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Publikacje
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[70690] Artykuł:

The laser textured surfaces of the silicon carbide analyzed with the bootstrapped tribology model

Czasopismo: 26th International Conference on Metallurgy and Materials   Strony: 1252-1257
ISBN:  978-80-87294-79-6
Wydawca:  TANGER LTD, KELTICKOVA 62, SLEZSKA, OSTRAVA 710 00, CZECH REPUBLIC
Opublikowano: 2017
Miejsce wydania:  Ostrava
 
  Autorzy / Redaktorzy / Twórcy
Imię i nazwisko Wydział Katedra Do oświadczenia
nr 3
Grupa
przynależności
Dyscyplina
naukowa
Procent
udziału
Liczba
punktów
do oceny pracownika
Liczba
punktów wg
kryteriów ewaluacji
R. Dwornicka Niespoza "N" jednostki20.00.00  
Norbert Radek orcid logo WMiBMKatedra Inżynierii Eksploatacji i Przemysłowych Systemów Laserowych*Takzaliczony do "N"Inżynieria mechaniczna2015.003.00  
M. Krawczyk Niespoza "N" jednostki20.00.00  
P. Osocha Niespoza "N" jednostki20.00.00  
J. Pobędza Niespoza "N" jednostki20.00.00  

Grupa MNiSW:  Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science)
Punkty MNiSW: 15
Klasyfikacja Web of Science: Proceedings Paper


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Keywords:

Bootstrap  uncertainty  non-parametric approach  tribology  laser beam texturization 



Abstract:

The surface layer properties of super-hard materials may be modified by a laser beam treatment and the regular texturization is one of possible approaches. The designed experiment was conducted based on two main controlled factors: the average diameter of cavities and the level of the blackening. The obtained dataset was processed with a response surface model for the coefficient of friction as the outcome. Typical statistical analysis of data with quantitative control factors bases on many theoretical assumptions, with the most important and the most influential one: the normality of random noises distributions. Such assumption is often used but rarely it is analyzed for its weakness and large uncertainty in results. The paper describes a non parametric approach to the statistical analysis of the uncertainty. It releases us from the direct assumption of particular probability distributions. The bootstrap is formally the method based on random resampling with replacement from the source dataset. It allows to identify the whole distribution shape of raw data and related confidence intervals without additional assumptions about particular distribution. The paper presents the analysis processed for the practical case: original, commercially available rings for front sealing sintered from SiC with a texture modified by a laser beam and tested on tribological tester for changed properties. The paper contains notes on encountered difficulties and possible guidelines for similar analysis.