Using Machine Learning Radial Basis Function (RBF) Method for Predicting Lubricated Friction on Textured and Porous Surfaces

Abstract

The coefficient of friction (CoF) obtained from tribological tests conducted on textured and porous surfaces was analysed using the machine learning Radial Basis Function (RBF) method. Non-textured and non-porous samples were taken as reference surfaces. Test parameters, such as entrainment velocity and slide-roll ratio (SRR), along with geometric characteristics of surface features (e.g. texture width and depth, coverage area, circularity, spatial distribution and directionality, among others), were selected as training dataset for the machine learning RBF model. The surface features were divided into designed patterns (dimples and grooves) manufactured by laser texturing, and randomised cavities (surface pores) resulted from the sintering process. The principal outcomes of this study are the effective use of the machine learning RBF method for tribological applications, as well as a critical discussion on its feasibility for the experimental dataset selected and the preliminary results obtained. Main results show that the Hardy multiquadric radial basis function provided an overall correlation coefficient of 0.934 for 35 poles. The application of the suggested machine learning technique and methodology can be extended to other experimental results available in the literature to train more robust models for predicting tribological performances of textured and structured surfaces.

The Use of Artificial Intelligence in Tribology—A Perspective

“…Furthermore, Boidi et al [34] employed the radial basis function (RBF) method predicting the friction coefficient in lubricated contacts with textured and porous surf The RBF model was trained with friction data obtained from tribological tests condu on surfaces with different features and for a range of entrainment velocity and slide ratio. The main results show that the hardy multiquadric radial basis function prov satisfactory overall correlation with the experimental data.…”

“…Framework of the metamodel of optimal prognosis to predict the tribological behavior of micro-textured EHL contacts utilized in [33]. Furthermore, Boidi et al [34] employed the radial basis function (RBF) method for predicting the friction coefficient in lubricated contacts with textured and porous surfaces. The RBF model was trained with friction data obtained from tribological tests conducted on surfaces with different features and for a range of entrainment velocity and slide-roll ratio.…”

Fast laser surface texturing of spherical samples to improve the frictional performance of elasto-hydrodynamic lubricated contacts

“…The tribological effects of surface texturing are still a common research topic scrutinised by many scientists, and the number of papers related to this topic has recently risen significantly [7]. Some authors even ventured to apply machine learning tools for predicting the coefficient of friction on textured surfaces [8].…”

Modeling of Friction Phenomena of Ti-6Al-4V Sheets Based on Backward Elimination Regression and Multi-Layer Artificial Neural Networks

“…They considered the optimization of mixtures of diesel oil with rapeseed and sunflower oils for use in diesel engines. Boidi et al [ 48 ] employed radial basis function neural networks to predict the COF in lubricated contacts with textured surfaces. It was shown that hardy multiquadratic radial basis functions provided satisfactory overall correlation with the experimental results.…”