Effect of Processing Parameters on the Hardness and Sliding Wear Behaviour of Sintered Cu-CNT Composite

Abstract

Copper has high thermal conductivity, superior corrosion and oxidation resistance, while CNT enhances strength and wear resistance properties to the matrix. There are several potential applications for these composites such as break shoes, cylinder liners as well as piston rings where high strength, hardness and wear resistance as well as good thermal conductivity are some of the desired properties required for the developed materials. Looking at its potential outcome, in the present research, an attempt is made to develop Cu-CNT composite with optimized process parameters for improved hardness and wear characteristics. In the present investigation, the composites are fabricated via powder metallurgy route. Copper metal in powdered form with 99.5 per cent purity and multi walled CNT are used as for the matrix and reinforcement. In the present study three processing parameters are considered namely ultrasonic agitation energy, volume percentage inclusion of reinforcement with respect to matrix and the sintering temperature which predominantly govern the tribological properties. The influences of these processing parameters on the hardness and wear resistance of the fabricated composites are then investigated in details. The study also includes the estimation of elastic properties of the fabricated composites using the mathematical model proposed and simulation of wear depth using general purpose FEA software package ANSYS Mechanical APDL, which is then followed by the experimental validation. From the study it is observed that the sintering temperature and amount of reinforcements has immense influence on the mechanical and wear properties of the fabricated composite. The variation of sonication energy for the dispersion of the particles for mixing is not having much significance for the same. An inverse correlation of wear depth with the hardness of the material could also be observed. The simulation data is found to be well in agreement with the experimental observations.