Synthesized Nanoaluminum Oxide with Al2024 to Investigate Wear Behavior by Grey Relational Approach and ANN

Abstract

Aluminum metal matrix composites (AMCs) have been employed in automobile manufacturing to reduce weight. Also this research concentrates on the tribological performances on the processed AMCs by the stir casting liquefying method. The aluminum alloy Al2024 was employed to nanoparticles of aluminum oxide for the preparation of AMCs with constant processing condition of stirring speed to produce the homogeneous dispersion. The processed composites were further investigated to identify the wear characteristics. Therefore, the dry sliding condition was achieved on the processed composites. The input parameters of dry sliding conditions are sliding distance, functional load, and sliding velocity, and the output characteristics are wear rate and coefficient of friction (COF). Those input parameters are framed by the Taguchi L9 array and parameters were further employed to optimize with grey relational analysis. From the L9 parameters, the better wear rate and COF were accumulated in the following parameter: 2,100 mm of sliding distance, 25 N of functional load, and 2.5 m/s of sliding velocity, respectively. Then the wear rates and COF values are subjected to produce the predicted responses with supporting of artificial neural network. Most of the predicted values are much higher than the actual wear response vales. The wear resistance of all the samples composed better performances with dispersion of nanoaluminum oxide particles on the Al2024 alloy.