Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4778
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dc.contributor.authorAbbod, MF-
dc.contributor.authorMahfouf, M-
dc.contributor.authorLinkens, DA-
dc.contributor.authorSellars, CM-
dc.date.accessioned2011-03-04T10:59:00Z-
dc.date.available2011-03-04T10:59:00Z-
dc.date.issued2007-
dc.identifier.citationMaterials Science Forum, 539-543: 2449-2454en_US
dc.identifier.issn0255-5476-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/4778-
dc.descriptionThis is a post print version of the article, the official published version can be obtained from the link below.en_US
dc.description.abstractDuring the last decade Genetic Programming (GP) has emerged as an efficient methodology for teaching computers how to program themselves. This paper presents research work which utilizes GP for developing mathematical equations for the response surfaces that have been generated using hybrid modelling techniques for predicting the properties of materials under hot deformation. Collected data from the literature and experimental work on aluminium are utilized as the initial training data for the GP to develop the mathematical models under different deformation conditions and compositions.en_US
dc.description.sponsorshipFinancial support from the UK EPSRC (Engineering and Physical Sciences Research Council) under grant number GR/R70514/01 was used in this study.en_US
dc.language.isoenen_US
dc.publisherTrans Tech Publicationsen_US
dc.subjectAlloy Materialsen_US
dc.subjectGenetic Programmingen_US
dc.subjectMaterial Propertyen_US
dc.subjectModelingen_US
dc.subjectStrainen_US
dc.subjectStressen_US
dc.titleEvolutionary computing for metals properties modellingen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.4028/www.scientific.net/MSF.539-543.2449-
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Electrical Engineering Research Papers

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