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

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.