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Application of tribological artificial neural networks in machine elements

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journal contribution
posted on 2022-12-02, 13:49 authored by Jack WalkerJack Walker, Harry QuestaHarry Questa, A Raman, Mahdi Mohammad-PourMahdi Mohammad-Pour, SR Bewsher, G Offner

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 is compared directly against ball-on-disc experimental measurements to validate its accuracy.

Funding

AVL List GmbH

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Tribology Letters

Volume

71

Issue

1

Publisher

Springer

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Acceptance date

2022-10-30

Publication date

2022-11-23

Copyright date

2022

ISSN

1023-8883

eISSN

1573-2711

Language

  • en

Depositor

Dr Mahdi Mohammad Pour. Deposit date: 13 November 2022

Article number

3

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