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Modeling of tribological properties of alumina fiber reinforced zinc-aluminum composites using artificial neural network

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dc.contributor.authors Genel, K; Kurnaz, SC; Durman, M;
dc.date.accessioned 2020-02-26T08:47:18Z
dc.date.available 2020-02-26T08:47:18Z
dc.date.issued 2003
dc.identifier.citation Genel, K; Kurnaz, SC; Durman, M; (2003). Modeling of tribological properties of alumina fiber reinforced zinc-aluminum composites using artificial neural network. MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 363, 210-203
dc.identifier.issn 0921-5093
dc.identifier.uri https://doi.org/10.1016/S0921-5093(03)00623-3
dc.identifier.uri https://hdl.handle.net/20.500.12619/50032
dc.description.abstract It is known that the strength of a metal, as well as wear resistance can be successfully improved by fiber reinforcement. In this study, multiple-layer feed-forward artificial neural network (ANN) modeling for tribological behavior of short alumina fiber reinforced zinc-aluminum composites has been established. The specific wear rate and coefficient of friction obtained from a series of the wear tests were used in the formation of training sets of ANN. Samples of composite material with 10, 15, 20 and 30 vol.% fiber contents were prepared by the pressure die-casting method. Wear tests with pin-on-disc arrangement had performed at a constant sliding speed of 1 m/s under four different loads (5, 10, 20 and 40 N). The results of experimental tests showed that wear behavior and friction coefficient of the composites were significantly affected by the fiber volume fraction. The specific wear rate decreased with increasing fiber volume fraction and increased with increasing load. The coefficients of friction of the composites were higher than that of the unreinforced matrix alloy. The modeling results confirm the feasibility of the ANN and its good correlation with the experimental results. The degrees of accuracy of the prediction were 94.2 and 99.4% for specific wear rate and friction coefficient, respectively. It is concluded that ANN is an excellent analytical tool if it is well trained. This means considerable cost and time saving. Finally, using ANN modeling data and experimental data, 3D plots and empirical expressions for specific wear rate and friction coefficient related to load and fiber volume fraction were established. (C) 2003 Elsevier B.V. All rights reserved.
dc.language English
dc.publisher ELSEVIER SCIENCE SA
dc.subject Metallurgy & Metallurgical Engineering
dc.title Modeling of tribological properties of alumina fiber reinforced zinc-aluminum composites using artificial neural network
dc.type Article
dc.identifier.volume 363
dc.identifier.startpage 203
dc.identifier.endpage 210
dc.contributor.department Sakarya Üniversitesi/Mühendislik Fakültesi/Makine Mühendisliği Bölümü
dc.contributor.saüauthor Genel, Kenan
dc.contributor.saüauthor Kurnaz, Süleyman Can
dc.contributor.saüauthor Durman, Mehmet
dc.relation.journal MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
dc.identifier.wos WOS:000186851000026
dc.identifier.doi 10.1016/S0921-5093(03)00623-3
dc.identifier.eissn 1873-4936
dc.contributor.author Genel, Kenan
dc.contributor.author Kurnaz, Süleyman Can
dc.contributor.author Durman, Mehmet


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