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Das charakteristische Existenzgebiet der Blockstrukturen mit zweidimensional begrenzten Bauelementen endet im System Nb2O5/WO3, wie bereits frühere Untersuchungen gezeigt haben, unter Gleichgewichtsbedingungen bei einem Maximalwert von 2,654 O/O/ΣM (M  Nb, W). Es gelang nun, in den mit den Verhältnissen Nb2O5: WO3 = 6:1, 7:3, 8:5 sowie 9:8 auftretenden Phasen im weiten Umfang Nb durch W zu substituieren, wobei ihre ursprüngliche Blockstruktur und das jeweilige Verhältnis O/ΣM erhalten blieben. So bildet z. B. die «9:8»‐Phase W4/4[Nb18W7O69] bei unveränderter Größe der Bauelemente ([5 × 5]‐Blöcke) einen Mischkristall W4/4[Nb11W14O69]; das Verhältnis W/Nb wird damit drastisch von 0,444 auf 1,364 erhöht. Durch metastabile Oxydation der so erhaltenen Mischkristalle bei Temperaturen um 500°C, z. B. an der Luft, gelangt man in das System Nb2O5/WO3 zurück. Das Existenzgebiet der Blockstrukturen konnte in dieser Weise bis weit über die Grenze bei 2,654 O/ΣM hinaus zu höheren W/Nb‐Werten erweitert werden.

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The multilayer perceptron type of neural network (MLP) was used to investigate the influence of chemical composition, heat treatment and thermodynamically stable phases on the creep rupture strength of 9-12 % Cr-steels. The model is based on extensive sets of data from literature. Additionally, methods for the minimization of Gibb's energy in complex multi-component systems are employed for thermodynamic generation of features. Chemical composition and operational temperature of the material serve to determine the complex state of equilibrium. This information supplies additional input parameters for the neural network. The created model allows precise prediction and adjustment of the long-term behaviour of 9-12 % Cr-steels by means of application of optimal heat treatment and material composition. In addition, relationships and dependencies between thermodynamically determined states and the creep rupture behaviour of ferritic 9-12 % Cr-steels are characterised. One aim of the model developed on the basis of neural networks in combination with the method minimizing the Gibb's energy in complex systems is to make the prediction of creep rupture behaviour possible and reproducible within the available data volume. Processes occurring near thermodynamic equilibrium can be described sufficiently by means of classical thermodynamics. Problems occurring with prediction of states and processes are caused by a more or less considerable deviation from the state of equilibrium. An indirect consideration of these deviations from the state of equilibrium can be achieved by joining complex thermodynamic approaches with neural networks. This allows the description of metastable and non-eqilibrium phases, i.e. dynamic systems. This includes modelling of time-dependent processes in materials, the creep rupture strength of 9-12 % Cr-steels being determined on the basis of experimental and thermodynamic data. The main target of the symbiosis of neural networks and complex thermodynamics is to combine the advantages of these methods, to compensate their deficiencies and to predict the creep rupture behaviour of materials. steel research 72 (2001) NO.9 observations '-, :L J . -_ 207 steel research 72 (2001) NO.9

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