Abstract
As a step in the further development of models and databases to support design of new steels, i.e., the “steel genome”, the growth of bainitic ferrite plates is accounted for by a thermodynamic and kinetic approach. The thermodynamic aspects are represented by CALPHAD databases and a Gibbs energy barrier for growth Bm. Experimental information on ferrite-plate growth rates for a number of Fe-C alloys, some of high-purity, are analyzed in terms of a modified Zener–Hillert model and the barrier as well as some kinetic parameters are evaluated. It is found that the barrier varies in a smooth way with carbon content and lengthening rate. In order to improve the agreement with the experimental information it was necessary to adjust the diffusion coefficient of carbon in austenite at low temperatures. It is concluded that the representation of the experimental data is satisfactory.
Highlights
THE materials genome is the combination of models and databases that supports efficient materials design,[1] the aim being to decrease the development time and costs for materials and processes
The CALPHAD type of models and databases for thermochemical and diffusional data may be seen as role models for materials genomic databases
The purpose of the present work is to provide a practical formalism based on the CALPHAD methodology and databases for predicting the lengthening rate of bainitic ferrite, an important part of the bainite transformation
Summary
THE materials genome is the combination of models and databases that supports efficient materials design,[1] the aim being to decrease the development time and costs for materials and processes. The CALPHAD type of models and databases for thermochemical and diffusional data may be seen as role models for materials genomic databases. The bainite transformation in steels is considered because it has received a lot of attention over the last decade. Such steels hold promises of excellent engineering properties, e.g., high hardness and strength, good wear resistance and toughness, to mention a few. In order to optimize product and performance, models are increasingly being used to facilitate tailoring of material properties
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