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On the data-driven modeling of reactive extrusion

hal.structure.identifierLaboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
dc.contributor.authorIBAÑEZ, Ruben
hal.structure.identifierIngénierie des Matériaux Polymères [IMP]
dc.contributor.authorCASTERAN, Fanny
hal.structure.identifierLaboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
dc.contributor.authorARGERICH, Clara
hal.structure.identifierNotre Dame University-Louaize [Lebanon] [NDU]
dc.contributor.authorGHNATIOS, Chady
hal.structure.identifierLaboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
dc.contributor.authorHASCOET, Nicolas
hal.structure.identifierLaboratoire Angevin de Mécanique, Procédés et InnovAtion [LAMPA]
dc.contributor.authorAMMAR, Amine
hal.structure.identifierIngénierie des Matériaux Polymères [IMP]
dc.contributor.authorCASSAGNAU, Philippe
hal.structure.identifierLaboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
dc.contributor.authorCHINESTA, Francisco
dc.date.accessioned2021-05-14T09:33:49Z
dc.date.available2021-05-14T09:33:49Z
dc.date.issued2020
dc.identifier.issn2311-5521
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/76082
dc.description.abstractEnThis paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.
dc.language.isoen
dc.publisherMDPI
dc.subject.enmachine learning
dc.subject.enreactive extrusion
dc.subject.endata-driven
dc.subject.enpolymer processing
dc.title.enOn the data-driven modeling of reactive extrusion
dc.typeArticle de revue
dc.identifier.doi10.3390/fluids5020094
dc.subject.halSciences de l'ingénieur [physics]/Mécanique [physics.med-ph]/Génie mécanique [physics.class-ph]
dc.subject.halSciences de l'ingénieur [physics]/Mécanique [physics.med-ph]/Mécanique des fluides [physics.class-ph]
dc.subject.halInformatique [cs]/Apprentissage [cs.LG]
bordeaux.journalFluids
bordeaux.page94
bordeaux.volume5
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.institutionINRAE
bordeaux.institutionArts et Métiers
bordeaux.peerReviewedoui
hal.identifierhal-02902692
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02902692v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Fluids&rft.date=2020&rft.volume=5&rft.issue=2&rft.spage=94&rft.epage=94&rft.eissn=2311-5521&rft.issn=2311-5521&rft.au=IBA%C3%91EZ,%20Ruben&CASTERAN,%20Fanny&ARGERICH,%20Clara&GHNATIOS,%20Chady&HASCOET,%20Nicolas&rft.genre=article


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