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.
Recommended Citation
M. Harris et al., "Improved Methodology for Automated SEM/EDS Non-Metallic Inclusion Analysis of Mini-Mill and Foundry Steels," Proceedings of the AISTech 2015 (2015, Cleveland, OH), Association for Iron & Steel Technology (AIST), May 2015.
Meeting Name
AISTech 2015 (2015: May 4-7, Cleveland, OH)
Department(s)
Materials Science and Engineering
Research Center/Lab(s)
Peaslee Steel Manufacturing Research Center
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2015 Association for Iron & Steel Technology (AIST), all rights reserved.
Publication Date
07 May 2015
