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dc.contributor.authorWan, Z.
dc.contributor.authorZhang, S.
dc.contributor.authorTeo, Kok Lay
dc.date.accessioned2017-01-30T14:36:14Z
dc.date.available2017-01-30T14:36:14Z
dc.date.created2012-03-26T20:01:27Z
dc.date.issued2012
dc.identifier.citationWan, Z. and Zhang, S.J. and Teo, K.L. 2012. Two-step based sampling method for maximizing the capacity of V-belt driving in polymorphic uncertain environment. Proceedings of the Institution of Mechanical Engineers,‎ Part C: Journal of Mechanical Engineering Science. 226 (1): pp. 177-191.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/39695
dc.description.abstract

In this article, a two-step based sampling method is developed to solve the problem of maximizing the power transmission capacity in a system of V-belt driving with polymorphic uncertainties. The problem is first formulated as an optimization model, where there are a non-linear objective function and some linear or non-linear constraints associated with some parameters which are of uncertain nature. These uncertain parameters are interval parameters, random interval parameters, fuzzy parameters, or fuzzy interval parameters. To find a robust solution of the problem, the model of polymorphic uncertainty is converted into a non-linear interval programming problem; then, for a given satisfaction level, an interval solution for the original model is found by the developed two-step based sampling method. The proposed method is applied into a real-world design problem of V-belt driving, and numerical results indicate that both the model and the developed algorithm are useful to solve the maximization problem of the capacity in the system of V-belt driving.

dc.publisherProfessional Engineering Publishing Ltd..
dc.titleTwo-step based sampling method for maximizing the capacity of V-belt driving in polymorphic uncertain environment
dc.typeJournal Article
dcterms.source.volume226
dcterms.source.startPage177
dcterms.source.endPage191
dcterms.source.issn0954-4062
dcterms.source.titleJournal of Mechanical Engineering Science
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available


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