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How to Leverage Artificial Intelligence to Accelerate Data Collection and Analysis of Diffusion Multiples
- 1. 1 How to Leverage Artificial Intelligence to Accelerate Data Collection and Analysis of Diffusion Multiples Ji-Cheng (JC) Zhao The Ohio State University NIST Workshop on Artificial Intelligence for Materials Science (AIMS) August 8, 2018
- 2. 2 • An example of alloy design using CALPHAD & regression analysis • Diffusion multiples for phase diagram mapping • Precipitation kinetics & microstructure • Property mapping • Summary Outline
- 3. 3 • An example of alloy design using CALPHAD & regression analysis • Diffusion multiples for phase diagram mapping • Precipitation kinetics & microstructure • Property mapping • Summary Outline
- 4. 4 900 1000 1100 1200 1300 1400 Measured γ’ Solvus (°C) Calculatedγ’Solvus(°C) Caron & Khan Dharwadkar Henry Sponseller 900 1000 1100 1200 1300 1400 Al,Ti,Ta,Nb,... TCP phases (σ, µ, P, Laves) Ni Mo, W, Re, Cr, Co... γ’ γ Al,Ti,Ta,Nb,... TCP phases (σ, µ, P, Laves) Ni Mo, W, Re, Cr, Co... γ’γ’ γ CALPHAD Benchmark for Multicomponent Alloys Zhao & Henry: Adv. Eng. Mater., 4, 501, 2002.
- 5. 5 0.1 1 10 100 0.1 1 10 100 PredictedTimetoFail(h) Measured Time to Fail (h) Regression done using only alloy chemistry 0.1 1 10 100 0.1 1 10 100 PredictedTimetoFail(h) Measured Time to Fail (h) Regression done using alloy chemistry and predicted γ' volume fraction γ’ volume fraction included Computational thermodynamics for composition optimization Regression-based prediction of creep strength in Ni-base superalloys γ’ volume fraction not included Crude machine learning of legacy data with model input Zhao & Henry: Adv. Eng. Mater., 4, 501, 2002. 760 ºC rupture life 760 ºC rupture life
- 6. 6The National Academies Press, Washington, D.C., 2012 GTD222 GTD262 4 years from concept to production • 2X creep strength ↑ • Ta replacement by Nb: cost ↓ • Computational design: validated with only one set of 4 alloys. Now widely used in GE gas turbines A Successful Example of Accelerated Alloy Design: GTD262 “The rapid development of GTD262 is the first successful landmark that has helped establish within GE the credibility of computational alloy design and its associated methodologies, models, and databases” Inventors: L. Jiang J.-C. Zhao G. Feng
- 7. 7 • An example of alloy design using CALPHAD & regression analysis • Diffusion multiples for phase diagram mapping • Precipitation kinetics & microstructure • Property mapping • Summary Outline
- 8. 8 • Local equilibrium at phase interfaces defines the tie-lines • Interdiffusion creates all single-phase compositions 0 10 20 30 40 50 60 70 80 90 100 0 100 200 300 400 500 600 Distance, microns Composition,at.% Co, at.% Cr, at.% fcc σ bcc Co Cr fcc σ bcc Co Cr 100 µm1100°C, 1000h Diffusion-Multiple Approach: the idea
- 9. 9 Mo Mo Mo Cr FeCr FeCo Co Ni Ni Diffusion-Multiple Approach: diffusion multiple preparation Cao & Zhao: J. Phase Equili. Diff., 37, 25, 2016.
- 10. 10 Mo Fe Ni 0 5 12 14 100 µm Ni Mo Fe δ γ P αFe 1 2 3 4 5 0 6 7 8 9 10 11 12 13 14 µ 15 16 1100°C 1500 hrs Mo FeNi γ αFe µ δ P 0 1 2 3 4 5 6 7 8 9 10 11 121314 15 16 1100 °C Diffusion-Multiple Approach: phase diagram mapping Mo Mo Mo Cr FeCr FeCo Co Ni Ni
- 11. 11 Ni Co Mo 1100°C fcc µ Co9Mo2 δ Cr Ni Co Nb Mo CALPHAD Assessments High-fidelity thermodynamic databases Co Cr Mo Co9Mo2 bccfcc µ R σ 1100°C At.% Co Cr Mo Co9Mo2 bccfcc µ R σ 1100°C At.% Diffusion-Multiple Approach: phase diagram mapping
- 12. 12 Diffusion-Multiple Approach: phase diagram mapping ? 2 diffusion multiples ? Zhu et al.: Intermetallics, 93, 20, 2018.
- 13. 13 Diffusion-Multiple Approach: phase diagram mapping Diffusion multiple Zhu et al.: J. Alloys Comp., 691, 110, 2017.
- 14. 14 Zhao: in Methods for Phase Diagram Determination, Elsevier, 2007.
- 15. 15
- 16. 16
- 17. 17 Snoeyenbos, Wark, Zhao: Microsc. Microanal. 14 (Suppl. 2), 2176, 2008
- 18. 18 • An example of alloy design using CALPHAD & regression analysis • Diffusion multiples for phase diagram mapping • Precipitation kinetics & microstructure • Property mapping • Summary Outline
- 19. 19 1200°C single-anneal Fe-Cr-Mo Mo Cr Fe Mo Mo Mo Cr FeCr FeCo Co Ni Ni 500μm Phase Precipitation High-Throughput Study of Precipitation Cao & Zhao: J. Phase Equili. Diff., 37, 25, 2016.
