Database Clustering after Automatic Feature Analysis of Nonmetallic Inclusions in Steel

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.

Keywords

• nonmetallic inclusions
• steel
• automatic feature analysis
• ca-treatment
• cluster analysis
• database of chemical compositions