Determination of the Minimum Scan Size to Obtain Representative Textures by Electron Backscatter Diffraction


A new method for analyzing microstructure is proposed to evaluate the long-range dependence of texture. The proposed method calculates the average disorientation as a function of distance between data points as measured by electron backscatter diffraction patterns. This method gives a measure of clustering of texture and is used to evaluate accurately the effective grain size. This procedure in conjunction with Information theory is used to estimate a representative scan size for various materials. Analyses show that the optimal scan size depends on grain morphology and crystallographic texture. The results also indicate that on an average the optimal scan size needs to be 10 times the effective grain size.

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  1. 1.

    Defined here as the maximum intensity in pole figures.

  2. 2.

    The details in this are fascinating but will not be discussed here to avoid diversion from the main thrust of the current study.

  3. 3.

    Grain size reported in Table I is grain radius.


  1. 1.

    J.A. Venables and C.J. Harland: Philos. Mag., 1973, vol. 27, no. 5, pp. 1193–1200.

    Article  CAS  Google Scholar 

  2. 2.

    B. Adams, S. Wright, and K. Kunze: Metall. Trans. A, 1993, vol. 24A, pp. 819–31

    CAS  Google Scholar 

  3. 3.

    K. Kunze, S.I. Wright, B.L. Adams, and D.J. Dingley: Texture Microstruct., 1993, vol. 20, nos. 1–4, pp. 41–54.

    Article  Google Scholar 

  4. 4.

    D.R. Steinmetz and S. Zaefferer: Mater. Sci. Technol., 2010, vol. 26, no. 6, pp. 640–45.

    CAS  Google Scholar 

  5. 5.

    A. Brahme, J. Fridy, H. Weiland, and A.D. Rollett: Model. Simul. Mater. Sci. Eng., 2009, vol. 17 (1), p. 015005.

  6. 6.

    O.M. Ivasishin, S.V. Shevchenko, N.L. Vasiliev, and S.L. Semiatin: Mater. Sci. Eng. A, 2006, vol. 433, nos. 1–2, pp. 216–32.

    Google Scholar 

  7. 7.

    D. Raabe and L. Hantcherli: Comput. Mater. Sci., 2005, vol. 34, no. 4, pp. 299–313.

    Article  CAS  Google Scholar 

  8. 8.

    A. Bhattacharyya, E. El-Danaf, S.R. Kalidindi, and R.D. Doherty: Int. J. Plast., 2001, vol. 17, no. 6, pp. 861–83.

    Article  CAS  Google Scholar 

  9. 9.

    D. Raabe, M. Sachtleber, Z. Zhao, F. Roters, and S. Zaefferer: Acta Mater., 2001, vol. 49, no. 17, pp. 3433–41.

    Article  CAS  Google Scholar 

  10. 10.

    A. Musienko, A. Tatschl, K. Schmidegg, O. Kolednik, R. Pippan, G. Cailletaud: Acta Mater., 2007, vol. 55, no. 12, pp. 4121–36.

    Article  CAS  Google Scholar 

  11. 11.

    K. Inal, M.H. Simha, and R.K. Mishra: J. Eng. Mater. Technol., 2008, vol. 130, pp. 021003-1–021003-8.

  12. 12.

    R. Lebensohn, R. Brenner, O. Castelnau, and A. Rollett: Acta Mater., 2008, vol. 56, no. 15, pp. 3914–26.

    Article  CAS  Google Scholar 

  13. 13.

    V.V. Fedorov: Theory of Optimal Experiments, Academic Press, New York, 1972.

  14. 14.

    C.A. Schuh, M. Kumar, and W.E. King: Acta Mater., 2003, vol. 51, no. 3, pp. 687–700.

    Article  CAS  Google Scholar 

  15. 15.

    F.J. Humphreys: J. Microsc., 1999, vol. 195, no. 3, pp. 170–85.

    Article  CAS  Google Scholar 

  16. 16.

    S.I. Wright, M.M. Nowell, and J.F. Bingert: Metall. Mater. Trans. A, 2007, vol. 38A, pp. 1845–55.

    Article  CAS  Google Scholar 

  17. 17.

    T. Baudin and R. Penelle: Metall. Trans. A, 1993, vol. 24, pp. 2299–2311.

    Article  Google Scholar 

  18. 18.

    K. Davut and S. Zaefferer: Metall. Mater. Trans. A, 2010, vol. 41A, pp. 2187–96.

    Article  CAS  Google Scholar 

  19. 19.

    H.J. Bunge: Texture Analysis in Materials Science, Butterworths, London, 1982.

  20. 20.

    B. Beausir, C. Fressengeas, N.P. Gurao, L.S. Toth, and S. Suwas: Acta Mater., 2009, vol. 57, no. 18, pp. 5382–95.

    Article  CAS  Google Scholar 

  21. 21.

    B. Adams, P. Morris, T. Wang, K. Willden, and S. Wright: Acta Metall., 1987, vol. 35, no. 12, pp. 2935–46.

    Article  CAS  Google Scholar 

  22. 22.

    N.R. Barton and P.R. Dawson: Model. Simul. Mater. Sci. Eng., 2001, vol. 9, no. 5, p. 433.

    Article  CAS  Google Scholar 

  23. 23.

    A. Vorhauer, T. Hebesberger, and R. Pippan: Acta Mater., 2003, vol. 51, no. 3, pp. 677–86.

    Article  CAS  Google Scholar 

  24. 24.

    R.M. Gray: Entropy and Information Theory, Springer, New York, 2009.

  25. 25.

    S. Kullback and R.A. Leibler: Ann. Math. Stat., 1951, vol. 22, no. 1, pp. 79–86.

    Article  Google Scholar 

  26. 26.

    J.H. Chang and W.S. Lee: J. Inf. Sci., 2005, vol. 31, pp. 420–32.

    Article  Google Scholar 

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This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and General Motors of Canada. The authors also gratefully acknowledge the High Performance Computing Center at the University of Sherbrooke (RQCHP).

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Correspondence to Kaan Inal.

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Manuscript submitted February 23, 2012.

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Brahme, A., Staraselski, Y., Inal, K. et al. Determination of the Minimum Scan Size to Obtain Representative Textures by Electron Backscatter Diffraction. Metall Mater Trans A 43, 5298–5307 (2012).

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  • Fisher Information
  • Crystallographic Texture
  • EBSD Data
  • Confidence Index
  • Intermediate Part