Use of an Inverse Algorithm for 2D to 3D Particle Size Conversion and its Application to Non-metallic Inclusion Analysis in Steel
Abstract
Modern automated SEM/EDX analysis provides morphological and chemistry statistics for precise determination of phases, porosity and non-metallic inclusions using two-dimensional observations (polished random sections). However, the knowledge of the real three-dimensional distribution of these features in the microstructure is necessary for metallurgical process analysis and product property predictions. A special algorithm for the conversion of two-dimensional round particle distribution obtained from an automated SEM/EDX or optical imaging to the real three-dimensional spherical particle distribution was developed based on inverse modeling. A program generates a virtual two-dimensional distribution from 9-12 sets of normal distributions of spherical particles covering all size ranges using arbitrary slicing. These sets are optimized by comparison with particle sections observed in experiments. The applied inverse algorithm uses the built-in optimizer and error function in EXCEL. The method was applied for three-dimensional analysis of near spherical phases and inclusions in different iron alloys. In ductile iron with spherical graphite, the method revealed that spheroids could be mono or polydimensional depending on solidification kinetics. In high-alloyed Al-Mn steel, different dimensional classes of nitrides, sulfides and oxides were observed and related to the free energy of formation.
Recommended Citation
S. N. Lekakh et al., "Use of an Inverse Algorithm for 2D to 3D Particle Size Conversion and its Application to Non-metallic Inclusion Analysis in Steel," AISTech - Iron and Steel Technology Conference Proceedings, vol. 1, pp. 1061 - 1068, Ronald E. Ashburn, Aug 2013.
Department(s)
Materials Science and Engineering
Keywords and Phrases
Inverse converter; Non-metallic inclusions; Particle size
International Standard Book Number (ISBN)
978-193511733-9
International Standard Serial Number (ISSN)
1551-6997
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Ronald E. Ashburn, All rights reserved.
Publication Date
14 Aug 2013