PolyProc: A Modular Processing Pipeline for X-ray Diffraction Tomography

  • Jiwoong Kang
  • Ning Lu
  • Issac Loo
  • Nancy Senabulya
  • Ashwin J. ShahaniEmail author
Thematic Section: 3D Materials Science
Part of the following topical collections:
  1. 3D Materials Science 2019


Direct imaging of three-dimensional microstructure via X-ray diffraction-based techniques gives valuable insight into the crystallographic features that influence materials properties and performance. For instance, X-ray diffraction tomography provides information on grain orientation, position, size, and shape in a bulk specimen. As such techniques become more accessible to researchers, demands are placed on processing the datasets that are inherently “noisy,” multi-dimensional, and multimodal. To fulfill this need, we have developed a one-of-a-kind function package, PolyProc, that is compatible with a range of data shapes, from planar sections to time-evolving and three-dimensional orientation data. Our package comprises functions to import, filter, analyze, and visualize the reconstructed grain maps. To accelerate the computations in our pipeline, we harness computationally efficient approaches: for instance, data alignment is done via genetic optimization; grain tracking through the Hungarian method; and feature-to-feature correlation through k-nearest neighbors algorithm. As a proof-of-concept, we test our approach in characterizing the grain texture, topology, and evolution in a polycrystalline Al–Cu alloy undergoing coarsening.


3D data processing X-ray diffraction contrast tomography Grain mapping Microstructure evolution 



