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• W2002673896 abstract "The potential of using neural network in prediction of wear loss quantities of some aluminum–copper–silicon carbide composite materials has been studied in the present work. Effects of addition of copper as alloying element and silicon carbide as reinforcement particles to Al–4 wt.%Mg metal matrix have been investigated. Different Al–Cu alloys and composites were subjected to dry sliding wear test using pin-on-disk apparatus under 40 N normal load with rotational speed of counter face disk of 150 rpm at room conditions (∼20 °C and ∼50% relative humidity). The experimental results were firstly coded prior to training in a feed forward back propagation artificial neural network (ANN) and the results were compared with experimental results. The average value of absolute relative error of un-coded values reaches 2.40%." @default.
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• W2002673896 date "2009-02-01" @default.
• W2002673896 modified "2023-09-27" @default.
• W2002673896 title "Prediction of tribological behavior of aluminum–copper based composite using artificial neural network" @default.
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• W2002673896 doi "https://doi.org/10.1016/j.jallcom.2008.03.035" @default.
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