Modelling Simul. Mater. Sci. Eng. 8 (2000) 445 C A And C P F E M

Modelling Simul. Mater. Sci. Eng. 8 (2000) 445–462. Printed in the UK PII: S0965-0393(00)11463-9

Coupling of a crystal plasticity finite-element model with a
probabilistic cellular automaton for simulating primary static
recrystallization in aluminium

Dierk Raabe† and Richard C Becker‡
† Max-Planck-Institut f¨ r Eisenforschung, Max-Planck-Str. 1, 40237 D¨ sseldorf, Germany
u u
‡ Lawrence Livermore National Laboratory, 7000 East Avenue, L-170 Livermore, CA 94550,
USA

Received 20 October 1999, accepted for publication 31 January 2000

Abstract. The paper presents a two-dimensional approach for simulating primary static
recrystallization, which is based on coupling a viscoplastic crystal plasticity finite-element model
with a probabilistic kinetic cellular automaton. The crystal plasticity finite-element model accounts
for crystallographic slip and for the rotation of the crystal lattice during plastic deformation. The
model uses space and time as independent variables and the crystal orientation and the accumulated
slip as dependent variables. The ambiguity in the selection of the active slip systems is avoided
by using a viscoplastic formulation that assumes that the slip rate on a slip system is related to
the resolved shear stress through a power-law relation. The equations are cast in an updated
Lagrangian framework. The model has been implemented as a user subroutine in the commercial
finite-element code Abaqus. The cellular automaton uses a switching rule that is formulated as a
probabilistic analogue of the linearized symmetric Turnbull kinetic equation for the motion of sharp
grain boundaries. The actual decision about a switching event is made using a simple sampling
nonMetropolis Monte Carlo step. The automaton uses space and time as independent variables and
the crystal orientation and a stored energy measure as dependent variables. The kinetics produced
by the switching algorithm are scaled through the mesh size, the grain boundary mobility, and the
driving force data. The coupling of the two models is realized by: translating the state variables used
in the finite-element plasticity model into state variables used in the cellular automaton; mapping
the finite-element integration point locations on the quadratic cellular automaton mesh; using the
resulting cell size, maximum driving force, and maximum grain boundary mobility occurring in
the region for determining the length scale, time step, and local switching probabilities in the
automaton; and identifying an appropriate nucleation criterion. The coupling method is applied
to the two-dimensional simulation of texture and microstructure evolution in a heterogeneously
deformed, high-purity aluminium polycrystal during static primary recrystallization, considering
local grain boundary mobilities and driving forces.

1. Motivation for coupling different spatially discrete microstructure and texture
simulation methods

Time- and space-discretized simulation approaches such as the crystal plasticity finite-element
method or cellular automata are increasingly gaining momentum as powerful tools for
predicting microstructures and textures. The major advantage of such discrete methods is
that they consider material heterogeneity as opposed to classical statistical approaches, which
are based on the assumption of material homogeneity.
Although the average behaviour of materials during deformation and heat treatment
can sometimes be sufficiently well described without considering local effects, prominent

0965-0393/00/040445+18$30.00 © 2000 IOP Publishing Ltd 445

446 D Raabe and R C Becker

examples exist where substantial progress in understanding and tailoring material response
can only be attained by taking material heterogeneity into account. For instance, in the field
of plasticity the quantitative investigation of ridging and roping, or related surface defects
observed in sheet metals, requires knowledge about local effects such as the grain topology or
the form and location of second phases. In the field of heat treatment, the origin of the Goss
texture in transformer steels, the incipient stages of cube texture formation during primary
recrystallization of aluminium, the reduction of the grain size in microalloyed low-carbon steel
sheets, and the development of strong {111} uvw textures in steels can hardly be predicted
without incorporating local effects such as the orientation and location of recrystallization
nuclei and the character and properties of the grain boundaries surrounding them.
Although spatially discrete microstructure simulations have already profoundly enhanced
our understanding of microstructure and texture evolution over the last decade, their potential
is sometimes simply limited by an insufficient knowledge about the external boundary
conditions that characterize the process and an insufficient knowledge about the internal
starting conditions that are, to a large extent, inherited from the preceding process step. It
is thus an important goal to improve the incorporation of both types of information into such
simulations. External boundary conditions prescribed by real industrial processes are often
spatially nonhomogeneous. They can be investigated using experiments or process simulations
that consider spatial resolution. Spatial heterogeneities in the internal starting conditions, i.e. in
the microstructure and texture, can be obtained from experiments or microstructure simulations
that include spatial resolution.
In this paper we use the results obtained from a crystal plasticity finite-element simulation
as starting conditions for a discrete recrystallization simulation carried out with a probabilistic
cellular automaton. The coupling between both methods consists of: extracting and translating
the state variables of the finite-element plasticity model (texture and accumulated shear) into
state variables of the cellular automaton model (texture and dislocation density); mapping
these data on the cellular automaton grid; scaling the cellular automaton mesh in terms of
the derived cell size, maximum occurring driving force and grain boundary mobility; and in
establishing an adequate nucleation criterion which makes use of these data.
The plan of the paper is as follows: we will separately present the basic features of both
simulation methods in sections 2 and 3, explain the coupling method in section 4, and present
the results of the recrystallization simulations in section 5.

