Table of Contents
Textures and Microstructures
Volume 32, Issue 1-4, Pages 197-219
http://dx.doi.org/10.1155/TSM.32.197

A Simulation of Recrystallization Textures of Al-Alloys With Consideration of the Probabilities of Nucleation and Growth

Los Alamos National Laboratory, Center for Materials Science, K765, Los Alamos NM 87545, USA

Accepted 28 September 1997

Copyright © 1999 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The characteristic recrystallization texture components of cold rolled Al-alloys can be traced back to a growth selection of grains with an approximate 40º 111 orientation relationship out of a limited spectrum of preferentially formed nucleus orientations. Accordingly, recrystallization textures can be modeled by the multiplication of a function f(g)nucl describing the probability of nucleation of the various orientations and a function f(g)grow representing their growth probability.

Whereas the growth probability can be accounted for by a 40 111 transformation of the rolling texture, the nucleation probability of the respective grains is given by the distribution of potential nucleus orientations, which is known from local texture analysis for the most important nucleation sites in cold rolled Al-alloys, cube-bands, grain boundaries and second-phase particles. If several nucleation sites are active simultaneously, the nucleation probabilities have to be weighted according to their respective proportions. For that purpose, the numbers of nuclei forming at the various nucleation sites were calculated according to a model approach proposed by Vatne et al. (Acta Mater 44, 1996, 4463–4473).

The paper describes the model for recrystallization texture simulation in Al-alloys and gives examples of recrystallization textures simulated regarding a variation of different microstructural parameters to demonstrate the predictive power of the model.