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An information-theoretic approach for obtaining property PDFs from macro specifications of microstructural variability. Nicholas Zabaras, Veera Sundararaghavan and Sethuraman Sankaran. Materials Process Design and Control Laboratory

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slide1

An information-theoretic approach for obtaining property PDFs from macro specifications of microstructural variability

Nicholas Zabaras, Veera Sundararaghavan and Sethuraman Sankaran

Materials Process Design and Control Laboratory

Sibley School of Mechanical and Aerospace Engineering188 Frank H. T. Rhodes Hall

Cornell University

Ithaca, NY 14853-3801

Email: zabaras@cornell.edu, vs85@cornell.edu, ss524@cornell.edu

URL: http://mpdc.mae.cornell.edu/

slide2

Idea Behind Information Theoretic Approach

Basic Questions:

1. Microstructures are realizations of a random field. Is there a principle by which the underlying pdf itself can be obtained.

2. If so, how can the known information about microstructure be incorporated in the solution.

3. How do we obtain actual statistics of properties of the microstructure characterized at macro scale.

Rigorously quantifying and modeling uncertainty, linking scales using criterion derived from information theory, and use information theoretic tools to predict parameters in the face of incomplete Information etc

Information

Theory

Linkage?

Information Theory

Statistical

Mechanics

maxent as a tool for microstructure reconstruction
MAXENT as a tool for microstructure reconstruction

Input: Given average (and lower moments) of grain sizes and ODFs

Obtain: microstructures that satisfy the given properties

  • Constraints are viewed as expectations of features over a random field. Problem is viewed as finding that distribution whose ensemble properties match those that are given.
  • Since, problem is ill-posed, we choose the distribution that has the maximum entropy.
  • Microstructures are considered as realizations of a random field which comprises of randomness in grain sizes and orientation distribution functions.
the maxent principle
The MAXENT principle

E.T. Jaynes 1957

The principle of maximum entropy (MAXENT) states that amongst the probability distributions that satisfy our incomplete information about the system, the probability distribution that maximizes entropy is the least-biased estimate that can be made. It agrees with everything that is known but carefully avoids anything that is unknown.

  • MAXENT is a guiding principle to constructPDFs based onlimited information
  • There is no proof behind the MAXENT principle. The intuition for choosing distribution with maximum entropy is derived from several diverse natural phenomenon and it works in practice.
  • The missing information in the input data is fit into a probabilistic model such that randomness induced by the missing data is maximized. This step minimizes assumptions about unknown information about the system.
slide5

MAXENT : a statistical viewpoint

MAXENT solution to any problem with set of features is

Parameters of the distribution

Input features of the microstructure

Fit an exponential family with N parameters (N is the number of features given), MAXENT reduces to a parameter estimation problem.

Commonly seen distributions

Mean, variance given

Mean provided

No information provided

(unconstrained optimiz.)

1-parameter exponential family

(similar to Poisson distribution)

Gaussian distribution

The uniform distribution

microstructural feature grain sizes
Microstructural feature: Grain sizes

Grain size obtained by using a series of equidistant, parallel lines on a given microstructure at different angles. In 3D, the size of a grain is chosen as the number of voxels (proportional to volume) inside a particular grain.

slide7

e3

^

e’3

^

^

e’2

^

e’1

^

e2

e1

^

Microstructural feature : ODF

Crystal/lattice

reference frame

  • CRYSTAL SYMMETRIES?

Same axis of rotation => planes

Each symmetry reduces the space by a pair of planes

Sample reference frame

crystal

RODRIGUES’ REPRESENTATION

FCC FUNDAMENTAL REGION

n

  • ORIENTATION SPACE

Euler angles – symmetries

Neo Eulerian representation

Particular crystal orientation

Rodrigues’ parametrization

Cubic crystal

maxent as an optimization problem

Partition Function

MAXENT as an optimization problem

Find

feature constraints

Subject to

features of image I

Lagrange Multiplier optimization

Lagrange Multiplier optimization

equivalent log linear model

Equivalent log-likelihood problem

Find

that minimizes

Kuhn-Tucker theorem: The that minimizes the dual function L also maximizes the system entropy and satisfies the constraints posed by the problem

Equivalent log-linear model

A

comparison

slide10

Gradient Evaluation

  • Objective function and its gradients:
  • Infeasible to compute at all points in one conjugate gradient iteration
  • Use sampling techniques to sample from the distribution evaluated at the previous point. (Gibbs Sampler)
slide11

Parallel Gibbs sampler algorithm

Improper pdf (function of lagrange multipliers)

continue till the samples converge to the distribution

Go through each grain of the microstructure and sample an ODF according to the conditional probability distribution (conditioned on the other grains)

Start from a random microstructure.

