a 3D solid model, JCAD, 20(2):58-66.
M. Marefat and R. L. Kashyap, 1990, Geometric reasoning for recognition of 3-D object features, IEEE Trans. Pattern Anal. Machine Intell., 12(10):949-965.
J. H. Vandenbrande and A. A. G. Requicha, 1993, Spatial reasoning for the automatic recognition of machinable features in solid models, IEEE Trans. Pattern Anal. Machine Intell., Vol. 15, pp. 1-17.
S. Gao and J. Shah, 1998, Automatic recognition of interacting machining features based on minimal condition subgraph, JCAD, 30(9):727-739.
K. Rahmani, B. Arezoo,2006, Boundary analysis and geometric completion for recognition of interacting machining features. Computer-Aided Design 38(8): 845-856.
K. Rahmani, B. Arezoo,2007, A hybrid hint-based and graph-based framework for recognition of interacting milling features, Computers in Industry, 58(4):304-312.
Y. Kim, 1990, Convex decomposition and solid geometric modeling, PhD dissertation, Stanford Univ.
H. Sakurai and C. Chin, 1993, Defining and recognizing cavity and protrusion by volumes, in Proc. ASME computers in Engineering Conf., pp. 59-65.
Hwang J., 1991, Applying the perceptron to 3D feature recognition, PhD, Arizona State Univ.
Henderson M.R., 1984, Extraction of feature information from three dimensional CAD data, PhD Thesis, Purdue Univercity, west Lafayette, IN, USA.
