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

Multidisciplinary design optimization (MDO) has been applied widely in the design of complex engineering systems. To ease MDO problems, analytical target cascading (ATC) organizes MDO process into multilevels according to the components of engineering systems, which provides a promising way to deal with MDO problems. ATC adopts a coordination strategy to coordinate the couplings between two adjacent levels in the design optimization process; however, existing coordination strategies in ATC face the obstacles of complicated coordination process and heavy computation cost. In order to conquer this problem, a quadratic exterior penalty function (QEPF) based ATC (QEPF-ATC) approach is proposed, where QEPF is adopted as the coordination strategy. Moreover, approximate models are adopted widely to replace the expensive simulation models in MDO; a QEPF-ATC and Kriging model combined approach is further proposed to deal with MDO problems, owing to the comprehensive performance, high approximation accuracy, and robustness of Kriging model. Finally, the geometric programming and reducer design cases are given to validate the applicability and efficiency of the proposed approach.

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Robust Design Optimization for Reducing Cogging Torque of a BLDC Motor through an Enhanced Taguchi Method

Journal of the Korean Magnetics Society ◽

10.4283/jkms.2014.24.5.160 ◽

2014 ◽

Vol 24 (5) ◽

pp. 160-164 ◽

Cited By ~ 1

Author(s):

Chang-Uk Lee ◽

Dong-Wook Kim ◽

Dong-Hun Kim

Keyword(s):

Taguchi Method ◽

Design Optimization ◽

Robust Design ◽

Robust Design Optimization ◽

Bldc Motor ◽

Cogging Torque

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Robust Design Optimization for Crashworthiness of Vehicle Side Impact

ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg ◽

10.1115/1.4035439 ◽

2017 ◽

Vol 3 (3) ◽

Cited By ~ 8

Author(s):

Souvik Chakraborty ◽

Tanmoy Chatterjee ◽

Rajib Chowdhury ◽

Sondipon Adhikari

Keyword(s):

Design Optimization ◽

New Product Development ◽

Robust Design ◽

Automobile Industry ◽

Human Error ◽

Differential Evolution Algorithm ◽

Side Impact ◽

Robust Design Optimization ◽

Parameter Uncertainties ◽

Design Engineers

Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, a deterministic optimum design often resides at the boundary of failure domain, leaving little or no room for modeling imperfections, parameter uncertainties, and/or human error. In this study, an operational model-based robust design optimization (RDO) scheme has been developed for designing crashworthiness of vehicle against side impact. Within this framework, differential evolution algorithm (DEA) has been coupled with polynomial correlated function expansion (PCFE). An adaptive framework for determining the optimum basis order in PCFE has also been presented. It is argued that the coupled DEA–PCFE is more efficient and accurate, as compared to conventional techniques. For RDO of vehicle against side impact, minimization of the weight and lower rib deflection of the vehicle are considered to be the primary design objectives. Case studies by providing various emphases on the two objectives have also been performed. For all the cases, DEA–PCFE is found to yield highly accurate results.

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