MRS  Dec 2010  Steel With  Copper Precipitates Dierk  Raabe
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MRS Dec 2010 Steel With Copper Precipitates Dierk Raabe

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Copper nanoprecipitates in steel studied by atom probe tomography and ab initio based Monte Carlo simulation ...

Copper nanoprecipitates in steel studied by atom probe tomography and ab initio based Monte Carlo simulation
Authors: O. Dmitrieva, P.-P. Choi, T. Hickel, N. Tillack,
D. Ponge, J. Neugebauer, D. Raabe
MRS Fall Meeting 2010

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  • 1. Copper nanoprecipitates in steel studied by atom probe tomography and ab initio based Monte Carlo simulation
    O. Dmitrieva, P.-P. Choi, T. Hickel, N. Tillack,
    D. Ponge, J. Neugebauer, D. Raabe
    Düsseldorf, Germany
    WWW.MPIE.DE
    d.raabe@mpie.de
    Thanksto: DFG
    MRS Fall Conference 30. November 2010 Dierk Raabe
  • 2. Overview
    • Motivation: Electromobility
    • 3. Fe-3%Si-1% (2%) Cu model system
    • 4. Fe-3%Si-Cu engineeringalloy
    • 5. Simulations (DFT + kMC, model system)
    • 6. Conclusionsandchallenges
    Raabe: Adv. Mater. 14 (2002), Roters et al. Acta Mater.58 (2010)
  • 7. 2
    Motivation
    Avoiduseof permanent magnetsanduseinsteadelectricalmagnets in synchronouselectricalengines
    High strength soft magneticsteels in carenginesreduce CO2emission
    www.mpie.de
  • 8. 3
    Electrical steels for electrical cars
  • 9. 4
    Electrical steels
    Standard soft magnetic steel: based on
    Fe3 wt. % Si 
    ferriticphase, coarse grains 
    soft-magnetic properties
    Improvement of the mechanical strength!
    precipitation hardening: 1-2 wt. % Cu, aging at 450°C
  • 10. 5
    {Jäniche et al., Werkstoffkunde Stahl}
    0,1 µm
    0,2 µm
    0,3 µm
    20 nm
    Nano-precipitates in soft magnetic Fe-3%Si steels
    magnetic loss (W/kg)
    15 nm
    size Cu precipitates (nm)
    {JP 2004 339603}
    nanoparticlestoosmallfor Bloch-wall interaction but
    effectiveasdislocationobstacles
    mechanicallyvery strong soft magnetsformotors
    Fe-Si steelwithCu nano-precipitates
  • 11. Overview
    • Motivation: Electromobility
    • 12. Fe-3%Si-1% (2%) Cu model system
    • 13. Fe-3%Si-Cu engineeringalloy
    • 14. Simulations (DFT + kMC, model system)
    • 15. Conclusionsandchallenges
    www.mpie.de
  • 16. 7
    Precipitation hardened materials
    Precipitation hardened electrical steels:
    nucleation of copper precipitates in iron-silicon matrix
    Fe
    Si 3 wt. %
    Cu 1 wt. %
    Fe
    Si 3 wt. %
    Cu 2 wt.%
    I.
    II.
    Cu
    Cu
    Cu
    Fe-3wt% Si
    • solute annealing 950°C-1150°C for 1h
    • 17. water quenching
    • 18. age hardening at 400-450°C for 10 min - 100 h
    www.mpie.de
  • 19. 8
    Precipitation hardened electrical steels
    Atom probe tomography
    10 min 120 min 6000 min
    Time, min
    www.mpie.de
  • 20. 9
    58Ni+2
    56Fe+2
    48Ti+2
    55Mn+2
    200 nm
    60Ni+2
    54Fe+2
    initiated evaporation
    by
    or
    24 26 28
    time of flight  mass / charge state
     Time of flight 
    spatial resolution
    layer-by-layer
    100 nm

