Reliability-based design optimization of an automotive structure using a variable uncertainty. (September 2016)

• Record Type: Journal Article
• Authors: Lim, Woochul; Jang, Junyong; Kim, Shinyu; Lee, Tae Hee; Kim, Jungho; Lee, Kyungwon; Lee, Changkun; Kim, Yongsuk
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 230:Number 10(2016:Oct.)
• Page Start: 1314
• Page End: 1323
• Publication Date: 2016-09
• Subjects: Akaike information criterion -- body-in-white -- engine cradle -- reliability-based design optimization -- tolerance guide -- variable uncertainty; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407015606825
• Languages: English
• ISSNs: 0954-4070

Abstract

Reliability-based design optimization is an optimization technique based on the stochastic approach. Many studies using this approach assume the uncertainty in the design variable to be constant. However, when the uncertainty depends on the values of the design variable, this assumption results in the wrong conclusions. Therefore, the uncertainty should be considered as a variable in reliability-based design optimization. The uncertainty in the thickness during optimization, such as the tolerance, had been assumed to be a constant in automotive structures. However, in practice, the tolerance of the thickness depends on the nominal thickness. Hence, in this paper, reliability-based design optimization of an automotive structure such as an engine cradle and a body-in-white with a variable uncertainty is carried out. General Motors Korea provides the tolerance guide which defines the dependence between the nominal thickness and the tolerance. The information is adopted to define the variable uncertainty. Thus, the variable uncertainty can modify the uncertainty with respect to the design point, resulting in an accurate reliability estimation. Finally, reliability-based design optimization with a variable uncertainty is performed using the Akaike information criterion method which determines the fittest distribution of the performance based on the maximum likelihood estimation of the candidate distributions. Consequently, the automotive structures are optimized to reduce the mass while still satisfying the target reliabilities of the performances when considering a variable uncertainty.