2. The crystal plasticity finite-element model

2.1. Crystal constitutive model
The deformation behaviour of the grains is determined by a crystal plasticity model, which
accounts for plastic deformation by crystallographic slip and for the rotation of the crystal lattice
during deformation. Consequently, the model uses space and time as independent variables and
the crystal orientation and the accumulated slip as dependent or state variables†. The crystal
kinematics follow those described by Asaro (1983), and the rate-dependent formulation follows
that developed by Peirce et al (1983). Here, however, the equations are cast in an updated
Lagrangian framework rather than the total Lagrangian. The model has been implemented
(Smelser and Becker 1989) as a user subroutine in the commercial finite-element code Abaqus
and has been used in several studies to simulate deformation in grains and single crystals
(Becker 1991, Becker et al 1991, 1995).
† The accumulated slip can be regarded as a state variable since it is used for the calculation of the slip system’s
resistance to shear.

Crystal plasticity FEM with cellular automaton 447

In the crystal model, the velocity gradient, L, is decomposed additively into elastic and
plastic parts:
L = L∗ + Lp . (1)
Each of these can be further decomposed into its symmetric and antisymmetric parts
representing, respectively, the rate of deformation tensor, D , and the spin tensor, Ω:
L∗ = D ∗ + Ω∗ (2)
L =D +Ω .
p p p
(3)
The plastic part of the rate of deformation tensor, D p , and the plastic spin, Ωp , can be
expressed in terms of the slip rates, γ α , along the crystallographic slip directions sα and
˙
on crystallographic slip planes with normals mα :
N N
1
Dp = γ α (s α ⊗ m α + m α ⊗ s α ) =
˙ γ α Pα
˙ (4)
2 α=1 α=1
N N
1
Ωp = γ α (s α ⊗ m α − m α ⊗ s α ) =
˙ γ α Wα
˙ (5)
2 α=1 α=1

where sα ⊗ mα and mα ⊗ sα are the dyadic products of the slip vectors.
For the simulations on aluminium, which has a face-centred cubic (fcc) crystal structure,
plastic deformation at low temperatures is typically assumed to occur on the 12 slip systems
with 110 slip directions and {111} slip planes, i.e. the slip vectors sα = √2 (110)T and
1

mα = √3 (111)T are orthonormal.
1

The elastic stretch and the elastic rotation of the crystal lattice lead to a change of sα and
mα . This effect is captured by the elastic part of the velocity gradient. The slip vectors evolve
during deformation according to
sα = L∗ · sα
˙ (6)
∗
mα = −mα · L .
˙ (7)
The slip vectors remain orthogonal so that the plastic portion of the deformation is nondilatant.
By assuming a stress potential in which the stress is related to the elastic distortion of the
crystal lattice, the rate of the Kirchhoff stress tensor, τ , is given by
˙
τ = C : D ∗ + D ∗ · τ + τ · D ∗ + Ω∗ · τ − τ · Ω∗
˙ (8)
where C is a fourth-order tensor of the elastic moduli and τ is the Kirchhoff stress tensor. Using
the additive decomposition of the rate of deformation tensor and the spin into its elastic and
plastic portions, and combining the second and third terms of equation (8) with the modulus
to deﬁne a new fourth-order tensor, K, the Jaumann rate tensor of the Kirchhoff stress rate can
be written
N
τ = K : D ∗ − K : D p − Ωp · τ + τ · Ωp = K : D ∗ − γ α Rα .
˙ (9)
α=1

The last three terms of equation (9) involve plastic deformation. They can be expressed in
terms of slip rates as
N N
γ α (K : Pα + Wα · τ − τ · Wα ) =
˙ γ α Rα
˙ (10)
α=1 α=1


where Pα and Wα are defined in equations (4) and (5). Using equation (10), the Jaumann
stress rate is given by
N
τ = K : D∗ − γ α Rα .
˙ (11)
α=1

The fourth-order modulus tensor, K, is given in terms of the crystal moduli and the current
stress state. The tensors Rα are functions of the stress state and of the known crystal geometry.
What remains is to specify the slip rates, γ α . In the rate-dependent constitutive formulation
˙
adopted here, the slip rate on a slip system is assumed to be related to the resolved shear stress
on this system, τ α = τ : Pα , through a power law relation:
τα 1/m
γ α = γ0α
˙ ˙ (12)
τα
ˆ
where the scalar scaling parameter τ α , which has the unit of stress, is a phenomenological
ˆ
measure for the slip system strength or resistance to shear, m = 0.002 is the strain rate
sensitivity exponent, and γ0α = 0.03 s−1 is a reference shear rate. The value of the strain rate
˙
sensitivity exponent is low and the material response is almost rate-independent.
With the slip rates given as explicit functions of the known resolved shear stresses, the
rate-dependent method avoids the ambiguity in the selection of active slip systems, which is
encountered in many rate-independent formulations where it must be solved using an additional
selection criterion. However, integration of the stress rate, equation (11), with the slip rate
defined by equation (12) produces a system of equations which is numerically ‘stiff’. The rate
tangent modulus method of Peirce et al (1983) is used to increase the stable time step size.
For the present simulations, the strengths of all of the slip systems at a material point are
taken to be equal, i.e. we adopt the Taylor hardening assumption. The hardening as a function
of accumulated slip
t N
γ = γ α dt
˙ (13)
0 α=1

