Flexible manufacturing processes such as laser metal deposition have a high potential for a production solely defined by software to cope with the current challenges of production systems. The determination of suitable machine parameters for the production of novel materials and geometries however requires extensive experimental effort. Existing simulative approaches do not offer sufficient accuracy to predict the relevant machine parameters in a satisfactory way. This paper presents a new concept, in which we apply a digital twin to provide a step towards a fully software-defined and predictable laser metal deposition process. The presented concept includes relevant data of the machines as well as data-driven machine learning models and physics-based simulation models. This enables a more reliable prediction of geometries of single tracks which was validated on a laser metal deposition machine.