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TECHNICAL PAPERS

Decision-Based Conceptual Design: Modeling and Navigating Heterogeneous Design Spaces

[+] Author and Article Information
William H. Wood

Department of Mechanical Engineering, University of Maryland, Baltimore, Baltimore, MD 21250e-mail: bwood@umbc.edu

Alice M. Agogino

Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720e-mail: aagogino@me.berkeley, edu

J. Mech. Des 127(1), 2-11 (Mar 02, 2005) (10 pages) doi:10.1115/1.1799612 History: Received February 10, 2003; Revised March 01, 2004; Online March 02, 2005
Copyright © 2005 by ASME
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References

Figures

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Engineering model generated using DBCD. Contour plot shows joint probability density, overlays show model abstractions. The dashed line uses a smoothing factor (b) of 0.01, the dot-dash line 0.05. Regression line overlaid has R2 of 0.635. Underlying data is shown as “⋅” marks.
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Performance and classification variables for motor components. Three decisions have been made: (i) To use a component; (ii) The component will be a rotational motor; and (iii) The motor will operate on dc power. Each decision has added variables to the design model. The objective minimizes motor mass with a constraint of 45 W on rated power. Current decisions are: frame type, commutation type, and magnet type. Depending on these decisions, the design model can expand to include new performance variables.
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Diagram of the IRTD method showing the typical design loop in which a designer provides approximate models, variables, and objectives that, along with classification decisions, direct the design process
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Probabilistic engineering model for motor catalog, after the constraint of 45 W has been applied. Because of the commitment, only four design parameters are “free” variables. Expected value of weight is overlaid as a dashed line on the probability density contours.
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Expected value decision making for various classification variables. The probability densities of motor weight given a power constraint of 45 W all overlap. Frameless, rare earth, and brushless produce the best expected value of weight for each classification variable. Among all classifications, brushless are the lightest. Considerable overlap among the pdf’s means that further information could help clarify the current decision.
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Decision making based on motor torque. The top left shows the expected value of weight plotted against torque; the distribution of motor torques in the constrained domain is overlaid (dot-dash, not to scale). The optimal decision over all choices (denoted by a “* ” overlaying the expected value plot) is a brushless motor.
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Decision making based on motor speed. The top-left plot shows the expected value of weight plotted against speed; probability of speed is overlaid (dot-dash, not to scale). The optimal decision (“* ”) changes from brushless below 500 rps to frameless above.
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Decision making based on motor length. The top-left plot shows the expected value of weight plotted against length; probability of length is overlaid (dot-dash, not to scale). The optimal decision (“* ”) changes from frameless for short motors to brushless for most other lengths.
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Decision making based on motor diameter. The top-left plot shows the expected value of weight plotted against diameter; probability of diameter is overlaid (dot-dash, not to scale). As in the length variable, the optimal decision (* ) changes from frameless for small motors to brushless larger ones.

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