is assumed to follow the macroscopic strain hardening behaviour obtained from a biaxial test
by fitting the experimental data to a Voce equation
σ = 445.21 − 258.0 exp(−5.1203ε) MPa (14)
where a satisfactory fit was obtained beyond ε = 0.08. The fit was adjusted by the average
Taylor factor using an approximate value of three to give the slip system resistance to shear,
equation (12), as a function of the accumulated shear:
τ α = 148.4 − 86.13 exp(−1.0768γ ) MPa.
ˆ (15)
When applied in a polycrystal simulation of a tensile test, this treatment of the slip system
hardening will approximately reproduce the hardening behaviour that was originally measured.
The cubic elastic constants used in the simulation are typical for aluminium: C11 = 108 GPa,
C12 = 62 GPa and C44 = 28.3 GPa (Smethells 1983).

2.2. Original specimen and finite-element discretization
The original specimen approximated here by the two-dimensional finite-element simulation
was a quasi two dimensional columnar grain polycrystal of high-purity aluminium created by
directional solidification (Becker 1998). The material was subsequently annealed to eliminate
small grains and irregularities from the grain boundaries. The resulting grain size was of


Figure 1. Distribution of the integration point locations in the finite-element mesh before
deformation. According to a section of the real specimen, the finite-element sample has a size
of 30 × 25 mm2 . The compression axis is along the 25 mm direction and extension is along the
30 mm direction.

the order of millimetres. A rectangular specimen 30 × 25 × 10 mm3 was excised for plane
strain compression in a channel die. The axis of the columnar grains was aligned with the
10 mm direction of the sample. This was also the constraint direction for the channel die
experiments. The compression axis was along the 25 mm direction and extension was along
the 30 mm direction. The specimen was etched to reveal the grain structure. The crystal
orientations of the 39 grains were determined using the electron backscatter technique in a
scanning electron microscope. Both the grain structure and the crystallographic orientations
taken from longitudinal sections were provided as input to the two-dimensional finite-element
analyses.
Because a fine spatial discretization is desired for coupling the deformation results with the
recrystallization model, a two-dimensional finite-element model was constructed. The finite-
element mesh was created using the package Maze (1993). This mesh generator uses a paving
algorithm in two dimensions to construct a mesh within each individual grain contour. The
nodal locations are the same for elements on both sides of a grain boundary. The deformation
was modelled as being continuous across grain boundaries. Grain boundary sliding and
separation are not permitted. The mesh (figure 1) used 36 977 quadrilateral elements in the
model plane.

3. The probabilistic cellular automaton

3.1. Fundamentals
The recrystallization model is designed as a cellular automaton with a probabilistic switching
rule (Raabe 1998a,b, 1999). Independent variables are time t and space x = (x1 , x2 , x3 ). Space
is discretized into an array of equally shaped quadratic cells. Each cell is characterized in terms
of the dependent variables. These are scalar (mechanical, electromagnetic) and configurational
(interfacial) contributions to the driving force and the crystal orientation g = g (ϕ1 , φ, ϕ2 ),


where g is the rotation matrix and ϕ1 , φ, ϕ2 the Euler angles. The driving force is the negative
change in Gibbs enthalpy Gt per transformed cell. The starting data, i.e. the crystal orientation
map and the spatial distribution of the driving force, must be provided by experiment, i.e.
orientation imaging microscopy via electron backscatter diffraction or by simulation, for
example a crystal plasticity finite-element simulation as in this study. Grains or subgrains
are mapped as regions of identical crystal orientation, but the driving force may vary inside
these areas.
The kinetics of the automaton result from changes in the state of the cells, which are
hereafter referred to as cell switches. They occur in accord with a switching rule, which
determines the individual switching probability of each cell as a function of its previous state
and the state of its neighbouring cells. The switching rule used in the simulations discussed
below is designed for the simulation of primary static recrystallization. It reflects that the state
of a nonrecrystallized cell belonging to a deformed grain may change due to the expansion
of a recrystallizing neighbour grain, which grows according to the local driving force and
boundary mobility. If such an expanding grain sweeps a nonrecrystallized cell the stored
dislocation energy of that cell drops to zero and a new orientation is assigned to it, namely that
of the growing neighbour grain.
To put this formally, the switching rule is cast in the form of a probabilistic analogue of the
linearized symmetric rate equation of Turnbull (1951), which describes grain boundary motion
in terms of isotropic single-atom diffusion processes perpendicular through a homogeneous
planar grain boundary segment under the influence of a decrease in Gibbs energy:
G + Gt /2 G − Gt /2
x = nνD λgb c exp −
˙ − exp − (16)
kB T kB T
where x is the grain boundary velocity, νD the Debye frequency, λgb is the jump width through
˙
the boundary, c is the intrinsic concentration of grain boundary vacancies or shuffle sources, n
is the normal of the grain boundary segment, G is the Gibbs enthalpy of motion through in the
interface, Gt is the Gibbs enthalpy associated with the transformation, kB is the Boltzmann
constant, and T is the absolute temperature. Replacing the jump width by the burgers vector
and the Gibbs enthalpy terms by the total entropy, S, and total enthalpy, H , leads to a
linearized form of equation (16):
S pV H
x ≈ nνD b exp −
˙ exp − (17)
kB kB T kB T
where p is the driving force and V is the atomic volume which is of the order of b3 , where
b is the magnitude of the Burgers vector. Summarizing these terms reproduces Turnbull’s
expression
Qgb
x = nmp = nm0 exp −
˙ p (18)
kB T
where m is the mobility. Equations (16)–(18) provide a well known kinetic picture of grain
boundary segment motion, where the atomistic processes† are statistically described in terms
of the pre-exponential factor of the mobility m0 = m0 ( g , n) and the activation energy of
grain boundary mobility Qgb = Qgb ( g , n). Both quantities may depend strongly on the
misorientation g across the boundary, the grain boundary normal n, and the impurity content
(Gottstein et al 1997, 1998, Doherty et al 1997, Molodov et al 1998).
† It must be emphasized in this context that thermal fluctuations, i.e. random forward and backward jumps of the
atoms through the grain boundary are already included in equation (16). It is not required to consider any additional
form of thermal fluctuation.


