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 language | English (US) |
|---|---|
| Title of host publication | Proceedings of the Industrial Engineering Research Conference |
| Editors | Deborah A. Mitta, Laura I. Burke, John R. English, Jennie Gallimore, Georgia-Ann Klutke, Gregory L. Tonkay |
| Publisher | Publ by IIE |
| Pages | 614-618 |
| Number of pages | 5 |
| ISBN (Print) | 0898061326 |
| State | Published - 1993 |
| Event | Proceedings of the 2nd Industrial Engineering Research Conference - Los Angeles, CA, USA Duration: May 26 1993 → May 28 1993 |
Other
| Other | Proceedings of the 2nd Industrial Engineering Research Conference |
|---|---|
| City | Los Angeles, CA, USA |
| Period | 5/26/93 → 5/28/93 |
All Science Journal Classification (ASJC) codes
- Engineering(all)
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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.