Abstract
Themethod of imprecision is a design method whereby a multi-objective design problem is resolved by maximizing the overall degree ofdesigner preference: values are iteratively selected based on combining the degree of preference placed on them. Consider, however, design problems that exhibit multiple uncertainty forms (noise). In addition to degrees of preference(imprecision) there areprobabilistic uncertainties caused by, for example, measuring and fabrication limitations. There are also parameters that can take on any valuepossible within a specified range, such as a manufacturing or tuning adjustment. Finally, there may be parameters which mustnecessarily satisfly all values within the range over which they vary, such as a horsepower requirement over a motor's different speeds. This paper defines a “best” set of design parameters for design problems with such multiple uncertainty forms and requirements.
Similar content being viewed by others
References
G. E. Box.Statistics for Experimenters. J. Wiley, New York, 1978.
R. T. Cox.The Algebra of Probable Inference. Johns Hopkins University Press, Baltimore, Maryland, 1961.
D. Dubois and H. Prade.Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, 1980.
D. Dubois and H. Prade. A class of fuzzy measures based on triangular norms.International Journal of General Systems, 8: 43–61, 1982.
D. Dubois and H. Prade.Possibility Theory: An Approach to the Computerized Processing of Information. Plenum Press, New York, 1988.
P. Halmos.Measure Theory. Springer-Verlag, New York, 1974.
D. Krantz, R. Luce, P. Suppes and A. Tversky.Foundations of Measurement, volume I. Academic Press, New York, 1971.
K. N. Otto and E. K. Antonsson. Trade-Off Strategies in Engineering Design.Research in Engineering Design, 3(2): 87–104, 1991.
K. N. Otto and E. K. Antonsson.Extensions to the Taguchi Method of Product Design.ASME Journal of Mechanical Design, 115(1): 5–13, 1993.
K. N. Otto and E. K. Antonsson. Tuning Parameters in Engineering Design.ASME Journal of Mechanical Design, 115(1): 14–19, 1993.
P. Papalambros and D. Wilde.Principles of Optimal Desing. Cambridge University Press, New York, 1988.
H. Royden.Real Analysis. Macmillan, New York, 1988.
J. N. Siddall,Probabilistic Engineering Design: Principles and Applications. Marcel Dekker, New York, 1983.
M. Sugeno. Theory of fuzzy integrals and its applications. Ph.D. thesis, Tokyo Institute of Technology, 1974.
M. Sugeno. Fuzzy measures and fuzzy integrals — a survey. In M. Gupta et al, editors,Fuzzy Automata and Decision Processes, pages 89–102. North-Holland, New York, 1977.
A. Sveshnikov.Problems in Probability Theory, Mathematical Statistics, and the Theory of Random Functions. Dover Publications, New York, 1968.
G. Taguchi.Introduction to Quality Engineering. Asian Productivity Organization, Unipub, White Plains, NY, 1986.
M. Tribus.Rational Descriptions, Decisions, and Designs. Pergamon Press, New York, 1969.
A. Ward.A Theory of Quantitative Inference for Artifact Sets, Applied to a Mechanical Design Compiler. Ph.D. thesis, MIT, 1989.
A. C. Ward and W. P. Seering. Extending the constraint propagation of intervals.Artificial Intelligence in Engineering Design and Manufacturing, 4(1): 47–51, 1990.
I. Wilson and M. Wilson.From Idea to Working Model. J. Wiley, New York, 1970.
K. L. Wood and E. K. Antonsson. Computations with Imprecise Parameters in Engineering Design: Background and Theory.ASME Journal of Mechanisms, Transmissions, and Automation in Design, 111(4): 616–25, December 1989.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Otto, K.N., Antonsson, E.K. Design parameter selection in the presence of noise. Research in Engineering Design 6, 234–246 (1994). https://doi.org/10.1007/BF01608402
Issue Date:
DOI: https://doi.org/10.1007/BF01608402