Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Current Trends and Applications of Machine Learning in Tribology : A Review

Title data

Marian, Max ; Tremmel, Stephan:
Current Trends and Applications of Machine Learning in Tribology : A Review.
In: Lubricants. Vol. 9 (1 September 2021) Issue 9 . - No. 86.
ISSN 2075-4442
DOI: https://doi.org/10.3390/lubricants9090086

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: tribology; machine learning; artificial intelligence; triboinformatics; databases; data mining; meta-modeling; artificial neural networks; monitoring; analysis; prediction; optimization
Institutions of the University: Faculties > Faculty of Engineering Science > Chair Engineering Design and CAD > Chair Engineering Design and CAD - Univ.-Prof. Dr.-Ing Stephan Tremmel
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 09 Sep 2021 09:00
Last Modified: 09 Sep 2021 09:00
URI: https://eref.uni-bayreuth.de/id/eprint/66992