3D Morphological Analysis and Synthesis of Industrial Materials Surfaces

  • Michael V. GlazoffEmail author
Thematic Section: 3D Materials Science
Part of the following topical collections:
  1. 3D Materials Science 2019


We present the original results pertaining to understanding topographical features on real industrial alloy surfaces. Quite often, such materials are subjected to different thermomechanical treatments with the goal of improving their yield strength, ductility, corrosion resistance, and other properties. Operations of casting, rolling, extruding, and stretching metal invariably leave their distinct “signatures” on the processed materials’ surfaces. It is highly desirable to be able to perform their characterization not in terms of the numerous surface roughness and/or waviness parameters, but rather in terms of the distinctive contributions of such operations. Indeed, if such a quantitative characterization were possible, it would be much easier to introduce corrective actions into the appropriate chain of production operations. We provide such possibility with the “morphological analysis and synthesis” techniques described in the paper. The results can be quantified either using surface topography measurements or studying light scattering from such surfaces. This last capability also gives a quick check of the “morphological similarity” of the “real” and modeled surfaces (images), which is important, e.g., in phase-field simulations. All calculations were performed using the MorphoHawk© software developed by the author and colleagues.


Mathematical morphology Functional surface metrology Analysis and synthesis of surface features 



This work was initiated some 20 years ago, when the author was working at the Alcoa Technical Center (now Arconic Technical Center) in New Kensington, PA. It was continued for the last 10 years at Idaho National Laboratory, Idaho Falls, ID. It would have been impossible without the generous help and assistance from his colleagues in both organizations: Mr. David E. Coleman, Dr. June M. Epp, Dr. Neville C. Whittle, Dr. Jonell M. Kerkhoff, Ms. Michelle Teichman (all from Arconic (former Alcoa Inc.)). The discussions with Prof. Dierk Raabe (University of Aachen, Germany) have been a great privilege and helped the author to identify the technical path to solving the 3D material characterization problems described in this paper. All these years the author continued collaborating with Prof. Yuri P. Pytyev (Dept. of Physics, Moscow State University, Russian Federation) and his many talented graduate students. At INL, the project was supported by the INL Directors, Dr. Steve Aumeier, Dr. Richard D. Boardman, and Dr. Eric Dufec. Last but certainly not least, a sincere gratitude is extended to Dr. Sergey N. Rashkeev (Qatar Foundation) for many years of fruitful collaboration. To all these colleagues and friends, the author would like to express his most sincere gratitude.


This manuscript has been authored by Battelle Energy Alliance, LLC under Contract No. DE-AC07-05ID14517 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the US Government.


