The Numerical Simulation of the Injection Filling of the Fluidity Probe Die with Pattern Waxes

Investment casting is a widely utilized casting technique that offers superior dimensional accuracy and surface quality. In this method, the wax patterns are employed in the layer-by-layer formation of a shell mold. As is customary, the patterns were created through the injection of molten or semi-solid wax into the die. The quality of the final casting is affected by the quality of the wax pattern. Furthermore, the filling of the die with wax can be associated with die-filling challenges, such as the formation of weld lines and misruns. In this study, the injection filling of the fluidity probe die with RG20, S1235, and S1135 pattern waxes was simulated using ProCast software. The thermal properties of the waxes, including thermal conductivity, heat capacity, and density across a wide temperature range, were determined with the assistance of a laser flash analyzer, a differential scanning calorimeter, and a dynamic mechanical analyzer. A favorable comparison of the acquired properties with those reported in the literature was observed. The Carreau model, which corresponds to non-Newtonian flow, was employed, and the parameters in the Carreau viscosity equation were determined as functions of temperature. Utilizing the thermal data associated with the wax patterns and the simulation outcomes, the interfacial heat transfer coefficients between the wax and the die were ascertained, yielding a value of 275–475 W/m2K. A strong correlation was observed between the experimental and simulated filling percentages of the fluidity probe across a wide range of injection temperatures and pressures. The analysis of the simulated temperature, fraction solid, viscosity, and shear rate in the wax pattern revealed that viscosity is a crucial factor influencing the wax fluidity. It was demonstrated that waxes with an initial high viscosity exhibit a low shear rate, which subsequently increases the viscosity, thereby hindering the wax flow.