A set-based approach is presented for solving multi-scale or multi-level design problems. The approach incorporates Bayesian network classifiers (BNC) for mapping design spaces at each level and flexibility metrics for intelligently narrowing the design space as the design process progresses. The approach is applied to a hierarchical composite materials design problem, specifically, the design of composite materials with macroscopic mechanical stiffness and loss properties surpassing those of conventional composites. This macroscopic performance is achieved by embedding small volume fractions of negative stiffness (NS) inclusions in a host material. To design these materials, the set-based, multilevel design approach is coupled with a hierarchical modeling strategy that spans several scales, from the behavior of microscale NS inclusions to the effective properties of a composite material containing those inclusions and finally to the macroscopic performance of components. The approach is shown to increase the efficiency of multi-level design space exploration, and it is particularly appropriate for top-down, performance-driven design, as opposed to bottom-up, trial-and-error modeling. The design space mappings also build intuitive knowledge of the problem and promising regions of the design space, such that it is almost trivial to identify designs that yield preferred system-level performance.
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ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 17–20, 2014
Buffalo, New York, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-4632-2
PROCEEDINGS PAPER
Bayesian Network Classifiers and Design Flexibility Metrics for Set-Based, Multiscale Design With Materials Design Applications
Jordan Matthews,
Jordan Matthews
The University of Texas at Austin, Austin, TX
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Timothy Klatt,
Timothy Klatt
The University of Texas at Austin, Austin, TX
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Carolyn C. Seepersad,
Carolyn C. Seepersad
The University of Texas at Austin, Austin, TX
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Michael Haberman,
Michael Haberman
The University of Texas at Austin, Austin, TX
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David Shahan
David Shahan
HRL Laboratories, Malibu, CA
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Jordan Matthews
The University of Texas at Austin, Austin, TX
Timothy Klatt
The University of Texas at Austin, Austin, TX
Carolyn C. Seepersad
The University of Texas at Austin, Austin, TX
Michael Haberman
The University of Texas at Austin, Austin, TX
David Shahan
HRL Laboratories, Malibu, CA
Paper No:
DETC2014-34436, V02BT03A008; 14 pages
Published Online:
January 13, 2015
Citation
Matthews, J, Klatt, T, Seepersad, CC, Haberman, M, & Shahan, D. "Bayesian Network Classifiers and Design Flexibility Metrics for Set-Based, Multiscale Design With Materials Design Applications." Proceedings of the ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2B: 40th Design Automation Conference. Buffalo, New York, USA. August 17–20, 2014. V02BT03A008. ASME. https://doi.org/10.1115/DETC2014-34436
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