- 20. 20 1200°C + 900°C dual-anneal Mo Cr Fe σ Chi 500μm Mo Cr Fe μ bcc solubility at 1200 °C bcc solubility at 900 °C
- 21. 21 Fe-Cr-Mo: 1200°C for 500h & 900°C for 500 h λ R Solubility limit a bc d e f R Cao & Zhao: J. Phase Equili. Diff., 37, 25, 2016.
- 22. 22 1200°C / 500 h + 800°C / 1000h
- 23. 23 • An example of alloy design using CALPHAD & regression analysis • Diffusion multiples for phase diagram mapping • Precipitation kinetics & microstructure • Property mapping • Summary Outline
- 24. 24 Huxtable, Cahill, Fauconnier, White, Zhao: Nature Mater., 3,298 (2004). Accepted Λ (W m-1 K-1) 1 10 100 MeasuredΛ(Wm-1K-1) 1 10 100 Au Al W Ru Mo Co CrNi Pd TaNb VTi Zr SiO2 8wt% YSZ Ni(80)Cr(20) Pt (f = 200 Hz) (f = 10 MHz) d Al (80nm) h, ΛAl, CAl G Sample Λ, C w0 = 4 µm Probe laser Pump laser (f = 10 MHz) d Al (80nm) h, ΛAl, CAl G Sample Λ, C w0 = 4 µm Micron-scale thermal conductivity mapping High-Throughput Property Mapping: thermal conductivity 2-4 µm resolution ±8% accuracy
- 25. 25 Ni Ni-54.5at%Al Wmol-1K-1 T[C] X[AL] 400 600 800 1000 1200 1400 1600 1800 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 ALNI 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 400 600 800 1000 1200 1400 1600 1800 X[AL] T[C]T[C] X[AL] 400 600 800 1000 1200 1400 1600 1800 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 ALNI 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 400 600 800 1000 1200 1400 1600 1800 X[AL] T[C] Zhao, Zheng, Cahill, Scripta Mater., 66, 935-938 (2012). High-Throughput Property Mapping: thermal conductivity
- 26. 26 High-Throughput Property Mapping: thermal conductivity A general model for thermal and electrical conductivity Wei, Antolin, Restrepo, Windl, Zhao: Acta Mater., 126 (2017) 272 Now thermal & electrical resistivity can be incorporated into CALPHAD
- 27. 27 Wei, Zheng, Cahill, Zhao: Rev. Sci. Instr. 2013 Pump ( f = 10 MHz ) Probe Sample (Λ, C) Al 8 µm d ≈ 600 nm fC d π2 Λ = Pump ( f = 100 KHz ) Probe Sample (Λ, C) Al 8 µm d ≈ 6 µm t (ns) 0 1 2 3 4 -Vin/Vout 0 5 10 15 Ni Gd MgO Si f = 9.8 MHz t (ns) 0.0 0.2 0.4 0.6 0.8 1.0 -Vin/Vout 0 5 10 15 Si Ni MgO Gd f = 123 KHz 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140 Accepted CP J mol-1 K-1 MeasuredCPJmol-1K-1 B Si MgAl2O4 Al2O3 Gd MgO Pd Ni Pt Heat Capacity Wei, Zheng, Cahill, Zhao, Rev. Sci. Instr. 84 (2013) 071301. High-Throughput Property Mapping: heat capacity
- 28. 28 ∆θ Probe Photodiode pair Sample Al (100 nm) Lock-in amplifier Pump (f ≈ 10 MHz) Zheng, Cahill, Weaver, Zhao: J. Appl. Phys., 104, 073509 (2008). Micron-scale measurement of CTE High-Throughput Property Mapping: thermal expansion
- 29. 29 High-Throughput Property Mapping: elastic constants Du & Zhao: npj Comput. Mater., 3 (2017) 17. Single-crystal elastic constants from polycrystalline samples PDMS film Si mold Expt. Model Substrate 700nm350nm Sample Only 1% of ~160,000 solid compounds has experimentally measured elastic constants
- 30. 30 • An example of alloy design using CALPHAD & regression analysis • Diffusion multiples for phase diagram mapping • Precipitation kinetics & microstructure • Property mapping • Summary Outline
- 31. 31 Accelerated Design of Structural & Multifunctional Materials Hardness Thermodynamic modeling Solution / precipitation strengthening Kinetic modeling Modulus Rh Pt Pd Hardness(GPa) 1.90 3.05 4.20 5.35 6.50 4.9 4.2 2.6 5.7 3.4 0 10 20 30 40 50 60 70 80 90 100 0 20 40 60 80 100 120 140 160 180 200 Distance, micron at.% Pd Pt Diffusion coefficients u Rh Cr Ru fcc hcp bcc A15 Phase diagrams Thermal conductivity Ordering Substitution Site occupancy Point defects ElasticModulus(GPa) Rh Pt Pd 134 183 231 279 327 167199231263 295 ElasticModulus(GPa) Rh Pt Pd 134 183 231 279 327 167199231263 295 ElasticModulus(GPa) Rh Pt Pd 134 183 231 279 327 167199231263 295 ElasticModulus(GPa) Rh Pt Pd 134 183 231 279 327 167199231263 295 Diffusion multiple Materials Property Microscopy Tools for Localized Measurement / Mapping Heat capacity Expansion coefficient Elastic constants Physical & other properties Summary Modified from Zhao: Annu. Rev. Mater. Res., 35, 51, 2005.
- 32. 32 Artificial Intelligence & automation are desperately need for acceleration Mo Mo Mo Cr FeCr FeCo Co Ni Ni Summary • Enormous amounts of data can be extracted from diffusion multiples • Active learning & autonomy/automation are the future • Interactive use of computed & prior data/knowledge • Data fusion & autonomous fault detection
- 33. 33 Thank you ! J.-C. Zhao zhao.199@osu.edu