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The investigation on the effect of sintering temperature and time intervals on workability behaviour of Al-SiC powder metallurgy composites during cold upsetting was attempted in the present work. Three levels of sintering temperature and time have been considered to evaluate their effect on workability behaviour. The amount of SiC reinforcement content has been varied as 0%, 10% and 20%. The experimental results were analyzed for workability under triaxial stress state condition as a function of the relative density. The Formability Stress Index (β σ ), the Formability Strain Index (β ε ), stress ratio parameters namely σ θ /σ eff and σ z /σ m were obtained for all the cases. As a result, the exhibited tremendous variations in the various parameters for different sintering temperatures and time intervals were studied and reported.

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In practice, lubricants are used to minimize the friction and wear of frictional surfaces. The disposal of mineral-based lubricating oil possesses environmental issues and forced the development of bio-degradable lubricating agents. The simultaneous mono-dispersion of metallic and metal oxides nanomaterials into lubricating agents may concurrently reveal superior thermo-physical and rheological characteristics. This paper proposes an experimental and theoretical investigation on the dynamic viscosity enhancement of flat platelets textured Graphene/NiO-coconut oil hybrid nanofluids. The results reveal that the dynamic viscosity enhancement of hybrid nanofluids increases with nanomaterial concentration and decreases with temperature. The squat hybrid nanomaterial concentration has less collusion probability and dynamic contact between the mono-dispersed hybrid nanomaterials as it has enough interfacing gaps to conquer superficial surface energy. The high nanomaterial concentration revamps the formation of lamellar-composite agglomerated particles and enhances the dynamic viscosity of base fluid. Further, a theoretical correlation is recommended to estimate the dynamic viscosity of hybrid nanofluid with minimum margin of deviation using artificial neural network (ANN).