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

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dc.contributor.authorTosic, Marko
dc.contributor.authorMarian, Max
dc.contributor.authorHabchi, Wassim
dc.contributor.authorLohner, Thomas
dc.contributor.authorStahl, Karsten
dc.date.accessioned2025-01-20T16:10:57Z
dc.date.available2025-01-20T16:10:57Z
dc.date.issued2024
dc.description.abstractThis 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.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.triboint.2024.109940
dc.identifier.eissn1879-2464
dc.identifier.issn0301-679X
dc.identifier.urihttps://doi.org/10.1016/j.triboint.2024.109940
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90240
dc.identifier.wosidWOS:001288803400001
dc.language.isoen
dc.revistaTribology international
dc.rightsacceso restringido
dc.subjectElastohydrodynamics
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectArtificial neural network
dc.subjectRegression
dc.subjectWorm gears
dc.subjectFilm thickness
dc.titleApplication of machine learning for film thickness prediction in elliptical EHL contact with varying entrainment angle
dc.typeartículo
dc.volumen199
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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