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Grain Boundary Detection and Phase Segmentation of SEM Ferrite–Pearlite Microstructure Using SLIC and Skeletonization

  • Subir Gupta
  • Jit Sarkar
  • Abhijit Banerjee
  • N. R. Bandyopadhyay
  • Subhas GangulyEmail author
Original Contribution
  • 57 Downloads

Abstract

In this paper, we report an efficient segmentation and grain boundary detection process using modern image processing operators like simple linear iterative clustering and skeletonization. Accurate phase segmentation is the major requirement for any phase identification and quantification operations. The proposed image processing methods have been experimented on the in-house generated 48 scanning electron microscopy (SEM) microstructures obtained from plain carbon steel samples containing 0.1, 0.22, 0.35 and 0.48 wt%C and have been subjected to both annealing and normalizing treatments. The microstructures for dataset have been captured in SEM using secondary electron mode over a wide range of magnification × 500–× 5000. The experimental results significantly validate the segmentation of ferrite and pearlite regions. Also, the grain boundary detection results appear to be plausibly effective in case of ferrite–ferrite and ferrite–pearlite boundaries. However, the grain boundary detection efficiency is found to be relatively poor in case of pearlite–pearlite boundary. The overall performance of the proposed image processing technique in context of ferrite–pearlite steel SEM images shows promising results in all circumstances of compositional range, heat treatment and magnification.

Keywords

Phase segmentation Grain boundary detection Ferrite–pearlite steel SLIC Skeletonization SEM microstructure 

