February/March_AMP

A D V A N C E D

M A T E R I A L S

P R O C E S S E S | F E B R U A R Y / M A R C H

2 0 1 7

1 7

description of microstructure in the

models.

Microscopy methods for unbiased

descriptions of microstructures have

undergone dramatic improvements.

The desire for 3D microstructural data

is relatively straightforward because

3D data provides access to important

geometrical and topological quantities

that cannot be determined or are erro-

neously quantified using classical stere-

ological methods applied to 2D images.

However, experimental 3D characteriza-

tion and quantification techniques still

require significantly more development

compared with conventional 2D analy-

sis. The state of the art for 3D materials

characterization is advancing and is

covered in the literature

[8-10]

. Depend-

ing on the area and features of inter-

est, different microscopy methods are

used to obtain 3D information. Meth-

ods used to obtain 3D statistics from

microstructures include stereology

[11]

serial sectioning with or without elec-

tron backscatter diffraction (EBSD)

[12]

to reconstruct microstructures in 3D,

3D FIB-SEM

[13]

, and high-energy diffrac-

tion microscopy

[14]

. However, because

it is not possible to measure all con-

ceivable microstructural features, char-

acterization must have a well-defined

scope.

LANL research on generating

digital materials models involves

DREAM.3D

[15]

, an open source software

package designed for the digital re-

construction, instantiation, quantifi-

cation, meshing, and visualization of

microstructures. This package allows

creation of synthetic microstructures

from automatically generated statistics

or from one’s own statistics. Recon-

structed volumes can be exported in

various formats, and statistical analysis

can be extracted for further use.

any challenges exist with regard

to understanding and represent-

ing complex physical processes

involved with ductile damage and fail-

ure in polycrystalline metallic materials.

Currently, the ability to accurately pre-

dict the macroscale ductile damage and

failure response of metallic materials

is lacking. Existing macroscale models

involve simple micromechanics of pore/

solid interactions, which do not take into

account microstructural effects. Recent

initiatives are driving greater interaction

between thematerials scienceanddesign

engineering communities as materials

research migrates to a science-based

capability to design materials for specific

applications. Such processing model

capabilities will predict the internal struc-

ture of materials under specific process-

ing conditions. Similarly, knowledge of

internal structures will enable materi-

als property models to predict material

performance under desired operating

conditions.

Past studies of the effect of mi-

crostructural and stress-state variables

on the evolution of damage show that

microstructure must not be neglect-

ed to enable predicting or avoiding

damage and failure. Other studies

show that physical processes of dam-

age nucleation and growth leading to

failure are substantially affected by

material anisotropy and the presence

of both intrinsic and extrinsic defects

and heterogeneities

[1-5]

. There is gen-

eral agreement that the mechanisms

of damage nucleation and growth un-

der mechanical loading strongly de-

pend on material processing and the

resulting microstructure

[6]

. However,

the underlying mechanisms and kinet-

ics controlling damage nucleation and

growth as a function of material micro-

structure and loading path are still not

well understood. The physics of ductile

damage and failure are approached

as an evolving process of nucleation,

growth, interaction, and coalescence,

which leads to failure. These stages

are observed experimentally in imag-

es captured using circular-differential

interference contrast (C-DIC) microsco-

py (Fig. 1). Unfortunately, these damage

and failure events cannot be predicted

without the use of mesoscale-aware

modeling tools. Crystal plasticity-based

simulations can predict the average

crystallographic texture and develop-

ment due to deformation, but cannot

predict the nature and extent of the lo-

cal intergranular misorientations due to

deformation

[7]

Research at Los Alamos National

Laboratory (LANL) is aimed at building

a coupled experimental and computa-

tional methodology that supports the

development of predictive damage

capabilities by:

•

Capturing real distributions of

microstructural features from real

material and implementing them as

digitally generated microstructures

in damage model development.

•

Distilling structure-property in-

formation to link microstructural

details to damage evolution under

a multitude of loading states.

MICROSTRUCTURE:

A3DCONCEPT

The internal structure of materials

is complex andmultiscale—and defined

by a large number of parameters. Micro-

structures of materials such as metals

are three-dimensional (3D). However,

most traditional methods developed to

study such materials depend on obser-

vations made in 2D sections due to their

opaque nature. Increasing demand for

modeling and simulation to predict ma-

terials properties and to provide more

realistic representations of microstruc-

tures have increased the need for high

accuracy characterization and analysis

of microstructures. Predictive represen-

tation of ductile damage and failure in

materials remains a significant compu-

tational challenge. Past quantification

efforts to characterize microstructural

aspects considered average quantities,

which in part were driven by the limited

Fig. 1 —

Stages in damage development observed experimentally in shocked tantalum:

(a) undeformed microstructure, (b) strain localization, and (c) void damage.