Journal of Materials Research and Technology (Sep 2023)

Influence of refining slag on transformation and separation of nonmetallic inclusions in 316L stainless steel

  • Cheng-song Liu,
  • Xiu-guo Ran,
  • Hua Zhang,
  • Jie Li,
  • Yong Wang,
  • Yuan-yuan Lu,
  • Li Xiong,
  • Hong-wei Ni

Journal volume & issue
Vol. 26
pp. 7199 – 7212

Abstract

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To determine the effects of C/A ratio (CaO/Al2O3) and the basicity B (CaO/SiO2) of Al2O3–CaO–SiO2–MgO(–CaF2) refining slags on the characteristics of inclusions and sulfur (S) content in 316L stainless steels, equilibrium experiments between the slag and steel were carried out at 1873 K under laboratory conditions. Thermodynamic investigations were performed to understand how the chemical compositions of the inclusions changed and to evaluate the desulfurization capabilities of the refining slags. Additionally, a mathematical model was established for predicting the separation of nonmetallic inclusions during refining and characterizing the movements of the inclusions at the “slag–steel” interface. First, the findings showed that increasing the slag basicity B in the range of 1.0–7.0 led to a reduction in the dissolved [O] mass fraction from 0.0033% to 0.0021% and the total oxygen (T.O.) from 0.0050% to 0.0031% while also slightly improving the cleanliness of the steel by increasing the C/A ratio within the range 1.0–2.2. Then, the average compositions of the inclusions in the steel were approximately (mol pct SiO2)/(mol pct MnO) = 0.57 (±0.08), and the Al2O3 content grew from 6.55% to 18.36% (mol pct), while increasing the slag basicity B resulted in an increase in the Al2O3 content of the inclusions. Once the B exceeded 5.0, further increases in the slag basicity did not continuously increase the desulfurization capability. Finally, all calculations of refining slag with the model yielded separation rates were higher than 80% for inclusions with diameters of 5 μm. For inclusion removal after slag refining, the results of the experiments exhibited consistent agreement with the predictions from the mathematical model.

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