Application of Thermo-Calc/DICTRA in Physics … of Thermo-Calc/DICTRA in Physics-Based Models to Support ... chem Quasi-Binary DG chem ... e J RdR V N J Z C a B m m a C m m R

Application of Thermo-Calc/DICTRA in

Physics-Based Models to Support

Materials by Design®

Herng-Jeng Jou (Joe)

[email protected], http://www.questek.com

Computational Thermodynamics & Kinetics Seminar

2011-04-06

Computational Thermodynamics & Kinetics Seminar2011-04-06

Materials By Design®

2

Outline

• Intro: QuesTek and Materials by Design

• Mechanistic Modeling With ThermoCalc and DICTRA

• Implementation With ThermoCalc and DICTRA

• Materials by Design Calculation Examples

• Future Directions



3

QuesTek Innovations LLC

• Founded 1997; privately-held; located in Evanston, IL

• 19 Employees, including 15 Engineers (9 PhD’s)

• Rapidly designing, developing, qualifying and inserting new materials using

computational methods on integrated basis

• Creates IP; licenses to OEMs or alloy producers/processors

• 4 alloys licensed: Ferrium® M54™, C61™, C64™ and S53®

• ~10 major new alloys in development, 30+ patents awarded or pending worldwide

• Working with many colleagues in industry and academia

• Serving industry and government

• Recipient of many business and technology awards

• Expanding our staff

Core Competencies and Key Services

• Mechanistic Modeling

• Systems-based Computational Design

• Material Invention / Obtaining IP

• Validating Performance through

Detailed Characterization

• Guiding Scale-Up of Manufacturing

• Obtaining Industry Certifications

• Licensing IP to Commercial Producers or

OEMs

• Commercializing Materials with Licensees



4

We Create Diverse Material Systems

• Iron-based

• Aluminum-based

• Nickel-based

• Copper-based

• Niobium-based

• Titanium-based



5

Ferrium C61 Ring and Pinion

Gear Steel

SCORE Off-Road Racing

Circuit, Baja 1000, etc.

Ferrium S53 UHS

Corrosion Resistant Alloy

Aircraft Landing Gear

A-10 Main Landing Gear

A-10 Nose Gear

Examples of QuesTek’s Computationally Designed Alloys



6

Modeling for the Purpose of Materials Design

Process

Structure

Property

Performance



7

Integrated Computational Materials Design



8

Example Flow-Block Diagram for UHS Gear Steels

Cas

t

Ingo

tFo

rge

Mac

hin

e

Carburize/Solution Treatment

Quench/Controlled Cooling

Tempering/Coating

Pow

der

Met

allu

rgy

Case

Dispersion Gradient

Residual Stress

Core

Matrix - Lath MartensiteNi - Cleavage Resistance

Co - SRO Recovery Resistance

Strengthening Dispersion(Cr, Mo, V, W, Fe)2CX

Avoid Fe3C, M6C, M23C6

Grain Refining Dispersiond/f

Micro-void Nucleation Resistance

Grain Boundary ChemistryCohesion Enhancement

Impurity Gettering

MicrosegregationMo, Cr secondary dendrites

PROCESSING PROPERTIESSTRUCTURE

SurfaceHardness

StressDistribution

ThermalResistance

CoreHardness

Toughness

Modulus

PERFORMANCE



9

Physics-based

Parametric Models

Use of Models in Materials by Design

Empirical ModelsMechanistic

Models

• Explorative Evaluation

• Trade-off Analysis

• Robust Analysis/Design

• Sensitivity Analysis

• Accelerated Insertion

of Materials

Type of Calculations:

Model Improvement



10

Outline



– Martensite/Bainite Kinetics Modeling

– Precipitation Modeling






11

Martensite Models (Ghosh and Olson, 1994)

FCC

BCC

x

G

DGchem

Quasi-Binary

DGchem

Critical Energy, DGcrit=DGs(n)+Wf

ss(Xi)+Wf^(T)

DGs(n=18) : Surface Energy, n is Defect Potency Size

• Wfss(Xi) : Frictional Work by Solute

Strengthening• Wf

^(T) : Frictional Work by Dislocation Forest

Balance

DGchem (T=Ms) = DGcrit (T=MS)Solve for MS

TC

Ni’(n,D), Pre-existing Potency Distribution

nnm*

P’(n), Autocatalytic Potency Distribution

nnm*

DGchem =DGs(n*)+Wfss(Xi)+Wf

^(T)

Volume Fraction (fM) Evolution:dfM/dn = (Ni’(n,D)+P’(n)fM)(1-fM)V(f)

with fM (n=)=0where:V: Volume of Martensitic Lath Sub-unitD: Grain Size

Solve for fM(n=nm*) and find fM(T)

Activated

fM(T) Model

MS Model



12

DGchem (xa, T=BS) = DGcrit (xa, T=BS)Solve for Bs and xa

Bainite Models (Olson, Bhadeshia and Cohen, 1989, 1990)

