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dc.contributor.authorKhalaj, Omid
dc.contributor.authorGhobadi, Moslem
dc.contributor.authorSaebnoori, Ehsan
dc.contributor.authorZarezadeh, Alireza
dc.contributor.authorShishesaz, Mohammadreza
dc.contributor.authorMašek, Bohuslav
dc.contributor.authorŠtádler, Ctibor
dc.contributor.authorSvoboda, Jiří
dc.date.accessioned2022-01-31T11:00:33Z-
dc.date.available2022-01-31T11:00:33Z-
dc.date.issued2021
dc.identifier.citationKHALAJ, O. GHOBADI, M. SAEBNOORI, E. ZAREZADEH, A. SHISHESAZ, M. MAŠEK, B. ŠTÁDLER, C. SVOBODA, J. Development of machine learning models to evaluate the toughness of OPH alloys. Materials, 2021, roč. 14, č. 21, s. 1-14. ISSN: 1996-1944cs
dc.identifier.issn1996-1944
dc.identifier.uri2-s2.0-85119253288
dc.identifier.urihttp://hdl.handle.net/11025/46706
dc.format14 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofseriesMaterialsen
dc.rights© authorsen
dc.titleDevelopment of machine learning models to evaluate the toughness of OPH alloysen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedOxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion- Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.en
dc.subject.translatedoxide precipitation-hardened (OPH) alloysen
dc.subject.translatedtensile testen
dc.subject.translatedtoughnessen
dc.subject.translatedartificial neural network (ANN)en
dc.subject.translatedparticle swarm optimizationen
dc.subject.translatedANFISen
dc.subject.translatedFe-Al-Oen
dc.identifier.doi10.3390/ma14216713
dc.type.statusPeer-revieweden
dc.identifier.document-number718800900001
dc.identifier.obd43934594
dc.project.IDGX21-02203X/Vylepšení vlastností současných špičkových slitincs
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