A new front-tracking Lagrangian model for the modeling of dynamic and post-dynamic recrystallization

TL;DR

This paper introduces an advanced front-tracking Lagrangian model for simulating dynamic and post-dynamic recrystallization, incorporating grain boundary migration and nucleation processes, with validated accuracy and improved computational performance.

Contribution

It extends previous models by including stored energy effects and grain nucleation, enabling more accurate simulation of recrystallization phenomena.

Findings

Model accurately simulates grain growth and recrystallization

Enhanced computational efficiency over classical FE methods

Validated against multiple test cases

Abstract

A new method for the simulation of evolving multi-domains problems has been introduced in previous works (RealIMotion), Florez et al. (2020) and further developed in parallel in the context of isotropic Grain Growth (GG) with no consideration for the effects of the Stored Energy (SE) due to dislocations. The methodology consists in a new front-tracking approach where one of the originality is that not only interfaces between grains are discretized but their bulks are also meshed and topological changes of the domains are driven by selective local remeshing operations performed on the Finite Element (FE) mesh. In this article, further developments and studies of the model will be presented, mainly on the development of a model taking into account grain boundary migration by (GBM) SE. Further developments for the nucleation of new grains will be presented, allowing to model Dynamic Recrystallization (DRX) and Post-Dynamic Recrystallization (PDRX) phenomena. The accuracy and the performance of the numerical algorithms have been proven to be very promising in Florez et al. (2020). Here the results for multiple test cases will be given in order to validate the accuracy of the model taking into account GG and SE. The computational performance will be evaluated for the DRX and PDRX mechanisms and compared to a classical Finite Element (FE) framework using a Level-Set (LS) formulation.