2016
DOI: 10.1177/0954406216654938
|View full text |Cite
|
Sign up to set email alerts
|

Simulation-based robust design of complex product considering uncertainties of metamodel, design variables, and noise parameters

Abstract: The uncertainties of design variables, noise parameters, and metamodel are important factors in simulation-based robust design optimization. Most conventional metamodel construction methods only consider one or two uncertainties. In this paper, a new surrogate modeling method simultaneously measuring all the uncertainties is proposed for simulation-based robust design optimization of complex product. The effect of metamodel uncertainty on product performance uncertainty is quantified through uncertainty propag… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
5
0

Year Published

2017
2017
2021
2021

Publication Types

Select...
6

Relationship

1
5

Authors

Journals

citations
Cited by 6 publications
(5 citation statements)
references
References 35 publications
0
5
0
Order By: Relevance
“…The expression of parallelism is y = a 1 x + a 0 (x A 4x4x B ). The ratio of projection length of segment AX to segment AB on the x-axis is t. Then y value at any point X on this sideline can be associated with y values of points A and B, as in equation (2). If y values of endpoints A and B satisfy normal distribution, the y-value distribution of point X can be obtained by equation (3)…”
Section: Discrete Statistical Expression Of Part Surface Error Characmentioning
confidence: 99%
See 2 more Smart Citations
“…The expression of parallelism is y = a 1 x + a 0 (x A 4x4x B ). The ratio of projection length of segment AX to segment AB on the x-axis is t. Then y value at any point X on this sideline can be associated with y values of points A and B, as in equation (2). If y values of endpoints A and B satisfy normal distribution, the y-value distribution of point X can be obtained by equation (3)…”
Section: Discrete Statistical Expression Of Part Surface Error Characmentioning
confidence: 99%
“…Therefore, a uniform assembly variation model is demanded to predict complicated product assembly variation for rigid–flexible tolerance optimization. 2…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…[5][6][7] Various metamodels have been extensively employed in conjunction with MCS to support UQ implementations. These metamodels are response surface model (RSM), 8 artificial neural network (ANN), 9 radial basis function (RBF), 10 polynomial chaos expansion (PCE), 11 Kriging, 12,13 support vector regression (SVR), 14,15 etc. However, the widespread applications of such metamodel-assisted UQ methods is impeded due to the increasing complexity of realistic engineering problems.…”
Section: Introductionmentioning
confidence: 99%
“…Nejlaoui et al 16 proposed a hybrid NSGA-II and MCS method for the robust design of rail vehicles. Liu et al 17 proposed an approach to quantify the metamodeling uncertainty propagated across multiple sub-models of a multi-level system and then improved the global fidelity of metamodels in the multi-level system's design. By applying Lyapunov approach, Zhou et al 18 presented a robust and multiobjective optimization of elliptical-orbit target under DPs' uncertainty.…”
Section: Introductionmentioning
confidence: 99%