Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction

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dc.contributor.authorVencl, Aleksandarcs
dc.contributor.authorSvoboda, Petrcs
dc.contributor.authorKlančnik, Simoncs
dc.contributor.authorBut, Adriancs
dc.contributor.authorVorkapić, Milošcs
dc.contributor.authorHarničárová, Martacs
dc.contributor.authorStojanović, Blažacs
dc.coverage.issue24cs
dc.coverage.volume11cs
dc.date.accessioned2023-08-03T11:00:27Z
dc.date.available2023-08-03T11:00:27Z
dc.date.issued2023-01-07cs
dc.description.abstractThree different and very small amounts of alumina (0.2, 0.3 and 0.5 wt. %) in two sizes (approx. 25 and 100 nm) were used to enhance the wear characteristics of ZA-27 alloy-based nanocomposites. Production was realised through mechanical alloying in pre-processing and compocasting pro-cesses. Wear tests were under lubricated sliding conditions on a block-on-disc tribometer, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. Experimental results were analysed by applying the response surface methodology (RSM) and a suitable mathematical model for the wear rate of tested nanocomposites was developed. Ap-propriate wear maps were constructed and the wear mechanism is discussed in this paper. The accuracy of the prediction was evaluated with the use of an artificial neural network (ANN). The architecture of the used ANN was 4-5-1 and the obtained overall regression coefficient was 0.98729. The comparison of the predicting methods showed that ANN is more efficient in predicting wear.en
dc.formattextcs
dc.format.extent13cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationLubricants. 2023, vol. 11, issue 24, 13 p.en
dc.identifier.doi10.3390/lubricants11010024cs
dc.identifier.issn2075-4442cs
dc.identifier.orcid0000-0003-3091-4025cs
dc.identifier.other180507cs
dc.identifier.researcheridF-5534-2012cs
dc.identifier.scopus57188955459cs
dc.identifier.urihttp://hdl.handle.net/11012/213705
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofLubricantscs
dc.relation.urihttps://www.mdpi.com/2057058cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2075-4442/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectZA-27 alloyen
dc.subjectAl2O3 nanoparticlesen
dc.subjectnanocompositesen
dc.subjectwearen
dc.subjectresponse surface methodologyen
dc.subjectartificial neural networken
dc.titleInfluence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Predictionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-180507en
sync.item.dbtypeVAVen
sync.item.insts2023.08.03 13:00:27en
sync.item.modts2023.08.03 12:15:55en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav konstruovánícs
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