For dealing with competing switches affecting the same cell, the deterministic rate
equation, equation (18), can be replaced by a probabilistic analogue that allows one to calculate
switching probabilities. First, equation (18) is separated into a deterministic part, x0 , which
˙
depends weakly on temperature, and a probabilistic part, w, which depends strongly on
temperature:
kB T m0 pV Qgb
x = x0 w = n
˙ ˙ exp − with
V kB T kB T
kB T m 0 pV Qgb
x0 = n
˙ w= exp − . (19)
V kB T kB T
The probability factor w represents the product of the linearized part pV /(kB T ) and
the nonlinearized part exp(−Qgb /(kB T )) of the original Boltzmann terms. According to
equation (19), nonvanishing switching probabilities occur for cells which reveal neighbours
with a different orientation and a driving force which points in their direction. The automaton
considers the ﬁrst-, second- (2D), and third- (3D) neighbour shells for the calculation of the
total driving force acting on a cell. The local value of the switching probability depends on
the crystallographic character of the boundary segment between such unlike cells.

3.2. The scaled and normalized switching probability
The cellular automaton is usually applied to starting data that have a spatial resolution far above
the atomic scale. This means that the automaton grid may have some mesh size λm b. If a
moving boundary segment sweeps a cell, the grain thus grows (or shrinks) by λ3 rather than
m
b3 . Since the net velocity of a boundary segment must be independent of the imposed value
of λm , an increase of the jump width must lead to a corresponding decrease of the grid attack
frequency, i.e. to an increase of the characteristic time step, and vice versa. For obtaining
a scale-independent grain boundary velocity, the grid frequency must be chosen in a way to
ensure that the attempted switch of a cell of length λm occurs with a frequency much below
the atomic attack frequency that attempts to switch a cell of length b. Mapping equation (19)
on a grid which is prescribed by an external scaling length λm leads to the equation
kB T m0
x = x0 w = n(λm ν)w
˙ ˙ with ν= (20)
V λm
where ν is the eigenfrequency of the chosen mesh characterized by the scaling length λm .
The eigenfrequency given by equation (20) represents the attack frequency for one
particular grain boundary with constant mobility. In order to use a whole spectrum of mobilities
and driving forces in one simulation it is necessary to normalize equation (20) by a common
grid attack frequency ν0 rendering it into
ν ν
x = x0 w = nλm ν0
˙ ˙ ˆ
w = x0
˙ ˆ ˆ
w = x0 w
˙ (21)
ν0 ν0
where the normalized switching probability amounts to
ν pV Qgb m0 p Qgb
w=
ˆ exp − = exp − . (22)
ν0 kB T kB T λm ν0 kB T
The value of the normalization or grid attack frequency ν0 can be identiﬁed by using the
physically plausible assumption that the maximum occurring switching probability cannot be
larger than one:
mmax p max Qmin
gb
w
ˆ max
= 0 min exp − 1 (23)
λm ν 0 kB T


where mmax is the maximum occurring pre-exponential factor of the mobility, p max is the
0
maximum possible driving force, ν0 is the minimum allowed grid attack frequency, and
min

Qgb is the minimum occurring activation energy. With w max = 1 in equation (23), one
min
ˆ
obtains the normalization frequency as a function of the upper bound input data:

mmax p max Qmin
gb
ν0 =
min 0
exp − . (24)
λm kB T

This frequency and the local values of the mobility and the driving force change equation (22)
into

mlocal p local Qlocal
gb mlocal p local (Qlocal − Qmin )
gb gb
wlocal =
ˆ 0
exp − = 0
exp −
λm ν 0min kB T mmax
0 p max kB T
mlocal p local
= . (25)
mmax p max

This expression is the central switching equation of the algorithm. It reveals that the local
switching probability can be quantified by the ratio of the local and the maximum mobility
mlocal /mmax , which is a function of the grain boundary character and by the ratio of the local and
the maximum driving pressure plocal /p max . The probability of the fastest occurring boundary
segment (characterized by mlocal = mmax , plocal = pmax , Qlocal = Qmin ) to realize a cell
0 0 gb gb
switch is equal to one. Equation (25) shows that the mesh size does not influence the switching
probability but only the time step elapsing during an attempted switch. The characteristic time
constant of the simulation t is 1/ν0 , equation (24).
min

