Application of Thermo-Calc/DICTRA in Physics-Based Models to Support Materials by Design

Transcription

1 Application of Thermo-Calc/DICTRA in Physics-Based Models to Support Materials by Design Herng-Jeng Jou (Joe)

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 Mechanistic Modeling Core Competencies and Key Services 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 Examples of QuesTek s Computationally Designed Alloys A-10 Main Landing Gear A-10 Nose Gear Ferrium C61 Ring and Pinion Gear Steel SCORE Off-Road Racing Circuit, Baja 1000, etc. Ferrium S53 UHS Corrosion Resistant Alloy Aircraft Landing Gear

6 Modeling for the Purpose of Materials Design Performance Property Structure Process

7 Integrated Computational Materials Design

8 Ingot Forge Machine Cast Powder Metallurgy Example Flow-Block Diagram for UHS Gear Steels PROCESSING STRUCTURE PROPERTIES Tempering/ Coating Case Dispersion Gradient Surface Hardness Quench/ Controlled Cooling Carburize/ Solution Treatment Residual Stress Core Matrix - Lath Martensite Ni - Cleavage Resistance Co - SRO Recovery Resistance Strengthening Dispersion (Cr, Mo, V, W, Fe) 2 C X Avoid Fe 3 C, M 6 C, M 23 C 6 Grain Refining Dispersion d/f Micro-void Nucleation Resistance Grain Boundary Chemistry Cohesion Enhancement Impurity Gettering Stress Distribution Modulus Thermal Resistance Core Hardness Toughness P E R F O R M A N C E Microsegregation Mo, Cr secondary dendrites

9 Use of Models in Materials by Design Model Improvement Empirical Models Physics-based Parametric Models Mechanistic Models Type of Calculations: Explorative Evaluation Trade-off Analysis Robust Analysis/Design Sensitivity Analysis Accelerated Insertion of Materials

10 Outline Intro: QuesTek and Materials by Design Mechanistic Modeling With ThermoCalc and DICTRA Martensite/Bainite Kinetics Modeling Precipitation Modeling Implementation With ThermoCalc and DICTRA Materials by Design Calculation Examples Future Directions

11 M S Model TC G Martensite Models (Ghosh and Olson, 1994) Quasi-Binary DG chem f M (T) Model x FCC BCC N i (n,d), Pre-existing Potency Distribution DG chem Balance DG chem (T=M s ) = DG crit (T=M S ) Solve for M S DG chem =DG s (n*)+w f ss (X i )+W f^(t) Critical Energy, DG crit =DG s (n)+w ss f (X i )+W f^(t) DG s (n=18) : Surface Energy, n is Defect Potency Size W ss f (X i ) : Frictional Work by Solute Strengthening W f^(t) : Frictional Work by Dislocation Forest n n m * P (n), Autocatalytic Potency Distribution Activated n n m * Volume Fraction (f M ) Evolution: df M /dn = (N i (n,d)+p (n)f M )(1-f M )V(f) Solve for f M (n=n m *) with f M (n=)=0 and find f M (T) where: V: Volume of Martensitic Lath Sub-unit D: Grain Size

12 B S Model Bainite Models (Olson, Bhadeshia and Cohen, 1989, 1990) G BCC FCC DG chem (with interstitial partitioning) Balance Critical Energy DG crit =DG s (n)+w f ss (X i )+W f^(t) TC DG chem DG chem (x a, T=B S ) = DG crit (x a, T=B S ) Solve for B s and x a x a x f B,stasis (T) Model can be formulated similar to f M (T) model Bainite Subunit Growth Rate Model G FCC BCC Carbon Diffusion, V d (x a, x I ) Interfacial Mobility Solute Trapping, V k (x a, x I ) DICTRA x I x a x BCC V n FCC DG d DG crit DG chem Set of Non-Linear Equations: V n (n) = V d = V k DG chem = DG d +W f ss (X i )+DG s (n)+w f^(t) TC x a x x I Solve for V n (n), x a, and x I Used to estimate bainite start C-curves

13 Martensite-Bainite Model Validation M S ( C) B S ( C)

14 Precipitation Growth and Nucleation Models Growth Model dr 1 R Growth: dt R s( R) where DG m PrecipiCalc 4πN exp 2 T G b a b DC DC C m 2 T G 1 e DC D jk DC i i CiC a CiC j a M j 0 v DICTRA j R DG Q RT k TC m m 2 ( R) V R m b m - DG Nucleation Model where Zeldovitch factor Z s Steady State Nucleation Rate J N R a C is Avogadro' s a: matrix b: precipitate DG m : effective driving force DG s : misfit strain energy SS V 2 4 N number, k is critical radius where dr/dt * N Zb V B b 2 m 2 a kb a a m TR e 4 C * R W k T B 1/ 2 3 * 16 work toform a critical nucleus WR DG 3 m b Vm * Na DGm rate of atomic impingemen t b 4RC b V m 0 is Boltzmann's constant J SS 0, T is temperature 2 ( R) dr

15 Diameter (m) Volume Fraction PrecipiCalc g Results for IN100 Disk Alloy T(t) Primary Secondary Dist 1 Dist 2 Dist T(t) Primary Secondary Tertiary Time (sec.) Tertiary <D>1 <D>2 <D> Time (sec.)

