Reliability-based design optimization of knuckle component using conservative method of moving least squares meta-models

Chang Yong Song, Jongsoo Lee

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

This paper discusses reliability-based design optimization (RBDO) of an automotive knuckle component under bump and brake loading conditions. The probabilistic design problem is to minimize the weight of a knuckle component subject to stresses, deformations, and frequency constraints in order to meet the given target reliability. The initial design is generated based on an actual vehicle specification. The finite element analysis is conducted using ABAQUS, and the probabilistic optimal solutions are obtained via the moving least squares method (MLSM) in the context of approximate optimization. For the meta-modeling of inequality constraint functions, a constraint-feasible moving least squares method (CF-MLSM) is used in the present study. The method of CF-MLSM based RBDO has been shown to not only ensure constraint feasibility in a case where the meta-model-based RBDO process is employed, but also to require low expense, as compared with both conventional MLSM and non-approximate RBDO methods.

Original languageEnglish
Pages (from-to)364-379
Number of pages16
JournalProbabilistic Engineering Mechanics
Volume26
Issue number2
DOIs
Publication statusPublished - 2011 Apr

Bibliographical note

Funding Information:
Haftka RT. Using bootstrap methods for conservative estimations of probability of failure. In: Proceedings of 2006 NSF design, service, and manufacturing grantees and research conference. NSF grant no. 0423280. 2006.

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Civil and Structural Engineering
  • Nuclear Energy and Engineering
  • Condensed Matter Physics
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Reliability-based design optimization of knuckle component using conservative method of moving least squares meta-models'. Together they form a unique fingerprint.

Cite this