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Heat treatment is usually preferred to achieve the pre-determined material properties of steel components thereby suited for several engineering applications. Gas quenching after austenitisation in vacuum is an established process for this purpose, as it is clean and environment friendly. The selection of quenching parameters depends on many factors such as sample geometry as well as material and the batches. The process parameters are adopted by many years of expert knowledge, complex calculations or from trial and error methods. This problem is addressed in this scientific study by developing a prognosis tool, which can predict the heat treatment results based on an artificial neural network (ANN). This is attained by training the ANN on the basis of experimental and numerical investigations. Therefore, the heat treatment experiments were carried out on specific components made from 42CrMo4 and 100Cr6 in a two chambered quenching setup, where N2 gas act as quenching fluid. The cooling behaviour will be investigated under the variation of process parameters such as gas pressure, geometry and batches. The development of the microstructure and hardness as a function of the process parameters are analysed metallographically. For the detailed investigation as well as to improve the training quality of ANN, FEM simulations are developed and validated, which serves afterwards to research the influence of parameter variation numerically. Thereby, sufficient data are generated numerically and experimentally for the successful training of the ANN of the prognosis tool, which can finally predict the heat treatment results.

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Plasma nitriding is widely used in various industrial applications to improve surface hardness and wear properties. Especially for tool steels, it is also used to improve the support and adhesion of diamond-like carbon (DLC) coatings. The properties of the nitrided zone produced by plasma nitriding are influenced by the applied process parameter, in particular temperature and time. However, for high-alloy tool steels, a deeper understanding of the underlying diffusion processes of the nitrogen and the interaction with the existing microstructure, as well as the effects on the case depth is still lacking. Therefore, in this study, specimens of high-alloy tool steel X153CrMoV12 were plasma nitrided at varying temperatures (480 °C, 520 °C, 560 °C) and treatment times (2 h, 4 h, 16 h). The resulting nitrided zones were investigated by optical and scanning electron microscopy (OM and SEM), depth-dependent glow discharge optical emission spectroscopy (GDOES), X-ray diffraction (XRD), and hardness measurements to characterize their microstructure, chemical composition, and hardness depending on the process parameters. The distribution of carbides (M7C3), e.g., chromium carbides, affects the diffusion of the nitrogen and the layer growth. An increase of temperature and duration leads to an increased layer thickness. The composition of the compound layer is, e.g., influenced by the process parameters: ε nitrides (Fe2–3N) occurred preferentially at lower temperatures, while γ′ nitrides (Fe4N) appeared mostly at higher temperatures. In order to investigate the influence of the carbides of the high-alloy tool steel on the nitriding process, a new methodology was developed by means of finite element analysis (FE), which makes it possible to analyze this influence on the development of the nitrogen concentration profile. This methodology makes it possible for the first time to map the heterogeneous nitrogen evolution and distribution.

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