16 M 2 C 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 Elastic Relaxation Factor b 0.3 coh 0 Surface Energy incoh Aspect Ratio a nm Diameter,d

17 AF1410 M 2 C Precipitation significant lower coarsening rate than t 1/3 t 1/3 Isothermal M 2 C 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/m 2, incoh =1.0J/m 2, r=2.5x10 14 (m/m 3 ), D pipe /D vol =100.

18 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

19 QuesTek s Proprietary Software Platform (CMD) Model developers, materials designers and developers isight, DAKOTA GUI Python/Shell Scripts or Console CMD Programs Standardized I/O command line interface isight Persistent and NV API s integrator API Model Implementations Numerical Library TCIPC ThermoCalc/ DICTRA TC-API CALPHAD Thermodynamics and Mobility Databases Integrator API CMD Program CMD Program CMD Program Command Line Interface Modularized Object-oriented (C++, Python) Standardized communication to enable customized integration

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 Generates Explorative Plots with one or two variations Combines/Overlays Multiple Results Adds External Data Results Manager Additional External Data Manager

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 DICTRA External Codes Accessibility Multiple Instances Programming Facility Error Detect Author PARROT Macro Slow No No Partial No Limited (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 Yes Full (C/C++/Python) Some QuesTek TC API API Fast Yes Yes Full No Full (C/C++) Yes TC-AB

24 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

25 Model Integration Example: Castable High Strength AA7xxx User Input Overall Compositions Surface Energy L1 2 /LIQ and liquid diffusion dt/dt or T(t) O 2 Content Surface Energy FCC/LIQ Thermo-Calc T L (FCC) LIQ Compositions w/o L1 2 PrecipiCalc Primary L1 2 L1 2 Particle Size Distribution (PSD) PSD Width Correction PrecipiCalc Inoculation Grain Size Corrected L1 2 PSD TC DICTRA

26 Model Integration Example: Design of Low Cost Ti Casting Alloys TC DICTRA

27 Design Exploration and Tradeoff Analysis Matrix + Strengthening Dispersion Design Grain Pinning Dispersion Design TC DICTRA TC

28 Sensitivity Analysis on S53 Compositions 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 s Property hardness HRc, toughness CVN 1000 Monte Carlo runs (12 minutes on a Pentium IV 2.2GHz CPU) TC TC TC DICTRA TC TC

29 Example of CMD/iSIGHT Robust Optimization TC Fail Original Compositions 3% Failure Robust Optimization New Compositions 0% Failure TC

30 S53 Design For Scale Solidification Simulation S53-3 Segregation Experience 300 lb 8 VAR ingot DICTRA hours Homogenization Simulation S53A Segregation Experience 3000 lb 17 VAR ingot DICTRA

31 Probabilistic Modeling of Manufacturing Variation: Forecast of Minimum Design Properties Process Variation PrecipiCalc YS Model YS Distribution DICTRA Mechanistic simulation + (n=15) gives good prediction of 1% minimum YS

32 Accelerated Insertion of Materials (AIM) Application Example: Ferrium S53 Property Predicted A-basis minimum = 280 ksi UTS AIM prediction A-basis minimum: 280 ksi UTS Experimental Data AIM Predictions +3 Mean value -3 DICTRA 3 10 AMS Specification MIL-HBK 5 A - Allowables 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

33 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

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 Major Milestones Computational Materials Qualification Acceleration Jan-14 Jan-13 Jan-12 Jan-11 Jan-10 Jan-09 Jan-08 Jan-07 Jan-06 Jan-05 Jan-04 Jan-03 Jan-02 Jan-01 Jan-00 Jan-99 5 design iterations S53 8.5yrs M54 ~6yrs Dates 1 design iteration MMPDS handbook update issued Additional property data developed 10 th multi-ton full-scale ingot produced Aerospace Materials Specification issued Static property data developed 3 rd multi-ton full-scale ingot produced 1 st multi-ton full-scale ingot produced Static properties demonstrated at prototype System design chart (design goals) established First S53 landing gear field service test flight occurred on Dec. 17 th, 2010

36 The Next Frontier