Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11698
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Main Title: Artificial neural network modeling of sliding wear
Author(s): Argatov, Ivan I.
Chai, Young S.
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/12897
http://dx.doi.org/10.14279/depositonce-11698
License: http://rightsstatements.org/vocab/InC/1.0/
Abstract: A 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.
Subject(s): wear coefficient
sliding wear
artificial neural network
specific wear rate
aluminum alloy matrix composites
Issue Date: 1-Apr-2021
Date Available: 29-Mar-2021
Language Code: en
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Journal Title: Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology
Publisher: SAGE
Volume: 235
Issue: 4
Publisher DOI: 10.1177/1350650120925582
Page Start: 748
Page End: 757
EISSN: 2041-305X
ISSN: 1350-6501
Notes: This 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.
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
TU Affiliation(s): Fak. 5 Verkehrs- und Maschinensysteme » Inst. Mechanik » FG Systemdynamik und Reibungsphysik
Appears in Collections:Technische Universität Berlin » Publications

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