3-D manufacturing feature recognition using super relation graph method

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Recognizing machining features such as slots, holes and pockets is one of the major tasks of CAD/CAM. This research proposes the super relation graph (SRG) method which uses artificial intelligence, pattern recognition, artificial neural networks and computational geometry techniques for extracting shape features. The nodes of the SRG represent the faces in depressions; the links represent either super-concavity or face-to-face relationships which are generated from a set of new definitions of relationships between two faces. SRG feature representation scheme carries more information than previous models and the results obtained are proved to be better than the ones generated from most of the prominent existing methods.

Original languageEnglish (US)
Title of host publicationProceedings of the Industrial Engineering Research Conference
EditorsDeborah A. Mitta, Laura I. Burke, John R. English, Jennie Gallimore, Georgia-Ann Klutke, Gregory L. Tonkay
PublisherPubl by IIE
Pages614-618
Number of pages5
ISBN (Print)0898061326
StatePublished - 1993
EventProceedings of the 2nd Industrial Engineering Research Conference - Los Angeles, CA, USA
Duration: May 26 1993May 28 1993

Other

OtherProceedings of the 2nd Industrial Engineering Research Conference
CityLos Angeles, CA, USA
Period5/26/935/28/93

Fingerprint

Computational geometry
Computer aided manufacturing
Pattern recognition
Artificial intelligence
Computer aided design
Machining
Neural networks

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Kao, C. Y., & Tirupatikumara, S. R. (1993). 3-D manufacturing feature recognition using super relation graph method. In D. A. Mitta, L. I. Burke, J. R. English, J. Gallimore, G-A. Klutke, & G. L. Tonkay (Eds.), Proceedings of the Industrial Engineering Research Conference (pp. 614-618). Publ by IIE.
Kao, Ching Yao ; Tirupatikumara, Soundar Rajan. / 3-D manufacturing feature recognition using super relation graph method. Proceedings of the Industrial Engineering Research Conference. editor / Deborah A. Mitta ; Laura I. Burke ; John R. English ; Jennie Gallimore ; Georgia-Ann Klutke ; Gregory L. Tonkay. Publ by IIE, 1993. pp. 614-618
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Kao, CY & Tirupatikumara, SR 1993, 3-D manufacturing feature recognition using super relation graph method. in DA Mitta, LI Burke, JR English, J Gallimore, G-A Klutke & GL Tonkay (eds), Proceedings of the Industrial Engineering Research Conference. Publ by IIE, pp. 614-618, Proceedings of the 2nd Industrial Engineering Research Conference, Los Angeles, CA, USA, 5/26/93.

3-D manufacturing feature recognition using super relation graph method. / Kao, Ching Yao; Tirupatikumara, Soundar Rajan.

Proceedings of the Industrial Engineering Research Conference. ed. / Deborah A. Mitta; Laura I. Burke; John R. English; Jennie Gallimore; Georgia-Ann Klutke; Gregory L. Tonkay. Publ by IIE, 1993. p. 614-618.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Y1 - 1993

N2 - Recognizing machining features such as slots, holes and pockets is one of the major tasks of CAD/CAM. This research proposes the super relation graph (SRG) method which uses artificial intelligence, pattern recognition, artificial neural networks and computational geometry techniques for extracting shape features. The nodes of the SRG represent the faces in depressions; the links represent either super-concavity or face-to-face relationships which are generated from a set of new definitions of relationships between two faces. SRG feature representation scheme carries more information than previous models and the results obtained are proved to be better than the ones generated from most of the prominent existing methods.

AB - Recognizing machining features such as slots, holes and pockets is one of the major tasks of CAD/CAM. This research proposes the super relation graph (SRG) method which uses artificial intelligence, pattern recognition, artificial neural networks and computational geometry techniques for extracting shape features. The nodes of the SRG represent the faces in depressions; the links represent either super-concavity or face-to-face relationships which are generated from a set of new definitions of relationships between two faces. SRG feature representation scheme carries more information than previous models and the results obtained are proved to be better than the ones generated from most of the prominent existing methods.

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Kao CY, Tirupatikumara SR. 3-D manufacturing feature recognition using super relation graph method. In Mitta DA, Burke LI, English JR, Gallimore J, Klutke G-A, Tonkay GL, editors, Proceedings of the Industrial Engineering Research Conference. Publ by IIE. 1993. p. 614-618