| Title: | Influence of Al2O3 nanoparticles addition in ZA-27 alloy-based nanocomposites and soft computing prediction |
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| Authors: | ID Vencl, Aleksandar (Author)
ID Svoboda, Petr (Author)
ID Klančnik, Simon (Author)
ID But, Adrian (Author)
ID Vorkapić, Miloš (Author)
ID Harničárová, Marta (Author)
ID Stojanović, Blaža (Author) |
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| Files: |  Vencl-2023-Influence_of_Al_sub_2__sub_O_sub_3_.pdf (14,10 MB)
MD5: 11F4E4F9676D249831316E4BE5B15918
 https://doi.org/10.3390/lubricants11010024
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | ZA-27 alloy, Al2O3 nanoparticles, nanocomposites, wear, response surface methodology, artificial neural network |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 30.11.2022 |
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| Article acceptance date: | 30.12.2022 |
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| Publication date: | 07.01.2023 |
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| Publisher: | MDPI |
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| Year of publishing: | 2023 |
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| Number of pages: | Str. 1-13 |
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| Numbering: | Letn. 11, Št. 1, št. članka 24 |
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| PID: | 20.500.12556/DKUM-87539  |
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| UDC: | 621.74:004.8 |
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| ISSN on article: | 2075-4442 |
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| COBISS.SI-ID: | 138621955 |
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| DOI: | 10.3390/lubricants11010024 |
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| Publication date in DKUM: | 20.03.2024 |
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| Views: | 365 |
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| Downloads: | 23 |
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| Metadata: |    |
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| Categories: | Misc.
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