TRIBOINFORMATICS: MACHINE LEARNING METHODS FOR FRICTIONAL INSTABILITIES

Michael Nosonovsky, Aleksandr S. Aglikov

DOI Number
https://doi.org/10.22190/FUME231208013N
First page
423
Last page
433

Abstract


The study of friction is traditionally a data-driven area with many experimental data and phenomenological models governing structure-property relationships. Triboinformatics is a new area combining Tribology with Machine Learning (ML) and Artificial Intelligence (AI) methods, which can help to establish correlations in data on friction and wear. This is particularly relevant to unstable motion, where deterministic models are difficult to build. There are several types of friction-induced instabilities including those caused by the velocity dependency of dry friction, coupling of friction with another process (wear, heat generation, etc.), the elastic Adams instabilities, and others. The onset of sliding is also an unstable process. ML/AI methods, such as Topological Data Analysis and various ML algorithms, which have been already used for various aspects of data analysis on friction, can be applied also to the frictional instabilities.


Keywords

Frictional instabilities, Painlevé paradoxes, Triboinformatics, Machine learning

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References


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DOI: https://doi.org/10.22190/FUME231208013N

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