# Predicting EHL film thickness parameters by machine learning approaches

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/predicting-ehl-film-thickness-parameters-by-machine-learning-approaches/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Max Marian,Jonas Mursak,Marcel Bartz,Francisco J. Profito,Andreas Rosenkranz,Sandro Wartzack |
| Citations | 67 |
| DOI | 10.1007/s40544-022-0641-6 |
| Fields | Engineering |
| Open Access | true |
| OA Status | diamond |
| OA URL | https://link.springer.com/content/pdf/10.1007/s40544-022-0641-6.pdf |
| OpenAlex ID | https://openalex.org/W4282599518 |
| Type | article |
| Year | 2022 |

## Paper authors

- [Francisco J. Profito](https://scholariq.org/researchers/francisco-j-profito/)

## Paper primary topic

- [Gear and Bearing Dynamics Analysis](https://scholariq.org/topics/gear-and-bearing-dynamics-analysis/)

## Paper topics

- [Gear and Bearing Dynamics Analysis](https://scholariq.org/topics/gear-and-bearing-dynamics-analysis/)
- [Tribology and Lubrication Engineering](https://scholariq.org/topics/tribology-and-lubrication-engineering/)
- [Adhesion, Friction, and Surface Interactions](https://scholariq.org/topics/adhesion-friction-and-surface-interactions/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.