SpringerLink
Log in

EDS-PhaSe: Phase Segmentation and Analysis from EDS Elemental Map Images Using Markers of Elemental Segregation

  • Original Research Article
  • Published:
Metallography, Microstructure, and Analysis Aims and scope Submit manuscript

Abstract

Scanning electron microscopy (SEM), combined with energy-dispersive spectroscopy (EDS), is an extensively used technique for in-depth microstructural analysis. Here, we present the EDS-Phase Segmentation (EDS-PhaSe) tool that enables phase segmentation and phase analysis using the EDS elemental map images. It converts the EDS map images into estimated composition maps for calculating markers of selective elemental redistribution in the scanned area and creates a phase-segmented micrograph while providing approximate fraction and composition of each identified phase. EDS-PhaSe offers two unique advantages. Firstly, it enables the direct processing of EDS elemental map images without requiring any raw or proprietary data/software, thereby allowing the analysis of EDS results available in the published literature as images. Secondly, it enables segmentation and analysis of phases even when the phase contrast is missing in backscattered micrographs, assisting in correlating the XRD and SEM-EDS data as shown in this work for a AlCoCrFeNi high-entropy alloy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Similar content being viewed by others

Code Availability

The code for EDS-PhaSe, wrapped in interactive Jupyter notebooks, is available at ‘IDEAsLab-Computational-Microstructure’ organization page on GitHub (https://github.com/IDEAsLab-Computational-Microstructure/EDS-PhaSe). Any enquiries related to the code may be directed to DB and PKR.

References

  1. J.I. Goldstein, D.E. Newbury, J.R. Michael, N.W.M. Ritchie, J.H.J. Scott, D.C. Joy (2018) Scanning Electron Microscopy and X-Ray Microanalysis. Springer, New York, NY, 2018. https://doi.org/10.1007/978-1-4939-6676-9.

  2. B. Münch, L. Martin, A. Leemann, Segmentation of elemental EDS maps by means of multiple clustering combined with phase identification. J. Microsc. 260, 411–426 (2015). https://doi.org/10.1111/jmi.12309

    Article  CAS  Google Scholar 

  3. R. Juránek, J. Výravský, M. Kolář, D. Motl, P. Zemčík, Graph-based deep learning segmentation of EDS spectral images for automated mineral phase analysis. Comput. Geosci. 165, 105109 (2022). https://doi.org/10.1016/j.cageo.2022.105109

    Article  Google Scholar 

  4. F. Georget, W. Wilson, K.L. Scrivener, edxia: Microstructure characterisation from quantified SEM-EDS hypermaps. Cem. Concr. Res. 141, 106327 (2021). https://doi.org/10.1016/j.cemconres.2020.106327

    Article  CAS  Google Scholar 

  5. J.B. Byrnes, A.A. Gazder, S.A. Yamini, Assessing phase discrimination via the segmentation of an elemental energy dispersive X-ray spectroscopy map: a case study of Bi2Te3 and Bi2Te2S. RSC Adv. 8, 7457–7464 (2018). https://doi.org/10.1039/C7RA08594J

    Article  CAS  Google Scholar 

  6. P.T. Durdziński, C.F. Dunant, M.B. Haha, K.L. Scrivener, A new quantification method based on SEM-EDS to assess fly ash composition and study the reaction of its individual components in hydrating cement paste. Cem. Concr. Res. 73, 111–122 (2015). https://doi.org/10.1016/j.cemconres.2015.02.008

    Article  CAS  Google Scholar 

  7. V. Shivam, Y. Shadangi, J. Basu, N.K. Mukhopadhyay, Evolution of phases, hardness and magnetic properties of AlCoCrFeNi high entropy alloy processed by mechanical alloying. J. Alloys Compd. 832, 154826 (2020). https://doi.org/10.1016/j.jallcom.2020.154826

    Article  CAS  Google Scholar 

  8. V. Shivam, D. Beniwal, Y. Shadangi, P. Singh, V.S. Hariharan, G. Phanikumar, D.D. Johnson, P.K. Ray, N.K. Mukhopadhyay, Effect of Zn addition on phase selection in AlCrFeCoNiZn high-entropy alloy. SSRN Electron. J. (Preprint). (2022). https://doi.org/10.2139/ssrn.4263461

    Article  Google Scholar 

  9. J.M. Cowley, Short-range order and long-range order parameters. Phys. Rev. 138, A1384–A1389 (1965). https://doi.org/10.1103/PhysRev.138.A1384

    Article  Google Scholar 

  10. Y. Rao, W.A. Curtin, Analytical models of short-range order in FCC and BCC alloys. Acta Mater. 226, 117621 (2022). https://doi.org/10.1016/j.actamat.2022.117621

    Article  CAS  Google Scholar 

  11. P. Singh, A.V. Smirnov, D.D. Johnson, Atomic short-range order and incipient long-range order in high-entropy alloys. Phys. Rev. B. 91, 224204 (2015). https://doi.org/10.1103/PhysRevB.91.224204

    Article  CAS  Google Scholar 

  12. D. Porter, K. Easterling, Phase Transformations in Metals and Alloys (Revised Reprint). CRC Press. (2009). https://doi.org/10.1201/9781439883570

    Article  Google Scholar 

  13. D. Beniwal, P.K. Ray, Learning phase selection and assemblages in high-entropy alloys through a stochastic ensemble-averaging model. Comput. Mater. Sci. 197, 110647 (2021). https://doi.org/10.1016/j.commatsci.2021.110647

    Article  CAS  Google Scholar 

  14. M. Wu, S. Wang, H. Huang, D. Shu, B. Sun, CALPHAD aided eutectic high-entropy alloy design. Mater. Lett. 262, 127175 (2020). https://doi.org/10.1016/j.matlet.2019.127175

