Home Technology Scale-bridging Microstructural Analysis – A Correlative Approach to Microstructure Quantification Combining Microscopic Images and EBSD Data
Article
Licensed
Unlicensed Requires Authentication

Scale-bridging Microstructural Analysis – A Correlative Approach to Microstructure Quantification Combining Microscopic Images and EBSD Data

  • M. Müller

    Martin Müller studied Material Science at Saarland University. After getting his master’s degree, he worked 3.5 years at Brück GmbH as a Materials and Welding Engineer. Since 2018, doctoral student at the Chair of Functional Materials. Research in characterization and classification of bainitic microstructures.

    EMAIL logo
    , D. Britz and F. Mücklich
Published/Copyright: July 27, 2021
Become an author with De Gruyter Brill

Abstract

A comprehensive description of complex material structures may require characterization using different methods and observations across several scales. This work will present a correlative approach including light optical microscopy, scanning electron microscopy and electron backscatter diffraction, enabling microstructure quantification which combines microscopic images and electron backscatter diffraction data. The parameters obtained from electron backscatter diffraction such as misorientation parameters or grain and phase boundary data are an ideal source of information, complementing microscopic images. Two case studies performed on bainitic microstructures will be presented to demonstrate practical applications of this approach.

Kurzfassung

Eine vollumfängliche Beschreibung komplexer Werkstoff-Gefüge kann eine Charakterisierung mit verschiedenen Methoden und über mehrere Betrachtungsskalen hinweg erfordern. In dieser Arbeit wird ein korrelativer Ansatz, bestehend aus Lichtmikroskopie, Rasterelektronenmikroskopie und Elektronenrückstreubeugung, vorgestellt, der eine kombinierte Gefüge-Quantifizierung aus den Mikroskop-Aufnahmen sowie den Daten der Elektronenrückstreubeugung ermöglicht. Die aus der Elektronenrückstreu beugung gewonnenen Parameter, z. B. Missorientierungsparameter oder Daten über sowohl Korn- als auch Phasengrenzen, stellen eine ideale, komplementäre Informationsquelle zu den Mikroskop-Aufnahmen dar. Zwei Fallstudien an bainitischen Gefügen stellen die praktischen Anwendungsmöglichkeiten des Ansatzes vor.

About the author

M. Müller

Martin Müller studied Material Science at Saarland University. After getting his master’s degree, he worked 3.5 years at Brück GmbH as a Materials and Welding Engineer. Since 2018, doctoral student at the Chair of Functional Materials. Research in characterization and classification of bainitic microstructures.

Acknowledgements

The authors would like to thank the Saarland State Chancellery for financial support within the ZuMat Project funded by the European Regional Development Fund (ERDF).

Danksagung

Die Autoren bedanken sich für die Förderung im Projekt ZuMat, gefördert von der Staatskanzlei des Saarlandes aus Mitteln des Europäischen Fonds für Regionale Entwicklung (EFRE).

References / Literatur

[1] Schwartz, A. J.; Kumar, M.; Adams, B. L.; Field, D. P.: Electron backscatter diffraction in materials science, 2nd Edition, Springer US, USA, 2009 DOI: 10.1007/978-0-387-88136-210.1007/978-0-387-88136-2Search in Google Scholar

[2] Britz, D.; Webel, J.; Gola, J.: Pract. Metallogr. 54 (2017) 10, 685–696 DOI: 10.3139/147.11048410.3139/147.110484Search in Google Scholar

[3] Britz, D.; Webel, J.; Schneider, A. S.; Mücklich, F.: Metall. Ital. 109 (2017) 3, 5 –10Search in Google Scholar

[4] Li, X.; Ramazani, A.; Prahl, U.; Bleck, W.: Mater. Charact. 142 (2018), 179–186 DOI: 10.1016/j.matchar.2018.05.03810.1016/j.matchar.2018.05.038Search in Google Scholar

[5] Gazder, A. A.; Al-Harbi, F.; Spanke, H.; Mitchell, D.R.G.; Pereloma, E. V.: Ultramicroscopy. 147 (2014), 114–132 DOI: 10.1016/j.ultramic.2014.07.00510.1016/j.ultramic.2014.07.005Search in Google Scholar

[6] Zitová, B.; Flusser, J.: Image Vis. Comput. 21 (2003) 11, 977–1000 DOI: 10.1016/S0262-8856(03)00137-910.1016/S0262-8856(03)00137-9Search in Google Scholar

[7] Goldstein, J.; Newbury, D. E.; Echlin, P.; Joy, D. C.; Romig Jr., A. D.; Lyman, C. E.; Fiori, C.; Lifshin, E.: Scanning Electron Microscopy and X-Ray Microanalysis: A Text for Biologists, Materials Scientists, and Geologists. 2nd Edition, Springer, New York, USA, 2012Search in Google Scholar

[8] Hecht, E.: Optics. 4th Edition, Addison-Wesley, Harlow, Essex, England, 2001Search in Google Scholar

