Artificial neural network modeling of sliding wear

dc.contributor.authorArgatov, Ivan I.
dc.contributor.authorChai, Young S.
dc.date.accessioned2021-03-29T07:26:11Z
dc.date.available2021-03-29T07:26:11Z
dc.date.issued2021-04-01
dc.date.updated2021-03-20T21:22:20Z
dc.descriptionThis publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.en
dc.descriptionDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.de
dc.description.abstractA widely used type of artificial neural networks, called multilayer perceptron, is applied for data-driven modeling of the wear coefficient in sliding wear under constant testing conditions. The integral and differential forms of wear equation are utilized for designing an artificial neural network-based model for the wear rate. The developed artificial neural network modeling framework can be utilized in studies of wearing-in period and the so-called true wear coefficient. Examples of the use of the developed approach are given based on the experimental data published recently.en
dc.identifier.eissn2041-305X
dc.identifier.issn1350-6501
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/12897
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-11698
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.otherwear coefficienten
dc.subject.othersliding wearen
dc.subject.otherartificial neural networken
dc.subject.otherspecific wear rateen
dc.subject.otheraluminum alloy matrix compositesen
dc.titleArtificial neural network modeling of sliding wearen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1177/1350650120925582en
dcterms.bibliographicCitation.issue4en
dcterms.bibliographicCitation.journaltitleProceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribologyen
dcterms.bibliographicCitation.originalpublishernameSAGEen
dcterms.bibliographicCitation.originalpublisherplaceLondonen
dcterms.bibliographicCitation.pageend757en
dcterms.bibliographicCitation.pagestart748en
dcterms.bibliographicCitation.volume235en
tub.accessrights.dnbdomain*
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme::Inst. Mechanik::FG Systemdynamik und Reibungsphysikde
tub.affiliation.facultyFak. 5 Verkehrs- und Maschinensystemede
tub.affiliation.groupFG Systemdynamik und Reibungsphysikde
tub.affiliation.instituteInst. Mechanikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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