Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (TRB) structures

Sun, G; Zhang, H; Fang, J; Li, G; Li, Q

Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (TRB) structures

Sun, G Zhang, H Fang, J

Li, G Li, Q

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Publication Type:: Journal Article
Citation:: Structural and Multidisciplinary Optimization, 2017, 55 (5), pp. 1899 - 1916
Issue Date:: 2017-05-01

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Field	Value	Language
dc.contributor.author	Sun, G	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Fang, J https://orcid.org/0000-0003-0119-6108	en_US
dc.contributor.author	Li, G	en_US
dc.contributor.author	Li, Q	en_US
dc.date.issued	2017-05-01	en_US
dc.identifier.citation	Structural and Multidisciplinary Optimization, 2017, 55 (5), pp. 1899 - 1916	en_US
dc.identifier.issn	1615-147X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125361
dc.description.abstract	© 2016, Springer-Verlag Berlin Heidelberg. Light weight and crashworthiness signify two main challenges facing in vehicle industry, which often conflict with each other. In order to achieve light weight while improving crashworthiness, tailor rolled blank (TRB) has become one of the most potential lightweight technologies. To maximize the characteristics of TRB structures, structural optimization has been adopted extensively. Conventional optimization studies have mainly focused on a single loading case (SLC). In practice, however, engineering structures are often subjected to multiple loading cases (MLC), implying that the optimal design under a certain condition may no longer be an optimum under other loading cases. Furthermore, traditional deterministic optimization could become less meaningful or even unacceptable when uncertainties of design variables and noises of system parameters are present. To address these issues, a multi-objective and multi-case reliability-based design optimization (MOMCRBDO) was developed in this study to optimize the TRB hat-shaped structure. The radial basis function (RBF) metamodel was adopted to approximate the responses of objectives and constraints, the non-dominated sorting genetic algorithm II (NSGA-II), coupled with Monte Carlo Simulation (MCS), was employed to seek optimal reliability solutions. The optimal results show that the proposed method is not only capable of improving the reliability of Pareto solutions, but also enhancing the robustness under MLC.	en_US
dc.relation.ispartof	Structural and Multidisciplinary Optimization	en_US
dc.relation.isbasedon	10.1007/s00158-016-1592-1	en_US
dc.subject.classification	Design Practice & Management	en_US
dc.title	Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (TRB) structures	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	55	en_US
utslib.for	0902 Automotive Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	55	en_US

Abstract:

© 2016, Springer-Verlag Berlin Heidelberg. Light weight and crashworthiness signify two main challenges facing in vehicle industry, which often conflict with each other. In order to achieve light weight while improving crashworthiness, tailor rolled blank (TRB) has become one of the most potential lightweight technologies. To maximize the characteristics of TRB structures, structural optimization has been adopted extensively. Conventional optimization studies have mainly focused on a single loading case (SLC). In practice, however, engineering structures are often subjected to multiple loading cases (MLC), implying that the optimal design under a certain condition may no longer be an optimum under other loading cases. Furthermore, traditional deterministic optimization could become less meaningful or even unacceptable when uncertainties of design variables and noises of system parameters are present. To address these issues, a multi-objective and multi-case reliability-based design optimization (MOMCRBDO) was developed in this study to optimize the TRB hat-shaped structure. The radial basis function (RBF) metamodel was adopted to approximate the responses of objectives and constraints, the non-dominated sorting genetic algorithm II (NSGA-II), coupled with Monte Carlo Simulation (MCS), was employed to seek optimal reliability solutions. The optimal results show that the proposed method is not only capable of improving the reliability of Pareto solutions, but also enhancing the robustness under MLC.

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http://hdl.handle.net/10453/125361