Abstract
Different types of uncertainties need to be addressed in a product design optimization process. In this paper, the uncertainties in both product design variables and environmental noise variables are considered. The reliability-based design optimization (RBDO) is integrated with robust product design (RPD) to concurrently reduce the production cost and the long-term operation cost, including quality loss, in the process of product design. This problem leads to a multi-objective optimization with probabilistic constraints. In addition, the model uncertainties associated with a surrogate model that is derived from numerical computation methods, such as finite element analysis, is addressed. A hierarchical experimental design approach, augmented by a sequential sampling strategy, is proposed to construct the response surface of product performance function for finding optimal design solutions. The proposed method is demonstrated through an engineering example.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 145-153 |
| Number of pages | 9 |
| Journal | Reliability Engineering and System Safety |
| Volume | 138 |
| DOIs | |
| State | Published - 2015 |
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Keywords
- Kriging metamodel
- optimization
- Robust design
- Sequential optimization and reliability assessment Sequential sampling
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Safety, Risk, Reliability and Quality
- Applied Mathematics
Cite this
Enhancing product robustness in reliability-based design optimization. / Zhuang, Xiaotian; Pan, Rong; Du, Xiaoping.
In: Reliability Engineering and System Safety, Vol. 138, 2015, p. 145-153.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Enhancing product robustness in reliability-based design optimization
AU - Zhuang, Xiaotian
AU - Pan, Rong
AU - Du, Xiaoping
PY - 2015
Y1 - 2015
N2 - Different types of uncertainties need to be addressed in a product design optimization process. In this paper, the uncertainties in both product design variables and environmental noise variables are considered. The reliability-based design optimization (RBDO) is integrated with robust product design (RPD) to concurrently reduce the production cost and the long-term operation cost, including quality loss, in the process of product design. This problem leads to a multi-objective optimization with probabilistic constraints. In addition, the model uncertainties associated with a surrogate model that is derived from numerical computation methods, such as finite element analysis, is addressed. A hierarchical experimental design approach, augmented by a sequential sampling strategy, is proposed to construct the response surface of product performance function for finding optimal design solutions. The proposed method is demonstrated through an engineering example.
AB - Different types of uncertainties need to be addressed in a product design optimization process. In this paper, the uncertainties in both product design variables and environmental noise variables are considered. The reliability-based design optimization (RBDO) is integrated with robust product design (RPD) to concurrently reduce the production cost and the long-term operation cost, including quality loss, in the process of product design. This problem leads to a multi-objective optimization with probabilistic constraints. In addition, the model uncertainties associated with a surrogate model that is derived from numerical computation methods, such as finite element analysis, is addressed. A hierarchical experimental design approach, augmented by a sequential sampling strategy, is proposed to construct the response surface of product performance function for finding optimal design solutions. The proposed method is demonstrated through an engineering example.
KW - Kriging metamodel
KW - optimization
KW - Robust design
KW - Sequential optimization and reliability assessment Sequential sampling
UR - http://www.scopus.com/inward/record.url?scp=84923221456&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84923221456&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2015.01.026
DO - 10.1016/j.ress.2015.01.026
M3 - Article
AN - SCOPUS:84923221456
VL - 138
SP - 145
EP - 153
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
ER -