T. Albert D. Prince Sahaya Sudherson K. Kalaiselvan N. Leema

Keywords: Powder metallurgy, Cu-B4C, Gwyddion, Wear, Artificial Neural Network

Abstract

ABSTRACT. In this work, the Copper composites Cu-x wt. % B4C (x = 0, 5, 10, 15, 20) were fabricated for the metallurgical and mechanical property evaluation as per ASTM standards. The metallurgical characterization tests on the samples include x-ray diffraction, optical microscopy, and scanning electron microscopy with EDX. Further, pin-on-disc apparatus was used to investigate the tribological behavior of composite specimens. An SEM micrograph of the worn surface and wear debris, along with the Gwyddion software, has been used to discuss the wear mechanisms in detail. The Artificial Neural Networks (ANN) classifier model is also constructed to describe the wear behavior in more detail. The experimental results inferred that the addition of Boron carbide particles has enhanced the Copper's corrosion resistance in a 1 M HCl electrolyte solution from 30.34% to 74.2%, 75.08%, and 83.29% with B and C ions. Also, it significantly enhance the mechanical and tribological characteristics considerably.

 

KEY WORDS: Powder metallurgy, Cu-B4C, Gwyddion, Wear, Artificial Neural Network

 

Bull. Chem. Soc. Ethiop. 2023, 37(4), 959-972.                                                              

DOI: https://dx.doi.org/10.4314/bcse.v37i4.12