DREAM.3D: An Overview - Carnegie Mellon...

DREAM.3D: An Overview

Michael Groeber, Sean Donegan & Mike Jackson

Michael Uchic, Dennis Dimiduk, Tony Rollett, Dave Rowenhorst, Joe Tucker, Will Lenthe, Joey Kleingers,

Marc DeGraef, Greg Rohrer, Megna Shah, Craig Przybyla, Greg Loughnane, Chris Woodward, Jeff Simmons

AFRL, BlueQuartz, CMU, NRL, UES, UCSB, WSU

Air Force Research Laboratory Materials and Manufacturing Directorate

Outline

• Historical Context for SIMPL & DREAM.3D

• Our Original Goal & How We Arrived Here

• It’s SIMPL

• Data Structure, Application Interaction and File Structure

• Living the DREAM.3D

• Examples of Datasets/Analyses Using DREAM.3D/SIMPL

• “New” Approaches to “Old” Ideas Enabled by SIMPL

• Who’s Using DREAM.3D/SIMPL

DREAM.3D – Digital Representation Environment for Analyzing Microstructure in 3D

SIMPL – Spatial Information Management Protocol Library

Outline



• It’s SIMPL






• Early-To-Mid 2010s

• Abstracted tool interaction and data

structure – Greatly lowered barrier to

entry and expanded breadth of application

The Evolution to DREAM.3D

2003 2011 2009 2007 2005 2013 2015

• Late 2000s and Early 2010s

• Large collaborative efforts – Added function,

but occurred manually & with point source

integration

• Mid-To-Late 2000s

• GUI front end – Improved user interaction,

but fixed workflow and structures

• Early-to-Mid 2000s

• Proof of concept – Led to disparate

command line tools

Off-shoots are suites of filters/apps & may not treat microstructure

directly or have similarities outside of managing spatial information

New View of SIMPL-Centric Structure

SIMPL

DREAM.3D

Thermal/Electrical

Field Analysis

Suite

Component

Scale Analysis

Toolbox

Processing

Simulation

Analysis Engine

Geographical

Data Analysis

Package

‘The Core’

DREAM.3D

‘The Core’ was common functions used by multiple

filters/plugins…and what we wanted to always be open-source

Medical Image

Analysis Kit

Weather Data

Management

Package

SIMPL is ‘The Core’ & is the foundation

that enables DREAM.3D

Provides ‘easy-to-use’, customizable programming environment

The Interface of SIMPL/DREAM.3D

∙ Filter/Module design → creates

scalable, customizable tool

∙ Dynamic workflow creation →

gives user autonomy

∙ Abstract data structure → allows

tool to adapt to users/applications

∙ Programming language vs.

program → creates learning curve

and user ‘responsibility’

∙ Tailored data structure → enables

pervasive API tools

High-level scripting language like:

Outline



• It’s SIMPL






Abstract Hierarchical Scheme

Feature Object

#NN

Size

ρdis

100s – 10,000s of Objects

(100s – 10,000s of Element Objects)

Ensemble Object 1s – 10s Objects

(100s – 10,000s of Feature Objects)

Element Object

ρdis

100,000s – 1,000,000,000s of Objects

GEOMETRY, LABELING, STATISTICS

'

Object–Attribute construct generalizes storage of digital, spatial information

Image Courtesy of L. Drummy

Images Courtesy

of J. Tiley Image Courtesy of J. Miller

Abstract Hierarchical Scheme

Feature Object 100s – 10,000s of Objects

(100s – 10,000s of Element Objects)

Ensemble Object 1s – 10s Objects

(100s – 10,000s of Feature Objects)

Element Object 100,000s – 1,000,000,000s of Objects

GEOMETRY, LABELING, STATISTICS

'


