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Pregled bibliografske jedinice broj: 1103379

Artificial intelligence-based predictive model of nanoscale friction using experimental data


Perčić, Marko; Zelenika, Saša; Mezić, Igor
Artificial intelligence-based predictive model of nanoscale friction using experimental data // Friction (2021) doi:10.1007/s40544-021-0493-5 (znanstveni, prihvaćen)


CROSBI ID: 1103379 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Artificial intelligence-based predictive model of nanoscale friction using experimental data

Autori
Perčić, Marko ; Zelenika, Saša ; Mezić, Igor

Vrsta, podvrsta
Radovi u časopisima, znanstveni

Izvornik
Friction (2021)

Status rada
Prihvaćen

Ključne riječi
nanoscale friction ; thin films ; data mining ; machine learning ; predictive AI-based model

Sažetak
A recent systematic experimental characterisation of technological thin films, based on elaborated design of experiments as well as probe calibration and correction procedures, allowed for the first time the determination of nanoscale friction under the concurrent influence of several process parameters, comprising normal forces, sliding velocities and temperature, thus providing an indication of the intricate correlations induced by their interactions and mutual effects. This created the preconditions to undertake in this work an effort to model friction in the nanometric domain with the goal of overcoming the limitations of currently available models in ascertaining the effects of the physicochemical processes and phenomena involved in nanoscale contacts. Due to the stochastic nature of nanoscale friction and the relatively sparse available experimental data, meta-modelling tools fail, however, at predicting the factual behaviour. Based on the acquired experimental data, data mining, incorporating various state-of-the-art machine learning (ML) numerical regression algorithms, is thus used. The results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical validation. It is hence shown that the black box ML methods provide effective predictions of the studied correlations with rather good accuracy levels, but the intrinsic nature of such algorithms prevents their usage in most practical applications. Genetic programming-based artificial intelligence (AI) methods are hence finally used. Despite the marked complexity of the analysed phenomena and the inherent dispersion of the measurements, the developed AI-based symbolic regression models allow attaining an excellent predictive performance with the respective prediction accuracy, depending on the sample type, between 72 and 91 %, but also result in an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process parameters. An effective tool for nanoscale friction prediction, adaptive control purposes, and further scientific and technological nanotribological analyses is thus obtained.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti



POVEZANOST RADA


Projekti:
MZO Ustanova-uniri-tehnic-18-32 - Inovativne mehatroničke konstrukcije za pametna tehnološka rješenja (Zelenika, Saša, MZO Ustanova - Natječaj za dodjelu sredstava potpore znanstvenim istraživanjima na Sveučilištu u Rijeci za 2018. godinu - projekti iskusnih znanstvenika i umjetnika) ( POIROT)

Ustanove:
Tehnički fakultet, Rijeka,
Sveučilište u Rijeci

Profili:

Avatar Url Saša Zelenika (autor)

Avatar Url Marko Perčić (autor)

Citiraj ovu publikaciju

Perčić, Marko; Zelenika, Saša; Mezić, Igor
Artificial intelligence-based predictive model of nanoscale friction using experimental data // Friction (2021) doi:10.1007/s40544-021-0493-5 (znanstveni, prihvaćen)
Perčić, M., Zelenika, S. & Mezić, I. (2021) Artificial intelligence-based predictive model of nanoscale friction using experimental data. Prihvaćen za objavljivanje u Friction. [Preprint] doi:10.1007/s40544-021-0493-5.
@unknown{unknown, year = {2021}, DOI = {10.1007/s40544-021-0493-5}, keywords = {nanoscale friction, thin films, data mining, machine learning, predictive AI-based model}, journal = {Friction}, doi = {10.1007/s40544-021-0493-5}, title = {Artificial intelligence-based predictive model of nanoscale friction using experimental data}, keyword = {nanoscale friction, thin films, data mining, machine learning, predictive AI-based model} }
@unknown{unknown, year = {2021}, DOI = {10.1007/s40544-021-0493-5}, keywords = {nanoscale friction, thin films, data mining, machine learning, predictive AI-based model}, journal = {Friction}, doi = {10.1007/s40544-021-0493-5}, title = {Artificial intelligence-based predictive model of nanoscale friction using experimental data}, keyword = {nanoscale friction, thin films, data mining, machine learning, predictive AI-based model} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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