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Computational investigation of testing parameter effects on abrasive wear behaviour of AL2O3 particle-reinforced MMCS using statistical analysis

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Abstract

The wear rate model of 7.3 vol.% Al2O3 particle-reinforced aluminium alloy composites with 16 and 66 µm particle sizes fabricated by molten metal mixing method was developed in terms of applied load, particle size of reinforcement, abrasive grain size and sliding distance based on the Taguchi method. The two-body abrasive wear behaviour of the specimens was investigated using a pin-on-disc abrasion test apparatus where the sample slid against different SiC abrasives under the loads of 2 and 5 N at the room conditions. The orthogonal array, signal-to-noise ratio and analysis of variance were employed to find out the optimal testing parameters. The test results showed that particle size of reinforcement was found to be the most effective factor among the other control parameters on abrasive wear, followed by abrasive grain size. Moreover, the optimal combination of the testing parameters was determined and predicted. The predicted wear rate results were compared with experimental results and found to be quite reliable.

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  1. Department of Mechanical Program, Kahramanmaraş MYO, Kahramanmaraş Sütçü Imam University, Karacasu Campus, 46100, Kahramanmaraş, Turkey

    Metin Kök

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Kök, M. Computational investigation of testing parameter effects on abrasive wear behaviour of AL2O3 particle-reinforced MMCS using statistical analysis. Int J Adv Manuf Technol 52, 207–215 (2011). https://doi.org/10.1007/s00170-010-2734-z

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