Image Courtesy of L. Drummy

Images Courtesy

of J. Tiley Image Courtesy of J. Miller

The Attribute Matrix


Position

Greyscale

Confidence

Volume

Quaternion

Chemistry

Phase

FeatureID

1 2 3 4 5 6 7 8 9 N-1 ……. N

Attribute

Array

Property

Vector

Attributes

Objects

Dynamically-defined & resized container allows for

flexible & efficient data storing

Data Structure Example

Hierarchical structure organizes information, geometries & their

relationships

Tree structure provides flexible growth path for

data during analysis and retains linkages

Attribute Arrays

Attribute Matrices

Data Containers

Data Container Array .dream3d File

ImageDataContainer

CellData

FeatureIds Grayscale

TriangleDataContainer

FaceData

FaceLabels Normals

VertexData

NodeType

ImageGeometry TriangleGeometry

Details of the SIMPL-App Interaction

SIMPL has protocols for

retrieving and returning

Attribute Arrays and

Property Vectors

SIMPL stores

parameters used to

generate and/or modify

Attributes

SIMPL has 100s of files,

~100,000 lines of code to

standardize pervasive

operations and runs on all

platforms (Windows, OSX,

Linux)

SIMPL–App interaction is defined protocol & enables clear growth path

Quantification Application(s)

SIMPL

• Manages Current Object Versions

• Brokers Application Interaction

• Controls I/O

• Manages Digital History of Data

Updated Element and Feature Objects

Current Element and Feature Objects

An Equally Flexible File Architecture

Flexible file structure well-suited to hold dynamically-defined information

File structure is like a ‘file system within a file’

Outline



• It’s SIMPL






DREAM.3D: An App Suite for Materials

Reconstruction Application(s)

Instantiation Application(s)

Archival Application(s)

Quantification Application(s)

SIMPL • Manages Current Object Versions

• Brokers Application Interaction

• Controls I/O

• Manages Digital History of Data

Identification Application(s)

SIMPL is material independent; Apps may be material & data-type dependent

* Images are example outputs

from existing applications for

specific processes

* Blue boxes represent a

suite of applications for

specific processes

* Red arrows represent

the transfer of information

to/from SIMPL to

Application

* Central box represents

SIMPL as a broker/manager

between applications

Meshing Application(s)

Processing Application(s)

Simulation Application(s)

1.25mm

• 1mm x 0.75mm x 0.65 mm (x, y, z)

• EBSD every section (512 x 384 pixels)

• BSE/SE images every section (4096 x 3072 pixels)

• Indents for indep. registration & material removal

• 170 sections

Multimodal, 3D Mesoscale Microscopy

Multi-modal data collection is critical to identify features & collect

information that are not available with a single data mode

Multimodal, 3D Mesoscale Microscopy

Storing information describing features intimately with ‘image’ information

allows for more straight-forward correlative analysis

Additive Manufacturing Example

Geometry

(CAD)

Process Parameters

CT Scans

+

+

• Processing conditions ‘known and

controlled’ spatially

• Structure can be correlated to

processing if properly registered

* Adapted from E. Schwalbach (AFRL) and V. Paquit (ORNL)

+

In-situ Monitoring

Data modes span range of resolution, representation type, time

Additive Manufacturing Example

* Adapted from E. Schwalbach (AFRL)

Initial investigations of processing–structure correlation points to more complex

interaction of process & geometry → could drive process optimization

Reconstruction Examples

Images & Analysis Courtesy of M. DeGraef and

J.C. Palauqui

Confocal laser microscopy of cotyledon → Classification of reconstructed

cells via moment invariant omega 3

Cast Ni Superalloy: Data Visualization

• Volume dimensions:

~1.8 x 1.8 x 0.19 mm

Pore morphology points to separate formation process

Correlated Data Analysis: CPM

Visual appearance of correlation is only marginally useful

• Polarized light optical microscopy exhibits contrast sensitive to c-axis orientation for

hexagonal materials

• Desire to identify and classify regions of common c-axis using optical microscope (speed,

cost and environment)


