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

• Published in: Probabilistic engineering mechanics Vol. 26; no. 2; pp. 364 - 379
• Main Authors: Song, Chang Yong, Lee, Jongsoo
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2011; Elsevier
• Subjects: Applied sciences; Automotive components; Automotive engineering; Automotive knuckle; Constraint-Feasible Moving Least Squares Method (CF-MLSM); Design engineering; Design optimization; Drives; Exact sciences and technology; Finite element method; Least squares method; Mechanical engineering. Machine design; Moving Least Squares Method (MLSM); Operational research and scientific management; Operational research. Management science; Optimization; Probabilistic methods; Probability theory; Reliability Based Design Optimization (RBDO); Reliability theory. Replacement problems; Shafts, couplings, clutches, brakes; Constraint-Feasible Moving Least Squares Method (CF-MLSM); Automotive knuckle; Reliability Based Design Optimization (RBDO); Moving Least Squares Method (MLSM); Motor car; Probabilistic approach; Specification; Ball joint; Humped road; Modeling; Optimization; Weight; Optimal design; Metaanalysis; Engineering design; Constraint-Feasible Moving Least Squares; Least squares method; Inequality constraint; Feasibility; Method (CF-MLSM); Reliability; Brake; Metamodel
• ISSN: 0266-8920; 1878-4275
• DOI: 10.1016/j.probengmech.2010.09.004

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.