A Graph Theory-Based Automated Twin Recognition Technique for Electron Backscatter Diffraction Analysis
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The present article introduces a new software, Microstructure Evaluation Tool for Interface Statistics (METIS), that performs high-throughput microstructure statistical analysis from electron backscatter diffraction maps. Emphasis is placed on the detection of twin domains in hexagonal close-packed metals. The numerical framework on which METIS is built leverages graph theory, group structures, and associated numerical algorithms to automatically detect twins and unravel both their intrinsic characteristics features and those pertaining to their interactions. The proposed graphical interface allows for the detection and correction of unlikely twin/parent associations rendering the approach applicable to highly deformed microstructures. Twin statistics and microstructural data are classified and saved in a relational database that can be interrogated via either GUI or SQL requests to reveal a wide spectrum of features of the microstructure. Illustration of the approach is performed in the case of zirconium.
KeywordsEBSD Twinning Zirconium Graph theory
The authors thank C.N. Tomé for the many useful and interesting discussions about twinning crystallography and the relevance of certain statistics.
P.-A. Juan thanks the support of the French State through the National Research Agency (ANR) under the program “Investment in the future” (Labex DAMAS referenced as ANR-11-LABX-0008-01) and the project MAGTWIN (referenced as ANR-12-BS09-0010-02) for its support. R.J. McCabe and L. Capolungo were fully supported by Office of Basic Energy Science, Project FWP 06SCPE401.
- 27.Aydiner CC, Bernier JV, Clausen B, Lienert U, Tomé CN, Brown DW (2009) Evolution of stress in individual grains and twins in a magnesium alloy aggregate. Phys Rev B 80(2). https://doi.org/10.1103/PhysRevB.80.024113
- 38.Henrie BL, Mason TA, Bingert JF (2005) Automated twin identification technique for use with electron backscatter diffraction. Mater Sci Forum 495–497:191–196. https://doi.org/10.4028/www.scientific.net/MSF.495-497.191 CrossRefGoogle Scholar
- 42.Bunge H-J (1982) Butterworths and Co.Google Scholar
- 43.Dam EB, Koch M, Lillholm M (1998) Datalogisk Institut Kobenhavns UniversitetGoogle Scholar
- 44.do Carmo MP (1992) Birkhauser BostonGoogle Scholar
- 45.Fulkerson B, Vedaldi A, Soatto S (2009) Class segmentation and object localization with superpixel neighborhoods. In: 2009 IEEE 12th International Conference - Computer Vision, pp 670–677. https://doi.org/10.1109/ICCV.2009.5459175
- 47.Bondy A, Murty USR (1976) North-HollandGoogle Scholar