Data fusion/co-registry allows determination of functional relationships

• Greyscale values fit to sine function:

A*sin(Bx+C)+D

• Sine parameters visually correlate to EBSD

IPF map

• Fusing data requires registering independent

reference marks or aligning mutual information

from two (or more) signals

• Once fused one data mode can be functionally

related to another data mode

• Once functional relationship is

known, one mode no longer

necessary and can be derived from

the other


CPM provides ‘similar’ feature identification 1/100th – 1/1000th the time

HEDM and DREAM.3D

Multiple Time Step Data (ex. Tomo) – Schuren, Shade, Turner

Crack path highly non-planar; May provide more accurate crack growth rates

Synthetic Building in D3D: Current Possibilities

Far-Field HEDM

(Data informed synthetic)

Carbon Foams

Mechanical Serial Sectioning (RoboMet) – Maruyama

Interfaces properties like curvature, flux, misorientation, energy, etc can

be stored in same structure as volumetric information

OMC Initial Results

Mechanical Serial Sectioning (RoboMet) – Uchic, Mollenhaur

Delaminations linked to specific interfaces; May point to process change

A “Big” Data Problem

Modern materials engineering is driven by process

modeling

Process modeling tools such as DEFORM and

ProCAST produce continuous descriptions of field

variables at the part geometry scale

Desire to predict part performance from simulated

processing to meet specification

*Data courtesy of A. Pilchak 29

Domain partitioning (or zoning) divides a space into

regions that are “similar”, by some metric

Want to automatically extract those regions of a CFV

that are more alike one another than any other region

These zones are then considered to have undergone

the same thermomechanical processing and

therefore to have similar microstructure and properties

Component ‘Zoning’

30

Determining the optimal number of zones can be difficult; algorithms exist that determine the

number of zones automatically, but are more sensitive to other input parameters

Want to avoid the “curse of dimensionality”, where many distance metrics begin to lose meaning

in very high dimensional space

Zones in CFVs should be those regions with the smallest gradient…

Compute gradient

Scalar

segmentation

Cleanup small zones

DREAM.3D – FFT Interaction: Synthetic TBCs

TBCs exhibit complex microstructure

and interface morphology

Generate microstructure

statistics Stitch individual

layers

Validate texture

DREAM.3D – FFT Interaction: Synthetic TBCs

Potts model

No Potts model

DREAM.3D exports

FFT file

Run thermolastic

FFT simulation

Phase contrast

Iterative procedure

contour map

z-smooth EED

POT

quantify hot spots

DREAM.3D – FFT Interaction: Hot Spot Correlation

columnar TC, textured TGO, (Ni,Pt)Al BC

Hot spots at the BC/TGO interface generally lie in regions of low elevation

(i.e., troughs)

splat TC, textured TGO, (Ni,Pt)Al BC

DREAM.3D – FFT Interaction: Hot Spot Correlation

Poly-xtal Ni: Microtension Samples

12%

Structure and constraints passed between scales using hierarchical scheme

• Micro-specimens tested and

characterized

• FEM simulations provide local stress &

strain states

• DD volumes could be created with

proper orientation and loading

conditions

• Quantification tools for assessing local

GND density in 3D EBSD and DD

simulations allow comparison metric

Dislocation Dynamics Examples

Discrete Dislocation Dynamics (ParaDis) – Rao

Local line density, ‘cell’ and ‘wall’ sizes may offer metrics to quantify DDD

Atomistics Examples

Atomistic Simulation (LAMMPS) – Rao

Synthetic generation of polycrystalline atomistic simulation input files

And Something Completely Different…..

