Experimental Study of Parameter Affecting the Thermal Conductivity of Composite Materials and Alloy: A Review

In the new era, the development of composite materials and alloy is very demanding for the fulfilment of the present demand of industries and society. The thermal analysis of composite materials and alloy is requiring its thermal properties like thermal conductivity, specific heat, etc. before their uses. The main focus of this research work is to study the effect of different parameters on the thermal conductivity of composite material and alloy based upon the experimental and analytical data available in the literature. Furthermore, a different method of finding the thermal conductivity of composite materials and alloy like finite element method, finite volume method, statistical model, non-conventional laser flash technique, machine learning method, spatially varying relaxation parameter lattice Boltzman method using melting process, equivalent inclusion method, hot plate method cut bar method, etc. also has been presented in this work. The machine learning method is a very fast, accurate and efficient method for predicting the conductivity of composites and alloy. From the intensive studies, it was found that temperature, particle size, density of materials and composition are generally affecting the thermal conductivity of composite materials. Furthermore, it was found that conductivity of composites material increases with an increase in the composition of filler material and a decrease in particle size of the filler.