Notes

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    D.A. Linkens, Y.Y. Yang, M. Chen, M.F. Abbod, A comparative study of neural and fuzzy algorithms for prediction of properties in steel processing. IFAC Proc. 33, 283–288 (2017).  https://doi.org/10.1016/s1474-6670(17)37007-6 CrossRefGoogle Scholar
  2. 2.
    P.J. García Nieto, E. García-Gonzalo, J.C. Álvarez Antón, V.M. González Suárez, R. Mayo Bayón, F. Mateos Martín, A comparison of several machine learning techniques for the centerline segregation prediction in continuous cast steel slabs and evaluation of its performance. J. Comput. Appl. Math. 330, 877–895 (2018).  https://doi.org/10.1016/j.cam.2017.02.031 MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    S. Krajewski, J. Nowacki, Dual-phase steels microstructure and properties consideration based on artificial intelligence techniques. Arch. Civ. Mech. Eng. 14, 278–286 (2014).  https://doi.org/10.1016/j.acme.2013.10.002 CrossRefGoogle Scholar
  4. 4.
    S.J. Lee, J.P. Yun, G. Koo, S.W. Kim, End-to-end recognition of slab identification numbers using a deep convolutional neural network. Knowl. Based Syst. 132, 1–10 (2017).  https://doi.org/10.1016/j.knosys.2017.06.017 CrossRefGoogle Scholar
  5. 5.
    B.L. DeCost, B. Lei, T. Francis, E.A. Holm, High throughput quantitative metallography for complex microstructures using deep learning: a case study in ultrahigh carbon steel. Microsc. Microanal. (2018).  https://doi.org/10.1017/s1431927618015635 CrossRefGoogle Scholar
  6. 6.
    F. Zhang, A. Ruimi, D.P. Field, Phase identification of dual-phase (DP980) steels by electron backscatter diffraction and nanoindentation techniques. Microsc. Microanal. 22, 99–107 (2016).  https://doi.org/10.1017/s1431927615015779 CrossRefGoogle Scholar
  7. 7.
    J.P. Papa, C.R. Pereira, V.H.C. De Albuquerque, C.C. Silva, A.X. Falcão, J.M.R.S. Tavares, Precipitates segmentation from scanning electron microscope images through machine learning techniques, in Lecture Notes Computer Science (LNCS) (Including Subseries Lecture Notes Artificial Intelligence and Lecture Notes Bioinformatics), vol. 6636 (2011), pp. 456–468.  https://doi.org/10.1007/978-3-642-21073-0_40
  8. 8.
    J. Ling, M. Hutchinson, E. Antono, B. DeCost, E.A. Holm, B. Meredig, Building data-driven models with microstructural images: generalization and interpretability. Mater. Discov. 10, 19–28 (2017).  https://doi.org/10.1016/j.md.2018.03.002 CrossRefGoogle Scholar
  9. 9.
    B.L. Decost, E.A. Holm, A computer vision approach for automated analysis and classification of microstructural image data. Comput. Mater. Sci. 110, 126–133 (2015).  https://doi.org/10.1016/j.commatsci.2015.08.011 CrossRefGoogle Scholar
  10. 10.
    M.D. Hecht, B.A. Webler, Y.N. Picard, Digital image analysis to quantify carbide networks in ultrahigh carbon steels. Mater. Charact. 117, 134–143 (2016).  https://doi.org/10.1016/j.matchar.2016.04.012 CrossRefGoogle Scholar
  11. 11.
    A. Chowdhury, E. Kautz, B. Yener, D. Lewis, Image driven machine learning methods for microstructure recognition. Comput. Mater. Sci. 123, 176–187 (2016).  https://doi.org/10.1016/j.commatsci.2016.05.034 CrossRefGoogle Scholar
  12. 12.
    S. Banerjee, S.K. Ghosh, S. Datta, S.K. Saha, Segmentation of dual phase steel micrograph: an automated approach. Meas. J. Int. Meas. Confed. 46, 2435–2440 (2013).  https://doi.org/10.1016/j.measurement.2013.04.057 CrossRefGoogle Scholar
  13. 13.
    G.F. Vander Voort, Metallography: Principles and Practice, Metallography (McGraw-Hill, New York, 1985).  https://doi.org/10.1016/0026-0800(85)90051-5 CrossRefGoogle Scholar
  14. 14.
    R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Sabine, Slic 6(2011), 1–8 (2011)Google Scholar
  15. 15.
    E. Nhancement, A Comparative study of histogram equalization based image enhancement techniques for brightness preservation and contrast. Signal Image Process. Int. J. 4, 11–25 (2013)Google Scholar
  16. 16.
    N. Otsu, A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979).  https://doi.org/10.1109/TSMC.1979.4310076 CrossRefGoogle Scholar
  17. 17.
    R.K. Pandey, S.S. Mathurkar, A review on morphological filter and its implementation. Int. J. Sci. Res. 6, 69–72 (2017).  https://doi.org/10.21275/art20163953 CrossRefGoogle Scholar
  18. 18.
    P. SrinivasaRao, M. Madhavi Latha, Generalized algorithm for two dimensional digital image skeletonization. Int. J. Comput. Appl. 95, 9–12 (2014).  https://doi.org/10.5120/16565-6230 CrossRefGoogle Scholar
  19. 19.
    H.K.D.H. Bhadeshia, Physical metallurgy of steels, in Physical Metallurgy, 5th edn., ed. by D.E. Laughlin, K. Hono (Newnes, London, 2014).  https://doi.org/10.1016/b978-0-444-53770-6.00021-6 CrossRefGoogle Scholar
  20. 20.
    R.W. Cahn, P. Haasen, Physical Metallurgy, vol. 1 (North-Holland, Amsterdam, 1996)Google Scholar

Copyright information

© The Institution of Engineers (India) 2019

Authors and Affiliations

  1. 1.School of Materials Science and EngineeringIndian Institute of Engineering Science and TechnologyShibpur, HowrahIndia
  2. 2.Deparment of Master of Computer ApplicationB C Roy Engineering CollegeDurgapurIndia
  3. 3.Boldink Technologies Private LimitedHowrahIndia
  4. 4.Deparment of Electronics and Communication EngineeringB C Roy Engineering CollegeDurgapurIndia
  5. 5.Department of Metallurgical EngineeringNational Institute of Technology RaipurRaipurIndia

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