[9] Lowe, D. G.: Int. J. Comput. Vis. 60 (2004) 2, 91–110 DOI: 10.1023/B:VISI.0000029664.99615.9410.1023/B:VISI.0000029664.99615.94Search in Google Scholar

[10] Arganda-Carreras, I.; Sorzano, C. O. S.; Marabini, R.; Carazo, J.M.; Ortiz-De-Solorzano, C.; Kybic, J.: Lect. Notes Comput. Sci. 4241 (2006), 85–95 DOI: 10.1007/11889762_810.1007/11889762_8Search in Google Scholar

[11] Schindelin, J.; Arganda-Carreras, I.; Frise, E.; Kaynig, V.; Longair, M.; Pietzsch, T.; Preibisch, S.; Rueden, C.; Saalfeld, S.; Schmid, B.; Tinevez, J.Y.; White, D.J.; Hartenstein, V.; Eliceiri, K.; Tomancak, P.; Cardona, A.: Nat Methods 9 (2012) 7, 676–682 DOI: 10.1038/nmeth.201910.1038/nmeth.2019Search in Google Scholar PubMed PubMed Central

[12] Singh Phogat, R.; Dhamecha, H.; Pandya, M.; Chaudhary, B.; Potdar, M.: Int. J. Sci. Eng. Res. 5 (2014) 12, 44–49Search in Google Scholar

[13] Sotiras, A.; Davatzikos, C.; Paragios, N.: IEEE Trans. Med. Imaging. 32 (2013) 7, 1153–1190 DOI: 10.1109/TMI.2013.226560310.1109/TMI.2013.2265603Search in Google Scholar PubMed PubMed Central

[14] Koll, L.; Tsipouridis, P.; Werner, E. A.: J. Microsc. 243 (2011) 2, 206–219 DOI: 10.1111/j.1365-2818.2011.03495.x10.1111/j.1365-2818.2011.03495.xSearch in Google Scholar PubMed

[15] Britz, D.; Hegetschweiler, A.; Roberts, M.; Mücklich, F.: Mater. Perform. Charact. 5 (2016) 5, 553–563 DOI: 10.1520/MPC2016006710.1520/MPC20160067Search in Google Scholar

[16] Feature Extraction – ImageJ, https://imagej.net/Feature_Extraction (accessed 28.04.2021)Search in Google Scholar

[17] MTEX Toolbox | MTEX, https://mtex-toolbox.github.io/ (accessed 28.04.2021)Search in Google Scholar

[18] Müller, M.; Britz, D.; Ulrich, L.; Staudt, T.; Mücklich, F.: Metals 630 (2020) 10, 1–19 DOI: 10.3390/met1005063010.3390/met10050630Search in Google Scholar

[19] Müller, M.; Britz, D.; Mücklich, F.: ASM Adv. Mater. Process. 179 (2021), 16–2110.31399/asm.amp.2021-01.p016Search in Google Scholar

[20] Gola, J.; Britz, D.; Staudt, T.; Winter, M.; Schneider, A. S.; Ludovici, M.; Mücklich, F.: Comput. Mater. Sci. 148 (2018), 324–335 DOI: 10.1016/j.commatsci.2018.03.00410.1016/j.commatsci.2018.03.004Search in Google Scholar

[21] Gola, J.; Webel, J.; Britz, D.; Guitar, A.; Staudt, T.; Winter, M.; Mücklich, F.: Comput. Mater. Sci. 160 (2019), 186–196 DOI: 10.1016/j.commatsci.2019.01.00610.1016/j.commatsci.2019.01.006Search in Google Scholar

[22] Webel, J.; Gola, J.; Britz, D.; Mücklich, F.: Mater. Charact. 144 (2018), 584–596 DOI: 10.1016/j.matchar.2018.08.00910.1016/j.matchar.2018.08.009Search in Google Scholar

[23] Zajac, S.; Schwinn, V.; Tacke, K. H.: Mater. Sci. Forum. 500–501 (2005), 387–394 DOI: 10.4028/www.scientific.net/MSF.500-501. 38710.4028/www.scientific.net/MSF.500-501. 387Search in Google Scholar

[24] Chen, Y. W.; Tsai, Y. T.; Tung, P. Y.; Tsai, S. P.; Chen, C. Y.; Wang, S. H.; Yang, J. R.: Mater. Charact. 139 (2018), 49–58 DOI: 10.1016/j.matchar.2018.01.04110.1016/j.matchar.2018.01.041Search in Google Scholar

[25] Zhao, H.; Wynne, B. P.; Palmiere, E. J.: Mater. Charact. 123 (2017), 339–348 DOI: 10.1016/j.matchar.2016.11.02410.1016/j.matchar.2016.11.024Search in Google Scholar

Received: 2021-05-02
Accepted: 2021-05-03
Published Online: 2021-07-27
Published in Print: 2021-07-31

© 2021 Walter de Gruyter GmbH, Berlin/Boston, Germany

Downloaded on 9.12.2025 from https://www.degruyterbrill.com/document/doi/10.1515/pm-2021-0032/html?lang=en
Scroll to top button