FCC

BCC

xa x

G

DGchem

DGchem (with interstitial partitioning)

Critical Energy DGcrit=DGs(n)+Wf

ss(Xi)+Wf^(T)

FCC

BCC

xa x

G

xI

DGd

DGchemDGcrit

• Carbon Diffusion, Vd (xa, xI)• Interfacial Mobility• Solute Trapping, Vk (xa, xI)

Set of Non-Linear Equations:• Vn(n) = Vd = Vk

• DGchem = DGd+Wfss(Xi)+DGs (n)+Wf

^(T)

Solve for Vn (n), xa, and xI

xax

xI

BCC FCC

Vn

BS Model

fB,stasis(T) Model can be formulated similar to fM(T) model

Balance

Bainite Subunit Growth Rate Model

Used to estimate bainite start C-curves

TC

TC

DICTRA



13

Martensite-Bainite Model Validation

MS (°C)

BS (°C)



14

Precipitation Growth and Nucleation Models

a: matrixb: precipitateDGm: effective driving forceDGs: misfit strain energy

G- where

)(2

exp)(

41 :Growth

12

s

2

0

e

kjk

ji

T

i

mmj

ji

T

im

m

m

v

CDCC

GC

CCCC

GCG

R

VRG

RT

QMRsR

RπNR

dt

dR

m

D

D

DD

DD

D

a

bab

a

b

re temperatuis , whereradius critical is

constant sBoltzmann' is number, sAvogadro' is

4t impingemen atomic of rate

3

16 nucleus critical a form work to

4factor h Zeldovitcwhere

)( Rate Nucleation StateSteady

*

2

3*

2/1

422

2

0

*

*

T0dR/dtR

kN

G

V

NR

V

GW

TRkN

VZ

dRRJeV

NZJ

C

Ba

m

m

aC

m

m

R

CBa

m

SSTk

W

m

aSS

B

R

D

D

b

b

b

a

b

b

Growth Model

Nucleation Model

TC

DICTRA

PrecipiCalc



15

PrecipiCalc g’ Results for IN100 Disk Alloy

10-9

10-8

10-7

10-6

10-5

0 10000 20000 30000 40000 50000 60000 70000 80000

<D>1<D>2<D>3

Dia

me

ter

(m)

Time (sec.)

10-9

10-8

10-7

10-6

10-5

0 10000 20000 30000 40000 50000 60000 70000 80000

<D>1<D>2<D>3

Dia

me

ter

(m)

Time (sec.)

T(t)

PrimarySecondary

Tertiary

0

0.1

0.2

0.3

0.4

0.5

0 10000 20000 30000 40000 50000 60000 70000 80000

Dist 1Dist 2Dist 3

Vo

lum

e F

raction

Time (sec.)

0

0.1

0.2

0.3

0.4

0.5

0 10000 20000 30000 40000 50000 60000 70000 80000

Dist 1Dist 2Dist 3

Vo

lum

e F

raction

Time (sec.)

T(t)

Primary

Secondary

Tertiary



16

M2C Model for Secondary Hardening Martensitic Steels

Enhancements to PrecipiCalc

• BCC-Cementite Para-Equilibrium Condition

• Heterogeneous Nucleation on Dislocation with pipe diffusion

• Simple Coherency Transition Model With A Modified HCP Description with Micromechanical Elastic Coherency Energy Parameterized by Aspect Ratio and Misfit (Compositions effects implemented into a TC’s TDB)

• Simplified Cementite Dissolution

• Simplified Dislocation Recovery Dynamics Diameter,d3nm

Elastic Relaxation Factor b

Surface Energy

Aspect Ratio a

1

35

0.3

0

incoh

coh



17

AF1410 M2C Precipitation

Isothermal M2C precipitation simulation of AF1410 steel at 510°C, along with the experimental results from G.B. Olson, T.J. Kinkus and J.M. Montgomery, Surface Science 246(1992) 238. Parameters used in the simulation include: surface energy coh=0.25J/m2, incoh=1.0J/m2, r=2.5x1014 (m/m3), Dpipe/Dvol=100.

significant lower coarsening rate than t1/3

t1/3



18

Outline








19

QuesTek’s Proprietary Software Platform (CMD)

• Modularized

• Object-oriented (C++, Python)

• Standardized communication to

enable customized integration

Command LineInterface

• Standardized I/O command line interface

• iSIGHT Persistent and NV API’s• integrator API

TCIPC

Model Implementations

CMD Programs

GUI

CMDProgram

Integrator API

CMDProgram CMD

Program

iSIGHT, DAKOTA

ThermoCalc/DICTRA

CALPHAD Thermodynamics and Mobility Databases

TC-APINumerical Library

Python/Shell Scripts or Console

Model developers, materials designers and developers



20

CMD GUI — Pre-Processor

• Operates All CMD Programs• Selects Models and CALPHAD Databases• Sets up Explorative, Full Factorial DoE, Sensitivity and AIM Analysis• Rapid Visualization of Results



