Generation of 3d synthetic microstructures for two-phase titanium alloys

Transcription

1 Generation of 3d synthetic microstructures for two-phase titanium alloys Sudipto Mandal A.D. (Tony) Rollett Carnegie Mellon University

2 Introduction In order to understand microstructure and texture development in titanium alloys, representative three dimensional microstructures of titanium alloys are to be constructed based on experimental EBSD scans. In the present study, information about grain size, shape, orientations for a beta-stabilized Ti alloy are extracted from experimental EBSD data and are used to create 3D microstructures using DREAM3D software. The alloy under consideration is Ti 5553 alloy which is a near beta alloy. It has good strength and hardenability characteristics. 2

3 Microstructure in titanium alloys 3 Fig: Schematic phase diagram associated with common titanium alloys, indicating alloy classification(banerjee,2012) Fig: Typical microstructure in α/β titanium alloys (a) colonies of α laths formed due to slow cooling in β processing (b) basketweave microstructure due to faster cooling in β processing (c) globular α(black) in transformed β grains(white) in α/β processed alloys (Nag,2008)

4 Variants in the BCC-to-HCP transformation 4 Fig: Geometrical representation of the Burgers OR with dashed lines showing the BCC crystal and continuous lines showing the HCP crystal (Menon et al.,1986) Histogram

5 Experimental data EBSD scans of the sample taken and the data analyzed Synthetic 3D Generate 3d microstructures in DREAM3D Cross-sections Take different cross-sections of 3d microstructures Compare with real Compare cross-sections with real microstructures(ebsd) Change parameters Alter the initial parameters based on the level of similarity until acceptable match found Outline 5

6 Experimental Data 6 Ti 5553 Alloy: Ti-5Al-5V-5Mo-3Cr Figure: Experimental data from IISc (a) SEM image of the scan area (b) cleaned IPF map having some non-indexed points (c) Pole figures of alpha and beta showing the Burgers OR

7 7 Figure: Experimental data from IISc (a) a section of the IPF map (b) corrected map (c) highlighted alpha phase (d)corrected phase map (e) Pole Fig of alpha phase

8 Synthetic Microstructure Generation: List of initial parameters Distribution type Parameters Size Log-normal μ, σ, min σ cutoff, max σ cutoff Omega 3 Beta α, β Shape (B/A) Beta α, β (C/A) Beta α, β Neighbor Log-normal μ, σ ODF Axis ODF Euler angles with weights and σ Axis euler angles with weights and σ 8 In addition to these, these parameters also need to be specified Second phase type( primary, precipitate, transformation) Cubic structure of both phases( primary BCC, precipitate HCP) Fraction of alpha particles in the microstructure

9 Sensitivity analysis: Assigned size of alpha particles 9 (a) μ=0.1 (b) μ=0.2 (c) μ=0.5 (d) μ=1.0 Fig: Effect of changing the size parameter μ(mean). Cross-sections of the 3d microstructures are shown

10 10 Fig: Comparison of real size distribution(from the EBSD data) with that of the ones obtained from simulated microstructures with different size parameters. Cross-sections of 3d microstructures are taken and then compared with the experimental 2d microstructures. The bars above the histogram denotes the mean and the standard deviation error.

11 Sensitivity analysis: Volume fraction of alpha phase 11 (a) f(α)=0.2 (b) f(α)=0.4 (c) f(α)=0.6 (d) f(α)=0.8 Fig: Effect of changing the alpha volume fraction

12 12 Fig: (a) Effect of alpha vol. fraction on size distribution. (b) Fraction parameter sensitivity

13 Shape of alpha particles: whisker 13 Figure : (a) 3d microstructure generated with particle shape ratio as 10:1:1 (b) X cross-section of the phase map (c) grainid map (d) one isolated particle magnified

14 Shape of alpha particles: disc 14 Figure : (a) 3d microstructure generated with particle shape ratio as 10:10:1 (b) X cross-section of the phase map (c) grainid map (d) one isolated particle magnified

15 Orientation data incorporated in the synthetic microstructure 15 Fig: Grain map, phase map & IPF color map of the synthetic microstructure. Size of the voxel is 128x128x128 and spacing is 0.15 microns.

16 Variants selection 16 Fig: Frequency of grains belonging to each variant and the corresponding total areas of the platelets based on the EBSD data. [-1 1-1] [ ] [1 1-1] [1 1 1 ] Variant

17 Variants in the BCC-to-HCP transformation 17 Fig: Geometrical representation of the Burgers OR with dashed lines showing the BCC crystal and continuous lines showing the HCP crystal (Menon et al.,1986) Histogram

18 Variants selection 18 Fig: Frequency of grains belonging to each variant and the corresponding total areas of the platelets based on the EBSD data. [-1 1-1] [ ] [1 1-1] [1 1 1 ] Variant

19 Variant triplets 19 Fig: Product of areas for all possible sets of variant triplets(220) to find if there s any bias for a particular direction. The triplets having variants of only a particular direction are marked with specific colors. [-1 1-1] [-1 1 1] [ 1 1-1] [ 1 1 1] Others

20 Variant triplets 20 Fig: Product of areas for all possible sets of variant triplets(220) to find if there s any bias for a particular direction. The triplets having variants of only a particular direction are marked with specific colors. [-1 1-1] [-1 1 1] [ 1 1-1] [ 1 1 1] Others

21 Variant triplets 21 Fig: Product of areas for all possible sets of variant triplets(220) to find if there s any bias for a particular direction. The triplets having variants of only a particular direction are marked with specific colors. [-1 1-1] [-1 1 1] [ 1 1-1] [ 1 1 1] Others

22 Experimental data EBSD scans of the sample taken and the data analyzed Synthetic 3D Generate 3d microstructures in DREAM3D Cross-sections Take different cross-sections of 3d microstructures Compare with real Compare cross-sections with real microstructures(ebsd) Change parameters Alter the initial parameters based on the level of similarity until acceptable match found Conclusions 22 3D synthetic microstructures with realistic a morphologies are being generated. Comparison with the EBSD scans based on grain size and shape statistics shows that quantitatively. The crystallography of the Burgers orientation relationships can be imposed on the synthetic microstructures. There is variants selection during the transformation as some particular plane or direction seem to be preferred over others.

23 Experimental data EBSD scans of the sample taken and the data analyzed Synthetic 3D Generate 3d microstructures in DREAM3D Cross-sections Take different cross-sections of 3d microstructures Compare with real Compare cross-sections with real microstructures(ebsd) Change parameters Alter the initial parameters based on the level of similarity until acceptable match found Future directions 23 Quantitative comparison of microstructures Automate the feedback loop Incorporate variants information in DREAM3D Deformation behavior using Viscoplastic FFT codes

24 Acknowledgements 24 This work is being supported by the Boeing Company. Experimental data were obtained from Indian Institute of Science (IISc) Bangalore. Interactions with Prof D. Banerjee and Shanoob are gratefully acknowledged. All synthetic microstructures were generated using a software package called DREAM3D. Associated package Stats Generator was used to generate statistics for DREAM3D. ITA for giving me the opportunity to present my research at the conference.

25 25 THANK YOU