Computational Model for Predicting Precipitation Evolution in Two-Phase Steel: First Application to Grain-Oriented Electrical Steel

Production of grain-oriented (GO) electrical steel represents a very complex technological process route with the fine dispersion of precipitates representing a key requirement to maximize final magnetic properties. This paper describes a novel computational model able to predict the kinetics of second-phase particles in dual phase steels by considering coprecipitation in ferrite and austenite. The model is applied to a typical industrial GO electrical steel subjected to production steps ranging from continuous casting to coiling after hot rolling. To facilitate interpretation of final results, these production steps are represented by a simplified thermo-mechanical profile although the model can process arbitrary profiles with different complexities. It is demonstrated that the methodology proposed in this work provides a comprehensive thermo-kinetics description of secondary phases with aluminium nitride (AlN) being the main precipitate. A bimodal distribution of AlN is predicted at the end of the cycle with two populations, one at nano-meter scale (< 200 nm) and one at micro-meter (> 200 nm) scale. Final distribution of AlN is also compared with experimental observations. For both populations, the main characteristic quantity (i.e. mean diameter) computed by the model is in agreement with measurements. This makes the developed technique a powerful tool for both qualitative and quantitative assessment of second-phase particles evolution in dual phase steels.