Image processing in experiments on, and simulations of plastic deformation of polycrystals

Comparisons between experiments and simulations of deformation of polycrystalline materials reveal some interesting challenges [1]. Addressing first the image processing issues, electron back-scatter diffraction (EBSD) [2] relies heavily on image transformations of electron diffraction patterns. High energy diffraction microscopy (HEDM) [3] also relies on thresholding of the diffractograms for peak identification [4]. By contrast to the standard finite element method, an image-based approach [5] that relies on the Fast Fourier Transform (FFT) has started to be used for simulating plastic deformation because it offers a more efficient solution of the same equations (e.g. mechanical equilibrium). It is possible, for example, to import directly a measured 3D image from HEDM into the FFT simulation code and simulate with no need for the time-consuming step of creating a 3D mesh. Common filters applied to orientation maps in particular, include grain average strain, Kernel Average Misorientation (KAM), Grain Orientation Spread (GOS), Intragranular Grain Misorientation (IGM).

[1]  A. Rollett,et al.  Three-dimensional plastic response in polycrystalline copper via near-field high-energy X-ray diffraction microscopy , 2012 .

[2]  Mahmood Fathy,et al.  A classified and comparative study of edge detection algorithms , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

[3]  Robert M. Suter,et al.  Tensile twin nucleation events coupled to neighboring slip observed in three dimensions , 2014 .

[4]  Tamás Ungár,et al.  Long-range internal stresses and asymmetric X-ray line-broadening in tensile-deformed [001]-oriented copper single crystals: The correction of an erratum , 1991 .

[5]  A. Rollett,et al.  Orientation image-based micromechanical modelling of subgrain texture evolution in polycrystalline copper , 2008 .

[6]  Hervé Moulinec,et al.  A numerical method for computing the overall response of nonlinear composites with complex microstructure , 1998, ArXiv.

[7]  A. Rollett,et al.  Statistics of High Purity Nickel Microstructure From High Energy X-ray Diffraction Microscopy , 2009 .

[8]  H. Poulsen,et al.  In Situ Measurement of Grain Rotation During Deformation of Polycrystals , 2001, Science.

[9]  Robert M. Suter,et al.  Adaptive reconstruction method for three-dimensional orientation imaging , 2013 .

[10]  Mukul Kumar,et al.  Electron Backscatter Diffraction in Materials Science , 2000 .

[11]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  R. Lebensohn N-site modeling of a 3D viscoplastic polycrystal using Fast Fourier Transform , 2001 .

[13]  J. Lind,et al.  In-situ High-Energy Diffraction Microscopy Study of Zirconium Under Uni-axial Tensile Deformation , 2013 .

[14]  Erik Knudsen,et al.  Mapping grains and their dynamics in three dimensions , 2006 .

[15]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[16]  Yi Liu,et al.  On the accuracy of the self-consistent approximation for polycrystals: comparison with full-field numerical simulations , 2004 .

[17]  Wenjin Liu,et al.  Influence of grain size in the near-micrometre regime on the deformation microstructure in aluminium , 2013 .

[18]  ScienceDirect Nuclear instruments & methods in physics research. Section B, Beam interactions with materials and atoms , 1984 .