Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-06-09T10:13:54.676Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated Inclusion Microanalysis in Steel by Computer-Based Scanning Electron Microscopy: Accelerating Voltage, Backscattered Electron Image Quality, and Analysis Time

Published online by Cambridge University Press:  10 November 2017

Dai Tang ,
Mauro E. Ferreira  and
Petrus C. Pistorius
Dai Tang
Affiliation:
Department of Materials Science and Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Mauro E. Ferreira
Affiliation:
Department of Materials Science and Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Petrus C. Pistorius*
Affiliation:
Department of Materials Science and Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
*
*Corresponding author. pistorius@cmu.edu
Get access

Abstract

Automated inclusion microanalysis in steel samples by computer-based scanning electron microscopy provides rapid quantitative information on micro-inclusion distribution, composition, size distribution, morphology, and concentration. Performing the analysis at a lower accelerating voltage (10 kV), rather than the generally used 20 kV, improves analysis accuracy and may improve spatial resolution, but at the cost of a smaller backscattered electron signal and potentially smaller rate of generation of characteristic X-rays. These effects were quantified by simulation and practical measurements.

Keywords

automated inclusion microanalysissteelenergy-dispersive X-ray analysis (EDX)scanning electron microscopy (SEM)backscattered electron (BSE) imaging
Type
Materials Science Applications
Information
Microscopy and Microanalysis , Volume 23 , Issue 6 , December 2017 , pp. 1082 - 1090
Copyright
© Microscopy Society of America 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Atkinson, H.V. & Shi, G. (2002). Characterization of inclusions in clean steels: A review including the statistics of extremes methods. Prog Mater Sci 48, 457–520.CrossRefGoogle Scholar
Goldstein, J.I., Newbury, D.E., Joy, D.C., Lyman, C.E., Echlin, P., Lifshin, E., Sawyer, L. & Michael, J.R. (2003). Scanning Electron Microscopy and X-ray Microanalysis, 3rd ed. New York, NY: Kluwer Academic.CrossRefGoogle Scholar
Izraeli, E.S., Tsach, T. & Levin, N. (2014). Optimizing FEG-SEM combined with an SDD EDX system for automated GSR analysis. X-Ray Spectrom 43, 29–37.Google Scholar
Kaushik, P., Lowry, M., Yin, H. & Pielet, H. (2012). Inclusion characterisation for clean steelmaking and quality control. Ironmaking Steelmaking 39(4), 284–300.Google Scholar
Merlet, C. (1994). An accurate computer correction program for quantitative electron probe microanalysis. Mikrochim Acta 114/115, 363–376.Google Scholar
Pistorius, P.C. & Patadia, A. (2012). The steel matrix affects microanalysis of CaO-Al2O3-CaS inclusions. In Clean Steel 8, pp. 1–12. Budapest, Hungary: Hungarian Mining and Metallurgical Society.Google Scholar
Pistorius, P.C., Patadia, A. & Lee, J. (2013). Correction of matrix effects on microanalysis of calcium aluminate inclusions. In AISTech 2013 Proceedings, pp. 1037–1044. Pittsburgh, PA: Association for Iron & Steel Technology.Google Scholar
Pistorius, P.C., Tang, D. & Ferreira, M.E. (2015). Consistency in automated inclusion microanalysis. In Clean Steel 9, pp. 1–8. Budapest, Hungary: Hungarian Mining and Metallurgical Society.Google Scholar
Pistorius, P.C. & Verma, N. (2011). Matrix effects in the energy dispersive X-ray analysis of CaO-Al2O3-MgO inclusions in steel. Microsc Microanal 17, 963–971.Google Scholar
Pouchou, J.L. & Pichoir, F. (1991). Quantitative analysis of homogeneous or stratified microvolumes applying the model “PAP”. In Electron Probe Quantitation, Heinrich, K.F.J. & Newbury, D.E. (Eds.), pp. 31–75. New York: Plenum Press.Google Scholar
Pretorius, E., Oltmann, H. & Cash, T. (2010). The effective modification of spinel inclusions by Ca Treatment in LCAK steel. Iron Steel Technol 7(7), 31–44.Google Scholar
Reischl, M., Frank, G., Martinez, C., Aigner, S., Lederhaas, B. & Michelic, S. (2011). Automated SEM/EDX particle analysis to determinate non-metallic inclusions in steel samples. Round robin tests aiming at studying the comparability of results from different measurement systems. Pract Metallogr 48(12), 643–659.CrossRefGoogle Scholar
Ritchie, N.W.M. (2009). Spectrum simulation in DTSA-II. Microsc Microanal 15, 454–468.Google Scholar
Salvat, F., Escuder, J.A., Bote, D. & Llovet, X. (2007). Simulation of EPMA spectra using PENELOPE. Microsc Microanal 13(S2), 1388–1389.Google Scholar
Story, S.R. (2006). Study of casting issues using rapid inclusion identification and analysis. In AISTech 2006 Proceedings, vol. I, pp. 879–889. Cleveland, OH: Association for Iron & Steel Technology.Google Scholar
Tang, D. & Pistorius, P.C. (2015). Optimizing speed and quality of automated inclusions analysis. In AISTech 2015 Proceedings, vol. III, pp. 3326–3337. Cleveland, OH: Association for Iron & Steel Technology.Google Scholar
Winkler, W., Angeli, J. & Mayr, M. (2007). Automated SEM-EDX cleanness analysis and its application in metallurgy. Berg Huettenmaenn Monatsh 152, 4–9.CrossRefGoogle Scholar