The switching probability expressed by equation (25) can also be formulated in terms of
the local time t = λm /x required by a grain boundary with velocity x to cross the automaton
˙ ˙
cell of size λm (Gottstein 1999):

mlocal p local x local
˙ t max
w local =
ˆ = = . (26)
mmax p max x max
˙ t local

Therefore, the local switching probability can also be regarded as the ratio of the distances
that were swept by the local grain boundary and the grain boundary with maximum velocity,
or as the number of time steps the local grain boundary needs to wait before crossing the
encountered cell. This reformulates the same underlying problem, namely that boundaries
with different mobilities and driving forces cannot equally switch the state of the automaton
in a given common time step.
There are two ways to cope with the problem. Either the time step is chosen such that the
boundary with minimum probability crosses the cell, then the automaton will always switch
state and boundaries with larger velocities will effect neighbouring cells. This is the approach
Reher (1998) and Marx et al (1995, 1997, 1998) have chosen in their modified automaton.
The alternative way is to clock the time step such as to have the boundary with the maximum
velocity to cross the cell during one time step. In such a case, more slowly moving boundaries
will not switch the cell and one would have to install a counter in the cell to account for that
delay. The approach used in this paper principally pursues the latter method and solves it
by using a stochastic decision rather than a counter to account for the insufficient sweep of
the boundary through the cell. Stochastic Markov-type sampling is equivalent to installing a
counter, since the probability to switch the automaton is proportional to the velocity ratio given
by equations (25) and (26), provided the chosen random number generator is truly stochastic.


3.3. The switching decision

Equations (25) and (26) allow one to calculate the switching probability of a cell as a function
of its previous state and the state of the neighbouring cells. The actual decision about a
switching event for each cell is made by a Monte Carlo step. The use of random sampling
ensures that all cells are switched according to their proper statistical weight, i.e. according to
the local driving force and mobility between cells. The simulation proceeds by calculating the
individual local switching probabilities w local according to equation (25) and evaluating them
ˆ
using a nonMetropolis Monte Carlo algorithm. This means that for each cell the calculated
switching probability is compared to a randomly generated number r which lies between zero
and one. The switch is accepted if the random number is equal or smaller than the calculated
switching probability. Otherwise the switch is rejected:

 accept switch if r mlocal p local
mmax p max
random number r between zero and one . (27)
 reject switch mlocal p local
if r > mmax pmax

Except for the probabilistic evaluation of the analytically calculated transformation
probabilities, the approach is entirely deterministic. Thermal fluctuations other than included
through equation (16) are not permitted. The use of realistic or even experimental input data
for the grain boundaries (e.g. Gottstein et al 1997, 1998, Gottstein and Shvindlerman 1999,
Molodov et al 1998) enables one to make predictions on a real time and space scale. The
switching rule is scalable to any mesh size and to any spectrum of boundary mobility and
driving force data. The state update of all cells is made in synchrony.

4. Coupling the crystal plasticity finite-element model with the probabilistic cellular
automaton

4.1. Basic considerations about coupling

The coupling between the crystal plasticity finite-element model and the probabilistic cellular
automaton was realized in four steps. First, the state variables of the finite-element plasticity
model (crystal orientation and accumulated shear) were extracted and translated into state
variables of the cellular automaton model (crystal orientation and dislocation density). Second,
the integration point locations from the distorted finite-element mesh were mapped on the
quadratic mesh of the automaton. Third, the resulting cell size, the maximum occurring driving
force, and the maximum occurring grain boundary mobility were extracted from the mapped
data for the determination of the length scale λm , the time step t = 1/ν0 which elapses
min

during the synchronous state update, equation (24), and the local switching probabilities w local ,
ˆ
equation (25). Fourth, an appropriate nucleation criterion was defined in order to determine
under which kinetic and thermodynamic conditions recrystallization started and which crystal
orientations the switched nucleation cells assumed.

4.2. Selection of state variables

The first step in coupling the two methods consists in extracting or, respectively, translating
appropriate state variables of the crystal plasticity finite-element model into state variables of
the cellular automaton model. The state variables required in the recrystallization model are the
crystal orientation and some measure for the stored elastic energy, e.g. the stored dislocation


Figure 2. Distribution of the integration point locations in the finite-element mesh at a logarithmic
strain of ε = −0.434.

density†. The state variables are given at the spatial coordinates of the integration points of
the finite-element mesh.
The crystal orientations at these coordinates, i.e. the microtexture, was not discretized but
directly used as calculated by the crystal plasticity finite-element method. Earlier calculations
(Raabe 1998a,b) used a discretization method where each orientation is represented by the
closest texture component from a set of discrete crystal orientations. The set contained
936 texture components which were equally distributed in orientation space. The use of a
continuous instead of a discrete orientation space enhances the calculation speed. However,
the required computer memory is enhanced as well.
The second state variable, i.e. the stored dislocation density was linearly related to the
value of the accumulated slip known for each nodal point in the finite-element model. It
should be noted at this point that recent crystal plasticity polycrystal simulations have not
only predicted stored elastic energy arising from the accumulated shear (translated here into
some stored dislocation density), but also some local residual elastic stresses which are
orientation dependent, although these are not proportional to the shear accumulated during
plastic deformation (Dawson et al 1999). At large plastic strains this additional elastic
incompatibility pressure might not be significant, since the driving forces stemming from
stored dislocations typically exceed those stemming from residual elastic stresses by at least
an order of magnitude. However, at small strains it could be important to add these elastic
stresses to the driving pressure.

