Simulation of primary static recrystallization with cellular operator model [Elektronische Ressource] / vorgelegt von Prantik Mukhopadhyay

Prantik Mukhopadhyay

Informations

• Publié par: rheinisch-westfalischen_technischen_hochschule_-rwth-_aachen
• Publié le: 01 janvier 2005
• Nombre de lectures: 10
• Langue: English
• Poids de l'ouvrage: 5 Mo

Extrait

Prantik Mukhopadhyay



Simulation of Primary Static Recrystallization with
Cellular Operator Model


















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Simulation of Primary Static Recrystallization with
Cellular Operator Model




Von der Fakultät für Georessourcen und Materialtechnik der
Rheinisch -Westfälischen Technischen Hochschule Aachen

zur Erlangung des akademischen Grades eines

Doktors der Ingenieurwissenschaften

genehmigte Dissertation

vorgelegt von M. Tech

Prantik Mukhopadhyay
aus Bishnupur, West Bengal, India





Berichter: Univ.-Prof. Dr. rer. nat. Günter Gottstein
Prof. Dr. –Ing. Dierk Raabe


Tag der mündlichen Prüfung: 28. September 2005

Diese Dissertation ist auf den Internetseiten der Hochschulbibliothek online verfügbar

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Contents:

1. Introduction 6

2. Recrystallization texture, microstructure and kinetics 8
2.1 Phenomenon, definitions and energies 8
2.2 Recrystallization texture 9
2.2.1 Definition and measurement of texture 9
2.2.2 Development of recrystallization texture 9
2.3 Microstructure evolution during recrystallization 11
2.4 Quantitative description of microstructure 13

3. Review on existing recrystallization models 15
3.1 Phenomenological models 15
3.2 Geometrical microstructure models 18
3.3 Vertex models 20
3.4 Discrete models 21
3.5 Texture models 26

4. Cellular operator model for primary static recrystallization 31
4.1 Necessity of this model 31
4.2 Sources and depiction of space variables 32
4.3 Preferential nucleation based on space discrete variables 37
4.4 Time dependent nucleation 45
4.5 Nuclei number distribution 45
4.6 Recovery models 47
4.7 Growth of nuclei by grain boundary movement 49
4.8 Recrystallization texture and grain distribution 54

5. Results 56
5.1 Recrystallization exponent in site saturation nucleation and isotropic
growth condition 56
5.2 The effect of grain boundary nuclei distribution 56
5.3 Influence of time dependent nucleation 58
5.4 The effect of nuclei density 61
5.5 The effect of orientation discrete and average driving force 63
5.6 Initial grain dimension on texture, microstructure and kinetics 64
5.7 Variation in subgrain size and particle radius 73
5.8 Back driving force on recrystallization kinetics 74
5.9 Solute drag on recrystallization kinetics 75
5.10 Recovery on recrystallization kinetics 76
5.11 Location discrete nucleation approach 78
5.12 Through thickness microstructure heterogeneity 80
5.13 Time dependence of different nucleation sources 82
5.14 Oriented growth 83
5.15 The particle stimulated nuclei orientations in deformed 4
microstructure 86
5.16 Texture engineering from laboratory scale to industrial scale 88
5.17 Industrial AA5182 strip production 91

6. Discussion 101
6.1 Nuclei distribution 101
6.2 Time dependent nucleation 102
6.3 Effect of initial microstructure 105
6.4 The simulation of recrystallization of AA5182 alloy from transfer
slab to final gauge 111

7.1 Sumary 113
7.2 Zusammenfassung 115

References 117






























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Acknowledgement

I would like to thank my academic advisor University-Prof. Dr. rer. nat. Günter
Gottstein, Guest Prof. Dr. Lasar Shvindlerman, Prof. Dr. Dierk Raabe and Prof. Dr. Oalf
Engler for their inspiration, encouragement during the course of this project.
I am indebted to my colleagues Dipl. Ing. Manfred Schneider, Dipl. Ing. Matthias
Goerdeler, Dipl. Ing. Mischa Crumbach, Dipl. Ing. Dirk Kirch, Dipl. Ing. H. Artz and
Dipl. Ing. Luc Neumann for their valuable comments, suggestions and association on
the simulation work and FEM calculations.
I would like to express thanks to system administrator Matthias Loeck and to Dipl.
Math Gerda Pomana for necessary discussions and programming.
I must take opportunity to thank all colleagues form the vir[FAB] project work for
supplying me the aluminium alloys used in that project and necessary experimental data
for validation of the simulation results and all the laboratory staffs and the members of
IMM-Aachen, who have helped me a lot.
Finally I gratefully acknowledge the support by the vir[FAB] consortium for their
collaborative support, the funding by the European Community and the Deutsche
Forschungsgemeinschaft (DFG) through the Collaborative Research Centre 370.














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1. Introduction.

A deformed material, e.g. a rolled sheet, is liable to softening by recrystallization during
an annealing treatment. Recrystallization proceeds by nucleation of strain free grains
and their growth by consumption of the deformed microstructure. A competing process
to recrystallization is recovery which consists of a rearrangement of deformation
induced dislocations in energetically more favourable patterns, in particular in low
angle grain boundaries.
A recrystallized microstructure is in first order defined by the distribution of its grain
diameters (grain size distribution) and the distribution of their crystallographic
orientations (crystallographic texture). Both recrystallization grain size and texture, for
instance obtained after hot rolling or by batch annealing after cold rolling depend on the
respective processing parameters. Texture is the cause of mechanical anisotropy.
Therefore, a textured sheet deforms inhomogeneously during a sheet forming process
and gives rise to earing, which is undesired since it can cause serious losses of
production time. A profound understanding of the development of texture during the
fabrication process of a sheet metal is prerequisite for enhanced productivity and
efficient quality improvement of the finished product.
The recrystallization during thermomechanical treatment of a material generates
textures which are characteristic of the material and the process parameters. In turn they
will have a strong influence on the plastic behavior and the development of deformation
texture during subsequent forming. Therefore, it is the ultimate goal of recrystallization
research to predict the recrystallization texture development during processing of a
material, which requires a reliable model that takes into account the physics and
mechanisms of recrytallization. The validation of a model requires at the same time
reliable experimental data that can be compared with model predictions. On the other
hand modelling can provide