# Using Machine Learning Radial Basis Function (RBF) Method for Predicting Lubricated Friction on Textured and Porous Surfaces

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/using-machine-learning-radial-basis-function-rbf-method-for-predicting/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Guido Boidi,Márcio Rodrigues da Silva,Francisco J. Profito,Izabel Fernanda Machado |
| Citations | 51 |
| DOI | 10.1088/2051-672x/abae13 |
| Fields | Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3048603960 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Izabel Fernanda Machado](https://scholariq.org/researchers/izabel-fernanda-machado/)

## Paper primary topic

- [Tribology and Lubrication Engineering](https://scholariq.org/topics/tribology-and-lubrication-engineering/)

## Paper topics

- [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/)
- [Lubricants and Their Additives](https://scholariq.org/topics/lubricants-and-their-additives/)

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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.