  1. 1.
    Andersson JO, Helander T, Höglund L, Shi PF, Sundman B (2002) Thermo-Calc and DICTRA, computational tools for materials science. Calphad 26:273–312. CrossRefGoogle Scholar
  2. 2.
    Kattner UR, Campbell CE (2009) Invited review: modelling of thermodynamics and diffusion in multicomponent systems. Mater Sci Technol 25(4):443–459CrossRefGoogle Scholar
  3. 3.
    Campbell CE, Kattner UR, Liu Z-K, (2014) File and data repositories for next generation CALPHAD. Scr Mater 70:7–11CrossRefGoogle Scholar
  4. 4.
    Olson GB, Jou H-J, Jung J, Sebastian JT, Misra A, Locci I, Hull D (2008) Precipitation model validation in 3rd generation aeroturbine disc alloys. In: Superalloys 2008. TMS, pp 923–932Google Scholar
  5. 5.
    Olson GB (1997) Computational design of hierarchically structured materials. Science 277(5330):1237–1242. CrossRefGoogle Scholar
  6. 6.
    Saunders N, Guo Z, Li X, Miodownik AP, Schillé J-P (2002) Using JMatPro to model materials properties and behavior, JOM, 55(12):60CrossRefGoogle Scholar
  7. 7.
    Davies RH, Dinsdale AT, Gisby JA, Robinson JAJ, Martin SM (2002) MTDATA - thermodynamics and phase equilibrium software from the National Physical Laboratory. CALPHAD 26(2):229–271. CrossRefGoogle Scholar
  8. 8.
    Bale CW, Chartrand P, Degterov SA, Eriksson G, Hack K, Ben Mahfoud R, Melançon J, Pelton AD, Petersen S (2002) FactSage thermochemical software and databases. Calphad 26(2):189–228. CrossRefGoogle Scholar
  9. 9.
    Chen S-L, Zhang F, Xie F-Y, Daniel S, Yan X-Y, Chang YA, Schmid-Fetzer R, Oates WA (2003). See also Y.A. Chang, et al. (2004) Phase Diagram Calculation: Past, Present and Future, Prog. Mater. Science 49(3-4) 313–345Google Scholar
  10. 10., PanEngine API, Phase-Field Modeling (2017) Calculating phase diagrams using PANDAT and PanEngine. JOM 55(12):48–51.
  11. 11.
    Chen L-Q (2002) Phase-field models for microstructure evolution. Annu Rev Mater Res 32:113–140CrossRefGoogle Scholar
  12. 12.
    Böttger B, Eiken J, Apel M (2009) Phase-field simulation of microstructure formation in technical castings – a self-consistent homoenthalpic approach to the micro–macro problem. J Comput Phys 228:6784–6795. CrossRefGoogle Scholar
  13. 13.
    Tonks MR, Gaston D, Millett PC, Anders D, Talbot P (2012) An object-oriented finite element framework for multiphysics phase field simulations. Comput Mater Sci 51(1):20–29. CrossRefGoogle Scholar
  14. 14.
    Rappaz M, Rettenmayr M (1998) Simulation of solidification. Curr Opinion Solid State Mater Sci 3(3):275–282. CrossRefGoogle Scholar
  15. 15.
    Anglada E, Meléndez A (2006) Successful applications in castings using ProCAST Inverse Module, 16th European Conference and Exhibition on Digital Simulation for Virtual Engineering, Toulouse, France. CrossRefGoogle Scholar
  16. 16. - MatNavi, databases on polymers, metals, ceramics, superconductors and more, accessed 04/08/2019
  17. 17.
    Agrawal A, Deshpande PD, Cecen A, Basavarsu GP, Choudhary AN, Kalidindi SR (2014) Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr Mater Manuf Innov 3:8CrossRefGoogle Scholar
  18. 18.
    Olson GB (2000) Designing a new material world. Science 288:993–998. CrossRefGoogle Scholar
  19. 19.
    Allison J, Li M, Wolverton C, Su XM (2006) Virtual aluminum castings: an industrial application of ICME. JOM 58:28–35CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Burnett TL, Kelley R, Winiarski B, Contreras L, Daly M, Gholinia A, Burke MG, Withers PJ (2016) Large volume serial section tomography by Xe plasma FIB dual beam microscopy. Ultramicroscopy 161:119–129. CrossRefGoogle Scholar
  22. 22.
    Kalinin SV, Sumpter BG, Archibald RK (2015) Big-deep-smart data in imaging for guiding materials design. Nat Mater 14:973–980. CrossRefGoogle Scholar
  23. 23.
    Serra J (1982) Image analysis and mathematical morphology. Academic Press, New YorkGoogle Scholar
  24. 24.