Each processor goes through only a subset of the grains.

Processor r

Processor 1

slide12

Convergence analysis w/o stabilization

Noise in function evaluation increases as step size for the next minima increases. This ensures that the impact on the next evaluation is reduced.

Optimization Schemes

Convergence analysis with stabilization

voronoi structure

Voronoi cell tessellation :

Division of n-D space into non-overlapping regions such that the union of all regions fill the entire space.

Division of into subdivisions so that for each point, pi there is an associated convex cell,

Voronoi structure

{p1,p2,…,pk}: generator points.

Cell division of k-dimensional space :

Voronoi tessellation of 3d space. Each cell is a microstructural grain.

slide14

Mathematical representation

  • OFF file representation (used by Qhull package)
  • Initial lines consists of keywords (OFF), number of vertices and volumes.
  • Next n lines consists of the coordinates of each vertex.
  • The remaining lines consists of vertices that are contained in each volume.

Volumes need to be hulled to obtain consistent representation with commercial packages

  • Brep (used by qmg, mesh generator)
  • Dimension of the problem.
  • A table of control points (vertices).
  • Its faces listed in increasing order of dimension (i.e., vertices first, etc) each associated with it the following:
  • The face name, which is a string.
  • The boundary of the face, which is a list of faces of one lower dimension.
  • The geometric entities making up the face. its type (vertex, curve, triangle, or quadrilateral),
  • its degree (for a curve or triangle) or degree-pair (for a quad), and
  • its list of control points

Convex hulling to obtain a triangulation of surfaces/grain boundaries

slide15

Preprocessing: stage 1

Growth of big grains to accommodate small grains entrenched in-between

  • Compute volumes of all grains
  • Adjust vertices of neighboring grains so that the new voronoi tessellation fills the volume of initial grain
  • Recompute surfaces and planes of the new geometry
slide16

Preprocessing: stage 2

  • Steps
  • Obtain input voronoi representation in OFF format.
  • Obtain the convex hull of the volumes/grains so that each surface is a triangle (triangulation of surfaces).
  • Use ANSYSTM to convert this representation to the universal IGES (Initial Graphics Exchange specification) format.
    • Surface database : To ensure non-duplication of surfaces, a database consisting of previously encountered hyper-planes is searched. When a new surface is created, if it is already in the database and if all the vertices of the surface were not present in a previous grain, no new surface is made.
  • Domain smoothing: The regions of the microstructure inside the region [0 1]3 is chosen. Edges are smoothed so that the boundaries represent edges of a k-dimensional cube of unit side.
monte carlo techniques for matching grain size distributions
Monte Carlo techniques for matching grain size distributions

Problem : Generate voronoi-cell structures whose grain size distribution matches grain size PDF obtained using MAXENT

  • Generate a database of microstructures using Monte Carlo schemes (on the generator points) based on voronoi tessellation
  • Obtain correlation coefficient between the MAXENT and actual grain size measure. Accept microstructures whose correlation is above a cutoff.

Assign random voronoi centers.

Evaluate grain size distribution

correlation coefficient > Rcut

Accept microstructure

slide18

Meshing

Tetrahedral element meshed. Grain boundaries conform with the mesh shapes.

