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The Alignment and Fusion of Multimodal 3D Serial Sectioning Datasets

  • Advances in Multi-modal Characterization of Structural Materials
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Abstract

As an example of data fusion in the context of 3D characterization of materials, this article demonstrates the procedures necessary to align and fuse separate imaging modes, traditional backscattered electron imaging (BSE) and electron backscattered diffraction mapping (EBSD), from serial-sectioning data. The fused data form a unified 3D reconstruction of additively manufactured 316L stainless steel processed by laser powder-bed fusion. We show that, by combining the relatively low-information yet high-fidelity BSE image stack with the more data-rich yet spatially distorted EBSD maps, the 3D reconstruction can leverage the strengths of both imaging techniques. The fully automated alignment procedures and frameworks rely on a number of optimized image warping techniques, with the result that spatial alignment errors are on the order of 0–3 \(\upmu {\hbox {m}}\) within a region of interest that is \(> 1\) mm.

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References

  1. I.M. Robertson, C.A. Schuh, J.S. Vetrano, N.D. Browning, D.P. Field, D. Juul Jensen, M.K. Miller, I. Baker, D.C. Dunand, R. Dunin-Borkowski, B. Kabius, T. Kelly, S. Lozano-Perez, A. Misra, G.S. Rohrer, A.D. Rollett, M.L. Taheri, G.B. Thompson, M.D. Uchic, X.L. Wang, and G. Was, J. Mater. Res. 26(11), 1341 (2011). https://doi.org/10.1557/jmr.2011.41

  2. B. Khaleghi, A. Khamis, F.O. Karray, and S.N. Razavi, Inf. Fus. 14(1), 28 (2013). https://doi.org/10.1016/j.inffus.2011.08.001

    Article  Google Scholar 

  3. T.L. Burnett, and P.J. Withers, Nat. Mater. 18(10), 1041 (2019). https://doi.org/10.1038/s41563-019-0402-8

    Article  Google Scholar 

  4. K. Thornton, and H.F. Poulsen, MRS Bull. 33(6), 587 (2008). https://doi.org/10.1557/mrs2008.123

    Article  Google Scholar 

  5. D.R. Clark, M. Rühle, and D.N. Seidman (eds.), Annu. Rev. Mater. Res., 42 (Annual Reviews, 2012)

  6. D.J. Jensen, Curr. Opin. Solid State Mater. Sci. 24(4), 100862 (2020). https://doi.org/10.1016/S1359-0286(20)30060-7

    Article  Google Scholar 

  7. R. Vincent, and P. Midgley, Ultramicroscopy 53(3), 271 (1994). https://doi.org/10.1016/0304-3991(94)90039-6

    Article  Google Scholar 

  8. P. Moeck, S. Rouvimov, E.F. Rauch, M. Véron, H. Kirmse, I. Häusler, W. Neumann, D. Bultreys, Y. Maniette, and S. Nicolopoulos, Cryst. Res. Technol. 46(6), 589 (2011). https://doi.org/10.1002/crat.201000676

    Article  Google Scholar 

  9. D.M. Saylor, A. Morawiec, and G.S. Rohrer, Acta Mater. 51(13), 3663 (2003). https://doi.org/10.1016/S1359-6454(03)00181-2

    Article  Google Scholar 

  10. D.J. Rowenhorst, A. Gupta, C.R. Feng, and G. Spanos, Scripta Materialia 55(1), 11 (2006). https://doi.org/10.1016/j.scriptamat.2005.12.061

    Article  Google Scholar 

  11. A. Lewis, J. Bingert, D. Rowenhorst, A. Gupta, A. Geltmacher, and G. Spanos, Mater. Sci. Eng. A 418(1–2), 11 (2006). https://doi.org/10.1016/j.msea.2005.09.088

    Article  Google Scholar 

  12. A. Deal, D.J. Rowenhorst, B. Laflen, I. Spinelli, T. Barbuto, Y. Huang, and T. Hanlon, in First International Conference on 3D Materials Science, ed. by M. De Graef, H.F. Poulsen, A.C. Lewis, J.P. Simmons, G. Spanos (Seven Springs, PA, 2012), pp. 13–18. https://doi.org/10.1002/9781118686768.ch2

  13. W. Ludwig, P. Reischig, A. King, M. Herbig, E.M. Lauridsen, G. Johnson, T.J. Marrow, and J.Y. Buffiere, Rev. Sci. Instrum. 80(3), 033905 (2009). https://doi.org/10.1063/1.3100200

