Database Clustering after Automatic Feature Analysis of Nonmetallic Inclusions in Steel

Andrey Zhitenev,Alexey Shamshurin,Sergey Ryaboshuk,Maria Salynova,Vladislav Kolnyshenko

doi:10.3390/met11101650

Andrey Zhitenev, Alexey Shamshurin + Show 3 more

Open Access

https://doi.org/10.3390/met11101650

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Journal: Metals	Publication Date: Oct 18, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: Peter the Great St. Petersburg Polytechnic University

Abstract

Non-metallic inclusions (NMIs) in steel have a negative impact on the properties of steel, so the problem of producing clean steels is actual. The existing metallographic methods for evaluating and analyzing nonmetallic inclusions make it possible to determine the composition and type of NMIs, but do not determine their real composition. The analysis of single NMIs using scanning electron microscope (SEM), fractional gas analysis (FGA), or electrolytic extraction (EE) of NMIs is too complicated. Therefore, in this work, a technique based on the automatic feature analysis (AFA) of a large number of particles by SEM was used. This method allows to obtain statistically reliable information about the amount, composition, and size of NMIs. To analyze the obtained databases of compositions and sizes of NMIs, clustering was carried out by the hierarchical method by constructing tree diagrams, as well as by the k-means method. This made it possible to identify the groups of NMIs of similar chemical composition (clusters) in the steel and to compare them with specific stages of the steelmaking process. Using this method, samples of steels produced at different steel plants and using different technologies were studied. The analysis of the features of melting of each steel is carried out and the features of the formation of NMIs in each considered case are revealed. It is shown that in all the studied samples of different steels, produced at different steel plants, similar clusters of NMIs were found. Due to this, the proposed method can become the basis for creating a modern universal classification of NMIs, which adequately describes the current state of steelmaking.

Highlights

Non-metallic inclusions (NMIs) have a negative effect on the casting of steel, leading to surface defects of continuously cast billets and sheets, and reduce the mechanical and corrosion properties of the final product [1,2,3,4,5]
The development of technological recommendations for obtaining clean steel is possible if information on the composition, size, and nature of inclusions is available; modern methods are required for their study and analysis
After electric arc furnace (EAF), the steel was processed in a ladle, the chemical composition was corrected, the melt was desulfurized with high basicity slag and deoxidized with an aluminum wire, and modified with SiCa

Summary

Introduction

Non-metallic inclusions (NMIs) have a negative effect on the casting of steel, leading to surface defects of continuously cast billets and sheets, and reduce the mechanical and corrosion properties of the final product [1,2,3,4,5]. 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 this study, a metallographic sample is placed in a scanning electron microscope chamber equipped with an energy-dispersive spectrometer and a motorized stage. Systems of rules are often used, according to which NMIs, depending on the content of the components, are assigned to different classes This method is proposed by the manufacturers of such systems [18,19,20], or independently developed by researchers [13]. The analysis of descriptive statistics to identify the reliability of clustering [21,22]

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