Fatigue optimization with combined ensembles of surrogate modeling for a truck cab

Fang, J; Gao, Y; Sun, G; Xu, C; Li, Q

Fatigue optimization with combined ensembles of surrogate modeling for a truck cab

Fang, J

Gao, Y Sun, G Xu, C Li, Q

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Publication Type:: Journal Article
Citation:: Journal of Mechanical Science and Technology, 2014, 28 (11), pp. 4641 - 4649
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Fang, J https://orcid.org/0000-0003-0119-6108	en_US
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Sun, G	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Li, Q	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Journal of Mechanical Science and Technology, 2014, 28 (11), pp. 4641 - 4649	en_US
dc.identifier.issn	1738-494X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118831
dc.description.abstract	© 2014, The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg. In this study, we propose the ensembles of surrogates for an industrial application of fatigue optimization problem that aims to maximize a truck cab’s fatigue life. After validating the numerical model, different ensembles of surrogates comprised polynomial response surface (PRS), radial basis function (RBF) and Kriging (KRG) models are established to approximate the fatigue life function. A hybrid PSO algorithm, which integrates the standard PSO with sequential quadratic programming (SQP), is implemented here to seek a quasiglobal optimum. Compared with individual surrogates, the ensembles of surrogates can attain more competent optima and yield a smaller surrogate error at the optimal point. Moreover, the hybrid PSO technique proves to search the better optima than the standard PSO in the fatigue optimization problem considered. Finally, it is found that a more accurate surrogate model may not necessarily produce a better optimum for the ensembles of surrogates, thus multiple ensembles are recommended without increasing much extra computational cost.	en_US
dc.relation.ispartof	Journal of Mechanical Science and Technology	en_US
dc.relation.isbasedon	10.1007/s12206-014-1032-3	en_US
dc.subject.classification	Mechanical Engineering & Transports	en_US
dc.title	Fatigue optimization with combined ensembles of surrogate modeling for a truck cab	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	28	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0102 Applied Mathematics	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	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 2014, The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg. In this study, we propose the ensembles of surrogates for an industrial application of fatigue optimization problem that aims to maximize a truck cab’s fatigue life. After validating the numerical model, different ensembles of surrogates comprised polynomial response surface (PRS), radial basis function (RBF) and Kriging (KRG) models are established to approximate the fatigue life function. A hybrid PSO algorithm, which integrates the standard PSO with sequential quadratic programming (SQP), is implemented here to seek a quasiglobal optimum. Compared with individual surrogates, the ensembles of surrogates can attain more competent optima and yield a smaller surrogate error at the optimal point. Moreover, the hybrid PSO technique proves to search the better optima than the standard PSO in the fatigue optimization problem considered. Finally, it is found that a more accurate surrogate model may not necessarily produce a better optimum for the ensembles of surrogates, thus multiple ensembles are recommended without increasing much extra computational cost.

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http://hdl.handle.net/10453/118831