| Autor: |
Perčić, M., Sasa Zelenika, Mezić, I., Peter, R., Krstulović, N. |
| Přispěvatelé: |
Billington, D., Leach, R. K., Phillips, D., Riemer, O., Savio, E. |
| Jazyk: |
angličtina |
| Rok vydání: |
2018 |
| Předmět: |
|
| Zdroj: |
Scopus-Elsevier |
| Popis: |
The main limitation in modelling nanoscale friction is the variety of physicochemical processes and their interactions in nanoscale contacts that depend on the materials and on the conditions relating to sliding velocity, normal forces, temperature etc. To assess the value of the nanoscale friction coefficients, in this work an experimental methodology using a scanning probe microscope (SPM) in the lateral force microscopy (LFM) mode is hence used on various thin film samples. The measurements are designed by employing the centroidal Voronoi tessellation technique with 50 sample points. By using Support Vector Machine-based machine learning, the trends of the values of the friction coefficients can thus be established despite the marked stochastic contributions, creating therefore the preconditions for obtaining a multi-dimensional model relating the value of nanometric friction to its influencing parameters. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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