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Journals ACM SIGART Bulletin Volume 1 Issue 3

SIGART Member Dissertation Abstracts Automatic Recognition of Machinable Features in Solid Models By Jan H. Vandenbrande University o f R o c h e s t e r Electrical Engineering D e p a r t m e n t Rochester, N Y Recognition of machining features such as holes, slots and pockets is essential for the fully automatic manufacture of mechanical parts. This thesis discusses an experimental feature recognizer that uses a blend of artificial intelligence (AI) and computational geometry techniques. The recognizer is implemented in a rapid prototyping test bed consisting of the KnowledgeCrafl (TM) AI environment tightly coupled with the PADL-2 solid modeler, running under Unix on a SUN 3/260 computer. It is capable of finding features with interacting volumes (e.g., two crossing slots), and takes into account nominal shape information, tolerancing and other available data. Machinable volumetric features (or simply "features") are solids removable by operations typically performed in 3-axis machining centers. Features are recognized by the characteristic traces they leave in the nominal geometry of a part. These traces, also called surface features, provide reliable clues or hints for the potential existence of volumetric features, even when feature interactions Occur. A generate-and-test strategy is used. Partial information on the presence of features is processed by OPS-5 production rules which generate hints and post them on a blackboard. The clues are assessed, and those judged promising are processed to ensure they correspond to actual features and to gather information neeessary for process planning. A solid feature is associated with each promising hint, its interaction with other features is represented by segmenting the feature into optional and required volumes, and the feature's accessibility is analyzed. Because some of the proposed features may rely on faulty hints, these are tested for validity in a second phase of feature finding. The validity tests ensure that the proposed features are accessible, do not intrude into the desired part, and satisfy other machinability conditions. The process continues until it produces a complete decomposition of the volume to be machined in terms of volumetric features that correspond to material removal operations. S I G A R T Bulletin, Vol. 1, No. 3

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SIGART Member Dissertation Abstracts: Automatic Recognition of Machinable Features in Solid Models

Vandenbrande, Jan H.
ACM SIGART Bulletin , Volume 1 (3)
Association for Computing Machinery – Oct 1, 1990

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