Census data has spatially discrete data with time evolution and

correlations that have potentially analogous analyses to materials

Outline



• It’s SIMPL






Enabling Multi-Scale Management

Computational management tools for structure hierarchy do not exist today

Ensemble

Object(s)

Feature

Object

Ensemble

Object(s)

Element

Object

Ensemble

Object(s)

Feature

Object

Ensemble

Object(s)

Element

Object

Multi-scale hierarchical

management can occur with

sufficient MSE guidance of links

between scales and knowledge

of digital representation

In construct of Objects with

Attributes, higher-scale

objects can have Attributes of

lower-scale information

DREAM.3D

+

‘Collection

Protocol’ Engine

Scheduler/

Resource Manager

DREAM.3D

+

‘Collection

Protocol’ Engine

DREAM.3D

+

‘Collection

Protocol’ Engine

SEM

+

EBS

D

SEM

+

EDS

Prep

Sub-

Syste

m

#2

OM

Robo

t

Arm

Prep

Sub-

Syste

m

#1

Prep

Sub-

Syste

m

#3

Prep

Sub-

Syste

m

#4

Robo-Met.3D Dual Beam

FIB-SEM LEROY

Broad Ion

Beam

Targeted, Protocol-Driven Collection

Automated systems to be driven in real-time by analysis and requirements

Automatable Resources

Decision Environment Quality/Requirement Rules

Enabling Links To Engineering Design

Outline



• It’s SIMPL






• DoD and DoE Laboratories

• Air Force Research Lab, OH+FL, USA

• Los Alamos National Lab, NM, USA

• Naval Research Lab, VA, USA

• Idaho National Lab, ID, USA

• NASA Langley, VA, USA

• Army Research Lab, MD, USA

• Sandia National Lab, NM, USA

• Oak Ridge National Lab, TN, USA

• OEMs/Industry

• GE Global, NY, USA

• GE Aviation, OH, USA

• Pratt&Whitney, CT, USA

• Rolls-Royce, IN, USA

• HRL Laboratories LLC, CA, USA

• U.S. Academia

• Ohio State Univ., OH, USA

• Carnegie Mellon Univ., PA, USA

• Cornell Univ., NY, USA

• Univ. of Michigan, MI, USA

• Drexel Univ., PA, USA

• Lehigh Univ., PA, USA

• Iowa State Univ., IA, USA

• Northwestern Univ., IL, USA

• Purdue Univ., IN, USA

• Georgia Tech, GA, USA

• Univ. of North Texas, TX, USA

• Johns Hopkins Univ., MA, USA

• Boise State Univ., ID, USA

• Univ. of Dayton, OH, USA

• Univ. of Pittsburgh, PA, USA

• Vanderbilt Univ., TN, USA

• Univ. of Kentucky, KY, USA

• Univ. of Cal. Santa Barbara, CA, USA

• Univ. of Florida, FL, USA

• Univ. of Tex. San Antonio, TX, USA

• Wright State Univ., OH, USA

• Case Western Univ.. OH, USA

• Univ. South Carolina, SC, USA

• Mississippi State Univ., MS, USA

• Arizona State Univ., AZ, USA

• Univ. of New Hampshire, NH, USA

• Univ. of Tennessee, TN, USa

User-base is international & spans spectrum of research areas

The Users of DREAM.3D

• International

• Ghent University, Belgium

• Queens Univ., Canada

• Seoul National Univ., S. Korea

• Univ. of Manchester, UK

• Univ. Lorraine, France

• Salzgitter Mannesmann Forschung , Germany

• Deakin University, Australia

• King Abdullah Univ., Saudi Arabia

• University College, Ireland

• Riso/DTU, Denmark

• Pohang Univ., Korea

Common frameworks become ‘Rosetta Stone’ for researchers

http://dream3d.bluequartz.net

http://www.openworm.org

Take-Away Messages

• SIMPL is an abstract tool for managing spatial

data from different modes, grids & scales

• SIMPL provides an objective environment for

models & analysis tools to interact

• Meta data is automatically captured & could be

easily augmented manually

• The vision of SIMPL is to exploit similarities of a

large portion of the data space

DREAM.3D: An Overview - Carnegie Mellon...

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