TY - JOUR
T1 - Integration of feature based design and feature recognition
AU - Han, Jung Hyun
AU - Requicha, Aristides A.G.
N1 - Funding 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.
PY - 1997/5
Y1 - 1997/5
N2 - 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.
AB - 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.
KW - Feature model conversion
KW - Feature recognition
KW - Multiple interpretations
UR - http://www.scopus.com/inward/record.url?scp=0001428101&partnerID=8YFLogxK
U2 - 10.1016/S0010-4485(96)00079-6
DO - 10.1016/S0010-4485(96)00079-6
M3 - Article
AN - SCOPUS:0001428101
SN - 0010-4485
VL - 29
SP - 393
EP - 403
JO - CAD Computer Aided Design
JF - CAD Computer Aided Design
IS - 5
ER -