Interpretation and Classification of Non-Metallic Inclusions

Abstract

The non-metallic inclusions in steel are one of the most important parameters of metallurgical quality. Despite the extensive progress in thermodynamic description of non-metallic inclusions formation in the liquid and solidifying steel, a complete simulation of their evolution during secondary metallurgy, casting and solidification is impossible. The processes for non-metallic inclusion formation, growth and elimination, taking into account macro- and micro-segregation evolution, are too complicated. Therefore, a statistically reliable estimation of the amount, morphology, size distribution and chemical composition of non-metallic inclusions obtained by modern experimental approaches could be an important basis for understanding steelmaking technology. Automated feature SEM/EDS analysis of the chemical composition, size, and volume fraction of more than 600 non-metallic inclusions in rail steel was conducted. Analysis of these non-metallic inclusions' database by developed software found the following clusters of inclusions: (1) oxides, Al-Ca-Si-Mn-S-O; (2) predominantly sulfides, Mn-S-Al-Ti-O; (3) oxides and sulfides, Mn-S-O; and (4) complex non-metallic inclusions, Ti-Mn-O(N). All found compositions of non-metallic inclusions were placed on the ternary diagram Mn-Al-S and they lined up from the Al corner to the MnS point on the Mn-S axis. The chemical compositions of the non-metallic inclusions evolved from Al-Ca-Si-Mn-S-O to Mn-S-Al-Ti-O and, finally, to the Mn-S-O+Ti-Mn-O(N) system. Interpretation of the chemical composition of non-metallic inclusions by thermodynamic modeling revealed the nature of each cluster with correspondence to the secondary metallurgy stage where they were formed. This information could be useful for improvement of steelmaking technology. Accumulation of cluster analysis results for non-metallic inclusions in different steels could be a basis for the development of a universal classification of non-metallic inclusions produced by modern secondary metallurgy technologies.

Extension of ASTM E2283 standard practice for the assessment of large exogenous nonmetallic inclusions in super duty steels

“…It was previously shown [27,28], and it was confirmed in the present paper, that small indigenous nonmetallic inclusions in wheel steel are the products of deoxidation and modification, and that they are liquid at the temperatures of ladle treatment and pouring off (Fig. 3, a).…”

Database Clustering after Automatic Feature Analysis of Nonmetallic Inclusions in Steel

“…A technique based on the automatic analysis of a large number of particles on a scanning electron microscope (AFA) [13][14][15][16][17][18][19][20] was used to study the NMIs in this work. A Tescan electron microscope with an Inca Feature attachment was used for the study.…”

“…Existing metallographic methods for evaluating and analyzing make it possible to determine the composition and type of NMIs (ASTM E1245 and ASTM E45 [6,7]) but do not determine their real composition. Other methods, for example, analysis of single NMIs using a scanning electron microscope (SEM) [8], fractional gas analysis (FGA) [9], or electrolytic extraction (EE) [10] of NMIs are too complicated or unrepresentative for widespread use or do not provide statistically objective information.…”

“…For an objective statistical assessment of the amount, size, and composition of NMIs in industrial steels, it is possible to use databases obtained by the method of automatic particle analysis (AFA) [11][12][13][14][15] and containing information on a large amount of NMIs. For systems [18][19][20], or independently developed by researchers [13].…”

“…However, this approach is not universal and contains uncertainty in the choice of rules in each specific case. Therefore, earlier the authors of this paper [14,15] used the k-means algorithm to identify the clusters of NMIs. However, the use of this method is not always convenient, since it requires a priori knowledge on the database structure and assumptions about the number of existing clusters.…”