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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Boidi, Guido [1] ; da Silva, Marcio Rodrigues [2, 3] ; Profito, Francisco J. [2] ; Machado, Izabel Fernanda [2]
Total Authors: 4
Affiliation:
[1] AC2T Res GmbH, Viktor Kaplan Str 2-C, A-2700 Wiener Neustadt - Austria
[2] Univ Sao Paulo, Escola Politecn, Ave Prof Melo Morais 2231, Sao Paulo, SP - Brazil
[3] Fac Tecnol Termomecan, Estr Alvarengas 4001, Sao Bernardo Do Campo, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES; v. 8, n. 4 DEC 2020.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 16/25067-9 - Study of the micro surface irregularities (texture and porosity) effect on the tribological behavior of a steel sintered by SPS
Grantee:Guido Boidi
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 17/21151-8 - Tribological effect of micro surface irregularities (surface porosity and texture) on lubricated sliding/rolling tests
Grantee:Guido Boidi
Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)