    Article  Google Scholar 

  14. A.D. Spear, S.F. Li, J.F. Lind, R.M. Suter, and A.R. Ingraffea, Acta Mater. 76, 413 (2014). https://doi.org/10.1016/j.actamat.2014.05.021

    Article  Google Scholar 

  15. W.C. Lenthe, M.P. Echlin, A. Trenkle, M. Syha, P. Gumbsch, and T.M. Pollock, J. Appl. Crystallogr. 48(4), 1034 (2015). https://doi.org/10.1107/s1600576715009231

    Article  Google Scholar 

  16. S.P. Donegan, and M.A. Groeber, Data Structures and Workflows for ICME (Springer, 2020), pp. 19–53. https://doi.org/10.1007/978-3-030-40562-5_2

  17. M.P. Echlin, A. Mottura, C.J. Torbet, and T.M. Pollock, Rev. Sci. Instrum. 83(2), 023701 (2012). https://doi.org/10.1063/1.3680111

    Article  Google Scholar 

  18. B.L. Boyce, and M.D. Uchic, MRS Bull. 44(4), 273 (2019). https://doi.org/10.1557/mrs.2019.75

    Article  Google Scholar 

  19. M.G. Chapman, M.D. Uchic, J.M. Scott, M.N. Shah, S.P. Donegan, P.A. Shade, W.D. Musinski, M. Obstalecki, M.A. Groeber, and D. Menasche et al., Microsc. Microanal. 25(S2), 342 (2019). https://doi.org/10.1017/s1431927619002447

    Article  Google Scholar 

  20. D.J. Rowenhorst, L. Nguyen, A.D. Murphy-Leonard, and R.W. Fonda, Curr. Opin. Solid State Mater. Sci. (2020). https://doi.org/10.1016/j.cossms.2020.100819

    Article  Google Scholar 

  21. M.V. Kral, and G. Spanos, Acta Mater. 47, 711 (1999). https://doi.org/10.1016/S1359-6454(98)00321-8

    Article  Google Scholar 

  22. M. Kral, M. Mangan, G. Spanos, and R. Rosenberg, Mater. Charact. 45(1), 17 (2000). https://doi.org/10.1016/s1044-5803(00)00046-2

    Article  Google Scholar 

  23. J. Alkemper, and P.W. Voorhees, J. Microsc.-Oxford 201, 388 (2001). https://doi.org/10.1046/j.1365-2818.2001.00832.x

    Article  Google Scholar 

  24. J. Alkemper, and P.W. Voorhees, Acta Mater. 49, 897 (2001). https://doi.org/10.1016/S1359-6454(00)00355-4

    Article  Google Scholar 

  25. J.E. Spowart, H.M. Mullens, and B.T. Puchala, JOM 55(10), 35 (2003). https://doi.org/10.1007/s11837-003-0173-0

    Article  Google Scholar 

  26. J.E. Spowart, Scr. Mater. 55, 5 (2006). https://doi.org/10.1016/j.scriptamat.2006.01.019

    Article  Google Scholar 

  27. M. De Graef, M.V. Kral, and M. Hillert, JOM 58(12), 25 (2006). https://doi.org/10.1007/BF02748491

    Article  Google Scholar 

  28. D.J. Rowenhorst, J.P. Kuang, K. Thornton, and P.W. Voorhees, Acta Mater. 54(8), 2027 (2006). https://doi.org/10.1016/j.actamat.2005.12.038

    Article  Google Scholar 

  29. D.J. Rowenhorst, A.C. Lewis, and G. Spanos, Acta Mater. 58(16), 5511 (2010). https://doi.org/10.1016/j.actamat.2010.06.030

    Article  Google Scholar 

  30. C. Kuglin, and D. Hines, in Proceedings of the IEEE 1975 International Conference on Cybernetics and Society (1975), pp. 163–165

  31. S. Kim, and W. Su, in 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5 (1993), vol. 5, pp. 153–156. https://doi.org/10.1109/ICASSP.1993.319770

  32. C. Lafond, T. Douillard, S. Cazottes, P. Steyer, and C. Langlois, Ultramicroscopy 186, 146 (2018). https://doi.org/10.1016/j.ultramic.2017.12.019

    Article  Google Scholar 

  33. S. Wang, JOM 59(10), 37 (2007). https://doi.org/10.1007/s11837-007-0129-x

    Article  Google Scholar 

  34. P.T. Brewick, S.I. Wright, and D.J. Rowenhorst, Ultramicroscopy 200, 50 (2019). https://doi.org/10.1016/j.ultramic.2019.02.013