4.3. Mapping procedure
The mesh of the finite-element model was aligned with each individual grain contour (figure 1).
Since the grains revealed different kinematics and different strain hardening behaviours during
deformation the mesh gradually became even more distorted with increasing strain. Figure 2
shows the distribution of the integration points at a logarithmic strain of ε = 0.434. The state
variables given at these points had to be mapped on the regular cellular automaton mesh that
consisted of quadratic cells.
Spatial compatibility between both types of models can, in principle, be attained by
either directly interpolating the finite-element data on a quadratic cellular automaton mesh
or by choosing an appropriate mapping procedure. The method we used is a Wigner–Seitz
† Recrystallization models working on a more microscopic scale would also aim at the incorporation of the dislocation
cell structure (e.g. Humphreys 1992, 1997, Doherty et al 1997). Progress in recrystallization and recovery could then
be described by discontinuous (recrystallization) and continuous (recovery) subgrain coarsening. The approach
presented here works on a somewhat larger scale where dislocation cell coarsening is not explicitly considered.


cellular automaton finite element

Figure 3. Schematic description of the Wigner–Seitz mapping algorithm. It consists of two steps.
First, a fine quadratic grid (circles) is superimposed on the finite-element mesh (crosses). Second,
values of the state variables at each of the integration points are assigned to the new grid points that
fall within the Wigner–Seitz cell corresponding to that integration point. The Wigner–Seitz cells
of the finite-element mesh are constructed from cell walls, which are the perpendicular bisecting
planes of all lines connecting neighbouring integration points, i.e. the integration points are in the
centres of the Wigner–Seitz cells.

type of mapping algorithm (Raabe 1999). It consisted of two steps. In the first step, a
fine quadratic automaton grid was superimposed on the distorted finite-element mesh. The
spacing of the points in the new grid was smaller than the spacing of the closest neighbour
points in the finite-element mesh. The absolute value of the cell size of the superimposed
quadratic cellular automaton mesh was thus determined by the size of the simulated specimen
(see section 2.2). It amounted to λm = 61.9 µm. While the original finite-element mesh
consisted of 36 977 quadrilateral elements, the cellular automaton mesh consisted of 217 600
cells. In the second step, values of the state variables at each of the integration points were
assigned to the new grid points that fell within the Wigner–Seitz cell corresponding to that
integration point. The Wigner–Seitz cells of the finite-element mesh were constructed from
cell walls, which were the perpendicular bisecting planes of all lines connecting neighbouring
integration points (figure 3), i.e. the integration points were in the centres of the Wigner–Seitz
cells.
The Wigner–Seitz procedure requires that the cellular automaton grid is finer than the
finite-element mesh, i.e. this mapping method produces clusters of cellular automaton sites
with identical state variable properties surrounding each finite-element interpolation point. In
the model these clusters correspond to regions of the same crystallographic orientation with
a uniform nonzero dislocation density. The size of these cellular automaton clusters depends
on the ratio between the average finite element point spacing and the cellular automaton site
spacing. Since, at a later stage of the simulation (section 4.5), a nucleation criterion must
be defined as a function of the local misorientation between neighbouring automaton cells,
and since this criterion is only satisfied at the boundaries of the cellular automaton clusters,
it is conceivable that the Wigner–Seitz approach might introduce a dependence on the finite-
element mesh size.


Figure 4. Distribution of the accumulated crystallographic shear strain in the finite-element sample
at a logarithmic deformation of ε = −0.434.

4.4. Scaling procedure
The maximum driving force in the region arising from the stored dislocation density amounted
to about 1 MPa. The temperature dependence of the shear modulus and of the Burgers vector
was considered in the calculation of the driving force. The grain boundary mobility in the
region was characterized by an activation energy of the grain boundary mobility of 1.46 eV
and a pre-exponential factor of the grain boundary mobility of m0 = 8.3 × 10−3 m3 N−1 s−1 .
Together with the scaling length λm = 61.9 µm we used these data for the calculation of
the time step t = 1/ν0 , equation (24), and of the local switching probabilities w local ,
min
ˆ
equation (25).

