Engineering design consists of a series of stages during which a number of decisions need to be made by the designer. Since the information available to the designer is limited during initial design stages, to make these decisions and be able to proceed further in the design process, the designer needs to depict the nature, visualize the form, and predict the behavior of the product through the use of aids called models. These models guide these decisions, therefore, the designer needs to ensure the downstream validity of these decisions by constructing models with sufficient accuracy and resolution. Because higher quality and accuracy of information is most often accompanied by a higher cost for a model, determining a satisfactory level of goodness for a model is a fundamental and pervasive question in engineering. Hence, a key aspect of design model construction is deciding whether a model is appropriate for a particular design specification or evaluation, considering accuracy and cost factors. This paper presents an approach for design model construction using utility theory. Since model selection is a design decision, uncertainties in parameters and models are considered by evaluating the confidence in the selection of any model. A method for proceeding in the reverse manner to determine the required goodness of a model is also discussed. We present this research through application to a race car sway bar.

Issue Section:
Technical Papers
Keywords:
utility theory, product development, product design
Topics:
Design, Engineering design, Errors, Finite element analysis, Public utilities, Uncertainty, Construction, Stiffness, Stress, Resolution (Optics)
1.
Hazelrigg
,
G. A.
,
1999
, “
On the Role and Use of Mathematical Models in Engineering Design
,”
J. Mech. Des.
,
121
, pp.
336
–
341
.
2.
Marczyk, J., 1999, Principles of Simulation Based Computer Aided Engineering, FIM Publications, Barcelona.
3.
Ulrich, Karl T., and Eppinger, S. D., 1995, Product Design and Development, McGraw-Hill, New York.
4.
Otto
,
K.
,
1994
, “
Measurement Methods for Product Evaluation
,”
Korean J. Chem. Eng.
,
7
, pp.
86
–
101
.
5.
Otto, K., and Wood, K. L., 1995, “Estimating Errors in Concept Selection,” Proc. of ASME Design Theory and Methodology Conference, Boston, ASME, New York, pp. 397–412.
6.
Von Neumann, J., and Morgenstern, O., 1964, Theory of Games and Economic Behavior (Science Editions), 3rd Edition, Wiley, New York.
7.
Keeney, R. L., and Raiffa, H., 1993, Decisions With Multiple Objectives: Preferences and Value Tradeoffs, Cambridge University Press, New York.
8.
Thurston
,
D. L.
,
Carnahan
,
J. V.
, and
Liu
,
T.
,
1994
, “
Optimization of Design Utility
,”
J. Mech. Des.
,
116
, pp.
801
–
808
.
9.
Thurston
,
D. L.
, and
Carnahan
,
J. V.
,
1992
, “
Fuzzy Ratings and Utility Analysis in Preliminary Design Evaluation of Multiple Attributes
,”
J. Mech. Des.
,
114
, pp.
648
–
658
.
10.
Otto, K. N., and Antonsson, E. K., 1993, “The Method of Imprecision Compared to Utility Theory for Design Selection Problems,” Proc. of ASME Design Theory and Methodology Conference, Albuquerque, ASME, New York, pp. 167–173.
11.
Locascio, A., and Thurston, D. L., 1994, “Quantifying the House of Quality for Optimal Product Design,” Proc. of ASME Design Theory and Methodology Conference, Minneapolis, ASME, New York, pp. 43–54.
12.
Magnusson, S. E., 1997, “Uncertainty Analysis: Identification, Quantification and Propagation,” Report 7002, Lund University.
13.
Draper
,
D.
,
1995
, “
Assessment and Propagation of Model Uncertainty
,”
J. R. Stat. Soc. Ser. B. Methodol.
,
57
(
1
), pp.
45
–
97
.
14.
Alvin, K. F., Oberkampf, W. L., Diegert, K. V., and Rutherford, B. M., 1998, “Uncertainty Quantification in Computational Structural Dynamics: A New Paradigm for Model Validation,” Proc. of 16th Int. Modal Analysis Conference, Santa Barbara, 2, pp. 1191–1197.
15.
Doraiswamy, S., Krishnamurty, S., and Grosse, I. R., 1999, “Decision Making in Finite Element Analysis,” Proc. of ASME Design Engineering Technical Conferences, Las Vegas, ASME, New York, Paper No. DETC99/CIE-9058.
16.
Doraiswamy, S., and Krishnamurty, S., 2000, “Bayesian Analysis in Engineering Model Assessment,” Proc. of ASME Design Engineering Technical Conferences, Baltimore, ASME, New York, Paper No. DETC2000/DTM-14546.
17.
Radhakrishnan, R., and McAdams, D. A., 2001, “A Framework for Sufficiency Estimation of Engineering Design Models,” Proc. of ASME Design Engineering Technical Conferences, Pittsburgh, ASME, New York, Paper No. 2001-DETC/DTM-21699.
18.
Shigley, J. E., and Mischke, C. R., 2001, Mechanical Engineering Design, 6th Edition, McGraw-Hill, New York.
19.
Sveshnikov, A., 1968, Problems in Probability Theory, Mathematical Statistics, and the Theory of Random Functions, Dover Publications, New York.
20.
McAdams, D. A., and Wood, K. L., 2000, “Theoretical Foundations for Tuning Parameter Tolerance Design,” Proc. of ASME Design Engineering Technical Conferences, Baltimore, ASME, New York, Paper No. DETC2000/DFM-14003.
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