Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU146623" target="_blank" >RIV/00216305:26210/23:PU146623 - isvavai.cz</a>

Alternative codes found

RIV/75081431:_____/23:00002497

Result on the web

<a href="https://www.mdpi.com/2057058" target="_blank" >https://www.mdpi.com/2057058</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/lubricants11010024" target="_blank" >10.3390/lubricants11010024</a>

Result language

angličtina

Original language name

Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction

Original language description

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 pro-cesses. 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. Ap-propriate 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Lubricants

ISSN

2075-4442

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

24

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

„“-„“

UT code for WoS article

000917744500001

EID of the result in the Scopus database

2-s2.0-85146793938