Abstract
Injection moulding of liquid silicone rubber (LSR) requires reliable computer-aided engineering simulations to support process optimisation, which in turn depend on accurate material data. In this study, thermo-physical and kinetic properties of a highly filled injection moulding (IM) grade of LSR were systematically characterised using complementary experimental approaches, and their impact on simulation fidelity was critically assessed. Specific heat capacity was measured using both modulated DSC and the standard sapphire method, revealing temperature dependence but no intrinsic change during curing, with sapphire-based data incorporating enthalpic effects more realistically for process prediction. Thermal conductivity was found to be nearly constant across the processing temperature range. Curing kinetics were investigated by calorimetry and rheology, with the former supporting an autocatalytic mechanism and the latter suggesting an nth-order model, reflecting differences in detection sensitivity and onset characterisation. When implemented into injection moulding simulations, viscosity primarily affected injection pressures, while differences in specific heat capacity and curing kinetics strongly influenced predicted curing profiles and cycle times. These results emphasise that dataset choice, particularly for curing-related parameters, is critical to achieving predictive accuracy in LSR injection moulding simulations. Unlike previous studies on LSR injection moulding, which typically adapt thermoplastic-inspired characterisation methods without systematically addressing their limitations, this work introduces an organised and comparative methodology to evaluate how different material characterisation techniques influence simulation outcomes. The proposed approach establishes a methodological framework that can guide future research and improve the reliability of process simulations for LSR and other polymeric systems.