Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 610071-6100711 |
| Number of pages | 5490641 |
| Journal | Journal of Mechanical Design, Transactions of the ASME |
| Volume | 131 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2009 Jun 1 |
ASJC Scopus subject areas
- Mechanics of Materials
- Mechanical Engineering
- Computer Science Applications
- Computer Graphics and Computer-Aided Design