We gratefully acknowledge financial support from the Army Research Office Young Investigator Program under award no. W911NF-18-1-0162. We also acknowledge the University of Michigan College of Engineering for financial support and the Michigan Center for Materials Characterization for use of the instruments and staff assistance.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Rowenhorst DJ, Gupta A, Feng CR, Spanos G (2006) 3D Crystallographic and morphological analysis of coarse martensite: combining EBSD and serial sectioning. Scr Mater 55:11–16. CrossRefGoogle Scholar
  2. 2.
    Uchic MD, Holzer L, Inkson BJ, Principe EL, Munroe P (2007) Three-dimensional microstructural characterization using focused ion beam tomography. Mater Res Soc Bull 32:408–416. CrossRefGoogle Scholar
  3. 3.
    Rowenhorst DJ, Lewis AC, Spanos G (2010) Three-dimensional analysis of grain topology and interface curvature in a β-titanium alloy. Acta Mater 58:5511–5519. CrossRefGoogle Scholar
  4. 4.
    Groeber MA, Haley BK, Uchic MD, Dimiduk DM, Ghosh S (2006) 3D reconstruction and characterization of polycrystalline microstructures using a FIB-SEM system. Mater Charact 57:259–273. CrossRefGoogle Scholar
  5. 5.
    Hounsfield GN (1973) Computerized transverse axial scanning (tomography): part 1. Description of system. Br J Radiol 46:1016–1022. CrossRefGoogle Scholar
  6. 6.
    Lauridsen EM, Schmidt S, Suter RM, Poulsen HF (2001) Applied crystallography tracking: a method for structural characterization of grains in powders or polycrystals. J Appl Cryst 34:744–750. CrossRefGoogle Scholar
  7. 7.
    Poulsen HF (2012) An introduction to three-dimensional X-ray diffraction microscopy. J Appl Cryst 45:1084–1097. CrossRefGoogle Scholar
  8. 8.
    Ludwig W et al (2009) New opportunities for 3D materials science of polycrystalline materials at the micrometre lengthscale by combined use of X-ray diffraction and X-ray imaging. Mater Sci Eng A 524:69–76. CrossRefGoogle Scholar
  9. 9.
    Suter RM, Hennessy D, Xiao C, Lienert U (2006) Forward modeling method for microstructure reconstruction using x-ray diffraction microscopy: single-crystal verification. Rev Sci Instrum 77:123905. CrossRefGoogle Scholar
  10. 10.
    Johnson G, King A, Honnicke MG, Marrow G, Ludwig W (2008) X-ray diffraction contrast tomography: a novel technique for three-dimensional grain mapping of polycrystals. II. The combined case. J Appl Cryst 41:310–318. CrossRefGoogle Scholar
  11. 11.
    Hayashi Y, Hirose Y, Seno Y (2015) Polycrystal orientation mapping using scanning three-dimensional X-ray diffraction microscopy. J Appl Cryst 48:1094–1101. CrossRefGoogle Scholar
  12. 12.
    King A, Reischig P, Adrien J, Peetermans S, Ludwig W (2014) Polychromatic diffraction contrast tomography. Mater Charact 97:1–10. CrossRefGoogle Scholar
  13. 13.
    Renversade L et al (2016) Comparison between diffraction contrast tomography and high-energy diffraction microscopy on a slightly deformed aluminium alloy. IUCrJ 3:32–42. CrossRefGoogle Scholar
  14. 14.
    McDonald SA et al (2015) Non-destructive mapping of grain orientations in 3D by laboratory X-ray microscopy. Sci Rep 5:14665. CrossRefGoogle Scholar
  15. 15.
    Holzner C et al (2016) Diffraction contrast tomography in the laboratory-applications and future directions. Microsc Today 24:34–43. CrossRefGoogle Scholar
  16. 16.
    Keinan R, Bale H, Gueninchault N, Lauridsen EM, Shahani AJ (2018) Integrated imaging in three dimensions: providing a new lens on grain boundaries, particles, and their correlations in polycrystalline silicon. Acta Mater 148:225–234. CrossRefGoogle Scholar
  17. 17.
    Sun J et al (2019) Grain boundary wetting correlated to the grain boundary properties: a laboratory-based multimodal X-ray tomography investigation. Scr Mater 163:77–81. CrossRefGoogle Scholar
  18. 18.
    Mcdonald SA et al (2017) Microstructural evolution during sintering of copper particles studied by laboratory diffraction contrast tomography (LabDCT). Sci Rep 7:5251. CrossRefGoogle Scholar
  19. 19.
    Sun J et al (2017) 4D study of grain growth in armco iron using laboratory x-ray diffraction contrast tomography. IOP Conf Ser Mater Sci Eng. CrossRefGoogle Scholar
  20. 20.
    Gürsoy D, Carlo DF, Xiao S, Jacobsen C (2014) TomoPy: a framework for the analysis of synchrotron tomographic data. J Synchrotron Rad 21:1188–1193. CrossRefGoogle Scholar
  21. 21.
    Bachmann F, Hielscher R, Schaeben H (2010) Texture analysis with MTEX-free and open source software toolbox. Solid State Phenom 160:63–68. CrossRefGoogle Scholar
  22. 22.
    Groeber MA, Jackson MA (2014) DREAM.3D: a digital representation environment for the analysis of microstructure in 3D. Integr Mater Manuf Innov 3:5. CrossRefGoogle Scholar
  23. 23.
    Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99. CrossRefGoogle Scholar
  24. 24.
    Chow CK, Tsui HT, Lee T (2004) Surface registration using a dynamic genetic algorithm. Pattern Recognit 37:105–117. CrossRefGoogle Scholar
  25. 25.
    Lomonosov E, Chetverikov D, Ekárt A (2005) Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm. Pattern Recogn Lett 27:1201–1208. CrossRefGoogle Scholar
  26. 26.
    Brunnstrom K, Stoddart AJ (1996) Genetic algorithms for free-form surface matching. Proc Int Conf Pattern Recogn. CrossRefGoogle Scholar
  27. 27.
    Goldberg DE (1989) Genetic algorithms in search, optimization & machine learning. Addison-Wesley, BostonGoogle Scholar
  28. 28.
    Underwood EE (1973) Quantitative stereology for microstructural analysis. In: McCall JL, Mueller WM (eds) Microstructural analysis. Springer, Boston, pp 35–66CrossRefGoogle Scholar
  29. 29.
    DeHoff RT, Aigeltinger EH, Craig KR (1972) Experimental determination of the topological properties of three-dimensional microstructures. J Microsc 95:69–91. CrossRefGoogle Scholar
  30. 30.
    Shahani AJ, Xiao X, Skinner K, Peters M, Voorhees PW (2016) Ostwald ripening of faceted Si particles in an Al–Si–Cu melt. Mater Sci Eng A 673:307–320. CrossRefGoogle Scholar
  31. 31.
    Roberts CG, Semiatin SL, Rollett AD (2007) Particle-associated misorientation distribution in a nickel-base superalloy. Scripta Mater 56:899–902. CrossRefGoogle Scholar
  32. 32.
    Bhattacharya A, Shen YF, Hefferan CM, Li SF, Lind J, Suter RM, and Rohrer GS (2019) Three-dimensional observations of grain volume changes during annealing of polycrystalline Ni. Acta Mater 167:40–50. CrossRefGoogle Scholar
  33. 33.
    Kuhn HW (1955) The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2:83–97. CrossRefGoogle Scholar
  34. 34.
    Bourgeois F, Lassalle JC (2002) An extension of the Munkres algorithm for the assignment problem to rectangular matrices. Commun ACM 14:802–804. CrossRefGoogle Scholar
  35. 35.
    Lind J et al (2014) Tensile twin nucleation events coupled to neighboring slip observed in three dimensions. Acta Mater 76:213–220. CrossRefGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2019

Authors and Affiliations

  • Jiwoong Kang
    • 1
  • Ning Lu
    • 2
  • Issac Loo
    • 3
  • Nancy Senabulya
    • 4
  • Ashwin J. Shahani
    • 2
    Email author
  1. 1.Department of Chemical EngineeringUniversity of MichiganAnn ArborUSA
  2. 2.Department of Materials Science and EngineeringUniversity of MichiganAnn ArborUSA
  3. 3.Department of Industrial and Operations EngineeringUniversity of MichiganAnn ArborUSA
  4. 4.Michigan Center for Materials CharacterizationUniversity of MichiganAnn ArborUSA

Personalised recommendations