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Inducing constraint activity in innovative design

Published online by Cambridge University Press:  27 February 2009

Jonathan Cagan  and
Alice M. Agogino
Jonathan Cagan
Affiliation:
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A.
Alice M. Agogino
Affiliation:
Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94720, U.S.A.
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Abstract

In this paper, a methodology for inducing trends in a first principle reasoning system for design innovation is presented. Dimensional Variable Expansion is used in 1stPRINCE (FIRST PRINciple Computational Evaluator) to create additional design variables and introduce new prototypes. Trends are observed at each generation of the prototype and induction is used to predict optimal constraint activity at the limit of the iterative procedure. The inductive mechanism is applied to a constant-radius beam under flexural load and a tapered beam of varying radius and superior performance is derived. A circular wheel is created from a primitive-prototype consisting of a rectangular, spinning block that is optimized for minimum resistance to spinning. Although presented as a technique to perform innovative design, the inductive methodology can also be utilized as an AI approach to shape optimization.

Type
Research Article
Information
AI EDAM , Volume 5 , Issue 1 , February 1991 , pp. 47 - 61
Copyright
Copyright © Cambridge University Press 1991

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References

Agogino, A. M. and Almgren, A. S. 1987. Techniques for integrating qualitative reasoning and symbolic computation in engineering optimization. Engineering Optimization 12(2), 117–135.CrossRefGoogle Scholar
Almgren, A. and Agogino, A. M. 1989. A generalization and correction of the welded beam optimal design problem using symbolic computation. ASME Journal of Mechanisms, Transmissions, and Automation in Design 111(1), 137–140.CrossRefGoogle Scholar
Azarm, S., Bhandarkar, S. M. and Durelli, A. J. 1988. On the experimental vs. numerical shape optimization of a hole in a tall beam. In Proceedings of ASME Design Automation Conference, Kissimmee, FL, September 25–28, pp. 257–264.Google Scholar
Cagan, J. 1990. Innovative Design of Mechanical Structures from First Principles. Ph.D. Dissertation, University of California, Berkeley, CA, April.Google Scholar
Cagan, J., and Agogino, A. M. 1987. Innovative design of mechanical structures from first principles. (AI EDAM) 1, 169–189.Google Scholar
Cagan, J. and Agogino, A. M. 1989. Inducing optimally directed non-routine designs. Preprints of International Round-Table Conference: Modeling Creativity and Knowledge-Based Creative Design, Heron Island, Queensland, Australia, December 11–14, pp. 95–117. A portion to be reprinted in Modeling Creativity and Knowledge-Based Creative Design, Gero, J. S. and Maher, M. L., (Eds), Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Choy, J. K. and Agogino, A. M. 1986. SYMON: Automated SYmbolic MONotonicity Analysis System for qualitative design optimization. In Proceedings of ASME 1986 International Computers in Engineering Conference, Chicago, July 24–26, pp. 305–310.Google Scholar
Courant, R. and Hilbert, D. 1937. Methods of Mathematical Physics, Vol. 1. New York, Interscience.Google Scholar
Haftka, R. T. and Grandhi, R. V. 1986. Structural shape optimization—a survey. Computer Methods in Applied Mechanics and Engineering, 57, 91–106.CrossRefGoogle Scholar
Howard, H. C., Wang, J., Daube, F. and Rafiq, T. 1989. Applying design-dependent knowledge in structural engineering design. (AIEDAM) 3, 111–123.CrossRefGoogle Scholar
Jain, P. and Agogino, A. M. 1990. Theory of design: an optimization perspective. Journal of Mechanism and Machine Theory 25(3), 287–303.CrossRefGoogle Scholar
Michalski, R. S. 1983. A theory and methodology of inductive learning. In Machine Learning: An Artificial Intelligence Approach (Michalski, R. S., Carbonell, J. G. and Mitchell, T. M., eds), pp. 83–134. Los Altos, CA: Morgan Kaufman.CrossRefGoogle Scholar
Papalambros, P. 1982. Monotonicity 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
Vanderplaats, G. N. 1984. Numerical Optimization Techniques for Engineering Design: With Applications. New York: McGraw-Hill.Google Scholar
Wilde, D. J. 1986. 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
Wylie, C. R. and Barrett, L. C. 1982. Advanced Engineering Mathematics. New York: McGraw-Hill.Google Scholar
Yang, R. J. and Botkin, M. E. 1987. A modular approach for three-dimensional shape optimization of structures. AIAA Journal 25(3), 492–497.CrossRefGoogle Scholar