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Tribological behavior of friction stir process surface hybrid composite AA5083/MWCNT/Al2SiO5 using multi-quadratic RBF algorithm

  • Carbon Letters
  • Abbr : Carbon Lett.
  • 2023, 33(7), pp.2363-2377
  • DOI : 10.1007/s42823-023-00577-1
  • Publisher : Korean Carbon Society
  • Research Area : Natural Science > Natural Science General > Other Natural Sciences General
  • Received : February 6, 2023
  • Accepted : June 17, 2023
  • Published : December 1, 2023

Samuel Ratna Kumar P. S. 1 Vaira Vignesh R. 2 Mashinini P. M. 1 Ramanathan S. 3

1Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg, South Africa
2Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India
3Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India

Accredited

ABSTRACT

The present research focuses on the tribological behavior of the AA5083 alloy-based hybrid surface composite using aluminosilicate and multi-walled-carbon nanotube through friction stir processing for automotive applications. The friction stir processing parameters (tool rotation and traverse speed) are varied based on full factorial design to understand their influence on the tribological characteristics of the developed hybrid composite. The surface morphology and composition of the worn hybrid composite are examined using a field-emission scanning electron microscope and an energy-dispersive x-ray spectroscope. No synergistic interaction is observed between the wear rate and friction coefficient of the hybrid composite plate. Also, adhesive wear is the major wear mechanism in both base material and hybrid composite. The influence of friction stir process parameters on wear rate and the friction coefficient is analyzed using the hybrid polynomial and multi-quadratic radial basis function. The models are utilized to optimize the friction stir processing parameters for reducing the rate of wear and friction coefficient using multi-quadratic RBF algorithm optimization.

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