    Article  CAS  Google Scholar 

  15. D. Beniwal, P.K. Ray, FCC vs. BCC phase selection in high-entropy alloys via simplified and interpretable reduction of machine learning models. Materialia. 26, 101632 (2022). https://doi.org/10.1016/j.mtla.2022.101632

    Article  CAS  Google Scholar 

  16. C. Liu, A. Garner, H. Zhao, P.B. Prangnell, B. Gault, D. Raabe, P. Shanthraj, CALPHAD-informed phase-field modeling of grain boundary microchemistry and precipitation in Al–Zn–Mg–Cu alloys. Acta Mater. 214, 116966 (2021). https://doi.org/10.1016/j.actamat.2021.116966

    Article  CAS  Google Scholar 

  17. D. Beniwal, Jhalak, P.K. Ray, Data-Driven Phase Selection, Property Prediction and Force-Field Development in Multi-Principal Element Alloys, in: A. Verma, S. Mavinkere Rangappa, S. Ogata, S. Siengchin (Eds.), Forcefields for Atomistic-Scale Simulations: Materials and Applications, Springer Nature, Singapore,: pp. 315–347. https://doi.org/10.1007/978-981-19-3092-8_16.

  18. W. Wang, H.-L. Chen, H. Larsson, H. Mao, Thermodynamic constitution of the Al–Cu–Ni system modeled by CALPHAD and ab initio methodology for designing high entropy alloys. Calphad. 65, 346–369 (2019). https://doi.org/10.1016/j.calphad.2019.03.011

    Article  CAS  Google Scholar 

  19. Q. Han, Z. Lu, S. Zhao, Y. Su, H. Cui, Data-driven based phase constitution prediction in high entropy alloys. Comput. Mater. Sci. 215, 111774 (2022). https://doi.org/10.1016/j.commatsci.2022.111774

    Article  CAS  Google Scholar 

  20. D. Beniwal, P.K. Ray, CoSMoR: decoding decision-making process along continuous composition pathways in machine learning models trained for material properties. Phys. Rev. Mater. 7, 043802 (2023). https://doi.org/10.1103/PhysRevMaterials.7.043802

    Article  CAS  Google Scholar 

  21. P. Singh, A.V. Smirnov, A. Alam, D.D. Johnson, First-principles prediction of incipient order in arbitrary high-entropy alloys: exemplified in Ti0.25CrFeNiAlx. Acta Mater. 189, 248–254 (2020). https://doi.org/10.1016/j.actamat.2020.02.063

    Article  CAS  Google Scholar 

  22. P. Singh, A.V. Smirnov, D.D. Johnson, Ta-Nb-Mo-W refractory high-entropy alloys: anomalous ordering behavior and its intriguing electronic origin. Phys. Rev. Mater. 2, 055004 (2018). https://doi.org/10.1103/PhysRevMaterials.2.055004

    Article  CAS  Google Scholar 

  23. D. Beniwal, P. Singh, S. Gupta, M.J. Kramer, D.D. Johnson, P.K. Ray, Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models. Npj Comput. Mater. 8, 1–11 (2022). https://doi.org/10.1038/s41524-022-00842-3

    Article  Google Scholar 

  24. L. Gránásy, G.I. Tóth, J.A. Warren, F. Podmaniczky, G. Tegze, L. Rátkai, T. Pusztai, Phase-field modeling of crystal nucleation in undercooled liquids—A review. Prog. Mater. Sci. 106, 100569 (2019). https://doi.org/10.1016/j.pmatsci.2019.05.002

    Article  CAS  Google Scholar 

  25. X.J. Zuo, Y. Coutinho, S. Chatterjee, N. Moelans, Phase field simulations of FCC to BCC phase transformation in (Al)CrFeNi medium entropy alloys. Mater. Theory. 6, 12 (2022). https://doi.org/10.1186/s41313-021-00034-4

    Article  Google Scholar 

Download references

Acknowledgements

DB acknowledges the financial support from Prime Minister’s Research Fellows (PMRF) scheme run by Ministry of Education, Government of India. PKR acknowledges the support from Science and Engineering Research Board, Department of Science and Technology grant (# CRG/2021/006974). Analysis of the Haynes-282 (OP and MJK) was supported by the US Department of Energy (DOE), Office of Fossil Energy, Crosscutting Research Program. Research was performed at Iowa State University and Ames Laboratory, which is operated by ISU for the US DOE under contract DE-AC02-07CH11358. We acknowledge support from Haynes International (Dr. Vinay Deodeshmukh), who provided the Haynes-282 alloy.

Author information

Authors and Affiliations

  1. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India

    Dishant Beniwal & Pratik K. Ray

  2. Materials Engineering Division, CSIR-National Metallurgical Laboratory, Jamshedpur, Jharkhand, 831007, India

    Vikas Shivam

  3. Ames National Laboratory, US-DOE, Ames, IA, 50011, USA

    Olena Palasyuk & Matthew J. Kramer

  4. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India

    Gandham Phanikumar

Authors
  1. Dishant Beniwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikas Shivam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olena Palasyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew J. Kramer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gandham Phanikumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pratik K. Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pratik K. Ray.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This invited article is part of a special topical focus in the journal Metallography, Microstructure, and Analysis on Microstructure Modeling.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beniwal, D., Shivam, V., Palasyuk, O. et al. EDS-PhaSe: Phase Segmentation and Analysis from EDS Elemental Map Images Using Markers of Elemental Segregation. Metallogr. Microstruct. Anal. 12, 924–933 (2023). https://doi.org/10.1007/s13632-023-01020-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13632-023-01020-7

Keywords

Search

Navigation