Characterization of Inclusion Populations in Mn-Si Deoxidized Steel
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CONTROL over the chemical composition of inclusions is crucial to avoid problems during the steelmaking process and to ensure the proper mechanical behavior of inclusions in subsequent metallurgical operations and applications that require good deformability. In some steel grades, to avoid hard alumina inclusions resulting from Al deoxidation, Mn-Si deoxidation is used instead to generate deformable inclusions, in addition to providing tight control of the chemical composition of inclusions through slag-metal interactions.[1] Additionally, the reduction of the number and size of inclusions must be promoted throughout the steelmaking process.[1,2] The resulting inclusions depend on numerous
ALFONSO GARCI´A-CARBAJAL, MARTI´N HERRERA-TREJO, MANUEL CASTRO-ROMA´N, ARTURO-ISAIAS MARTINEZENRIQUEZ are with the Centro de Investigacio´n y de Estudios Avanzados, CINVESTAV Saltillo, Av. Industria Metalu´rgica No. 1062, Parque Industrial Saltillo-Ramos Arizpe, 25900 Ramos Arizpe, Coah, Mexico. Contact email: [email protected] EDGARIVAN CASTRO-CEDEN˜O is with the Institut Jean Lamour UMR 7198 CNRS/Universite´ de Lorraine, LabEx DAMAS, 50840, 54011 Nancy Cedex, France. Manuscript submitted February 17, 2017.
METALLURGICAL AND MATERIALS TRANSACTIONS B
factors, including the time and sequence of deoxidizer and slag-former additions, the chemical composition of the slag and the stirring, among others. To improve the control over the inclusion population, it is desirable to have information about its evolution throughout the steelmaking process. Meeting this requirement implies having reliable methods for the assessment of steel cleanliness that provide indicators permitting the comparison and discrimination of inclusion populations. A widely used method for the characterization of inclusion populations is based on optical microscopy and image analysis, which provide indicators such as the number and size distribution, among others. Furthermore, describing the upper tail of the size distribution is of particular interest since it corresponds to larger inclusions that are potentially more harmful to the quality of the solidified product. The statistic of extreme values (SEV) theory has been used as a tool for describing the upper tail of the size distribution.[3] Murakami et al.[4–6] developed a methodology in which the Gumbel distribution was fitted to measurements of the maximum inclusion size in random inspection areas. This method allowed the estimation of a maximum expected inclusion size and further allowed the discrimination among inclusion populations of different samples. Shi et al.[7–11] developed another method that consisted of fitting the generalized Pareto (GP)
distribution to the measurements of inclusion size larger than a given threshold size. The authors compared their results with those obtained using Murakami’s method. Kanbe et al.[12] applied Murakami’s method to determine the maximum inclusion size in samples taken from a tundish and from a slab in the as-cast and as-hot-rolled condition. In other
