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- W2524167465 abstract "In industries such as injection molding, sheet metal cutting, etc., substantial costs and time delays are associated with design iterations that occur due to incomplete or non-existent manufacturability analysis of the design at the upstream design stage. The knowledge required to perform such analysis may be based on shape features called manufacturing features, that are physically distinguishable regions on parts, and that usually perform certain functions.Shape features are not always available in CAD designs, and therefore need to be extracted. Abstracting shape features from CAD is difficult because: (1) shape features vary substantially in topology and geometry, and, (2) recognition of shape features through subgraph matching is a computationally expensive solution.The current research towards feature abstraction draws its motivation from the manner in which humans recognize shapes. Among several visual inputs such as shadows, depth perception, and silhouette, the current research selects the silhouette as the basis for defining a 3D filter: The Differential Depth Filter. A human being obtains a silhouette by viewing a part from a given direction in space; similarly the filter is defined with respect to a view. The filter acts on the surface model of the part to reduce the number of topological entities on it, and making the problem computationally more tractable. The filtering process is repeated for a number of views, to obtain what is called a positive filtered object.A negative filter, that is defined as a positive filter of the solid complement of the model, is subsequently extracted. The positive and negative edges are transformed into entities of a higher abstraction level called loops. Features are defined in terms of loops and are searched for in the filtered and transformed model. Loops aid in the reduction of the search space, besides being able to find features having significant topological and geometric variations. Considerable success in feature recognition has been achieved for parts with varying topology and geometry and for large-sized parts with approximately 25,000 planar faces. To our knowledge, no known formalization of a feature recognition approach exists that has been successful in abstracting features with such geometric variation, topological variation, and large input data size." @default.
- W2524167465 created "2016-10-07" @default.
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- W2524167465 date "1991-05-01" @default.
- W2524167465 modified "2023-10-15" @default.
- W2524167465 title "Abstraction of manufacturing features from design" @default.
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