A laminae approach to constructing geometric feature volumes

ABSTRACT

The limiting factor for the majority of reported feature recognition (AFR) algorithms lie in their inability to handle anything more complex than the restricted geometric domain of 2.5D machined components. This paper describes a novel approach to recognising shape features on models comprising both simple and complex ruled surfaces. Specifically, the paper describes how the concept of 3D-laminae enables feature volumes bounded by complex ruled surfaces to be constructed. This generic feature recognition algorithm requires no predefined feature libraries and advocates the notion of neutral features, which separates the generic features identified by the extraction algorithm from those (features) classified subsequently to suit a discrete domain. The work concentrates on identifying machinable volumes (for manufacture by CNC machines) and the classifications presented apply specifically to this context. However, because the algorithm is capable of handling complex ruled surfaces, it is envisaged that the proposed methodology will be applicable to industries involved with the manufacture of dies and moulds.

REFERENCES

• 1: S Ansaldi , K De Floriani , B Falcidieno, An edge-face relational scheme for boundary representations, Computer Graphics Forum, v.4 n.4, p.319-332, Dec. 1985 [doi>10.1111/j.1467-8659.1985.tb00237.x]
• 2: Choi, B.K., Barash, M.M. and Anderson, D.C. Automatic recognition of machined surfaces from a 3D solid model. Computer-aidod Design, Vol. 16, No. 2, 1984, 81-86.
• 3: J. Corney , D. E. R. Clark, A feature recognition algorithm for multiply connected depressions and protrusions in 2 1/2 D objects, Proceedings of the first ACM symposium on Solid modeling foundations and CAD/CAM applications, p.171-183, June 05-07, 1991, Austin, Texas, United States [doi>10.1145/112515.112541]
• 4: L. de Floriani, Feature Extraction from Boundary Models of Three-Dimensional Objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.11 n.8, p.785-798, August 1989 [doi>10.1109/34.31442]
• 5: Di Stefano, P. Automatic extraction of form features for casting. Computer-Aided Design, Vol. 29, No. 11, 1997, 761-770.
• 6: Gaines, D.M. and Hayes, C.C. CUSTOM_CUT: a customizable feature recognizer. Computer-Aided Design, Vol. 31, No. 6, 1999, 85-100.
• 7: Gavankar, P., Chuang, S.H., Henderson, M.R. and Ganu, P. Graph-Based feature Extraction. Formal Methods in Design Workshop, Tue. Oct. 24, 1989, 167-183.
• 8: Satyandra K. Gupta , William C. Regli , Dana S. Nau, Manufacturing feature instances: which ones to recognize?, Proceedings of the third ACM symposium on Solid modeling and applications, p.141-152, May 17-19, 1995, Salt Lake City, Utah, United States [doi>10.1145/218013.218052]
• 9: Han, J.H., Regli, W.C. and Rosen, D. Special panel session for feature recognition. Proceedings of 1997 ASME Design Engineering Technical Conference. Sept. 14-17, 1997, Sacramento, California, DETC97/CIE-4423.
• 10: Hart, J.H and Requicha, A.A.G. Integration of feature based design and feature recognition. Computer-Aided Design, Vol. 29, No. 5, 1997, 393-403.
• 11: Han, J.H and Requicha, A.A.G. Modeler-independent feature recognition in a distributed environment. Computer-Aided Design, Vol. 30, No. 6, 1997, 453-463.
• 12: Hui, K.C. and Li, Y. A feature-based shape blending technique for industrial design. Computer-Aided Design, Vol. 30, No. I0, 1998, 823-834.
• 13: Jakubowski, R. Syntactic characterisation of machine part shapes. Cybernetics and Systems, Vol. 3, No. 1, 1982, 1- 24.
