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RESEARCH PAPERS

Robust Design for Multiscale and Multidisciplinary Applications

[+] Author and Article Information
Janet K. Allen

The Systems Realization Laboratory,  The G. W. Woodruff School of Mechanical Engineering, Georgia Tech Savannah, Savannah, GA

Carolyn Seepersad

The Mechanical Engineering Department,  The University of Texas, Austin, TX

HaeJin Choi

The Systems Realization Laboratory, The G. W. Woodruff School of Mechanical Engineering, Georgia Tech, Atlanta, GA

Farrokh Mistree1

The Systems Realization Laboratory, The G. W. Woodruff School of Mechanical Engineering, Georgia Tech Savannah, Savannah, GAfarrokh.mistree@me.gatech.edu

1

Corresponding author.

J. Mech. Des 128(4), 832-843 (Jan 30, 2006) (12 pages) doi:10.1115/1.2202880 History: Received October 17, 2005; Revised January 30, 2006

The intent in robust design is to improve the quality of products and processes by reducing their sensitivity to variations, thereby reducing the effects of variability without removing its sources. Robust design is especially useful for integrating information from designers working at multiple length and time scales. Inevitably this involves the integration of uncertain information. This uncertainty is derived from many sources and robust design may be classified based on these sources—uncertainty in noise or environmental and other noise factors (type I); uncertainty in design variables or control factors (type II); and uncertainty introduce by modeling methods (type III). Each of these types of uncertainty can be mitigated by robust design. Of particular interest are the challenges associated with the design of multidisciplinary and multiscale systems; these challenges and opportunities are examined in the context of materials design.

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Copyright © 2006 by American Society of Mechanical Engineers
Topics: Design
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References

Figures

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Figure 1

A P diagram showing information input and response in a product or process model. Robust design is classified based on the source of variability.

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Figure 2

The quality loss function and performance target for three manufactured products whose performance varies through different ranges and whose values of mean performance may or may not coincide with the desired performance

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Figure 3

Computing infrastructure for the robust concept exploration method (modified from Ref. 49)

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Figure 4

Comparing two designs with respect to a range of requirements

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Figure 5

Modes of coupling in a distributed robust design process

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Figure 6

A schematic of a two-level design process chain for robust multiscale, multiobjective design

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Figure 7

A hierarchy of length scales in complex systems and materials (59)

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Figure 8

An example of the random assignment of particle microstructure with a fixed set of input parameters (5)

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Figure 9

Propagated uncertainty in a multiscale design process

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Figure 10

Examples of processing-related variations in prismatic cellular materials. Clockwise from upper left: dimensional tolerances, porosity, cracked cell walls (80).

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