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1090 Dai Tang et al. REFERENCES


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Figure 14. Calculated automated inclusion microanalysis time versus measured analysis time on XL30 (accelerating voltage 10kV and spot size 5); scanning electron microscope (SEM) set- tings [magnification, energy-dispersive X-ray (EDX) live time, backscattered electron (BSE) pixel dwell time, and BSE image size] are listed in Table 3. Each symbol is for a different sample; con- tributions of BSE imaging, EDX analysis, and stage movement to the total analysis time are shown. “Dirty sample” refers to a sam- ple with a high concentration of inclusions (700 per mm2), for which EDX analysis took longer than BSE imaging.


evaluation. This paper discusses the effects of some of the fundamental instrument settings (accelerating voltage, spot size, and BSE image pixel dwell time) for two instru- ments: a tungsten filament SEM (FEI/ASPEX Explorer) and a field-emission SEM (FEI/Phillips XL30). Similar trends of poorer BSE spatial resolution and higher beam current with increased spot size are found for the two instruments. BSE imaging is responsible for a large fraction of the total analysis time, even after optimizing the pixel dwell time based on measured BSE image noise; a larger BSE detector would facilitate faster analysis. Use of the lower accelerating voltage (10 kV) (and the associated smaller interaction volume) improves detection of smaller inclusions and decreases the systematic error of inclusion microanalysis by EDX; the difference in X-ray count rate between 10 and 20kV depends on the details (inclusion depth; dependence of beam current on accelerating voltage; choice of spot size), but the difference can be unexpectedly small.


ACKNOWLEDGMENTS


Support of this work by the industrial members of the Center for Iron and Steelmaking Research and by FEI/ASPEX is gratefully acknowledged. The authors also acknowledge use of the Materials Characterization Facility at Carnegie Mellon University supported by the grant no. MCF-67785. Some of the material in this paper was first presented at the Clean Steel 9 conference (Hungary, 2015).


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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.


WINKLER, W., ANGELI,J.&MAYR, M. (2007). Automated SEM-EDX cleanness analysis and its application in metallurgy. Berg Huettenmaenn Monatsh 152,4–9.


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