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

Titelangaben

Marian, Max ; Tremmel, Stephan:
Current Trends and Applications of Machine Learning in Tribology : A Review.
In: Lubricants. Bd. 9 (1 September 2021) Heft 9 . - No. 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/CAD > Lehrstuhl Konstruktionslehre/CAD - Univ.-Prof. Dr.-Ing. Stephan Tremmel
Fakultäten
Fakultäten > Fakultät für Ingenieurwissenschaften
Fakultäten > Fakultät für Ingenieurwissenschaften > Lehrstuhl Konstruktionslehre/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: 09 Sep 2021 09:00
URI: https://eref.uni-bayreuth.de/id/eprint/66992