Process planning for machined parts typically requires that a part be described through machining features such as holes, slots and pockets. This paper presents a novel feature finder, which automatically generates a part interpretation in terms of machining features, by utilizing information from a variety of sources such as nominal geometry, tolerances and attributes, and design features. The feature finder strives to produce a desirable interpretation of the part as quickly as possible. If this interpretation is judged unacceptable by a process planner, alternatives can be generated on demand. The feature finder uses a hint-based approach, and combines artificial intelligence techniques, such as blackboard architecture and uncertain reasoning, with the geometric completion procedures first introduced in the OOFF system previously developed at USC.
Bibliographical noteFunding Information:
In the current implementationI,F * considerse vidence from threes ources:n ominal geometry,d esignf eatures, and tolerancesa nd attributesW. hen a hint is supported by multiplee videncest,h eir strengthsm ustb e combined. The following subsectionds ealw ith the manipulationo f evidencef rom various sources,i nitial assignmentso f strengthsto evidencesa, nd the combinationo f multiple evidences.
- Feature model conversion
- Feature recognition
- Multiple interpretations
ASJC Scopus subject areas
- Computer Science Applications
- Computer Graphics and Computer-Aided Design
- Industrial and Manufacturing Engineering