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Robust design optimization by spline dimensional decomposition
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DOI：10.1016/j.probengmech.2022.103218.png)
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摘要
En 中文
This article highlights new spline-empowered computational methods for solving robust design optimization (RDO) problems in complex mechanical systems. The methods are predicated on a spline dimensional decomposition (SDD) of a high-dimensional, discontinuous, or nonsmooth stochastic response for statistical moment analysis, a novel fusion of SDD and score functions for calculating the second-moment sensitivities with respect to the design variables, and standard gradient-based optimization algorithms. New closed-form formulae are derived for the design sensitivities that are concurrently determined along with the moments. The methods depend on how the statistical moment and sensitivity analyses are engaged with an optimization algorithm, engendering direct and multi-point single-step design processes. Numerical results reveal that the proposed methods deliver accurate and computationally efficient optimal solutions to RDO problems, including an industrial-scale shape design of a robotic gripper jaw.
Keyword:
RDO
Second-moment analysis
SDD
Orthonormalized B-splines
ANOVA dimensional decomposition
Score functions
Stochastic optimization
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