Heat treatment is an important post-treatment process for cast metal components with the purpose of increasing their mechanical properties. After casting, the components are placed in a heat treatment furnace, which is heated using either electrical heaters or gas burners. The components are held at elevated temperatures for a prescribed amount of time after which they are removed from the furnace and then cooled by air or by being submerged in water. This process – holding at high temperatures and then cooling the components in a controlled way – facilitates the necessary metallurgical transformations that give the components their final properties. Since heat treatment is conducted at high temperatures, typically in the 900-1100 ℃ range, it is very energy intensive. For this reason, foundries try to optimize this process in terms of temperature and time. While optimization can be done to some extent by using experimental trial-and-error, a complementary but less developed route is to set up and model a digital twin of the heat treatment process, which allows for much greater flexibility and quicker screening of new concepts. The goal of this Master's thesis project is to 1) set up a digital twin of a heat treatment process for cast components using OpenFOAM, an open source CFD and FEM environment, 2) create an AI training dataset using this twin, and then 3) train surrogate AI models that can simulate the heat treatment process orders of magnitude faster than OpenFOAM, thus making it possible to quickly search through parameter space for the optimal furnace settings.