Pregled bibliografske jedinice broj: 1070177
A supervised machine learning approach to a predictive model of nanoscale friction
A supervised machine learning approach to a predictive model of nanoscale friction // Proceedings of the 20th international conference of the EUSPEN - European society for precision engineering and nanotechnology / Leach, R. K. ; Billington, D. ; Nisbet, C. ; Phillips, D. (ur.).
online: European Society for Precision Engineering and Nanotechnology (EUSPEN), 2020. str. 69-70 (poster, međunarodna recenzija, kratko priopćenje, znanstveni)
CROSBI ID: 1070177 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
A supervised machine learning approach to a predictive model of nanoscale friction
Autori
Perčić, Marko ; Zelenika, Saša ; Mezić, Igor
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, kratko priopćenje, znanstveni
Izvornik
Proceedings of the 20th international conference of the EUSPEN - European society for precision engineering and nanotechnology
/ Leach, R. K. ; Billington, D. ; Nisbet, C. ; Phillips, D. - : European Society for Precision Engineering and Nanotechnology (EUSPEN), 2020, 69-70
ISBN
978-0-9957751-7-6
Skup
20th International conference of the European Society for Precision Engineering and Nanotechnology (EUSPEN)
Mjesto i datum
Online, 08.06.2020. - 12.06.2020
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Nanoscale friction ; mathematical modelling ; machine learning ; predictive model
Sažetak
Modelling of nanoscale friction presents a long-lasting challenge. In fact, while there are several generalised models that provide good results for macro- and micro-scale friction, due to the complex concurrent physicochemical interactions in nanoscale contacts, when modelling nanoscale friction there is a clear lack of reliable predicting tools. The modelling methodology proposed in this work is based on the recently performed multidimensional experimental measurements of thin-films’ nanoscale friction, where the concurrent effects of several process parameters are considered. Due to the stochastic nature of the considered phenomena, conventional regression methods yield poor predictive performances. A machine learning (ML) numerical paradigm is hence proposed. Via a comparative study it is hence shown that, while the best typical regression models result in coefficients of determination (R2) of the order of 0.3, the predictive performances of the used ML models, depending on the considered sample, yield R2 in the range from 0.54 to 0.9. The developed models provide also new insights into the functional dependence of the variable process parameters, but also sound basis for future extensions of existing friction models to the nanometric range.
Izvorni jezik
Engleski
Znanstvena područja
Strojarstvo, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Projekti:
uniri-tehnic-18-32
RC.2.2.06-0001 - Razvoj istraživačke infrastrukture na kampusu Sveučilišta u Rijeci (RISK) (Ožanić, Nevenka, EK - Operativni program Regionalna konkurentnost) ( CroRIS)
Ustanove:
Tehnički fakultet, Rijeka,
Sveučilište u Rijeci
