Application of machine learning for film thickness prediction in elliptical EHL contact with varying entrainment angle

verfasst von

Marko Tošić, Max Marian, Wassim Habchi, Thomas Lohner, Karsten Stahl

Abstract

This contribution demonstrates the potential of machine learning (ML) algorithms in predicting elastohydrodynamic lubrication (EHL) film thickness in elliptical contact with varying direction of lubricant entrainment, ranging from wide to slender elliptical configurations. The input parameters pertain to worm gear contacts, which are characterized by slender-like elliptical contact between a steel and a soft metal component. The study encompasses generating a database using numerical Finite Element Method (FEM) simulations, training artificial neural network (ANN) models, and evaluating their performance in terms of bias and variance. Key outcomes include the successful training of the ANN models, detailed analysis of the impact of tailored architecture on the ANN models' performance, and the superiority of the ANN compared to other ML regression algorithms. The study further identifies key input parameters that influence prediction accuracy and introduces a strategic dataset augmentation procedure to increase local and overall prediction accuracy. This strategic dataset augmentation enhances model robustness and precision while providing insights for expanding databases collaboratively. It holds potential for broader applications of ML for performance prediction of tribological contacts, thus paving the way for advanced ML models that consider additional factors and collaborative databases refined by multiple research groups.

Organisationseinheit(en)

Institut für Maschinenkonstruktion und Tribologie

Externe Organisation(en)

Technische Universität München (TUM)
Pontificia Universidad Catolica de Chile
Lebanese American University

Typ

Artikel

Journal

Tribology international

Band

199

Anzahl der Seiten

14

ISSN

0301-679X

Publikationsdatum

11.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

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

Elektronische Version(en)

https://doi.org/10.1016/j.triboint.2024.109940 (Zugang: Offen)