PolyProc: A Modular Processing Pipeline for X-ray Diffraction Tomography Thematic Section: 3D Materials Science First Online: 18 July 2019 Part of the following topical collections: 3D Materials Science 2019 Abstract
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. Keywords 3D data processing X-ray diffraction contrast tomography Grain mapping Microstructure evolution
The authors Jiwoong Kang and Ning Lu contributed equally to this work.
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.
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.
https://doi.org/10.1016/j.scriptamat.2005.12.061 CrossRef Google Scholar
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.
https://doi.org/10.1557/mrs2007.64 CrossRef Google Scholar
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.
https://doi.org/10.1016/j.actamat.2010.06.030 CrossRef Google Scholar
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.
https://doi.org/10.1016/j.matchar.2006.01.019 CrossRef Google Scholar
Hounsfield GN (1973) Computerized transverse axial scanning (tomography): part 1. Description of system. Br J Radiol 46:1016–1022.
https://doi.org/10.1259/0007-1285-46-552-1016 CrossRef Google Scholar
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.
https://doi.org/10.1107/s0021889801014170 CrossRef Google Scholar
Poulsen HF (2012) An introduction to three-dimensional X-ray diffraction microscopy. J Appl Cryst 45:1084–1097.
https://doi.org/10.1107/s0021889812039143 CrossRef Google Scholar
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.
https://doi.org/10.1016/j.msea.2009.04.009 CrossRef Google Scholar
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.
https://doi.org/10.1063/1.2400017 CrossRef Google Scholar
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.
https://doi.org/10.1107/s0021889808001726 CrossRef Google Scholar
Hayashi Y, Hirose Y, Seno Y (2015) Polycrystal orientation mapping using scanning three-dimensional X-ray diffraction microscopy. J Appl Cryst 48:1094–1101.
https://doi.org/10.1107/s1600576715009899 CrossRef Google Scholar
King A, Reischig P, Adrien J, Peetermans S, Ludwig W (2014) Polychromatic diffraction contrast tomography. Mater Charact 97:1–10.
https://doi.org/10.1016/j.matchar.2014.07.026 CrossRef Google Scholar
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.
https://doi.org/10.1107/s2052252515019995 CrossRef Google Scholar
McDonald SA et al (2015) Non-destructive mapping of grain orientations in 3D by laboratory X-ray microscopy. Sci Rep 5:14665.
https://doi.org/10.1038/srep14665 CrossRef Google Scholar
Holzner C et al (2016) Diffraction contrast tomography in the laboratory-applications and future directions. Microsc Today 24:34–43.
https://doi.org/10.1017/s1551929516000584 CrossRef Google Scholar
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.
https://doi.org/10.1016/j.actamat.2018.01.045 CrossRef Google Scholar
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.
https://doi.org/10.1016/j.scriptamat.2019.01.007 CrossRef Google Scholar
Mcdonald SA et al (2017) Microstructural evolution during sintering of copper particles studied by laboratory diffraction contrast tomography (LabDCT). Sci Rep 7:5251.
https://doi.org/10.1038/s41598-017-04742-1 CrossRef Google Scholar
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.
https://doi.org/10.1088/1757-899x/219/1/012039 Google Scholar
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.
https://doi.org/10.1107/s1600577514013939 CrossRef Google Scholar
Bachmann F, Hielscher R, Schaeben H (2010) Texture analysis with MTEX-free and open source software toolbox. Solid State Phenom 160:63–68.
https://doi.org/10.4028/www.scientific.net/SSP.160.63 CrossRef Google Scholar
Groeber MA, Jackson MA (2014) DREAM.3D: a digital representation environment for the analysis of microstructure in 3D. Integr Mater Manuf Innov 3:5.
https://doi.org/10.1186/2193-9772-3-5 CrossRef Google Scholar
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99.
https://doi.org/10.1023/a:1022602019183 CrossRef Google Scholar
Chow CK, Tsui HT, Lee T (2004) Surface registration using a dynamic genetic algorithm. Pattern Recognit 37:105–117.
https://doi.org/10.1016/s0031-3203(03)00222-x CrossRef Google Scholar
Lomonosov E, Chetverikov D, Ekárt A (2005) Pre-registration of arbitrarily oriented 3D surfaces using a genetic algorithm. Pattern Recogn Lett 27:1201–1208.
https://doi.org/10.1016/j.patrec.2005.07.018 CrossRef Google Scholar
Brunnstrom K, Stoddart AJ (1996) Genetic algorithms for free-form surface matching. Proc Int Conf Pattern Recogn.
https://doi.org/10.1109/icpr.1996.547653 Google Scholar
Goldberg DE (1989) Genetic algorithms in search, optimization & machine learning. Addison-Wesley, Boston
Underwood EE (1973) Quantitative stereology for microstructural analysis. In: McCall JL, Mueller WM (eds) Microstructural analysis. Springer, Boston, pp 35–66
CrossRef Google Scholar
DeHoff RT, Aigeltinger EH, Craig KR (1972) Experimental determination of the topological properties of three-dimensional microstructures. J Microsc 95:69–91.
https://doi.org/10.1111/j.1365-2818.1972.tb03712.x CrossRef Google Scholar
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.
https://doi.org/10.1016/j.msea.2016.06.077 CrossRef Google Scholar
Roberts CG, Semiatin SL, Rollett AD (2007) Particle-associated misorientation distribution in a nickel-base superalloy. Scripta Mater 56:899–902.
https://doi.org/10.1016/j.scriptamat.2007.01.034 CrossRef Google Scholar
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.
https://doi.org/10.1016/j.actamat.2019.01.022 CrossRef Google Scholar
Kuhn HW (1955) The Hungarian method for the assignment problem. Nav. Res. Logist. Q. 2:83–97.
https://doi.org/10.1002/nav.3800020109 CrossRef Google Scholar
Bourgeois F, Lassalle JC (2002) An extension of the Munkres algorithm for the assignment problem to rectangular matrices. Commun ACM 14:802–804.
https://doi.org/10.1016/j.imavis.2008.04.004 CrossRef Google Scholar
Lind J et al (2014) Tensile twin nucleation events coupled to neighboring slip observed in three dimensions. Acta Mater 76:213–220.
https://doi.org/10.1016/j.actamat.2014.04.050 CrossRef Google Scholar Copyright information
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