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dc.creatorCarlone, Pierpaolo
dc.creatorAleksendrić, Dragan
dc.creatorRubino, Felice
dc.creatorĆirović, Velimir
dc.date.accessioned2022-09-19T18:38:56Z
dc.date.available2022-09-19T18:38:56Z
dc.date.issued2018
dc.identifier.issn2195-4356
dc.identifier.urihttps://machinery.mas.bg.ac.rs/handle/123456789/2992
dc.description.abstractAutoclave curing is a common practice to manufacture high temperature thermoset matrix composites. The cycle design and optimization of the temperature-time curve is a key issue for a competitive production. In this paper artificial neural networks (ANN), as a technique of artificial intelligence, were used for prediction of the composite temperature profile during the autoclave curing process. Different neural network models have been investigated regarding their capabilities for prediction of the composite temperature profile. The new neural network model has been developed able to predict the composite temperature profile in the wide range of manufacturing conditions changing.en
dc.publisherPleiades journals
dc.rightsrestrictedAccess
dc.sourceLecture Notes in Mechanical Engineering
dc.subjectCuring processen
dc.subjectComposite materialen
dc.subjectArtificial neural networksen
dc.titleArtificial neural networks in advanced thermoset matrix composite manufacturingen
dc.typebookPart
dc.rights.licenseARR
dc.citation.epage88
dc.citation.issue9783319895628
dc.citation.other0(9783319895628): 78-88
dc.citation.rankM14
dc.citation.spage78
dc.citation.volume0
dc.identifier.doi10.1007/978-3-319-89563-5_5
dc.identifier.scopus2-s2.0-85046801777
dc.type.versionpublishedVersion


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