dc.creator | Rubino, Felice | |
dc.creator | Carlone, Pierpaolo | |
dc.creator | Aleksendrić, Dragan | |
dc.creator | Ćirović, Velimir | |
dc.creator | Sorrentino, Luca | |
dc.creator | Bellini, Costanzo | |
dc.date.accessioned | 2022-09-19T18:00:03Z | |
dc.date.available | 2022-09-19T18:00:03Z | |
dc.date.issued | 2016 | |
dc.identifier.issn | 0094-243X | |
dc.identifier.uri | https://machinery.mas.bg.ac.rs/handle/123456789/2418 | |
dc.description.abstract | The curing process of thermosetting resins plays a key role on the final quality of the composite material components. Soft computing techniques proved to be an efficient method to control and optimize the curing process, replacing the conventional experimental and numerical approaches. In this paper artificial neural network (ANN) and fuzzy logic control (FLC) were implemented together to predict and control the temperature and degree of cure profile during the autoclave curing process. The obtained outcomes proved the capability of ANNs and FLC with respect to the hard computing methods. | en |
dc.publisher | Amer Inst Physics, Melville | |
dc.rights | restrictedAccess | |
dc.source | Proceedings of The 19th International Esaform Conference on Material Forming (Esaform 2016) | |
dc.title | Hard and Soft Computing Models of Composite Curing Process Looking Toward Monitoring and Control | en |
dc.type | conferenceObject | |
dc.rights.license | ARR | |
dc.citation.other | 1769: - | |
dc.citation.rank | M33 | |
dc.citation.volume | 1769 | |
dc.identifier.doi | 10.1063/1.4963438 | |
dc.identifier.scopus | 2-s2.0-84994121536 | |
dc.identifier.wos | 000392692600034 | |
dc.type.version | publishedVersion | |