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http://dx.doi.org/10.3795/KSME-A.2012.36.11.1305

Efficient Robust Design Optimization Using Statistical Moment Based on Multiplicative Decomposition Considering Non-normal Noise Factors  

Cho, Su-Gil (Dept. of Automotive Engineering, Hanyang Univ.)
Lee, Min-Uk (Dept. of Automotive Engineering, Hanyang Univ.)
Lim, Woo-Chul (Dept. of Automotive Engineering, Hanyang Univ.)
Choi, Jong-Su (Dept. of Automotive Engineering, Hanyang Univ.)
Kim, Hyung-Woo (Ocean System Engineering Research Division, Korea Institute of Ocean Science & Technology)
Hong, Sup (Ocean System Engineering Research Division, Korea Institute of Ocean Science & Technology)
Lee, Tae-Hee (Dept. of Automotive Engineering, Hanyang Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers A / v.36, no.11, 2012 , pp. 1305-1310 More about this Journal
Abstract
The performance of a system can be affected by the variance of noise factors, which arise owing to uncertainties of the material properties and environmental factors acting on the system. For robust design optimization of the system performance, it is necessary to minimize the effect of the variance of the noise factors that are impossible to control. However, present robust design techniques consider the variation of design factors, and not the noise factors, as being important. Furthermore, it is necessary to assume a normal distribution; however, a normal distribution is often not suitable to estimate the variations. In this study, a robust design technique is proposed to consider the variation of noise factors that are estimated as non-normal distributions in a real experiment. As an example of an engineering problem, a deep-sea manganese nodule miner tracked vehicle is used to demonstrate the feasibility of the proposed method.
Keywords
Robust Design Optimization; Deep-Sea Ocean Mining Vehicle; Multiplicative Decomposition Method; Kriging Metamodel;
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