Prediction of tribological behavior of aluminum-copper based composite using artificial neural network

Mohammed Hayajneh, Adel Mahamood Hassan, Abdalla Alrashdan, Ahmad Turki Mayyas

    Research output: Contribution to journalArticlepeer-review

    76 Scopus citations

    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%.

    Original languageBritish English
    Pages (from-to)584-588
    Number of pages5
    JournalJournal of Alloys and Compounds
    Volume470
    Issue number1-2
    DOIs
    StatePublished - 20 Feb 2009

    Keywords

    • Artificial neural network (ANN)
    • Metal matrix composite
    • Metals and alloys
    • Wear

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