Creep modelling of polypropylenes using artificial neural networks trained with Bee algorithms

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dc.authoridEsen, Ismail/0000-0002-7853-1464
dc.authoridAydin, Mehmet/0000-0002-4890-5648
dc.contributor.authorDugenci, Muharrem
dc.contributor.authorAydemir, Alpay
dc.contributor.authorEsen, Ismail
dc.contributor.authorAydin, Mehmet Emin
dc.date.accessioned2024-09-29T15:55:21Z
dc.date.available2024-09-29T15:55:21Z
dc.date.issued2015
dc.departmentKarabük Üniversitesien_US
dc.description.abstractPolymeric materials, being capable of high mouldability, usability of long lifetime up to 50 years and availability at low cost properties compared to metallic materials, are in demand but finite element-based design engineers have limited means in terms of the limited material data and mathematical models. In particular, in the analysis of products with complex geometry, the stresses and strains of various amounts formed in the product should be known and evaluated in terms of a precise design of the product to fulfil life expectancy. Due to time and cost constraints, experimental data cannot be available for all cases required in analysis, therefore, finite element method-based simulations are commonly used by design engineers. This is also computationally expensive and requires a simpler and more precise way to complete the design more realistically. In this study, the whole creep behaviour of polypropylene for all stresses were obtained with 10% accuracy errors by artificial neural networks trained using existing experimental test results of the materials for a particular working range. The artificial neural network model was trained with traditional as well as heuristic based methods. It is demonstrated that heuristically trained ANN models have provided much accurate and precise results, which are in line with 10% accuracy of experimental data. (C) 2015 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.engappai.2015.06.016
dc.identifier.endpage79en_US
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-84941093646en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage71en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2015.06.016
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4607
dc.identifier.volume45en_US
dc.identifier.wosWOS:000362130500006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCreepen_US
dc.subjectPolypropyleneen_US
dc.subjectArtificial neural networksen_US
dc.subjectBees algorithmsen_US
dc.subjectHeuristically trained neural networksen_US
dc.titleCreep modelling of polypropylenes using artificial neural networks trained with Bee algorithmsen_US
dc.typeArticleen_US

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