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

Friction 2021年第6期9卷 1726-1748页

作者： Marko PERČIĆ saša zelenika Igor MEZIĆ University of Rijeka Faculty of Engineering51000 RijekaCroatia University of Rijeka Centre for Micro-and Nanosciences and Technologies&Center for Artificial Intelligence and Cybersecurity51000 RijekaCroatia University of California santa Barbara Department of Mechanical EngineeringSanta BarbaraCA 93105USA

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 *** 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 *** to the stochastic nature of nanoscale friction and the relatively sparse available experimental data,meta-modelling tools fail,however,at predicting the factual *** on the acquired experimental data,data mining,incorporating various state-of-the-art machine learning(ML)numerical regression algorithms,is therefore *** results of the numerical analyses are assessed on an unseen test dataset via a comparative statistical *** is therefore 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 *** programming-based artificial intelligence(AI)methods are consequently finally *** 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%,allowing also to attain an extremely simple functional description of the multidimensional dependence of nanoscale friction on the studied variable process param

关键词： nanoscale friction thin films data mining machine learning(ML) predictive artificial intelligence(AI)-based model

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An experimental methodology for the concurrent characterization of multiple parameters influencing nanoscale friction

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Friction 2020年第3期8卷 577-593页

作者： Marko PERCIC sasa zelenika Igor MEZIC Robert PETER Niksa KRSTULOVIC University of Rijeka Faculty of EngineeringVukovarska 58Rijeka 51000Croatia University of Rijeka Centre for Micro-and Nanosciences and TechnologiesRadmile Marej&ie 2Rjeka 51000 Croatia Department of Mechanical Engineering UC Santa BarbaraSanta BarbaraCA 93105USA University of Rijeka Department of PlysicsRadmile Matejtic 2Rijeka 51000Croatia Institute of Plnysics Bijenicka 46Zagreb 1000Croatia

A structured transdisciplinary method for the experimental determination of friction in the nanometric domain is proposed in this *** dependence of nanoscale friction on multiple process parameters on these scales,which comprise normal forces,sliding velocities,and temperature,was studied via the lateral force microscopy *** procedure used to characterize the stiffness of the probes used,and especially the influence of adhesion on the obtained results,is thoroughly *** analyzed thin films were obtained by using either atomic layer or pulsed laser *** developed methodology,based on elaborated design of experiments algorithms,was successfully implemented to concurrently characterize the dependence of nanoscale friction in the multidimensional space defined by the considered process *** enables the establishment of a novel methodology that extends the current state-of-the-art of nanotribological studies,as it allows not only the gathering of experimental data,but also the ability to do so systematically and concurrently for several influencing variables at ***,in turn,creates the basis for determining generalizing correlations of the value of nanoscale friction in any multidimensional experimental *** developments create the preconditions to eventually extend the available macro-and mesoscale friction models to a true multiscale model that will considerably improve the design,modelling and production of MEMS devices,as well as all precision positioning systems aimed at micro-and nanometric accuracy and precision.

关键词： nanoscale friction lateral force microscopy experimental determination methodology multivariate space contact mechanics

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