    Pyt’ev YP (1993) Morphological image analysis. Pattern Recognit Image Anal 3:1Google Scholar
  25. 25.
    Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB, 2nd edn. GatesmarkGoogle Scholar
  26. 26.
    Soille P (2004) Morphological image analysis, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  27. 27.
    Shih FY (2009) Image processing and mathematical morphology. CRC Press, Boca Raton. CrossRefGoogle Scholar
  28. 28.
    Dougherty ER, Lotufo RA (2003) Hands-on morphological image processing. SPIE Press, Washington, DC. CrossRefGoogle Scholar
  29. 29.
    Glazoff MV, Pytyev YuP, Rashkeev SN, Gering K (2016) Methods, apparatuses, and computer-readable media for projectional morphological analysis of N-dimensional signals, US Patent 9,342,896, May 16Google Scholar
  30. 30.
    Muralikrishnan B, Raja J (2009) Computational surface and roundness metrology. Springer, BerlinGoogle Scholar
  31. 31.
    Lou S, Jiang X, Scott PJ (2012) An introduction to morphological filters in surface metrology. In: Proceedings of The Queen’s Diamond Jubilee Computing and Engineering Annual Researchers’ Conference 2012: CEARC’12. University of Huddersfield, pp 7–13Google Scholar
  32. 32.
    De Backer A, Domain C, Becquart CS, Luneville L, Simeone D, Sand AE, Nordlund K (2018) A model of defect cluster creation in fragmented cascades in metals based on morphological analysis. J Phys Condens Matter 30:405701. CrossRefGoogle Scholar
  33. 33.
    Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675. CrossRefGoogle Scholar
  34. 34.
    Whitehouse DJ (1982) The parameter rash—is there a cure? Wear 83(1):75–78. CrossRefGoogle Scholar
  35. 35.
    Whitehouse DJ (2010) Handbook for surface and nano-metrology. CRC Press, Boca RatonGoogle Scholar
  36. 36.
    Whitehouse DJ (2008) Surfaces and their measurement, HPSGoogle Scholar
  37. 37.
    Raabe D, Sachtleber M, Weiland H, Scheele G, Zhao Z (2003) Grain-scale micromechanics of polycrystal surfaces during plastic straining. Acta Mater 51(6):1539–1560. CrossRefGoogle Scholar
  38. 38.
    Sachtleber M, Raabe D, Weiland H (2004) Surface roughening and color changes of coated aluminum sheets during plastic straining. J Mater Process Technol 148(1):68–76. CrossRefGoogle Scholar
  39. 39.
    Glazoff MV, Rashkeev SN, Pyt’ev YP, Yoon J-W, Sheu S (2009) Interplay between plastic deformations and optical properties of metal surfaces: a multiscale study. Appl Phys Letts 95(8):084106CrossRefGoogle Scholar
  40. 40.
    Sachtleber M, Raabe D (2002) private communicationGoogle Scholar
  41. 41.
    Glazoff MV (2006) Plastic deformation changes visual appearance of surfaces, Invited Talk at International Aluminum Congress, Essen, GermanyGoogle Scholar
  42. 42.
    Beckmann P, Spizzichino A (1963) Scattering of electromagnetic waves from rough surfaces Oxford, Pergamon. CrossRefGoogle Scholar
  43. 43.
    Bennett JM, Mattson L (1999) Introduction to surface roughness and scattering, 2nd edn. Optical Society of America, Washington, DCGoogle Scholar
  44. 44.
    Glazov MV, Rashkeev SN (1998) Light scattering from rough surfaces with superimposed periodic structures. Appl Phys B Lasers Opt 66:217–223. CrossRefGoogle Scholar
  45. 45.
    Bass G, Fuchs JM (1963) Wave scattering of electromagnetic waves from rough surfacesGoogle Scholar
  46. 46.
    Prutton M (1994) Introduction to surface physics. Oxford University Press, OxfordGoogle Scholar
  47. 47.
    Rykowski R, Chittim K, Wadman S (2005) Imaging Sphere. Photonics Spectra, pp 64–68Google Scholar
  48. 48.
    Glazoff MV, Dufek EJ, Shalashnikov EA (2016) Morphological analysis and synthesis for understanding electrode microstructure evolution as a function of applied charge/discharge cycles. Appl Phys A 122:894CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection  2019

Authors and Affiliations

  1. 1.Department of Materials Science and EngineeringIdaho National LaboratoryIdaho FallsUSA

Personalised recommendations