Details
| Original language | English |
|---|---|
| Article number | 109940 |
| Number of pages | 14 |
| Journal | Tribology international |
| Volume | 199 |
| Early online date | 5 Jul 2024 |
| Publication status | Published - Nov 2024 |
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.
Keywords
- Artificial intelligence, Artificial neural network, Elastohydrodynamics, Film thickness, Machine learning, Regression, Worm gears
ASJC Scopus subject areas
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
- Physics and Astronomy(all)
- Surfaces and Interfaces
- Materials Science(all)
- Surfaces, Coatings and Films
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In: Tribology international, Vol. 199, 109940, 11.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Application of machine learning for film thickness prediction in elliptical EHL contact with varying entrainment angle
AU - Tošić, Marko
AU - Marian, Max
AU - Habchi, Wassim
AU - Lohner, Thomas
AU - Stahl, Karsten
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024/11
Y1 - 2024/11
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Artificial neural network
KW - Elastohydrodynamics
KW - Film thickness
KW - Machine learning
KW - Regression
KW - Worm gears
UR - http://www.scopus.com/inward/record.url?scp=85200209282&partnerID=8YFLogxK
U2 - 10.1016/j.triboint.2024.109940
DO - 10.1016/j.triboint.2024.109940
M3 - Article
AN - SCOPUS:85200209282
VL - 199
JO - Tribology international
JF - Tribology international
SN - 0301-679X
M1 - 109940
ER -