journal article·2022-07Open access

On the comparative inclusion analysis in steels: Spark-DAT, ASCAT and optical microscopy

Sirlene Trugilho Perin Passigatti, Márcia Spelta de Oliveira, José Roberto de Oliveira, Raphael Mariano de Souza, Estéfano Aparecido Vieira

Abstract

In the last decades, the control of amount, size, morphology, and chemical composition of non-metallic inclusions in steel has been investigated, particularly at the stage of steel refining, where endogenous and exogenous inclusions can affect the castability and the mechanical properties of the material, thus affecting the quality and performance of the process and final product. In this way, identifying the origin of inclusions and the steelmaking stages where they are formed is essential. The purpose of this study was to compare the results of methodologies for inclusions characterization and analysis using Spark-DAT, ASCAT and optical microscope systems, aiming to validate these systems for inclusions amount and size measuring control during steelmaking. Samplings of ultra-low (ULC) and medium (MC) carbon steels were carried out and both steels killed with silicon and/or aluminum. The MC steel presented higher concentration of inclusions with size in a range between 1 and 5 μm and this behavior is associated with calcium treatment. The total amount of inclusions per square area, presents significant difference between the measurement systems, however correlations could be noted. Spark-DAT and ASCAT showed better correlations. The study also showed the predominancy of oxide inclusions type. For sulfide inclusions there are a discrepancy between Spark-DAT and ASCAT, so that inclusions such as CaS and MnS are revealed but only ASCAT system showed the TiS inclusion type.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Metallurgical Processes and Thermodynamics · Microstructure and Mechanical Properties of Steels · Iron and Steelmaking Processes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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