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<p>We compare three methods for quantifying the volume fraction of nonmetallic inclusions in steel: (i)&#xa0;automatic image analysis in accordance with ASTM E1245-03, (ii)&#xa0;fractional gas analysis (FGA) of&#xa0;O, N, and S with conversion to the volume fraction of inclusions, and (iii)&#xa0;spark optical emission spectrometry with pulse discrimination analysis (spark OES-PDA) supported by proprietary software that converts spectral line intensities into the volume fraction of nonmetallic inclusions, calibrated against quantitative metallography data.</p><p>We further propose an automated method to infer the oxide composition of inclusions from FGA data. The method is based on a&#xa0;mathematical model describing CO evolution during carbothermic reduction and the dissociation of oxide inclusions under non-isothermal heating. Model parameters are estimated using RMSProp with automatic differentiation, minimizing a&#xa0;loss function that balances data-fit accuracy with physically motivated constraints.</p><p>All the three methods yield broadly consistent estimates for the volume fraction of inclusions, with systematic differences among the methods. In all cases, the FGA-based calculation produced the highest values. On average, measurements per ASTM E1245 were 0.024% lower than the FGA calculation, whereas PDA results either agreed with it or were, on average, 0.017% lower, depending on specimen type.</p>

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Comparison of methods for determining the volume fraction of nonmetallic inclusions in steel

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Anton O. Morozov,
  • Olga A. Komolova,
  • Konstantin V. Grigorovich,
  • Yuri K. Feldman,
  • Dmitry S. Tokar,
  • Anton Yu. Uem,
  • Andrey S. Krylov

摘要

We compare three methods for quantifying the volume fraction of nonmetallic inclusions in steel: (i) automatic image analysis in accordance with ASTM E1245-03, (ii) fractional gas analysis (FGA) of O, N, and S with conversion to the volume fraction of inclusions, and (iii) spark optical emission spectrometry with pulse discrimination analysis (spark OES-PDA) supported by proprietary software that converts spectral line intensities into the volume fraction of nonmetallic inclusions, calibrated against quantitative metallography data.

We further propose an automated method to infer the oxide composition of inclusions from FGA data. The method is based on a mathematical model describing CO evolution during carbothermic reduction and the dissociation of oxide inclusions under non-isothermal heating. Model parameters are estimated using RMSProp with automatic differentiation, minimizing a loss function that balances data-fit accuracy with physically motivated constraints.

All the three methods yield broadly consistent estimates for the volume fraction of inclusions, with systematic differences among the methods. In all cases, the FGA-based calculation produced the highest values. On average, measurements per ASTM E1245 were 0.024% lower than the FGA calculation, whereas PDA results either agreed with it or were, on average, 0.017% lower, depending on specimen type.