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

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

3. Fe-3%Si-1% (2%) Cu model system

4. Fe-3%Si-Cu engineeringalloy

5. Simulations (DFT + kMC, model system)

6. ConclusionsandchallengesRaabe: 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

12. Fe-3%Si-1% (2%) Cu model system

13. Fe-3%Si-Cu engineeringalloy

14. Simulations (DFT + kMC, model system)

15. Conclusionsandchallengeswww.mpie.de

17. water quenching

18. age hardening at 400-450°C for 10 min - 100 hwww.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

26. Fe-3%Si-1% (2%) Cu model system

27. Fe-3%Si-Cu engineeringalloy

28. Simulations (DFT + kMC, model system)

29. Conclusionsandchallengeswww.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

34. Fe-3%Si- 1% (2%) Cu model system

35. Fe-3%Si-Cu engineeringalloy

36. Simulations (DFT + kMC, model system)

37. Conclusionsandchallengeswww.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

47. Fe-3%Si-1% (2%) Cu model system

48. Fe-3%Si-Cu engineeringalloy

49. Simulations (DFT + kMC, model system)

50. Conclusionsandchallengeswww.mpie.de

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 experimentswww.mpie.de