• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer

Center for Artificial Intelligence and Cybersecurity – AIRI

  • Home
  • About Us
    • Center Activities
    • Vision, Mission and Goals
    • Center Faculty
    • Steering Committee
    • Press
  • Research
    • Scientific Projects
    • Research Papers
  • Laboratories
    • Machine Learning
    • Natural Speech & Language Processing
    • Blockchain Technology
    • Information Processing & Pattern Recognition
    • AI in Medicine
    • Data Mining
    • Computer Vision
    • Complex Networks
    • Human-Computer Interaction
    • Maritime Cybersecurity
    • Autonomous Navigation
    • AI in Mechatronics
    • AI in Education
    • Hybrid Computational Methods
    • Drug Design
    • Legal Aspects of AI
    • Ethically Aligned AI
    • Cultural Complexity
  • Collaboration
    • Industry Collaboration
    • Industry Projects
    • International Collaboration
  • News
  • Contact

A supervised machine learning approach to a predictive model of nanoscale friction

01.08.2020

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.

Authors:
Perčić, Marko ; Zelenika, Saša ; Mezić, Igor
Journal:
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. - UK : EUSPEN, 2020, 69-70
Publishing date:
12.06.2020

Primary Sidebar

Latest Projects

Transversal Skills in Applied Artificial Intelligence (TSAAI)

INNO2MARE – Strengthening the capacity for excellence of Slovenian and Croatian innovation ecosystems to support the digital and green transitions of maritime regions

European Digital Innovation Hub Adria Croatia

ABsistemDCiCloud

Machine Learning for Knowledge Transfer in Medical Radiology

Latest Research Papers

Fracture Recognition in Paediatric Wrist Radiographs: An Object Detection Approach

Rapid extraction of skin physiological parameters from hyperspectral images using machine learning

Extended Energy-Expenditure Model in Soccer: Evaluating Player Performance in the Context of the Game

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Block-Adaptive Rényi Entropy-Based Denoising for Non-Stationary Signals

Latest News

Recognition of the Faculty of Information and Digital Technologies

Assoc. prof. Jonatan Lerga received the Croatian Academy of Sciences and Arts award

Dr. Sc. Nikola Lopac successfully defended his doctoral dissertation

Presentation at the conference “Digital Innovation and Technology for People”

Assoc. prof. dr. sc. Jonatan Lerga presented AIRI Center at the IEEE Rijeka : Computer Society Congress 2021

We provide the expertise for solving real world problems using AI

If your company wants to implement artificial intelligence in your products or services, or increase your level of cybersecurity, our multidisciplinary team of scientists is your ideal partner.

Contact us

Footer

Center for Artificial Intelligence and Cybersecurity
  • jlerga@airi.uniri.hr
  • +385 51 406 500

University of Rijeka

University of Rijeka

About the Center

  • About Us
  • News
  • Privacy Policy
  • Contact

Center Activities

  • Laboratories
  • Scientific Projects
  • Industry Projects
  • Research Papers
  • Industry Collaboration
  • International Collaboration

Footer bottom left

© 2020 Center for Artificial Intelligence and Cybersecurity, all rights reserved.

Designed & developed by Nela Dunato Art & Design