0
Research Papers

Practical Implementation of Robust Design Assisted by Response Surface Approximation and Visual Data-Mining

[+] Author and Article Information
Koji Shimoyama1

Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japanshimoyama@edge.ifs.tohoku.ac.jp

Jin Ne Lim

Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japanlim@edge.ifs.tohoku.ac.jp

Shinkyu Jeong

Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japanjeong@edge.ifs.tohoku.ac.jp

Shigeru Obayashi

Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japanobayashi@ifs.tohoku.ac.jp

Masataka Koishi

CAE Laboratory, Yokohama Rubber Co., Ltd., 2-1 Oiwake, Hiratsuka, Kanagawa 254-8601, Japankoishi@hpt.yrc.co.jp

1

Corresponding author.

J. Mech. Des 131(6), 061007 (May 19, 2009) (11 pages) doi:10.1115/1.3125207 History: Received June 28, 2008; Revised March 02, 2009; Published May 19, 2009

A new approach for multi-objective robust design optimization was proposed and applied to a practical design problem with a large number of objective functions. The present approach is assisted by response surface approximation and visual data-mining, and resulted in two major gains regarding computational time and data interpretation. The Kriging model for response surface approximation can markedly reduce the computational time for predictions of robustness. In addition, the use of self-organizing maps as a data-mining technique allows visualization of complicated design information between optimality and robustness in a comprehensible two-dimensional form. Therefore, the extraction and interpretation of trade-off relationships between optimality and robustness of design, and also the location of sweet spots in the design space, can be performed in a comprehensive manner.

FIGURES IN THIS ARTICLE
<>
Copyright © 2009 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Comparison between deterministic optimization and robust optimization (in minimization problem)

Grahic Jump Location
Figure 2

Flowchart of multi-objective robust design optimization process

Grahic Jump Location
Figure 3

Kriging model (in minimization problem)

Grahic Jump Location
Figure 4

Self-organizing map

Grahic Jump Location
Figure 5

Automobile tire structure

Grahic Jump Location
Figure 6

Cross validation of Kriging-based RSs for tire stiffnesses: (a) Ex, (b) Ey, and (c) Ez

Grahic Jump Location
Figure 7

Nondominated solutions obtained through deterministic optimization (shown as values estimated by RSs): (a) (σEy∕μEy) versus μEy and (b) (σEz∕μEz) versus μEz

Grahic Jump Location
Figure 8

Nondominated solutions obtained through robust optimization (shown as values estimated by RSs): (a) (σEy∕μEy) versus μEy and (b) (σEz∕μEz) versus μEz

Grahic Jump Location
Figure 9

SOMs of nondominated solutions obtained through deterministic optimization (shown as values estimated by RSs): (a) Ex, (b) Ey, and (c) Ez

Grahic Jump Location
Figure 10

SOMs of nondominated solutions obtained through robust optimization (shown as values estimated by RSs): (a) μEx, (b) μEy, (c) σEy∕μEy, (d) μEz, and (e) σEz∕μEz

Grahic Jump Location
Figure 11

Profiles of tire stiffness values against the variation of pint for nondominated solutions obtained through deterministic optimization and robust optimization (shown as real values calculated by ABAQUS ): (a) Ex versus pint, (b) Ey versus pint, and (c) Ez versus pint

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In