21

CMD GUI — Post-Processor

Results Manager

Additional External Data Manager

• Generates Explorative Plots with one or two variations

• Combines/Overlays Multiple Results• Adds External Data



22

CMD GUI — Sensitivity and AIM Analysis

compositions and model inputs

(such as HT conditions)

described as uncertainty

probability distribution



23

Approaches for Integrating Thermo-Calc/DICTRA

Software Type Speed DICTRAExternal

Codes

Access-

ibility

Multiple

Instances

Programming

Facility

Error

DetectAuthor

PARROT Macro Slow No No Partial NoLimited

(PARROT)Yes TC-AB

TCLib IPC Slow No Yes Full No Full (C) Yes TC-AB

TQ API Fast No Yes Partial No Full (F77) Yes TC-AB

TCIPC IPC Slow Yes Yes Full YesFull

(C/C++/Python)Some QuesTek

TC API API Fast Yes Yes Full No Full (C/C++) Yes TC-AB



24

Outline








25

Overall Compositions

User Input

O2Content

Surface Energy L12/LIQ and

liquid diffusion

Surface Energy FCC/LIQ

TL (FCC)

LIQ Compositions

w/o L12

L12 Particle Size

Distribution (PSD)

Corrected L12PSD

dT/dt or T(t)

Thermo-Calc

PrecipiCalcPrimary L12

PrecipiCalcInoculation

PSD WidthCorrection

Grain Size

Model Integration Example: Castable

High Strength AA7xxx

DICTRATC



26

Model Integration Example: Design of Low Cost Ti Casting Alloys

DICTRATC



27

Design Exploration and Tradeoff Analysis

Matrix + Strengthening Dispersion Design

Grain Pinning Dispersion Design

DICTRA TCTC



28

Composition Sampling(wt%, ±6):C ± 0.01 Cr ± 0.2 Mo± 0.1 W ± 0.1 Co ± 0.3 Ni ± 0.1 V ±0.02

CMD/

iSIGHT

Variations of:Structure — carbide solvus Ts, martensite

Ms, precipitation control DG’sProperty — hardness HRc, toughness CVN

1000 Monte Carlo runs (12 minutes on a Pentium IV 2.2GHz CPU)

Sensitivity Analysis on S53 Compositions

TC

DICTRA

TC

TC

TC

TC



29

Example of CMD/iSIGHT Robust Optimization

Original Compositions 3% Failure

Fail

Robust Optimization

New Compositions 0% Failure

TC

TC



30

S53 — Design For Scale

S53-3 Segregation Experience

300 lb 8” VAR ingot

S53A Segregation Experience

3000 lb 17” VAR ingot

12 hours

Solidification Simulation

Homogenization Simulation

24

DICTRA

DICTRA



31

Probabilistic Modeling of Manufacturing Variation:

Forecast of Minimum Design Properties

PrecipiCalc YS Model YS Distribution

Mechanistic simulation+ (n=15) gives good prediction of 1% minimum YS

Process VariationDICTRA



32

Accelerated Insertion of Materials (AIM)

Application Example: Ferrium S53®

Predicted A-basis minimum = 280 ksi UTS A-basis minimum: 280 ksi UTS

• AIM methodology has demonstrated reliable predictions for design minimums

• Allows designers to apply design models to estimate property variation prior to

full design allowable development

• Reduces costs and risks of material design and development

AIM prediction

Experimental Data

Pro

pert

y

Mean value

+3

-3

3 10AMS

Specification

MIL-HBK 5

“A”- Allowables

AIM Predictions

DICTRA



33

Outline








34

Future Directions

• Model uncertainty and prediction confidence level

• Integration with additional system design framework

and methods

• New computer hardware and software architectures

• Further integration with component-level process

simulations

• Robustness improvement

• Cross platform



35

Computational Materials Qualification Acceleration

•MMPDS handbook update issued

•Additional property data developed

•10th multi-ton full-scale ingot produced

•Aerospace Materials Specification issued

•Static property data developed

•3rd multi-ton full-scale ingot produced

•1st multi-ton full-scale ingot produced

•Static properties demonstrated at prototype

•System design chart (design goals) establishedJan-99

Jan-00Jan-01Jan

-02Jan

-03Jan-04Jan-05Jan

-06Jan

-07Jan-08Jan-09Jan-10Jan-11Jan

-12Jan-13Jan-14

Maj

or

Mile

sto

nes

Dates

5 design

iterations

1 design

iteration

S53

8.5yrsM54

~6yrs

First S53 landing gear field

service test flight occurred

on Dec. 17th, 2010



36

The Next Frontier …

Documents

Application of Thermo-Calc/DICTRA in Physics … of Thermo-Calc/DICTRA in Physics-Based Models to Support ... chem Quasi-Binary DG chem ... e J RdR V N J Z C a B m m a C m m R