Characterization of Inclusion Size Distributions in Steel Wire Rods

• Published in: Materials Vol. 15; no. 21; p. 7681
• Main Authors: Huazano-Estrada, Pablo, Herrera-Trejo, Martín, Castro-Román, Manuel de J, Ruiz-Mondragón, Jorge
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.11.2022; MDPI
• Subjects: Analysis; Confidence intervals; Continuous casting; Control rods; Extreme value theory; Extreme values; Fractals; inclusion; Inclusions; Liquid alloys; Maximum likelihood method; Normal distribution; Population (statistical); Population density; population density function; Random variables; Size distribution; Statistical analysis; Steel products; Steel wire; Wire rod; population density function; inclusion; extreme value theory; size distribution
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma15217681

The control of inclusions in steel components is essential to guarantee strong performance. The reliable characterization of inclusion populations is essential not only to evaluate the quality of the components but also to allow the use of analytical procedures for the comparison and discrimination of inclusion populations. In this work, inclusion size distributions in wire rod specimens from six plant-scale heats were measured and analyzed. For the measurements, the metallographic procedure specified in the ASTM E2283 standard was used. The population density function (PDF) approach and the extreme value statistical procedure specified in the ASTM E2283 standard were used to analyze the whole size distribution and the upper tail of the size distribution, respectively. The PDF approach allowed us to identify differences among inclusion size distributions and showed that new inclusions were not formed after the liquid steel treatment process. The extreme value statistical procedure led to the prediction of the maximum inclusion length for each heat, which was used for the statistical discrimination of heats. Furthermore, the estimation of the probability of finding an inclusion larger than a given inclusion size using the extreme value theory allowed us to order the heats for different critical inclusion sizes.