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Attaining material sustainability by incorporating nanoparticles additives to improve the mechanical properties of polypropylene composites: Data driven modelling

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dc.contributor.authorAlsaffar M.A.en_US
dc.contributor.authorAli J.M.en_US
dc.contributor.authorAbdel Ghany M.A.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorid57210601717en_US
dc.contributor.authorid57197302318en_US
dc.contributor.authorid57215843327en_US
dc.contributor.authorid56862160400en_US
dc.date.accessioned2023-05-29T09:06:54Z
dc.date.available2023-05-29T09:06:54Z
dc.date.issued2021
dc.descriptionAdditives; Forecasting; Graphene; Graphene Nanoplatelets; Neural networks; Planning; Plastic products; Support vector machines; Sustainable development; Tensile strength; Toughness; Cable insulation; Data driven modelling; Effect of parameters; Hybrid support vector machines; Maleic anhydride grafted polypropylene; Nanoparticles additives; Polypropylene composite; Research interests; Polypropylenesen_US
dc.description.abstractPolypropylene is commonly employed in several industrial applications such as packaging, cable insulation and automotive. Research interest has focused on how to improve its mechanical properties to reduce the effect of low impact toughness of polypropylene. One of the sustainable ways to achieve this is by incorporating graphene nanoplatelets to form a composite. This study investigates the application of a hybrid support vector machine (SVM) and artificial neural networks (ANN) model to predict the effect of incorporating graphene on the mechanical properties of polyproline composites. The effect of parameters such as maleic anhydride grafted polypropylene (MAPP), Talc, and exfoliated graphene nanoplatelets on the tensile strength and modulus of the polypropylene composites was modelled by using ANN. Testing various topologies was accomplished. An optimized ANN structure of 3-7-2 indicating 3 input-layer, 7 hidden layer, and 2 output-layer was tested. Both the SVM and the ANN predict well the mechanical properties of polyproline composites. However, the ANN with R2 of 0.999 offers the best predictions. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12001
dc.identifier.doi10.1088/1755-1315/779/1/012001
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85109719434
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85109719434&doi=10.1088%2f1755-1315%2f779%2f1%2f012001&partnerID=40&md5=24a6840cd6a77dd2451af7dc775bce0d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26110
dc.identifier.volume779
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.titleAttaining material sustainability by incorporating nanoparticles additives to improve the mechanical properties of polypropylene composites: Data driven modellingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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