Application of Tribological Artificial Neural Networks in Machine Elements

J. Walker*, H. Questa, A. Raman, M. Ahmed, M. Mohammadpour, S. R. Bewsher, G. Offner

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung

Abstract

Traditionally, analytical equations used in tribo-dynamic modelling, such as those used for predicting central film thickness within elastohydrodynamic lubricated contacts, have led to timely computations, but tend to lack the accuracy of numerical solvers. However, it can be shown that data-driven solutions, such as machine learning, can significantly improve computational efficiency of tribo-dynamic simulations of machine elements without comprising accuracy relative to the numerical solution. During this study, artificial neural networks (ANNs) are trained using data produced via numerical solutions, which are constrained by the regimes of lubrication to ensure the quality of the training data set. Multiple ANNs are then implemented to predict EHL central film thickness, as well as viscous and boundary friction, in multiple commonly used machine elements, such as a rolling element bearing and a spur gear. The viscous and boundary friction ANN prediction are compared directly against ball-on-disc experimental measurements to validate its accuracy.

Originalspracheenglisch
Aufsatznummer3
FachzeitschriftTribology Letters
Jahrgang71
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - Feb. 2023

ASJC Scopus subject areas

  • Werkstoffmechanik
  • Maschinenbau
  • Oberflächen und Grenzflächen
  • Oberflächen, Beschichtungen und Folien

Fingerprint

Untersuchen Sie die Forschungsthemen von „Application of Tribological Artificial Neural Networks in Machine Elements“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren