Investigating the role of nonmetallic fillers in particulate-reinforced mold composites using EAs

Abstract

In the soft tooling (ST) process, flexible polymeric materials (namely, silicone rubber, polyurethane, etc.) are used for making mold for producing wax pattern. Due to low thermal conductivity of mold materials, the ST process takes longer time for cooling. Hence, to reduce the cooling time, thermal conductive fillers are included in mold materials. But addition of fillers affects various properties of ST process (such as stiffness of the mold box) and the influences may vary according to the types of materials used. Therefore, in the present work, multiobjective optimizations of equivalent thermal conductivity and effective modulus of elasticity of composite mold materials are conducted using evolutionary algorithms in order to investigate the role of various nonmetallic fillers in particulate reinforced mold material composites. We have adopted NSGA-II to optimize the conflicting objectives-maximization of thermal conductivity and minimization of modulus of elasticity. A recently proposed innovization procedure is used to unveil salient properties associated with the trade-off solutions. The obtained Pareto fronts are used successfully to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mold material. The optimal selection of materials is suggested in consideration with the cost implication factor based on the findings through investigations.