Hostname: page-component-6898cfcb55-zkb6p Total loading time: 0 Render date: 2026-11-11T19:27:04.567Z Has data issue: false hasContentIssue false
  • English
  • Français

Symbolic calculus for volumetric reasoning about process plans

Published online by Cambridge University Press:  27 February 2009

Huaming Lee,
Jon Scott,
Jon Sims Williams and
David Cox
Huaming Lee
Affiliation:
Faculty of Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom
Jon Scott
Affiliation:
Faculty of Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom
Jon Sims Williams
Affiliation:
Faculty of Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom
David Cox
Affiliation:
Faculty of Engineering, University of Bristol, Bristol BS8 1TR, United Kingdom
Get accessRights & Permissions [Opens in a new window]

Abstract

A symbolic calculus for reasoning about process plans is proposed in this paper. The main focus of attention is the selection and sequencing of material removal operations for components in accordance with the design geometry. This is a central issue in automated process planning. The proposed symbolic calculus defines a computational formalism for symbolic manipulation of feature volumes, so that reasoning about volumetric removals can be treated in a logical manner by using well-defined procedures of algorithmic synthesis. This potentially encourages a more generic approach to the automation of outline and detailed process planning. The underlying philosophy is that a properly interpreted object topology upon the feature model allows the logical synthesis of volumetric removal sequences. The number of sequences is constrained by algorithms within the planning system that consider part geometry as expressed by features. This reduces the problem space associated with plan synthesis. Some of the geometrically viable sequences have the potential for further development to form viable machining removal sequences, or outline process plans.

Keywords

Volumetric ReasoningFeature VolumeVolumetric RemovalObject GeometryObject TopologyAdjacency InterpenetrationOwnershipTopologic InferenceProblem SpaceReverse Analysis Strategy

Information

Type
Articles
Information
AI EDAM , Volume 10 , Issue 3 , June 1996, pp. 183 - 198
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Butterfield, W.R., Green, M.K., Scott, D.C., & Stoker, W.J. (1986). Part features for process planning. (Report No. C-85-PPP-03, CAM-1). Arlington, TX.Google Scholar
Case, K., & Gao, J. (1993). Feature technology: An overview. Int. J. Comput. Integral. Manufact. 6 (1 & 2), 2–12.CrossRefGoogle Scholar
Chamberlain, M.A., Joneja, A., & Chang, T.C. (1993). Protrusion-features handling in design and manufacturing planning. J. Computer-Aided Design 25 (1), 19–28.CrossRefGoogle Scholar
Chang, T.C., & Wysk, R.A. (1985). An introduction to automated process planning systems. Prentice-Hall Inc., Englewood Cliffs, NJ.Google Scholar
Chang, T.C. (1990). Expert process planning for manufacturing Addison-Wesley Publishing Co., Reading, MA.Google Scholar
Cox, D., McMahon, C., & Tannock, J. (1995). Approach to linear and geometrical tolerancing for computer-aided design. J. Design Manufacturing 5, 223–240.Google Scholar
Erve, A.H., & Kals, H.J. (1986). XPLANE, a generative computer aided process planning system for part manufacturing. Ann. CIRP 2, 324–329.Google Scholar
Gindy, N. (1989). A hierarchical structure for form features. Int. J. Product. Res. 27 (12), 2089–2103.CrossRefGoogle Scholar
Gindy, N., Huang, X., & Ratchev, T. (1991). Feature-based component model for computer aided process planning systems. Symp. Feature-Based Approaches to Design Process Plan.CrossRefGoogle Scholar
Ham, I., & Lu, S. (1988). Computer-aided process planning: The present and the future. Ann. CIRP 37 (2), 591–601.CrossRefGoogle Scholar
Homen de Mello, L.S., & Sanderson, A.C. (1990). AND/OR graph representation of assembly. IEEE J. Robotics Automat. 6 (2), 188–199CrossRefGoogle Scholar
Juri, A., Saia, A., & Pennington, A. (1990). Reasoning about machining operations using feature-based models. Int. J. Product. Res. 28 (1), 153–171.CrossRefGoogle Scholar
Kang, T., & Nnaji, B. (1993). Feature representation and classification for automated process planning systems. J. Manufact. Syst. 12 (2), 133–145.CrossRefGoogle Scholar
Kanumury, M., & Chang, T.C. (1991). Process planning in an automated manufacturing environment. J. Manufact. Syst. 10 (1), 67–78.CrossRefGoogle Scholar
Karinthi, R. (1990). An Algebraic Approach to Feature Interactions. Ph.D. thesis, University of Maryland, College Park, MD.Google Scholar
Karinthi, R., & Nau, D.S. (1989). Geometric reasoning as a guide to process planning. ASME Int. Conf. Comput. Eng.Google Scholar
Karinthi, R., & Nau, D. (1992). Geometric reasoning using a feature algebra. AAAI Press/MIT Press.Google Scholar
Lee, S. (1992). Backward assembly planning. AAAI Press/MIT Press.Google Scholar
Mendelson, B. (1975). Introduction to topology. Allyn & Bacon Inc., New York.Google Scholar
Mill, F.G., Salmon, J.C., & Pedley, A.G. (1993). Representation problems in feature-based approaches to design and process planning. Int. J. Comput. Integrat. Manufact. 6 (1&2), 27–33.CrossRefGoogle Scholar
Prabhu, P., Elhence, S., Wang, H., & Wysk, R. (1990). An operations network generator for computer aided process planning. J. Manufact. Syst. 9 (4), 283–291.CrossRefGoogle Scholar
Requicha, A.G. (1977). Mathematical models of rigid solid objects. (Tech. Report No. TM-28). Rochester, NY: University of Rochester.Google Scholar
Salomons, O., Houten, F., & Kals, H. (1993). Review of research in feature-based design. J. Manufact. Syst. 12 (2), 113–132.CrossRefGoogle Scholar
Shah, J.J. (1991). Assessment of features technology. Computer-Aided Design 23 (5), 331–343.CrossRefGoogle Scholar
Shah, J.J., & Rogers, M.T. (1988 a). Expert form feature modelling shell. Computer-Aided Design 20 (9), 515–524.CrossRefGoogle Scholar
Shah, J.J., & Rogers, M.T. (1988 b). Functional requirements and conceptual design of the feature-based modelling system. Computer-Aided Eng. J. 9–15.CrossRefGoogle Scholar
Shah, J.J., Sreevalsan, P., & Mathew, A. (1991). Survey of CAD/feature-based process planning and NC programming techniques. Computer-Aided Eng. J.CrossRefGoogle Scholar
Smith, J., Cohen, P., Davis, J., & Shahrukh, I. (1992). Process plan generation for sheet metal parts using an integrated feature-based expert system approach. Int. J. Product. Res. 30 (5), 1175–1190.CrossRefGoogle Scholar
Thomas, F., & Torras, C. (1988). A group theoretic approach to the computation of symbolic part relations. IEEE J. Robotics Automat. 4 (6), 622–634.CrossRefGoogle Scholar
Van Houten, F., Erve, A., Jonkers, F., & Kals, H. (1989). PART, a CAPP System with a flexible architecture. Proc. 2nd CIRP Int. Workshop CAPP.Google Scholar
Vosniakos, G., & Davis, B. (1993). Knowledge-based selection and sequencing of hole-making operations for prismatic parts. Int. J. Adv. Manufact. Technol. 8, 9–16.CrossRefGoogle Scholar
Weill, R., Spur, G., & Evershiem, W. (1982). Survey of computer-aided process planning systems. An. CIRP 31 (2), 539–551.CrossRefGoogle Scholar