Literature DB >> 28432905

Statistical analysis of dislocations and dislocation boundaries from EBSD data.

C Moussa1, M Bernacki2, R Besnard3, N Bozzolo2.   

Abstract

Electron BackScatter Diffraction (EBSD) is often used for semi-quantitative analysis of dislocations in metals. In general, disorientation is used to assess Geometrically Necessary Dislocations (GNDs) densities. In the present paper, we demonstrate that the use of disorientation can lead to inaccurate results. For example, using the disorientation leads to different GND density in recrystallized grains which cannot be physically justified. The use of disorientation gradients allows accounting for measurement noise and leads to more accurate results. Misorientation gradient is then used to analyze dislocations boundaries following the same principle applied on TEM data before. In previous papers, dislocations boundaries were defined as Geometrically Necessary Boundaries (GNBs) and Incidental Dislocation Boundaries (IDBs). It has been demonstrated in the past, through transmission electron microscopy data, that the probability density distribution of the disorientation of IDBs and GNBs can be described with a linear combination of two Rayleigh functions. Such function can also describe the probability density of disorientation gradient obtained through EBSD data as reported in this paper. This opens the route for determining IDBs and GNBs probability density distribution functions separately from EBSD data, with an increased statistical relevance as compared to TEM data. The method is applied on deformed Tantalum where grains exhibit dislocation boundaries, as observed using electron channeling contrast imaging.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Dislocations; Disorientation Gradient; Electron BackScatter Diffraction; Geometrically Necessary Boundaries; Incidental Dislocation Boundaries; Tantalum

Year:  2017        PMID: 28432905     DOI: 10.1016/j.ultramic.2017.04.005

Source DB:  PubMed          Journal:  Ultramicroscopy        ISSN: 0304-3991            Impact factor:   2.689


  1 in total

1.  Isotonic regression for metallic microstructure data: estimation and testing under order restrictions.

Authors:  Martina Vittorietti; Javier Hidalgo; Jilt Sietsma; Wei Li; Geurt Jongbloed
Journal:  J Appl Stat       Date:  2021-03-05       Impact factor: 1.416

  1 in total

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