NDLI: Probabilistic analytical target cascading combined with Kriging metamodel for multidisciplinary robust design optimization

Content Provider	IEEE Xplore Digital Library
Author	Fengxiang Wang Mi Xiao Liang Gao
Copyright Year	2015
Description	Author affiliation: State Key Lab. of Digital Manuf. Equip. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China (Fengxiang Wang; Mi Xiao; Liang Gao)
Abstract	Robust design under a multidisciplinary environment is a challenging problem in modern quality engineering because of the organizational and computational burden. In this paper, a probabilistic analytical target cascading (PATC) methodology combined with Kriging metamodel (PATC-KRG) is proposed for robust design of engineering systems considering both objective robustness and feasibility robustness. PATC is an effective methodology for hierarchical multilevel optimization under uncertainty. However, when mathematical models are complex and highly nonlinear, especially when simulations are used, multidisciplinary robust design optimization (MRDO) will be highly computationally expensive. To obtain better computational efficiency, in the PATC-KRG method, Kriging metamodels are used to replace the complicated models, and an exponential penalty function (EPF) formulation is employed for target-response coordination in PATC. A geometric programming problem is used to demonstrate the feasibility and computational efficiency of the PATC-KRG method. Results indicate that the PATC-KRG method can achieve a higher efficiency in support of MRDO.
Starting Page	1407
Ending Page	1411
File Size	1785273
Page Count	5
File Format	PDF
e-ISBN	9781467380669
DOI	10.1109/IEEM.2015.7385879
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Computational modeling Probabilistic logic robust design Robustness exponential penalty function Joining processes Probabilistic analytical target cascading Kriging Design optimization
Content Type	Text
Resource Type	Article

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