Abstract
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.
Highlights
Increasing requirements concerning the operational conditions and durability of desired microstructure
The target of the present study is to build a software solution based on a neural network, which is capable of generating chemical compositions of High-speed steels (HSSs) for defined parameters regarding stable phases and their compositions
A database consisting of random chemical compositions and corresponding thermodynamic equilibrium values was created and proved to be suitable for training a neutral network
Summary
Gero Egels,* Nils Wulbieter, Sebastian Weber, and Werner Theisen the chemical composition to obtain a Increasing requirements concerning the operational conditions and durability of desired microstructure. The amount and composition of stable phases in HSS does often have nonlinear dependencies on the overall chemical composition, which makes it difficult to determine bottleneck of collecting experimental data This is done by training a neural network with a set of thermodynamically calculated data instead of experimental data. For the development of a new steel, it might be desirable to be able to define certain stable phases as well as their amounts and compositions at a temperature and to receive the corresponding chemical composition for the steel This represents the reverse way of how classical CALPHAD calculations work. The target of the present study is to build a software solution based on a neural network, which is capable of generating chemical compositions of HSS for defined parameters regarding stable phases and their compositions
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