TY - JOUR
T1 - Formation and characterization of non-metallic inclusions in steel produced by melting secondary ferrous scrap in an electric arc furnace
T2 - A case study of AISI 1066 steel
AU - Kholmirzaev, Nozimjon
AU - Turakhodjaev, Nodir
AU - Khasanov, Jamshidbek
AU - Yang, Chong
AU - Yusupov, Bekzod
AU - Saidkhodjaeva, Shokhista
AU - Bektemirov, Abdujalol
AU - Sadikova, Nargiza
AU - Nurdinov, Zokirjon
AU - Yusupov, Nuriddin
AU - Juraboev, Davronbek
AU - Nurmurodov, Bobomurod
N1 - Publisher Copyright:
© 2026 SciCell s.r.o.. All rights reserved.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - deep learning
KW - inclusion size distribution
KW - metallurgical cleanliness
KW - non-metallic inclusions
KW - oxide and sulfide
KW - scrap-based
UR - https://www.scopus.com/pages/publications/105044728083
U2 - 10.36547/ams.32.2.2284
DO - 10.36547/ams.32.2.2284
M3 - 文章
AN - SCOPUS:105044728083
SN - 1335-1532
VL - 32
SP - 116
EP - 123
JO - Acta Metallurgica Slovaca
JF - Acta Metallurgica Slovaca
IS - 2
ER -