4.5. Nucleation criterion for recrystallization
The nucleation process during primary static recrystallization has been explained for pure
aluminium in terms of discontinuous subgrain growth (Humphreys 1992, 1997, Doherty
et al 1997). According to this model nucleation takes place in areas which reveal high
misorientations among neighbouring subgrains and a high driving force for curvature-
driven subgrain coarsening. The present simulation approach works above the subgrain
scale, i.e. it does not explicitly describe cell walls and subgrain coarsening phenomena.
Instead, we incorporated nucleation on a more phenomenological basis using the kinetic
and thermodynamic instability criteria known from classical recrystallization theory (Himmel
1963, Haessner 1978, Gottstein 1984, Humphreys and Hatherly 1995). The kinetic instability
criterion means that a successful nucleation process leads to the formation of a mobile, large-
angle grain boundary. The thermodynamic instability criterion means that the stored energy
changes across the newly formed large-angle grain boundary providing a net driving force.
Nucleation in this simulation is performed in accord with these two aspects, i.e. potential
nucleation sites must fulfil both the kinetic and the thermodynamic instability criterion. In
the simulations two phenomenological nucleation models were implemented based on these
instability criteria.
The first nucleation model is a variant of the subgrain coalescence model and is capable
of creating new orientations. At the beginning of the simulation the kinetic conditions for
nucleation were checked by calculating the misorientations among all neighbouring cells. If
a pair of cells revealed a misorientation above 15◦ , the thermodynamic criterion, i.e. the local
value of the dislocation density was also checked. If the dislocation density was larger than
some critical value of its maximum value in the sample (we checked 10%, 30%, 50%, 70%,
80% and 90%), the two cells were recrystallized, i.e. a new orientation midway between the two


(a) (b)

(c) (d)

Figure 5. 2D simulations of primary static recrystallization in a deformed aluminium polycrystal on
the basis of crystal plasticity finite-element data. The figure shows the change both in microtexture
(upper images) and in dislocation density (lower images), which was derived from the value of
the accumulated crystallographic shear, as a function of the annealing time during isothermal
recrystallization. The white areas in the lower images indicate a stored dislocation density of zero,
i.e. they are recrystallized. The black lines in both figures indicate misorientations above 15◦
and the thin grey lines indicate misorientations between 5◦ and 15◦ , irrespective of the rotation
axis. The orientation image given in the upper figures represents different crystal orientations by
different grey levels. The simulation parameters are: annealing temperature, 800 K; site-saturated
nucleation conditions; kinetic instability criterion, misorientation above 15◦ ; thermodynamic
instability criterion, dislocation density larger than 70% of the maximum occurring value; maximum
occurring driving force, 1 MPa; activation energy of the grain boundary mobility, 1.46 eV; pre-
exponential factor of the grain boundary mobility, m0 = 8.3 × 10−3 m3 N−1 s−1 and mesh size
of the cellular automaton grid (scaling length), λm = 61.9 µm. The images show a crystal in the
following states of recrystallization: (a) 0%, (b) 3%, (c) 13%, (d) 22%, (e) 32%, (f ) 48%, (g) 82%
and (h) 93% recrystallized.

original orientations was created and a dislocation density of zero was assigned to them. The
generation of the new orientation was based on the idealized picture of subgrain coalescence
(Hu 1963). If the two recrystallized cells had a misorientation above 15◦ with respect to the
nonrecrystallized neighbour cells they could grow into the surrounding matrix.
The second nucleation model is even simpler and does not create new orientations. At the
beginning of the simulation, the thermodynamic criterion, i.e. the local value of the dislocation
density, was first checked for all grid points. If the dislocation density was larger than some
critical value of its maximum value in the sample (we checked 10%, 30%, 50%, 70%, 80%


(e) (f )

(g) (h)

Figure 5. (Continued)

and 90%), the cell was spontaneously recrystallized without any orientation change, i.e. a
dislocation density of zero was assigned to it and the original crystal orientation was preserved.
In the next step the ordinary growth algorithm was started according to equations (25)–(27), i.e.
the kinetic conditions for nucleation were checked by calculating the misorientations among
all spontaneously recrystallized cells (preserving their original crystal orientation) and their
immediate neighbourhood considering the first-, the second-, and the third-neighbour shells. If
any such pair of cells revealed a misorientation above 15◦ , the cell flip of the unrecrystallized
cell was calculated according to equations (25)–(27). In case of a successful cell flip, the
orientation of the first recrystallized neighbour cell was assigned to the flipped cell. All
simulation results presented hereafter used the second nucleation model.

5. Simulation of primary static recrystallization

Figure 4 shows the starting conditions prior to the simulated annealing treatment, i.e. the
distribution of the accumulated crystallographic shear in the sample after a total logarithmic
strain of ε = −0.434. The distribution of the integration points of the finite-element mesh
was shown in figure 2. Figure 4 reveals three major areas with large values of the accumulated
shear (bright areas). These areas can be referred to as deformation bands.
Figure 5 shows the change both in microtexture and in dislocation density, which was
assumed to be proportional to the accumulated crystallographic shear, as a function of the


(a) (b)

Figure 6. Simulated recrystallized volume fraction ((a) Avrami diagram) and interface fraction
((b) Cahn–Hagel diagram) as functions of the annealing time. The temperature was 800 K.

