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Gero Egels, Nils Wulbieter, Sebastian Weber, Werner Theisen

• Increasing requirements concerning the operational conditions and durability of tools create a demand for the optimization of tool steels. High‐speed steels (HSSs), for example, contain high amounts of carbides embedded in a secondary hardenable martensitic matrix. The wear behavior and the mechanical properties of HSS can be optimized for a certain application by adjusting the type and amount of carbides, as well as their compositions and the composition of the matrix. Computational thermodynamics based on the calculation of phase diagrams method allow the estimation of arising phases as well as phase compositions during the solidification or the heat treatment of a steel. However, in complex alloy systems, for example, HSS, the relationships between the content of alloying elements and the stability and the composition of phases can be complicated and nonlinear. Therefore, it can be difficult to find alloy compositions that are suitable to achieve a desired microstructure with iterative calculations. To handle this difficulty, a computational tool is developed, which determines compositions to obtain predefined HSS microstructures. The computational tool is based on a neural network that was previously trained with a thermodynamically calculated database. The efficiency of this approach is experimentally verified by producing and investigating laboratory melts of different HSS.