Author : B N V S Ganesh Gupta K and D Santha Rao

Increasing demands for operating properties of fabricated elements on one hand, and a necessity of reducing mass of a structure on the other, triggers materials engineering research into producing surface layers representing required functional properties. Methods commonly used in the production of surface layers, such as surfacing, spraying or re-melting with a laser beam have been known for years. A new method is the friction stir processing (FSP) of surface layers. The FSP process is primarily used for the modification of microstructure in near-surface layers of processed metallic components this deformation is produced by forcibly inserting a non-consumable tool into the workpiece, and revolving the tool in a stirring motion as it is pushed laterally through the workpiece. This is promising process for the automotive and aerospace industries where new material will need to be developed to improve resistance to wear, creep, and fatigue. The main objective of this project is to be producing copper reinforced metal matrix composite layers using micro sized TiB2 particles via friction stir processing (FSP) in order to enhance surface mechanical properties. Taguchi method was used to optimize these factors for maximizing the mechanical properties of surface composites. The fabricated surface composites were examined by optical microscope (OM) and scanning electron microscopy (SEM) for dispersion reinforcement particles. It was found that TiB2 particles are uniformly dispersed in the stir zone. Mechanical properties like tensile, Impact, hardness, were also evaluated. The results showed that functional characteristics of surface composites increased with the increase in vol. % of the micro sized TiB2 reinforcement particles. The observed mechanical properties are correlated with microstructure and fracture features.

Keywords: Friction Stir Processing, Copper, TiB2 Micro reinforcement particles, Micro Structure, Stir Zone, Orthogonal Array, Signal –to-Noise ratio, Grey Relational Analysis, ANOVA.