    Article  Google Scholar 

  35. G. Nolze, Mater. Sci. Technol. 22(11), 1343 (2006). https://doi.org/10.1179/174328406x130894

    Article  Google Scholar 

  36. G. Nolze, Ultramicroscopy 107(2–3), 172 (2007). https://doi.org/10.1016/j.ultramic.2006.07.003

    Article  Google Scholar 

  37. M.A. Linne, and S. Daly, Mater. Charact. (2019). https://doi.org/10.1016/j.matchar.2019.109984

    Article  Google Scholar 

  38. M.A. Groeber, B.K. Haley, M.D. Uchic, D.M. Dimiduk, and S. Ghosh, Mater. Charact. 57(4–5), 259 (2006). https://doi.org/10.1016/j.matchar.2006.01.019

    Article  Google Scholar 

  39. S. Zaefferer, S.I. Wright, and D. Raabe, Metall. Mater. Trans. A 39(2), 374 (2008). https://doi.org/10.1007/s11661-007-9418-9

    Article  Google Scholar 

  40. M.P. Echlin, M. Straw, S. Randolph, J. Filevich, and T.M. Pollock, Mater. Charact. 100, 1 (2015). https://doi.org/10.1016/j.matchar.2014.10.023

    Article  Google Scholar 

  41. Y. Zhang, A. Elbrønd, and F. Lin, Mater. Charact. 96, 158 (2014). https://doi.org/10.1016/j.matchar.2014.08.003

    Article  Google Scholar 

  42. M.A. Charpagne, F. Strub, and T.M. Pollock, Mater. Charact. 150, 184 (2019). https://doi.org/10.1016/j.matchar.2019.01.033

    Article  Google Scholar 

  43. V.S. Tong, and T.B. Britton, Ultramicroscopy 221, 113130 (2021). https://doi.org/10.1016/j.ultramic.2020.113130

    Article  Google Scholar 

  44. J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, IEEE Trans. Med. Imag. 22(8), 986 (2003). https://doi.org/10.1109/TMI.2003.815867

    Article  Google Scholar 

  45. E.B. Gulsoy, J.P. Simmons, and M. De Graef, Scr. Mater. 60(6), 381 (2009). https://doi.org/10.1016/j.scriptamat.2008.11.004

    Article  Google Scholar 

  46. E.B. Gulsoy, Computational Tools for Analysis of Automated Three-Dimensional Microstructural Characterization Data. Ph.D. thesis, Carnegie Mellon Pittsburgh, Pennsylvania (2010)

  47. Q. Razlighi, N. Kehtarnavaz, and S. Yousefi, J. Vis. Commun. Image Represent. 24(7), 977 (2013). https://doi.org/10.1016/j.jvcir.2013.06.010

    Article  Google Scholar 

  48. Z. Chen, W. Lenthe, J.C. Stinville, M. Echlin, T.M. Pollock, and S. Daly, Exp. Mech. 58(9), 1407 (2018). https://doi.org/10.1007/s11340-018-0419-y

    Article  Google Scholar 

  49. B. Winiarski, A. Gholinia, K. Mingard, M. Gee, G. Thompson, and P. Withers, Ultramicroscopy 226, 113315 (2021). https://doi.org/10.1016/j.ultramic.2021.113315

    Article  Google Scholar 

  50. T. Britton, J. Jiang, Y. Guo, A. Vilalta-Clemente, D. Wallis, L. Hansen, A. Winkelmann,and A. Wilkinson, Mater. Charact. 117, 113 (2016). https://doi.org/10.1016/j.matchar.2016.04.008

    Article  Google Scholar 

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Acknowledgements

The authors would like to gratefully acknowledge Michael Kirka at Oakridge National Laboratory for providing the sample material for this work. We also would like to acknowledge the financial support of the Office of Naval Research under the Agile ICME Toolkit project (N0001421WX00899, N0001420WX00405) and the US Naval Research Laboratory (Contract No. 63-1P95, 63-1G76).

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  1. National Research Council Postdoctoral Associate at The U.S. Naval Research Laboratory, Washington, DC, 20375, USA

    L. T. Nguyen

  2. The U.S. Naval Research Laboratory, Washington, DC, 20375, USA

    D. J. Rowenhorst

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Nguyen, L.T., Rowenhorst, D.J. The Alignment and Fusion of Multimodal 3D Serial Sectioning Datasets. JOM 73, 3272–3284 (2021). https://doi.org/10.1007/s11837-021-04865-x

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