Advanced Steel Metallurgy: Simulation & Modeling
Program Organizers: Chirag Mahimkar, Big River Steel; Justin Raines, SSAB Americas; Kip Findley, Colorado School of Mines; Alla Sergueeva, NanoSteel Company Inc; Daniel Branagan, The NanoSteel Co
Tuesday 2:00 PM
November 3, 2020
Room: Virtual Meeting Room 39
Location: MS&T Virtual
2:00 PM Invited
Modelling of Precipitation and Grain Growth in Ti-Nb Microalloyed Steels: Alexis Graux1; Sophie Cazottes1; M. Perez1; M. Bugnet1; Damien Fabregue1; 1Univ. Lyon, INSA Lyon, MATEIS, UMR CNRS 5510, F-69621
To achieve high mechanical properties in microalloyed steels, fine precipitates are used to control grain growth during heat treatments. Thus, it is of prime importance to be able to predict the austenite grain growth kinetic coupled with precipitation. The model described has been used in Ti-Nb microalloyed steels. After a careful characterization of the precipitation state by SEM and TEM, a thermodynamic based model has been developed. The solubility product was determined by CALPHAD database and then a model based on the classical nucleation and growth theory was implemented to be able to describe the evolution of (Ti,Nb)C size distributions during isothermal heat treatments. The calculated precipitate size distributions were coupled to a simple grain growth model based on Zener pinning accounting for the pressure due to the whole precipitate size distribution. The obtained grain growth kinetics are in good agreement with the experimental obtained ones after different heat treatments.
2:20 PM
Modeling and Experimental Validation of the Precipitation Kinetics of Vanadium Carbide in Austenitic Steel: Paul Lambert1; Daniel Bechetti1; Keith Knipling2; Maya Nath1; Matthew Draper1; Charles Fisher1; 1Naval Surface Warfare Center - Carderock Division; 2Naval Research Laboratory
Computational thermodynamics modeling tools are highly effective for predicting phase fractions and other equilibrium material properties. Non-equilibrium phenomena such as precipitate nucleation/growth are inherently more challenging to model, and existing calibration datasets for these models are comparatively less robust. Thus, accurate prediction of the structure-process-property relationships for some classes of materials (e.g. precipitation-strengthened alloys) must rely on the coordinated use of modeling techniques and judicious experimental validation. In this work, we present modeling and characterization efforts within an Integrated Computational Materials Engineering (ICME) framework aimed at property prediction in austenitic steels precipitation-strengthened by vanadium carbide (VC) particles. Precipitation modeling software was used to predict VC precipitate characteristics as a function of alloy composition and aging conditions, and these predictions were experimentally validated for selected alloy systems and heat treatment schedules. Results of this study will be discussed in the broader context of ICME approaches to rapid design of new materials.