Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-24T19:16:13.064Z Has data issue: false hasContentIssue false
  • English
  • Français

Innovative design of mechanical structures from first principles

Published online by Cambridge University Press:  27 February 2009

Jonathan Cagan  and
Alice M. Agogino
Jonathan Cagan
Affiliation:
Intelligent Systems Research Group, Department of Mechanical Engineering, University of California at Berkeley, Berkeley CA 94720, U.S.A.
Alice M. Agogino
Affiliation:
Intelligent Systems Research Group, Department of Mechanical Engineering, University of California at Berkeley, Berkeley CA 94720, U.S.A.
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper a unique design methodology known as 1stPRINCE (FIRST PRINciple Computational Evalualor) is developed to perform innovative design of mechanical structures from first principle knowledge. The method is based on the assumption that the creation of innovative designs of physical significance, concerning geometric and material properties, requires reasoning from first principles. The innovative designs discovered by 1stPRINCE differ from routine designs in that new primitives are created. Monotonicity analysis and computer algebra are utilized to direct design variables in a globally optimal direction relative to the goals specified. In contrast to strict constraint propagation approaches, formal qualitative optimization techniques efficiently search the solution space in an optimizing direction, eliminate infeasible and suboptimal designs, and reason with both equality and inequality constraints. Modification of the design configuration space and the creation of new primitives, in order to meet the constraints or improve the design, are achieved by manipulating mathematical quantities such as the integral. The result is a design system which requires a knowledge base only of fundamental equations of deformation with physical constraints on variables, constitutive relations, and fundamental engineering assumptions; no pre-compiled knowledge of mechanical behavior is needed. Application of this theory to the design of a beam under torsion leads to designs of a hollow tube and a composite rod exhibiting globally optimal behavior. Further, these optimally-behaved designs are described symbolically as a function of the material properties and system parameters. This method is implemented in a LISP environment as a module in a larger intelligent CAD system that integrates qualitative, functional and numerical computation for engineering applications.

Type
Research Article
Information
AI EDAM , Volume 1 , Issue 3 , August 1987 , pp. 169 - 189
Copyright
Copyright © Cambridge University Press 1987

