Phase transformations from a modelling perspective
Channel: bhadeshia123
Date: 2023-02-17

A talk by Professor D. Ernst Gamsjäger,  Montan Universität Leoben (Austria), delivered as a part of the ``Metallic Microstructures European Lectures Online" series.

Information about stability ranges for various phases is crucial for materials design and process engineering. These stability ranges are represented by phase diagrams, which are constructed by means of appropriate modelling approaches using thermodynamic databases.

In the first part of the lecture the Debye-Einstein fitting approach for pure components and end members is compared with the frequently used temperature polynomials. Modelling of non-equilibrium processes also benefit from equilibrium thermodynamics. A thermodynamic system not far from equilibrium can be divided into a sufficiently large number of sub-systems, where local equilibrium is assumed to hold in each sub-system. As long as the neighbouring sub-systems are in a different local equilibrium state a thermodynamic force (e.g. a difference in chemical potentials) drives a thermodynamic flux (e.g. the diffusive flux of a component in the system).

Irreversible state changes will be considered in the second part of the lecture where the kinetics of growth and shrinkage of oxide crystals is investigated. The interfacial reaction and diffusion in the liquid bulk are considered as possible rate-controlling processes. The modelling approach combined with data analysis from key experiments reveals the underlying dissipative processes of solidification and melting phenomena, here, diffusion in the liquid bulk material and/or the interfacial reactions.

Solid/solid phase transformations are the topic of the third part of the lecture. In particular the consequences of various heat treatments on the microstructure of steels is explored. Whereas dilatometry, metallographic investigations and hardness measurements help to distinguish between martensite, ferrite, bainite and pearlite, evaluating the X-ray diffractograms allows to quantify the phase fraction of austenite. The experimental investigations are evaluated by machine learning algorithms with the potential to relate microstructures to material properties. The classification of martensite, ferrite, bainite and pearlite is supported by an unsupervised machine learning algorithm (here hierarchical clustering) on the basis of measured X-ray diffractograms.