Prediction of Tribological Properties of Al MMCs using ANN Models - Kumar G. B., Veeresh;Rao, C. S. P.;Selvaraj, N.
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The superior properties of composite materials over conventional materials have been well acknowledged by the research community. In particular, Aluminum Metal Matrix Composites (Al-MMCs) are sought over other conventional engineering materials owing to their excellent mechanical properties and outstanding wear resistance. Al-MMCs are widely used in aerospace, marine and automotive industries for different applications. Wear is a complex phenomenon and the most important reason for the damage and consequent failure of machine parts. A lot of experiments have to be conducted in order to study…mehr

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Produktbeschreibung
The superior properties of composite materials over conventional materials have been well acknowledged by the research community. In particular, Aluminum Metal Matrix Composites (Al-MMCs) are sought over other conventional engineering materials owing to their excellent mechanical properties and outstanding wear resistance. Al-MMCs are widely used in aerospace, marine and automotive industries for different applications. Wear is a complex phenomenon and the most important reason for the damage and consequent failure of machine parts. A lot of experiments have to be conducted in order to study the wear behavior resulting in wastage of both manpower and money. In several Artificial Intelligence (AI), an Artificial Neural Networks (ANN) help in reducing the cost of experiments when implemented with care and enough data in prediction of wear. Al-MMCs subjected to wear studies and with the obtained data, an ANN model was developed to predict the tribological properties of the Al6061 and Al7075 reinforced with SiC and Al2O3 MMCs. The predicted values of tribological properties of MMCs using a well trained ANN were found in good agreement with experimental values.
Autorenporträt
Dr. Veeresh Kumar G. B. is specialized in the field of Fabrication and Evaluation of Physical, Mechanical, and Tribological Characterization of Metal Matrix Composites with professional experience in disciplines of Material Science and Metallurgy, Composite Materials, Manufacturing Technology, Production Management, and Artificial Neural Networks.