SIGART Member Dissertation Abstracts: Automatic Recognition of Machinable Features in Solid Models
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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 Knowledge Craft (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.