Simulation of the Texture Evolution During Annealing of Cold Rolled Bcc and Fcc Metals Using a Cellular Automation Approach

Abstract

In this study both primary static recrystallization and static recovery of cold rolled bcc and fcc metals and alloys are numerically simulated using a three-dimensional model that is based on a modified cellular automaton approach. The model considers the influence of the initial deformation texture and microstructure on both static recovery and primary static recrystallization with a high spatial resolution. The cellular automaton technique provides both local and statistical information about the kinetics, the morphology and the texture change during annealing. The influence of nucleation and growth can be studied in detail. The simulations are compared to experimental results obtained on fcc and bcc polycrystals.

A texture-component Avrami model for predicting recrystallization textures, kinetics and grain size

“…Owing to these features, cellular automata provide a discrete method of simulating the evolution of dynamical systems which contain large numbers of similar components on the basis of local interactions. Various applications in the field of recrystallization and recovery modelling were discussed in [32][33][34][35][36][37][38][39][40][41][42][43].…”

Modeling and Simulation of Steel Rolling with Microstructure Evolution: An Overview

“…The CA is a probabilistic approach in which the recrystallized cells are initially randomly selected. Many studies have successfully predicted recrystallized texture in the annealing process using CA [ 54,106,107 ]…”

“…The CA is a probabilistic approach in which the recrystallized cells are initially randomly selected. Many studies have successfully predicted recrystallized texture in the annealing process using CA [54,106,107] Here, a representative study on the texture evolution of interstitial free (IF) steels after cold rolling is given by Park et al [108] The study presents reliable prediction results through integration of the CA model with CP. The parameters in the CP model are determined through backfitting to the mechanical test results of the material (i.e., uniaxial tension or compression).…”

Computationally Efficient Cellular Automata‐Based Full‐Field Models of Static Recrystallization: A Perspective Review

“…The texture evolution in bcc and fcc metals during annealing was evaluated in ref. [85]. Some aspects of coupling with the FE models were also proposed.…”