Simulation of primary static recrystallization with cellular operator model
type: CreativeWork
description: 1. Based on the modified cellular automata approach of Reher [60] a cellular operator model has been developed that is capable of accounting for spatial and temporal inhomogeneity on a finer scale. For this a scalable subgrid automaton is introduced that allows for a high spatial resolution on demand and still high computational efficiency. The scalable subgrid permits to track the minute changes of growth front during recrystallization owing to local variations of boundary mobility and net driving force. This approach substantially improves the prediction of grain morphology and grain statistics.2. This new cellular operator model for recrystallization (CORe) has been connected to the grain cluster deformation texture model GIA to account for different nucleation mechanisms. The GIA model renders the deformation texture in terms of grain cluster that in sum properly reflects the deformation texture. Moreover, the GIA model is interfaced to a work hardening model 3IVM that provides information on the dislocation densities of the individual grains in a cluster, depending on the total amount of slip. The individual clusters are evaluated with regards to their behaviour during deformation. Divergence of grain orientation is taken as measure for nucleation in transition band, large stored energy differences (e.g. large difference in dislocation density) across grain boundaries are interpreted as nucleation along grain boundaries and stored energy differences across the deformation zone around the randomly distributed second phase particles are considered as particle stimulated nucleation. Each process contributes specific nucleus orientation. 3. The information on the property of grain to a specific nucleation mechanism is coupled with the information on the local dislocation density to predict the absolute number of nuclei of a specific orientation and location as needed for quantitative recrystallization texture prediction. For this purpose a statistical model for the…
abstract: 1. Based on the modified cellular automata approach of Reher [60] a cellular operator model has been developed that is capable of accounting for spatial and temporal inhomogeneity on a finer scale. For this a scalable subgrid automaton is introduced that allows for a high spatial resolution on demand and still high computational efficiency. The scalable subgrid permits to track the minute changes of growth front during recrystallization owing to local variations of boundary mobility and net driving force. This approach substantially improves the prediction of grain morphology and grain statistics.2. This new cellular operator model for recrystallization (CORe) has been connected to the grain cluster deformation texture model GIA to account for different nucleation mechanisms. The GIA model renders the deformation texture in terms of grain cluster that in sum properly reflects the deformation texture. Moreover, the GIA model is interfaced to a work hardening model 3IVM that provides information on the dislocation densities of the individual grains in a cluster, depending on the total amount of slip. The individual clusters are evaluated with regards to their behaviour during deformation. Divergence of grain orientation is taken as measure for nucleation in transition band, large stored energy differences (e.g. large difference in dislocation density) across grain boundaries are interpreted as nucleation along grain boundaries and stored energy differences across the deformation zone around the randomly distributed second phase particles are considered as particle stimulated nucleation. Each process contributes specific nucleus orientation. 3. The information on the property of grain to a specific nucleation mechanism is coupled with the information on the local dislocation density to predict the absolute number of nuclei of a specific orientation and location as needed for quantitative recrystallization texture prediction. For this purpose a statistical model for the…
author: Mukhopadhyay, Prantik
language: eng
keywords: info:eu-repo/classification/ddc/620, Ingenieurwissenschaften, Recrystallization, Cellular Operator Model
date published: 2005
date modified: 2026-07-30T19:42:48Z
provider: RWTH Aachen University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Publikationsserver der RWTH Aachen University