Microsc. Microanal. 23, 1082–1090, 2017 doi:10.1017/S1431927617012648
© MICROSCOPY SOCIETY OF AMERICA 2017
Automated Inclusion Microanalysis in Steel by Computer-Based Scanning Electron Microscopy: Accelerating Voltage, Backscattered Electron Image Quality, and Analysis Time
Dai Tang, Mauro E. Ferreira, and Petrus C. Pistorius* Department ofMaterials Science and Engineering, CarnegieMellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
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.
Key words: automated inclusion microanalysis, steel, energy-dispersive X-ray analysis (EDX), scanning electron microscopy (SEM), backscattered electron (BSE) imaging
INTRODUCTION
Automated analysis of inclusions by computer-controlled scanning electron microscopes (SEM) equipped with back- scattered electron (BSE) detectors and fast energy-dispersive X-ray (EDX) analyzers has been widely used in the steel industry to evaluate steel cleanliness (e.g., see Pretorius et al., 2010; Kaushik et al., 2012). It provides a convenient tool to evaluate micro-inclusion size, shape, and composition for modern cleaner steels; manual SEM analysis of micro- inclusions in such steels would require the examination of unrealistically large areas to achieve statistically significant results (Atkinson & Shi, 2002). Round-robin tests (Reischl et al., 2011; Kaushik et al., 2012) showed comparable results from automated inclusion microanalysis on different instruments and different analyzers when instrument settings were standardized (Reischl et al., 2011). The two major steps of automated inclusion analysis are
capturing BSE images of the polished steel sample surface (to identify “features” that may be inclusions), and conducting
the feature morphology passes a user-defined morphology filter. For EDX analysis the electron beam is placed at the
*Corresponding author.
pistorius@cmu.edu Received June 11, 2017; accepted October 11, 2017
EDX analysis of the individual features. Features are identi- fied in BSE images based on the difference in brightness (gray level) between inclusions and the steel matrix. The apparent size and morphology of each feature are directly determined from these BSE images [by means of a rotation chord algorithm in the case of the ASPEX system (Winkler et al., 2007)]. EDX analysis is only performed if the bright- ness of the feature is lower than a user-defined threshold and
center of the feature, or rasters across the exposed surface of the feature. Thanks to the development of fast EDX detec- tors, statistical information about micro-inclusions includ- ing composition, size distribution, morphology, and concentration in the steel sample can be obtained in a reasonable time (Story, 2006). Inclusion detection by BSE imaging and analysis by
EDX consume most of the time required for automated inclusion microanalysis. For “clean steels” with relatively few inclusions (typically fewer than 100 inclusions/mm2), BSE imaging is often the slowest step. Further improvement of analysis speed requires quantification of BSE image noise, BSE image spatial resolution, and inclusion brightness (relative to the user-defined threshold) in the BSE images. The work presented here deals with oxide inclusions in
steel; the specific examples considered are inclusions in aluminum-killed steels that were calcium treated; the inclu- sions typically contain CaO, Al2O3, and MgO, often with associated sulfides (mixtures of CaS and MnS); the sulfide normally precipitates as a shell around the oxide. When optimizing analysis of such inclusions, different accelerating voltages need to be considered. The most commonly used voltage to date appears to have been 20 kV. Reasons for using this relatively high accelerating voltage include higher beam current (beneficial for the BSE image quality and the speed of EDX analysis), and sufficient overvoltage for excitation of characteristic lines of heavier elements such as copper and nickel. However, matrix effects cause systematic errors in CaO–Al2O3–MgO inclusion EDX analysis at 20 kV: The calcium to aluminum (Ca/Al) ratio is underestimated for smaller and shallower inclusions (inclusions with apparent diameter smaller than 1 μm or with a spherical-cap shape), and is overestimated for larger and deeper inclusions
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