Solid-State Microstructure Evolution In Steels.
Abstract
This paper deals with the simulation of microstructure evolution in steels, specifically
eutectoid steels, where competitive diffusive (pearlitic) and diffusionless (martensitic)
transformations may take place.
Diffusion-controlled transformations are modelled by using the classical Johnson-Mehl-
Avrami-Kolmogorov law for isothermal transformations, while the martensitic transformation
is assumed to obey either the Koistinen-Marburger or the Yu laws.
The non-isothermal evolution of diffusive transformations is derived from the isothermal
transformation kinetics either by invoking the additivity rule, or by integrating the rate form of
the Johnson-Mehl-Avrami-Kolmogorov law in time. The ability of both techniques to build continuous
cooling transformation (CCT) diagrams from isothermal transformation (IT) diagrams
is evaluated.
Microstructure evolution is coupled with the thermal analysis, performed using the finite
element method.
A finite element analysis of a quench problem is finally carried out to evaluate the performance
of the model.
eutectoid steels, where competitive diffusive (pearlitic) and diffusionless (martensitic)
transformations may take place.
Diffusion-controlled transformations are modelled by using the classical Johnson-Mehl-
Avrami-Kolmogorov law for isothermal transformations, while the martensitic transformation
is assumed to obey either the Koistinen-Marburger or the Yu laws.
The non-isothermal evolution of diffusive transformations is derived from the isothermal
transformation kinetics either by invoking the additivity rule, or by integrating the rate form of
the Johnson-Mehl-Avrami-Kolmogorov law in time. The ability of both techniques to build continuous
cooling transformation (CCT) diagrams from isothermal transformation (IT) diagrams
is evaluated.
Microstructure evolution is coupled with the thermal analysis, performed using the finite
element method.
A finite element analysis of a quench problem is finally carried out to evaluate the performance
of the model.
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