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dc.rights.licenseAttribution-NonCommercial 3.0 United States*
dc.contributor.authorMiladinovic, Slavica-
dc.contributor.authorRankovic, Vesna-
dc.contributor.authorBabic, Miroslav-
dc.contributor.authorStojanovic, Blaza-
dc.contributor.authorVelickovic Sandra-
dc.date.accessioned2023-02-21T11:15:01Z-
dc.date.available2023-02-21T11:15:01Z-
dc.date.issued2017-
dc.identifier.isbn978‐86‐6335‐041‐0en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/16712-
dc.description.abstractPrediction of tribological characteristics of hybrid composites with A356 matrix using artificial neural networks (ANN) was performed in this paper. During experiment next parameters were varied: sliding speed, load, sliding distance and wt.% of reinforcement. The obtained experimental results were used to form the artificial neural network in which were varied number of neurons in the hidden layer, number of layers, the activation function and the function of training. Training of the neural network was performed for the wear rate, and optimal regression coefficient was equal to 0.994, for the network 4-15-10-1. Using neural networks to predict the wear rate greatly reduces the time and cost of experiment.en_US
dc.language.isoenen_US
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/us/*
dc.source15th International Conference on Tribology – Serbiatrib ‘17, Faculty of Engineering University of Kragujevac, Kragujevac, Serbiaen_US
dc.subjectpredictionen_US
dc.subjectwear rateen_US
dc.subjectartificial neural networken_US
dc.subjectcoefficient of frictionen_US
dc.titlePrediction of tribological behavior of aluminium matrix hybrid composites using artificial neural networksen_US
dc.typeconferenceObjecten_US
Налази се у колекцијама:Faculty of Engineering, Kragujevac

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