• 14: Qiang Ji , Michael M. Marefat, Machine interpretation of CAD data for manufacturing applications, ACM Computing Surveys (CSUR), v.29 n.3, p.264-311, Sept. 1997 [doi>10.1145/262009.262012]
• 15: S. Joshi , T. C. Chang, Graph-based heuristics for recognition of machined features from a 3D solid model, Computer-Aided Design, v.20 n.2, p.58-66, March, 1988 [doi>10.1016/0010-4485(88)90050-4]
• 16: Kyprianou, L.K. Shape Classification in Computer Aided Design. Ph.D. thesis, Christ College, University of Cambridge, Cambridge, UK, 1980.
• 17: Lee, J.Y. and Kim, K. A feature-based approach to extracting machining features. Computer-Aided Design, Vol. 30, No. 12, 1997, pp. 895-905.
• 18: Lichtenstein, C. and Engler, F. Streamlined, A Metaphor for Progress, Lars Muller Publishers, Switzerland, 1991.
• 19: Lim, T., Comey, J. and Clark, D.E.R. Exact tool sizing for feature accessibility. Int. J. Adv. Manufacturing Technology, Vol. 16, 2000, 791-802.
• 20: Lim, T. Lamina-based Feature Recognition and Applications in Manufacturing. Ph.D. thesis, Heriot-Watt University, Edinburgh, UK, July 2000.
• 21: Little, G., Clark, D.E.R., Tuttle, R. and Comey, J. The Heriot-Watt FeatureFinder: A graph-based approach to recognition. Proceedings of 1997 ASME Design Engineering Technical Conference., Sept. 14-17, 1997, Sacramento, California, DETC97/CIE-4276.
• 22: Little, G., Clark, D.E.R., Tuttle, R. and Comey, J. Delta- Volume Decomposition for Multi-Sided Components. Computer-Aided Design, Voi. 30, No.9, 1998,695-705.
• 23: William Clement Regli, III, Geometric algorithms for recognition of features from solid models, University of Maryland at College Park, College Park, MD, 1996
• 24: Regli, W.C. and Gaines, D.M. A repository for design, process planning and assembly. Computer-Aided Design, Vol. 29, No. 13, 1998, 1019-1035.
• 25: Jami J. Shah , Martti Mantyla , Dana S. Nau, Advances in Feature-Based Manufacturing, Elsevier Science Inc., New York, NY, 1994
• 26: J. J. Shah, Assessment of features technology, Computer-Aided Design, v.23 n.5, p.331-343, June 1991 [doi>10.1016/0010-4485(91)90027-T]
• 27: N. Sormaz , D. E. R. Clark , J. R. Corney , J. R. Tuttle, A feature recognition algorithm for NC-machining, Proceedings of the fifth IFIP TC5/WG5.2 international workshop on geometric modeling in computer aided design on Product modeling for computer integrated design and manufacture, p.223-233, January 1997, Airlie, Virginia, United States
• 28: Staley, S.M., Henderson, M.R. and Anderson, D.C. Using syntactic pattern recognition to extract feature information from a solid geometric data base. Computers in Mechanical Engineering, Vol. 2, Sept. 1983, 61-65.
• 29: Stage, R., Roberts, C. and Henderson, M. Generating resource based flexible form manufacturing features through objective driven clustering. Computer-Aided Design, Vol. 31, 1999, 119-130.
• 30: Spatial Technology Inc., Three-Space, Ltd., and Applied Geometry, Corp., 2425 55 th Street Building A, Boulder, CO 80301-5740. ACIS Geometric Modeler Application Guide, Version 4.0, Issue 1, 1998.
• 31: CAM-I Current Status of Features Technology, Technical Report R-88-GM-04.1, Revised Nov. 1988.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Feature evaluation and selection

Additional Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.6 Segmentation
          Subjects: Edge and feature detection
      I.4.7 Feature Measurement
          Subjects: Feature representation
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation

  J. Computer Applications

General Terms:
Algorithms, Design, Measurement, Performance, Theory

Keywords:
CAD/CAM, feature recognition, geometric reasoning, laminae, non-2.5D geometry