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Hulya Kacar Durmus, Erdogan Ozkaya and Cevdet Meric, “The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 Al alloy”, Materials & Design, Vol 27, Issue 2, Pgs 156-159, 2006.

has been cited by the following article:

• TITLE: Mechanical and Tribological Behavior of Particulate Reinforced Aluminum Metal Matrix Composites – a review

  AUTHORS: G. B. Veeresh Kumar, C. S. P. Rao, N. Selvaraj

  KEYWORDS: Al-MMCs, Density, Hardness, Mechanical Properties, Wear, Prediction.

  JOURNAL NAME: Journal of Minerals and Materials Characterization and Engineering, Vol.10 No.1, January 20, 2011

  ABSTRACT: Aluminum Metal Matrix Composites (MMCs) sought over other conventional materials in the field of aerospace, automotive and marine applications owing to their excellent improved properties. These materials are of much interest to the researchers from few decades. These composites initially replaced Cast Iron and Bronze alloys but owing to their poor wear and seizure resistance, they were subjected to many experiments and the wear behavior of these composites were explored to a maximum extent and were reported by number of research scholars for the past 25 years. In this paper an attempt has been made to consolidate some of the aspects of mechanical and wear behavior of Al-MMCs and the prediction of the Mechanical and Tribological properties of Aluminum MMCs.