The simulation of casting processes is a powerful tool that helps predict the defects in the final component. In practice, material data are often extracted from the literature, and significant deviations can occur between the simulation and real casting. Thermal analysis-driven data are a useful strategy to acquire reliable material data, even more so in the case of aluminium metal matrix composites (MMCs) reinforced with silicon carbide particles (SiCp).  High processing costs are a drawback of composites in automotive applications, highlighting the need for simulation techniques based on reliable datasets. The castability and solidification behaviour of aluminium-based composites depend on the reinforcement particle shape, size, and content, among other factors. The calibration of the material dataset for the simulation maximises the defect prediction accuracy and minimises the production costs. The present study investigates the castability and thermophysical properties of aluminiumbased composites reinforced with different SiCp contents ranging from 0 to 30 wt.%. The materials were produced by casting to gather relevant data as input for the material database of the casting simulation of the brake rotor. The simulation model predicted shrinkage defects by dividing the casting into zones with different liquid-phase fractions. The shrinkage porosities were caused by changing the melt and solid phase densities at the temperature change. The laws of heat and mass transfer between the different casting phases and moulds were used to forecast the shrinkage porosity, cold shuts, and hotspots. Material properties, such as thermal diffusivity, thermal expansion, and specific heat capacity, were evaluated as a function of temperature and simulation software with density and thermal conductivity. Computer-aided cooling curves were imported to create a new dataset of aluminium-based composites with different reinforcement additions. A simulation based on the adapted material database was validated in terms of solidification and defect prediction.