Studies on effective thermal conductivity of particle-reinforced polymeric flexible mould material composites

Abstract

Due to poor thermal conductivity of conventional flexible polymeric mould materials, the solidification of (wax/plastic) patterns in soft tooling (ST) process takes a longer time. This problem can be solved by increasing the effective thermal conductivity of mould materials through (high thermal conductive) particle reinforcement. Therefore in this study, the equivalent thermal conductivities (ETCs) of particle-reinforced polymeric mould materials, namely silicone rubber and polyurethane are experimentally observed using hot disc technique. Findings show that not only the amount of filler content and type of filler material, but also particle size has significant influence on the effective thermal conductivity of polymer and it starts increasing drastically at 20-30 per cent volume fraction of filler content. To predict the cooling time in ST process, it is important to have an appropriate model of ETC. In this study, a new method is proposed based on a genetic algorithm fuzzy (GA-fuzzy) approach to model the effective thermal conductivity of a two-phase particle-reinforced polymer composites (PCs). The effectiveness of the model is extensively tested in comparison with various empirical expressions reported in literature based on the experimental measurements. It has been found that the model based on GA-fuzzy approach not only outperforms the existing models, but also possesses a generic one applicable to a wide range of two-phase particle-reinforced PCs.