    high
    voltage
     10 kV
    +
    3D Atom Probe Tomography
    LEAP (Local Electrode Atom Probe) 3000X HR
    3-dimensional
    reconstructed
    model of specimen
    (about 100 Millions of atoms)
  • 21. 10
    APT results: 3D atomic maps (Laser mode at 60K, 0.4 nJ)
    Cu 1 wt.% 
    120 min
    Fe; Cu
    nm
    nm
    Iso-concentration surfaces at Cu 11 at.%
    www.mpie.de
  • 22. 11
    APT results: 3D atomic maps (Laser mode at 60K, 0.4 nJ)
    Cu 1 wt.% 
    Cu 2 wt.% 
    6000 min
    120 min
    6000 min
    120 min
    20 nm
    20 nm
    20 nm
    20 nm
    450°C aging
    Iso-concentration surfaces at Cu 11 at.%
    Fe-Si-Cu, LEAP 3000X HR
    www.mpie.de
  • 23. 12
    APT results: quantification
    Cu 1 wt.% 
    Cu 2 wt.% 
    6000 min
    120 min
    6000 min
    120 min
    3.2 nm ± 0.8 nm
    5.0 nm ± 0.8 nm
    4.3 nm ± 0.8 nm
    6.5 nm ± 1.4 nm
    Cluster:
    Fe 71 at%
    Cu 24 at%
    Si 5 at%
    Cluster:
    Fe 62.9 at%
    Cu 32.1 at%
    Si 5.0 at%
    Cluster:
    Fe 60.7 at%
    Cu 35.2 at%
    Si 4.1 at%
    Cluster:
    Fe 51.8 at%
    Cu 44.6 at%
    Si 3.6 at%
    Matrix (without clusters):
    Cu 0.2 at% Cu 0.1 at% Cu 0.2 at% Cu 0.1 at%
    “Enrichment factor”:
    content in cluster
    content in alloy
    Fe 0.68
    Cu 40.1
    Si 0.75
    Fe 0.65
    Cu 44.0
    Si 0.68
    Fe 0.77
    Cu 17
    Si 0.78
    Fe 0.56
    Cu 31.9
    Si 0.58
    clusters number density, m-3
    6.110230.910236.41023 0.61023
    volume fraction of clusters
     2%  2%  5%  3-5%
    www.mpie.de
  • 24. 13
    APT results: Proximity histogram
    bigger cluster
    Cu 2 wt.%; 450°C/6000 min
    smaller cluster
    20 nm
    Cluster size
    5 nm
    8 nm
    nm
    matrix
    cluster center
    • kinetic reasons?
    (different stages of particle growth)
    • slow diffusion; misfit ?
    residual Fe-content
    diffuse interface
    www.mpie.de
  • 25. Overview
    • Motivation: Electromobility
    • 26. Fe-3%Si-1% (2%) Cu model system
    • 27. Fe-3%Si-Cu engineeringalloy
    • 28. Simulations (DFT + kMC, model system)
    • 29. Conclusionsandchallenges
    www.mpie.de
  • 30. 15
    APT results
    Overall content in the analysis volume:
    Fe 92.1 at.%
    Cu 1.49 at.%
    Al 0.56 at.%
    Si 5.83 at. %
    www.mpie.de
  • 31. O. Dmitrieva
    16
    3D-concentration profiles
    Fe Si 17924-1FB
    3wt%Si, 2wt%Cu
    Iso-concentration-Surfaces at Cu 6 at.%
    www.mpie.de
  • 32. 17
    Cluster analysis
    (cluster analysis:
    Nmin=100,
    dmax=0.7 nm)
    d = 5.0 ± 1.0 nm
    (density corrected:
    d = 6.0 ± 1.4 nm)
    www.mpie.de
  • 33. Overview
    • Motivation: Electromobility
    • 34. Fe-3%Si- 1% (2%) Cu model system
    • 35. Fe-3%Si-Cu engineeringalloy
    • 36. Simulations (DFT + kMC, model system)
    • 37. Conclusionsandchallenges
    www.mpie.de
  • 38. Modeling: ab-initio, DFT / GGA, binding energies
    Fe-Si steelwithCu nano-precipitates
  • 39. Modeling: ab-initio, DFT / GGA, binding energies
    Fe-Si steelwithCu nano-precipitates
  • 40. Modeling: ab-initio, DFT / GGA, binding energies
    Fe-Si steelwithCu nano-precipitates
  • 41. Modeling: ab-initio, DFT / GGA, binding energies
    Fe-Si steelwithCu nano-precipitates
  • 42. 23
    Ab-initio, bindingenergies: Cu-Cu in Fematrix
    Fe-Si steelwithCu nano-precipitates
  • 43. 24
    Ab-initio, bindingenergies: Si-Si in Fematrix
    Fe-Si steelwithCu nano-precipitates
  • 44. 25
    Ab-initio, bindingenergies
    For neighbor interaction energy take difference (in eV)‏
    (repulsive) = 0.390
    (attractive) = -0.124
    (attractive) = -0.245
    Fe-Si steelwithCu nano-precipitates
  • 45. 26
    Ab-initio, usebindingenergies in kinetic Monte Carlo model
    www.mpie.de
  • 46. Overview
    • Motivation: Electromobility
    • 47. Fe-3%Si-1% (2%) Cu model system
    • 48. Fe-3%Si-Cu engineeringalloy
    • 49. Simulations (DFT + kMC, model system)
    • 50. Conclusionsandchallenges
    www.mpie.de
  • 51. 28
    Conclusionsandchallenges
    • Soft magnetic steels for electromobility
    • 52. Increase mechanical strength without loosing good soft magnetic properties
    • 53. Use nanoprecipitates with size below Bloch wall thickness
    • 54. APT suited to provide statistics and composition of nanoparticles
    • 55. Ab-initio thermodynamics in system Fe-Si-Cu
    • 56. Kinetics: QM – kMC
    • 57. Coupling DFT/kMC with atomic-scale experiments
    www.mpie.de