Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-04T19:38:32.589Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated process planning: reasoning from first principles based on geometric relation constraints

Published online by Cambridge University Press:  27 February 2009

Cornelius Nevrinceanu ,
Vassilios Morellas  and
Max Donath
Cornelius Nevrinceanu
Affiliation:
The Productivity Center and the Department of Mechanical Engineering, University of Minnesota, MN, U.S.A.
Vassilios Morellas
Affiliation:
The Productivity Center and the Department of Mechanical Engineering, University of Minnesota, MN, U.S.A.
Max Donath
Affiliation:
The Productivity Center and the Department of Mechanical Engineering, University of Minnesota, MN, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

While previous work in automated process planning established plan ordering on an empirical basis alone, we derive our process plans based on the Holding-Under-Uncertainty Principle. We will introduce the principle, and we will describe the operational requirements needed to make this principle implementable in practice. The principle takes into account the form and geometric tolerances needed to locate features in deriving plan steps. Rather than just focusing on technological features, our planning strategy is controlled by the geometric relationships among features. By implementing a constraint propagation paradigm, we ensure that the tolerances accumulated in generating the part geometry remain within the tolerances specified by the design.

Type
Research Article
Information
AI EDAM , Volume 7 , Issue 3 , August 1993 , pp. 159 - 179
Copyright
Copyright © Cambridge University Press 1993

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.)

References

ASME 1974. Preferred Limits and Fits for Cylindrical Parts. USA Standard, USAS B4.1–1967.Google Scholar
ASME 1983. Dimensioning and Tolerancing. USA Standard, ANSI Y14.5M-1982.Google Scholar
Brooks, S. L., Hummel, K. E. and Wolf, M. L. 1987. XCUT: A Rule Based Expert System for the Automated Process Planning Machined Parts. Technical Report, BDX–613–3768, Bendix Kansas City Division, Kansas City, Missouri, June.Google Scholar
Brown, P. F. and Ray, S. R. 1989. NBS AMRF Process Planning System: System Architecture. NISTR 88–3828, NIST, Gaithersburg, MD, March.Google Scholar
Chang, T. and Wysk, R. 1985. An Introduction to Automated Process Planning Systems. New Jersey: Prentice-Hall.Google Scholar
de Kleer, J. and Sussman, G. 1980. Propagation of constraints applied to circuit synthesis. Circuit Theory and Applications 8, 127–144.Google Scholar
Descotte, Y. and Latombe, J. 1981. GARI: a problem solver that plans how to machine mechanical parts. Proceedings of the Seventh Joint International Conference on Artificial Intelligence, 766–772.Google Scholar
Descotte, Y. and Latombe, J. 1985. Making compromises among antagonist constraints in a planner. Artificial Intelligence 21, 183–217.Google Scholar
Hayes, C. and Wright, P. 1989. Automating process planning: using feature interactions to guide search. Journal of Manufacturing Systems 8(1), 1–15.Google Scholar
Iwata, K. and Sugimura, N. 1987. An integrated CAD/CAPP system with ‘know-hows’ on machining accuracies of parts. Transactions of the ASME, Journal of Engineering for Industry 109, 128–133.CrossRefGoogle Scholar
Jain, A. and Donath, M. 1989. Knowledge representation system of robot-based automated assembly. Transactions of the ASME, Journal of Dynamic Systems Measurement and Control 111(3), 462–469.Google Scholar
Nau, D. and Chang, T. 1985. A knowledge-based approach to generative process planning, in Computer-Aided/Intelligent Process Planning, eds Lin, C. R., Chang, T.-C. and Komanduri, R., ASME, pp. 65–71.Google Scholar
Nau, D. 1987. Automated Process Planning Using Hierarchical Abstraction. 1987 Texas Instruments Conference on AI for Industrial Automation, July.Google Scholar
Nevrinceanu, C. 1987. Reasoning from First Principles in Process Planning for Precision Machining. Ph.D. Dissertation. Department of Mechanical Engineering, University of Minnesota.Google Scholar
Nevrinceanu, C. and Donath, M. 1987. Planning with constraints in precision machining, In: Knowledge Based Expert Systems in Engineering: Planning and Design, eds. Sriram, D. and Adey, R. A., Boston: Computational Mechanics Publications.Google Scholar
Russell, B. 1938. Introduction to Mathematical Philosophy. New York: Macmillan.Google Scholar
Stallman, R. and Sussman, G. 1977. Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence 9, 135–196.Google Scholar
Steele, G. Jr 1980. The Definition and Implementation of a Computer Programming Language Based on Constraints. Ph.D. Dissertation, MIT AI Lab Technical Report AI-TR-595, August.Google Scholar
Sussman, G. J. and Steele, G. Jr 1980. CONSTRAINTS—a language for expressing almost-hierarchical descriptions. Artificial Intelligence 14, 1–39.Google Scholar