Modelling Phase Transformations in Steel Alloys
Mathias Mul (COSSE student, double degree with TU Berlin)

Supervisor: Fred Vermolen

Site of the project:
Tata Steel
Breedbandweg 1
1951 MC Velsen-Noord

Supervisor Tata Steel: Kees Bos

start of the project: November 2013

In May 2014 the Interim Thesis has appeared and a presentation has been given.

The Master project has been finished in September 2014 by the completion of the Masters Thesis and a final presentation has been given. For working address etc. we refer to our alumnipage.

Summary of the master project:
The mechanical properties of steel are determined by its composition and microstructure. There are many ways to adapt the microstructure of steel alloys. Thermal treatment is widely used to give steel its properties for specific purposes. Effects on the internal structure of a steel alloy under cooling or heating can be modelled on micro scale. The average grain size is the most interesting microstructure parameter, because it has been extensively correlated to mechanical properties. An explanation for the different behaviour lies within the transformation of the iron lattice from face centred cubic (fcc) at higher temperatures to body centred cubic (bcc) at lower temperatures. The high temperature phase is referred to as austenite and the low temperature phase is referred to as ferrite. Therefore, the phase change from fcc to bcc is also called austenite-ferrite transformation. (image below)

Steel consists mainly of the elements iron (Fe) and carbon (C). The movement of the interface between austenite and ferrite depends partly on the carbon concentration at the interface. Ferrite is saturated with carbon at much lower concentrations than austenite. Therefore, during the transformation, carbon is pushed ahead of the moving interface. This can be modelled by the diffusion equation, which mathematically describes carbon spreading through the austenite. (image below)

The goal of this thesis is to model grain growth of different phases in an accurate and efficient way. This can be done using Cellular Automata (CA) models. A CA model is a discrete model with a regular grid of cells. Each cell has a set of properties, most importantly its state, neighbourhood, and transformation rule. Given an initial state, each time step the state of a cell is updated by a rule that usually is a mathematical function of the states of its neighbours.

Results of simulations to estimate the stability of the interface.

Contact information: Kees Vuik

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