Title

Improved Methodology for Automated SEM/EDS Non-Metallic Inclusion Analysis of Mini-Mill and Foundry Steels

Abstract

Automated Feature Analysis (AFA) provides the means to rapidly characterize large inclusion populations. System settings must be optimized to properly detect and interpret the important inclusion characteristics. The effects of sample area and AFA parameter settings (step size, magnification and threshold) on inclusion characterization results has been investigated and optimized. Methodologies for determining average inclusion chemistry, total element concentrations within inclusions, and for using joint ternary diagrams with size visualization to represent inclusion populations are presented. These methodologies were applied to samples collected from industrial steel mill and steel foundries and demonstrated in this study.

Meeting Name

AISTech 2015 (2015: May 4-7, Cleveland, OH)

Department(s)

Materials Science and Engineering

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Association for Iron & Steel Technology (AIST)

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