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

Addanki, S. and Davis, E. S. 1985. A representation for complex domains. Proceedings of the Ninth International Joint Conference on Artificial Intelligence 2, 443–446.Google Scholar
Agogino, A. M. and Almgren, A. S. 1987 a. Symbolic computation in computer-aided optimal design. In: Gero, J. S. (Ed ) Expert Systems in Computer-Aided Design. Amsterdam: North-Holland, pp. 267–284.Google Scholar
Agogino, A. M. and Almgren, A. S. 1987 b Techniques for integrating qualitative reasoning and symbolic computation in engineering optimization. Engineering Optimization 12 (2), 117–135.CrossRefGoogle Scholar
Agogino, A. M. and Guha, R. 1987. Object-oriented data structures for reasoning about functionality, manufacturability, and diagnosability of mechanical systems. Working Paper 87–0901-P, Berkeley Expert Systems Technology Laboratory, Mechanical Engineering Department, University of California, Berkeley, CA 94720. (Prepared for an invited presentation at the 1987 ASME Design Automation Conference, Boston, MA, August 29, 1987).Google Scholar
Beer, F. P. and Johnston, E. R. 1981. Mechanics of Materials. New York: McGraw Hill.Google Scholar
Bennett, J. S. and Engelmore, R. S. 1979. SACON: A knowledge-based consultant for structural analysis. Proc. 6th IJCAI, Tokyo, vol. 1, pp 47–49.Google Scholar
Brown, D. C. and Chandrasekaran, B. 1986. Knowledge and control for a mechanical design expert system. Computer July, 92–100.Google Scholar
Cagan, J. and Genberg, V. 1987. PLASHTRAN, an expert consultant on two-dimensional finite element modelling techniques. Engineering with Computers, 2, 199–208.CrossRefGoogle Scholar
Choy, J. K. and Agogino, A. M. 1986. SYMON: Automated SYmbolic MONotonicity analysis system for qualitative design optimization. Proceedings ASME 1986 International Computers in Engineering Conference, Chicago, July 24–26, pp. 305–310.Google Scholar
Coyne, R. D., Roseman, M. A., Radford, A. D. and Gero, J. S. 1987. Innovation and creativity in knowledge-based CAD. In: Gero, J. S. (Ed.) Expert Systems in Computer-Aided Design. Amsterdam: North-Holland, pp. 435–465.Google Scholar
Dixon, J. R., Howe, A. H., Cohen, P. R. and Simmons, M. K. 1987. DOMINIC I: Progress toward domain independence in design by iterative design. Engineering with Computers, 2, 137–145.CrossRefGoogle Scholar
Dyer, M. G., Flowers, M. and Hodges, J. 1986. EDISON: An engineering design invention system operating naively. Proc. First International Conference on Applications of Artificial Intelligence to Engineering Problems, Southampton, U.K., April 15–18, p. 327.Google Scholar
DeKleer, J. and Brown, J. 1983. A qualitative physics based on confluences. Artificial Intelligence, 24, 7–84.CrossRefGoogle Scholar
Fateman, R. J. 1982. Vaxima Primer for VAX/UNIX 6.31. Academic Computer Services Library, University of California, Berkeley, CA, July.Google Scholar
Forbus, K. 1983. Qualitative process theory. Artificial Intelligence, 24, 85–168.CrossRefGoogle Scholar
Hayes, P. 1985. The second naive physics manifesto, In: Brachman, and Levesque, (Eds.) Readings in Knowledge Representation, Los Angeles, CA: Morgan Kaufmann, pp. 467–485.Google Scholar
Haug, E. J. and Arora, J. S. 1979. Applied Optimal Design. New York: Wiley-Interscience.Google Scholar
Lenat, D. B. 1983. EURISKO: A program that learns new heuristics and domain concepts: The nature of heuristics III: Program design and results. Artificial Intelligence 21, 61–98.CrossRefGoogle Scholar
Macfarlane, J. F. and Donath, M. 1988. Device oriented qualitative reasoning for dynamic physical systems. ASME reprints, ASME Winter Annual Meeting, Chicago, ILL (obtainable from J. F. Macfarlane, Lawrence Livermore National Laboratory, P.O. Box 808, Livermore, CA94550, U.S.A.Google Scholar
Michelena, N. F and Agogino, A. M. 1988. Multiobjective hydraulic cylinder design. Transactions of the ASME, Journal of Mechanisms, Transmissions, and Automation in Design, 110, 81–87.CrossRefGoogle Scholar
Mittal, S. and Araya, A. 1986. A knowledge-based framework for design. Proceedings, AAAI Fifth National Conference on Artificial Intelligence, Philadelphia, PA, August 11–15, 2, 856–865.Google Scholar
Mittal, S., Dym, C. L. and Morjaria, M. 1985. PRIDE: An expert system for the design of paper handling systems. In: Dym, C. L., Ed. Applications of Knowledge-Based Systems to Engineering Analysis and Design. New York:ASME, 99–115Google Scholar
Murthy, S. S. and Addanki, S. 1987. PROMPT: An innovative design tool. Proceedings, AAAI Sixth National Conference on Artificial Intelligence, Seattle, WA, July 13–17, 2, 637–642.Google Scholar
Papalambros, P. 1982. Monotomcity in goal and geometric programming. Journal of Mechanical Design 104, 108–113.CrossRefGoogle Scholar
Papalambros, P. and Wilde, D. J 1979. Global non-iterative design optimization using monotonicity analysis. Journal of Mechanical Design 101, 645–649.CrossRefGoogle Scholar
Radford, A. D. and Gero, J. S. 1985. Multicriteria optimization in architectural design. In: Gero, J S., Ed. Design Optimization. New York: Academic Press.Google Scholar
Rand, R. H. 1984. Computer Algebra in Applied Mathematics: An Introduction to MACSYMA. Marshfield, MA: Pitman Publishing.Google Scholar
Rege, A. and Agogino, A. M 1988 Topological framework for representing and solving probabilistic inference problems in expert systems. IEEE Transactions of Systems, Man, and Cybernetics, 18 (13), 402–412.CrossRefGoogle Scholar
Reklaitis, G. V., Ravindran, A. and Ragsdell, K. M. 1983. Engineering Design Optimization. New York: John Wiley.Google Scholar
Stefik, M. J. 1981. Planning with constraints. Artificial Intelligence, 111–140.CrossRefGoogle Scholar
Timoshenko, S. P. and Gere, J. M. 1961. Theory of Elastic Stability. New York: McGraw-Hill.Google Scholar
Ulrich, K. and Seering, W. 1987. Conceptual design as novel combination of existing device features. Advances in Design Automation-Proceedings, ASME Design Automation Conference, Boston, Sept. 27–30, 1, 295–300Google Scholar
Vanderplaats, G. N. 1984. Numerical Optimization Techniques for Engineering Design: With Applications. New York: McGraw-Hill.Google Scholar
Wilde, D. J. 1985. A maximal activity principle for eliminating overconstrained optimization cases. Transactions of the ASME, Journal of Mechanisms, Transmissions, and Automation in Design 108, 312–314.CrossRefGoogle Scholar
Xerox Corporation 1983. The Loops Manual. Xerox Corp: Palo Alto.Google Scholar