4.7 Article

In-situ observation of bulk 3D grain evolution during plastic deformation in polycrystalline Cu

Journal

INTERNATIONAL JOURNAL OF PLASTICITY
Volume 67, Issue -, Pages 217-234

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijplas.2014.10.013

Keywords

Microstructures; Crystal plasticity; Polycrystalline material; Non-destructive evaluation; High-energy X-ray diffraction microscopy

Funding

  1. US Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering [DESC0002001]
  2. US Department of Energy, Office of Science, Office of Basic Energy Sciences [DE-AC02-06CH11357]
  3. Los Alamos National Laboratory's Directed Research and Development (LDRD-DR Project) [20140114DR]
  4. Lawrence Livermore National Laboratory [DE-AC52-07NA27344]
  5. National Science Foundation [DMR080072]
  6. National Energy Research Scientific Computing Center - Office of Science of the US Department of Energy [DE-AC02-05CH11231]

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We present a non-destructive in-situ measurement of three-dimensional (3D) microstructure evolution of 99.995% pure polycrystalline copper during tensile loading using synchrotron radiation. Spatially resolved three-dimensional crystallographic orientation fields are reconstructed from the measured diffraction data obtained from a near-field high-energy X-ray diffraction microscopy (nf-HEDM), and the evolution of about 5000 3D bulk grains is tracked through multiple stages of deformation. Spatially resolved observation of macroscopic texture change, anisotropic deformation development, and the correspondence of different crystallographic parameters to defect accumulation are illustrated. Moreover, correlations between different crystallographic parameters, such as crystal rotation evolution, short- and long-range orientation gradient development, microstructural features, and grain size effects are investigated. The current state of data mining tools available to analyze large and complicated diffraction data is presented and challenges associated with extracting meaningful information from these datasets are discussed. (C) 2014 Elsevier Ltd. All rights reserved.

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