The Use of Artificial Intelligence in Tribology—A Perspective
Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at h...
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doaj-4d65b78b66004eb9a306bfa5375e0ea32020-12-27T00:02:05ZengMDPI AGLubricants2075-44422021-12-0192210.3390/lubricants9010002The Use of Artificial Intelligence in Tribology—A PerspectiveAndreas Rosenkranz0Max Marian1Francisco J. Profito2Nathan Aragon3Raj Shah4Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Santiago 7820436, ChileEngineering Design, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), 91058 Erlangen, GermanyDepartment of Mechanical Engineering, Polytechnic School, University of São Paulo, São Paulo 17033360, BrazilKoehler Instrument Company, Holtsville, NY 11742, USAKoehler Instrument Company, Holtsville, NY 11742, USAArtificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.https://www.mdpi.com/2075-4442/9/1/2artificial intelligencemachine learningartificial neural networkstribology |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andreas Rosenkranz Max Marian Francisco J. Profito Nathan Aragon Raj Shah |
spellingShingle |
Andreas Rosenkranz Max Marian Francisco J. Profito Nathan Aragon Raj Shah The Use of Artificial Intelligence in Tribology—A Perspective Lubricants artificial intelligence machine learning artificial neural networks tribology |
author_facet |
Andreas Rosenkranz Max Marian Francisco J. Profito Nathan Aragon Raj Shah |
author_sort |
Andreas Rosenkranz |
title |
The Use of Artificial Intelligence in Tribology—A Perspective |
title_short |
The Use of Artificial Intelligence in Tribology—A Perspective |
title_full |
The Use of Artificial Intelligence in Tribology—A Perspective |
title_fullStr |
The Use of Artificial Intelligence in Tribology—A Perspective |
title_full_unstemmed |
The Use of Artificial Intelligence in Tribology—A Perspective |
title_sort |
use of artificial intelligence in tribology—a perspective |
publisher |
MDPI AG |
series |
Lubricants |
issn |
2075-4442 |
publishDate |
2021-12-01 |
description |
Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems. |
topic |
artificial intelligence machine learning artificial neural networks tribology |
url |
https://www.mdpi.com/2075-4442/9/1/2 |
work_keys_str_mv |
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1724370102526148608 |