Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F23%3A00002497" target="_blank" >RIV/75081431:_____/23:00002497 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/00216305:26210/23:PU146623
Výsledek na webu
<a href="https://cer.ihtm.bg.ac.rs/bitstream/id/23662/lubricants-11-00024-v2.pdf" target="_blank" >https://cer.ihtm.bg.ac.rs/bitstream/id/23662/lubricants-11-00024-v2.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction
Popis výsledku v původním jazyce
Three different and very small amounts of alumina (0.2, 0.3 and 0.5 wt. %) in two sizes (approx. 25 and 100 nm) were used to enhance the wear characteristics of ZA-27 alloy-based nanocomposites. Production was realised through mechanical alloying in pre-processing and compocasting processes. Wear tests were under lubricated sliding conditions on a block-on-disc tribometer, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. Experimental results were analysed by applying the response surface methodology (RSM) and a suitable mathematical model for the wear rate of tested nanocomposites was developed. Appropriate wear maps were constructed and the wear mechanism is discussed in this paper. The accuracy of the prediction was evaluated with the use of an artificial neural network (ANN). The architecture of the used ANN was 4-5-1 and the obtained overall regression coefficient was 0.98729. The comparison of the predicting methods showed that ANN is more efficient in predicting wear.
Název v anglickém jazyce
Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction
Popis výsledku anglicky
Three different and very small amounts of alumina (0.2, 0.3 and 0.5 wt. %) in two sizes (approx. 25 and 100 nm) were used to enhance the wear characteristics of ZA-27 alloy-based nanocomposites. Production was realised through mechanical alloying in pre-processing and compocasting processes. Wear tests were under lubricated sliding conditions on a block-on-disc tribometer, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m. Experimental results were analysed by applying the response surface methodology (RSM) and a suitable mathematical model for the wear rate of tested nanocomposites was developed. Appropriate wear maps were constructed and the wear mechanism is discussed in this paper. The accuracy of the prediction was evaluated with the use of an artificial neural network (ANN). The architecture of the used ANN was 4-5-1 and the obtained overall regression coefficient was 0.98729. The comparison of the predicting methods showed that ANN is more efficient in predicting wear.
Klasifikace
Druh
J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS
CEP obor
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OECD FORD obor
20501 - Materials engineering
Návaznosti výsledku
Projekt
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Návaznosti
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Lubricants
ISSN
2075-4442
e-ISSN
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Svazek periodika
11
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
14
Strana od-do
1-13
Kód UT WoS článku
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EID výsledku v databázi Scopus
2-s2.0-85146793938