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Tribological performance of random sinter pores vs. deterministic laser surface textures: an experimental and machine learning approach
OA Location
Author(s)
Boidi, Guido
Grützmacher, Philipp
Varga, Markus
da Silva, Márcio Rodrigues
Gachot, Carsten
Type
Chapter
Abstract
This work critically scrutinizes and compares the tribological performance of randomly distributed surface pores in sintered materials and precisely tailored laser textures produced by different laser surface texturing techniques. The pore distributions and dimensions were modified by changing the sintering parameters, while the topological features of the laser textures were varied by changing the laser sources and structuring parameters. Ball-on-disc tribological experiments were carried out under lubricated combined sliding-rolling conditions. Film thickness was measured in-situ through a specific interferometry technique developed for the study of rough surfaces. Furthermore, a machine learning approach based on the radial basis function method was proposed to predict the frictional behavior of contact interfaces with surface irregularities. The main results show that both sintered and laser textured materials can reduce friction compared to the untextured material under certain operating conditions. Moreover, the machine learning model was shown to predict results with satisfactory accuracy. It was also found that the performance of sintered materials could lead to similar improvements as achieved by textured surfaces, even if surface pores are randomly distributed and not precisely controlled.
Editor(s)
Pintaude, Giuseppe
Cousseau, Tiago
Date Issued
2021-11-11
Citation
Tribology, 2021
URI
URL
DOI
Journal / Book Title
Tribology
Copyright Statement
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
https://www.intechopen.com/online-first/78932
Grant Number
EP/N025954/1
Publication Status
Published