Pixel based meshing scheme. Boundary is distorted since element shapes and sizes are fixed.

slide19

Mesh refinement

Tetrahedral mesh

Hexahedral mesh

Input to homogenization tool to obtain plastic property and eventually property statistics

first order homogenization scheme
(First order) homogenization scheme

How does macro loading affect the microstructure

  • Microstructure is a representation of a material point at a smaller scale
  • Deformation at a macro-scale point can be represented by the motion of the exterior boundary of the microstructure. (Hill, R., 1972)

Materials Process Design and Control Laboratory

homogenization of deformation gradient
Homogenization of deformation gradient

How does macro loading affect the microstructure

Microstructure without cracks

Use BC:

= 0 on the boundary

Note w = 0 on the volume is the Taylor assumption, which is the upper bound

Materials Process Design and Control Laboratory

implementation
Implementation

Macro

Meso

Micro

Largedef formulation for macro scale

Update macro displacements

Macro-deformation gradient

Homogenized (macro) stress, Consistent tangent

Boundary value problem for microstructure

Solve for deformation field

Consistent tangent formulation (macro)

meso deformation gradient

Mesoscale stress, consistent tangent

Integration of constitutive equations

Continuum slip theory

Consistent tangent formulation (meso)

Materials Process Design and Control Laboratory

homogenized properties
Homogenized properties

Z

Y

X

(a)

(b)

(d)

(c)

Materials Process Design and Control Laboratory

slide24

2D random microstructures: evaluation of property statistics

Problem definition: Given an experimental image of an aluminium alloy

(AA3302), properties of individual components and given the expected

orientation properties of grains, it is desired to obtain the entire variability

of the class of microstructures that satisfy these given constraints.

Polarized light micrograph of aluminium alloy AA3302

(source Wittridge NJ et al. Mat.Sci.Eng. A, 1999)

slide25

MAXENT distribution of grain sizes

Grain sizes:Heyn’s intercept method. An equidistant network of parallel lines drawn on a microstructure and intersections with grain boundaries are computed.

Input constraints in the form of first two moments. The corresponding MAXENT distribution is shown on the right.

slide26

0.14

0.12

0.1

0.08

Orientation distribution function

0.06

0.04

0.02

0

-2

-1

0

1

2

Orientation angle (in radians)

Assigning orientation to grains

Given: Expected value of the orientation distribution function.

To obtain: Samples of orientation distribution function that satisfies the given ensemble properties

Input ODF (corresponds to a pure shear deformation, Zabaras et al. 2004)

Ensemble properties of ODF

from reconstructed distribution

slide27

80

70

60

Bounding plastic curves over a set

Equivalent Strain (MPa)

of microstructural samples

50

40

30

0

0.05

0.1

0.15

0.2

Equivalent Stress

Evaluation of plastic property bounds

Orientations assigned to individual grains from the ODF samples obtained using MAXENT.

Bounds on plastic properties obtained from the samples of the microstructure

slide28

MAXENT tool

3D random microstructures – evaluation of property statistics

Problem definition: Given lower moments of grain sizes and an input ODF,

obtain 3D reconstructions of the microstructure and evaluate statistics of

its homogenized properties

Input constraints: macro grain size observable. First three grain size moments , expected value of the ODF are given as constraints.

Output: Entire variability (PDF) of grain size and ODFs in the microstructure is obtained.

slide30

Microstructure meshing

Nodal degrees of freedom

500000

Nodal degrees of freedom

50000

Nodal degrees of freedom

300000

No. of hexahedral elements

~140000

No. of brick elements

~13500

No. of hexahedral elements

~95000

slide31

ODF reconstruction using MAXENT

Input ODF

Reconstructed samples using MAXENT

slide32

Ensemble properties

Ensemble properties of reconstructed samples of microstructures

Input ODF

slide33

Stress-contours obtained using conforming meshes

Orientations are sampled from the PDF of ODF’s obtained using MAXENT and grains are assigned these orientations. These microstructures are interrogated for evaluating homogenized properties.

Microstructure

Conforming hexahedral mesh

Sharp stress-contours are seen near the grain boundaries which is attributed to a comparatively higher heterogeneities near these boundaries.

slide34

30

25

20

Equivalent stress (MPa)

15

10

5

0

0

0.2

0.4

0.6

0.8

1

Equivalent strain

-3

x 10

Homogenized equivalent stress-strain curves

slide35

Applications (many …)

Statistics of plastic properties

modeling grain boundary physics
MODELING GRAIN BOUNDARY PHYSICS

Equivalent stress contours

  • Include failure mechanisms
  • Grain boundary properties
  • Local stress concentrations develop to cause the emission of a few partial dislocations from grain boundaries, and
  • these high stresses drive the partial dislocations across the grain interiors
  • MD studies indicate that this is the major mechanism
  • of the limited inelastic deformation in the grain interiors of nanocrystalline
  • materials.