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Marian, Max ; Tremmel, Stephan:
Current Trends and Applications of Machine Learning in Tribology : A Review.
In: Lubricants.
Bd. 9
(2021)
Heft 9
.
- 86.
ISSN 2075-4442
DOI: https://doi.org/10.3390/lubricants9090086
Abstract
Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes.
Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Keywords: | tribology; machine learning; artificial intelligence; triboinformatics; databases; data mining; meta-modeling; artificial neural networks; monitoring; analysis; prediction; optimization |
| Institutionen der Universität: | Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Konstruktionslehre und CAD > Lehrstuhl Konstruktionslehre und CAD - Univ.-Prof. Dr.-Ing. Stephan Tremmel Fakultäten Fakultäten > Fakultät für Ingenieurwissenschaften Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Konstruktionslehre und CAD |
| Titel an der UBT entstanden: | Ja |
| Themengebiete aus DDC: | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften |
| Eingestellt am: | 09 Sep 2021 09:00 |
| Letzte Änderung: | 20 Dec 2024 10:13 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/66992 |