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Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B4C Composites Using Artificial Neural Networks

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Acta Metallurgica Sinica (English Letters) Aims and scope

Abstract

Al–Cu–Mg/B4Cp metal matrix composites with reinforcement of up to 20 wt% were produced using the powder metallurgy technique. The effects of reinforcement ratio, reinforcement size, milling time, and compact pressure on the density and porosity of the composites reinforced with 0, 5, 10, and 20 wt% B4C particles were studied. Moreover, an artificial neural network model has been developed for the prediction of the effects of the manufacturing parameters on the density and porosity of powder metallurgy Al–Cu–Mg/B4Cp composites. This model can be used for predicting the densification behavior of Al–Cu–Mg/B4Cp composites produced under reinforcement of different sizes and amounts with various milling times and compact pressures. The mean absolute percentage error for the predicted values did not exceed 1.6%.

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Acknowledgments

The authors are grateful to the Karadeniz Technical University Research Fund for financially supporting this research (No: 2010.112.010.4). The researchers would also like to thank the Gundogdu Exotherm Service for providing the Al 2024 powders.

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Authors and Affiliations

  1. Department of Metallurgical and Materials Engineering, Engineering Faculty, Karadeniz Technical University, Trabzon, Turkey

    Temel Varol & Aykut Canakci

  2. Department of Industrial Engineering, Engineering Faculty, Karadeniz Technical University, Trabzon, Turkey

    Sukru Ozsahin

Authors
  1. Temel Varol
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  2. Aykut Canakci
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  3. Sukru Ozsahin
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Correspondence to Aykut Canakci.

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Varol, T., Canakci, A. & Ozsahin, S. Modeling of the Prediction of Densification Behavior of Powder Metallurgy Al–Cu–Mg/B4C Composites Using Artificial Neural Networks. Acta Metall. Sin. (Engl. Lett.) 28, 182–195 (2015). https://doi.org/10.1007/s40195-014-0184-6

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  • DOI: https://doi.org/10.1007/s40195-014-0184-6

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