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Herein, the abrasive wear behavior of different high‐alloyed powder metallurgical (PM) tool steels is investigated at elevated temperatures (400–600 °C) in a dry‐pot wear tester containing Al2O3 particles. To identify the influence of the microstructure, PM tool steels with different hot hardnesses, carbide types, and carbide volume contents are selected. Wear tracks are analyzed by scanning electron microscopy (SEM) to clarify wear mechanisms. The results show that there is no direct correlation between wear resistance and only one material property such as hot hardness, carbide content, or carbide type. More important seems to be the best possible compromise between a sufficient hot hardness of the metallic matrix and a high volume content of carbides that are harder than the attacking abrasive particles at the respective temperature. When the test temperatures surpass the tempering temperature of the investigated steels, there is a pronounced change in wear behavior due to the stronger embedding of abrasive particles into the wear surface. It is thus necessary to discuss the microstructural properties as a function of temperature, considering interactions with the abrasive particles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]