annealing time during recrystallization. The annealing temperature was 800 K. The simulation
assumed site-saturated nucleation conditions using the second nucleation criterion described
in the preceding section, i.e. potential nuclei were spontaneously formed at t = 0 s in cells with
a dislocation density larger than 70% of the maximum value in the sample. These potential
nuclei then grew or remained unchanged in accord with equations (25)–(27).
The upper images in figure 5 show the orientation images where each grey level represents
a specific crystal orientation. The grey level is calculated as the magnitude of the Rodriguez
orientation vector. The cube component serves as a reference orientation. The lower images
in figure 5 show the stored dislocation densities. The white areas are recrystallized, i.e. the
stored dislocation content of the affected cells was dropped to zero. The black lines in both
figures indicate misorientations above 15◦ irrespective of the rotation axis. The thin grey lines
in both figures indicate misorientations above 5◦ and below 15◦ irrespective of the rotation
axis.
The incipient stages of recrystallization (figures 5(a)–(c)) reveal that nucleation is
concentrated in areas with large accumulated local shear strains and lattice curvatures
(figure 4). This means that the spatial distribution of the nuclei is very inhomogeneous. The
deformation bands with high localized stored energy and lattice curvature produce clusters
of similarly oriented nuclei. Less deformed areas between the bands show a negligible
density of nuclei. The following stages of recrystallization (figures 5(d)–(f )) reveal that
the nuclei do not grow freely into the surrounding deformed material as described by
Avrami–Johnson–Mehl theory, but impinge upon each other and thus compete, already,
at a very early stage of the transformation. The late stages of recrystallization show an
incomplete and spatially heterogeneous transformation of the deformed material (figures 5(g)
and (h)).
The observed deviation from Avrami–Johnson–Mehl-type growth, i.e. the early
impingement is reflected by the kinetic behaviour which differs from the classical sigmoidal
kinetics observed under homogeneous nucleation conditions (figure 6). The kinetics simulated
on the basis of the finite-element data in conjunction with the chosen nucleation model reveal
an Avrami exponent of about 1.4, which is for below the theoretical value for site-saturated


Figure 7. Magnification of three selected areas where moving large-angle grain boundaries did
not sweep the deformed material. This pronounced recovery is due to insufficient misorientations
between the deformed and the recrystallized areas entailing a drop in grain boundary mobility
(orientation pinning).

nucleation conditions in two dimensions of two (figure 6(a)). Figure 6(b) shows the
corresponding Cahn–Hagel plot. A more systematic analysis of such nucleation behaviour,
which is characterized by an early growth competition, might help to identify approaches for
the optimization and even tailoring of recrystallization kinetics, texture, and grain size.
Another interesting result of the simulation is the partial recovery of deformed material.
Figure 7 shows three selected areas where moving large angle-grain boundaries did not
sweep the deformed material. An analysis of the state variable values at these coordinates
and of the grain boundaries involved substantiates that not insufficient driving forces, but
insufficient misorientations between the deformed and the recrystallized areas—entailing a
drop in grain boundary mobility—were responsible for this effect. Previous authors referred
to this mechanisms as orientation pinning (Juul Jensen 1997).
Figure 8 shows the crystallographic textures of the microstructures presented in figure 5.
The orientation distribution functions were calculated by replacing each single orientation by a
Gauss-type scattering functions using a scatter width of 3◦ . The textures are given in Euler space
where each coordinate ϕ1 , φ, ϕ2 represents a certain crystal orientation. In order to emphasize
the main texture components in Euler space, only areas with an orientation density above
random (f (g) = 1) are plotted. The initial texture is characterized by a number of isolated
components, some partial texture fibres, and some smeared-out components. During the
annealing treatment most of the scattered components vanish and the main texture components
are shifted.


(a) (b)

Figure 8. Starting (a) and end (b) texture of the microstructures shown in figure 5. The orientation
distribution functions were calculated by using Gauss-type scattering functions with a scatter width
of 3◦ . The textures are given in Euler space where each coordinate ϕ1 , φ, ϕ2 represents a certain
crystal orientation. In order to emphasize the main texture components, the graphs show only areas
in Euler space with an orientation density above random (f (g ) = 1).

6. Conclusions

We presented an approach for simulating recrystallization by coupling a viscoplastic crystal
plasticity finite-element model with a cellular automaton. The coupling between both models
consisted of: extracting and translating the microtexture and stored energy data predicted by
the finite-element simulation into the cellular automaton model; mapping these data on the
quadratic cellular automaton mesh; scaling the cellular automaton in terms of the derived
cell size, maximum driving force, and maximum grain boundary mobility occurring in the
region; and establishing an adequate nucleation criterion, which makes use of these data.
The coupling method was used to simulate the formation of texture and microstructure in a
deformed high-purity aluminium polycrystal during static primary recrystallization. It was
observed that nucleation was concentrated in areas with a large accumulated shear and large
lattice curvature. The spatial distribution of the nuclei was very inhomogeneous. Deformation
bands with high stored energy and large curvature showed a high density of nuclei, whilst
less deformed areas did not produce nuclei. The clustering of nuclei led to a deviation from
Avrami–Johnson–Mehl-type kinetics with a 2D Avrami exponent significantly below two.
The observed partial recovery of deformed material was explained in terms of insufficient
misorientations between some of the deformed and the recrystallized areas, which entailed
a local drop in grain boundary mobility. The initial texture after deformation was changed
during the recrystallization treatment.

Acknowledgments

One of the authors (DR) gratefully acknowledges the financial support by the Deutsche
Forschungsgemeinschaft through the Heisenberg programme. The authors are grateful to
G Gottstein and L S Shvindlerman for stimulating discussions.


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