TY - CONF
T1 - Integration of feature based design and feature recognition
AU - Han, J. H.
AU - Requicha, A. A.G.
N1 - Funding Information:
The researchr eportedi n this paper was supportedi n part by the National ScienceF oundation under grant DDM-92-14996,a nd the Industrial Associateso f the ProgrammableA utomation Laboratory, Institute for Roboticsa ndI ntelligentS ystemsU, niversityo f Southern California. Researcho n integrationw ith FBDS is being conductedin collaborationw ith theF raunhoferI nstitute for ComputerG raphics,D armstadtG, ermany.W ork on interfacingw ith theC IMPLEX systemw ass upportedb y CAM-I. An earlierv ersiono f this paperw asp resenteda t the 1995A SME Computerisn EngineeringC onference.
PY - 1995
Y1 - 1995
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.
UR - http://www.scopus.com/inward/record.url?scp=0029419133&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:0029419133
SP - 569
EP - 578
T2 - Proceedings of the 1995 Database Symposium
Y2 - 17 September 1995 through 20 September 1995
ER -