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Formation and characterization of non-metallic inclusions in steel produced by melting secondary ferrous scrap in an electric arc furnace: A case study of AISI 1066 steel

  • Nozimjon Kholmirzaev
  • , Nodir Turakhodjaev
  • , Jamshidbek Khasanov
  • , Chong Yang
  • , Bekzod Yusupov
  • , Shokhista Saidkhodjaeva
  • , Abdujalol Bektemirov
  • , Nargiza Sadikova
  • , Zokirjon Nurdinov
  • , Nuriddin Yusupov
  • , Davronbek Juraboev
  • , Bobomurod Nurmurodov
  • Tashkent State Technical University
  • Andijan State Technical Institute
  • Almalyk State Technical Institute
  • Uzbek-Japan Innovation Center of Youth

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigated the formation of non-metallic inclusions and their characterization during the melting of AISI1066 steel from secondary metal scrap in an electric arc furnace. The analyses were carried out using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). In addition, a deep learning-based segmentation method was applied to improve the identification of inclusions. The results confirmed a heterogeneous microstructure consisting of nonmetallic inclusions dispersed within a Fe-based matrix. The size of the inclusions ranged from a few micrometers up to approximately 40 µm. The majority of inclusions were found in the 10–25 µm range. SEM analysis showed that the inclusions predominantly exhibit irregular and globular morphologies. EDS results confirmed that they consist of manganese sulfides (MnS), oxides (Al2O3 and SiO2), and complex multi-phase particles. The elevated sulfur (S) (0.12 wt.%) and copper (Cu) (0.81 wt.%) contents in the investigated samples indicate that deoxidation and modification processes were not sufficiently effective. The deep learning-based model enabled accurate separation of non-metallic inclusions from the alloy composition. According to the model results, inclusions ≥30 µm primarily represent the oxide phase fraction, while fine inclusions <20 µm represent the sulfide phase fraction.

Original languageEnglish
Pages (from-to)116-123
Number of pages8
JournalActa Metallurgica Slovaca
Volume32
Issue number2
DOIs
StatePublished - 2026

Keywords

  • deep learning
  • inclusion size distribution
  • metallurgical cleanliness
  • non-metallic inclusions
  • oxide and sulfide
  • scrap-based

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