Non-metallic Inclusions In Steel Research Articles

Three-Dimensional Imaging of Non-metallic Inclusions in Steel Using Ionoluminescence

AbstractObtaining information on the morphology, size, distribution, and chemical composition of non-metallic inclusions in steel helps control the steel production process and the quality of the steel products. Two-dimensional analysis is commonly used to acquire this information; however, more accurate data can be obtained through three-dimensional analysis, leading to a better control of the quality of steel products and their production process. Currently, several techniques are proposed for the three-dimensional analysis of non-metallic inclusions; however, they are time consuming. Herein, the author presented a method to rapidly obtain three-dimensional images of non-metallic inclusions in steel using ionoluminescence (IL). A three-dimensional image of MgO·Al2O3 spinel inclusions was constructed based on two-dimensional IL images obtained every 10 min during argon–ion bombardment. The proposed IL imaging can cover an oval-shaped area of 1.17 mm × 0.26 mm on the semi-major and in the semi-minor axes, respectively, at a single measurement. Three-dimension images of MgO·Al2O3 spinel inclusions with sizes more than 20 μm can be obtained within 4 hours. Therefore, the IL imaging proposed here can provide a precise and rapid account of the effects of non-metallic inclusions on steel products and the steel production process.

Study on Influence of Rare Earth Ce on Micro and Macro Properties of U75V Steel.

Non-metallic inclusions in steel have great influence on the continuity of the steel matrix and the mechanical properties of steel. The precipitation sequence of Ce inclusions in molten steel is predicted by thermodynamic calculations. The results show that Ce content will affect the precipitation sequence of rare earth inclusions in molten steel, and the formation of CeO2, Ce2O3 and CeAlO3 will be inhibited with the increase in Ce content. Our laboratory smelted the test steel without rare earth additive and the test steel with rare earth Ce additive (0.0008%, 0.0013%, 0.0032%, 0.0042%). It was found that the MnS inclusions and inclusions containing Al, Ca, Mg and Si oxides or sulfides in the steel after rare earth addition were modified into complex inclusions containing CeAlO3 and Ce2O2S. The size of inclusion in steel was reduced and the aspect ratio of inclusion was improved. The addition of Ce also improved the grain size of U75V steel and significantly refined the pearlite lamellar spacing. After mechanical property testing of the test steel, it was found that when Ce is increased within 0.0042%, the tensile and impact properties of U75V steel are also improved.

Relation of nonmetallic inclusions to the cyclic properties of steel 42CrMo4 after steel melt cleaning by filtration and related processes

Nonmetallic inclusions (NMIs) detrimentally influence the steel properties. The application of metal melt filter systems promises to reduce NMIs in steel. In the current work, NMIs are analyzed from metallographic sections after metal melt filtration or cleaning by a related process of the steel 42CrMo4 using immersion filtration, flow-through filtration as well as filters floating on top of the melt. Analyses by automated scanning electron microscopy (ASPEX) as well as light optical microcopy (LOM) revealed the chemistry, size distribution, and morphology of NMIs. The filter systems had a significant effect on these characteristics of NMIs. The threshold value ΔKth against fatigue crack propagation was found to depend on the amount of NMIs within the steel. A higher amount of NMIs caused less crack deflection leading to less roughness-induced crack closure and in turn resulted in lower threshold values at a stress ratio of R = 0.1. The comparison of the present analysis of NMIs with fatigue data from previous work indicated that the analysis of NMIs from metallographic sections can be used to approximate the fatigue strength of a batch in the same way as the analysis of NMIs from fatigue fracture surfaces for single specimens, as suggested by Murakami.

Modification of Non-metallic Inclusions in Steel by Calcium Treatment: A Review

Modification of Non-metallic Inclusions in Steel by Calcium Treatment: A Review

Modeling the Precipitation of Aluminum Nitride Inclusions during Solidification of High‐Aluminum Steels

High‐aluminum advanced high‐strength steels have received increasing interest due to their excellent combination of strength and ductility. The control of nonmetallic inclusions in steel is among the most important issue for the production of high‐aluminum steel. This study proposes a model for the evolution of size distributions of AlN inclusions during solidification of steel based on the Kampmann–Wagner numerical model and microsegregation calculation. Both homogeneous (pure AlN) and heterogeneous precipitations (AlN+oxide or MnS) are described using homogeneous nucleation and free growth models of Greer et al. respectively. The model is applied to calculate the size distributions of AlN in high‐Al (up to 5%) Fe–Cr–Al steels and high‐ and medium‐manganese steels and validated by the experimental data in the literature. The model correctly describes the influence of cooling rates on the precipitation of AlN in Fe–Cr–Al steels and high‐manganese steels, and the sizes of AlN are reduced and the number densities of AlN inclusions are increased by increasing the cooling rate. The effects of nitrogen and aluminum concentration in medium‐manganese steel on inclusion precipitation are investigated by the model calculation. The increases in nitrogen and aluminum concentration facilitate the homogeneous precipitation of AlN inclusions.

Thermodynamics of the Formation of Non-Metallic Inclusions during the Deoxidation of GCr15 Bearing Steel

The non-metallic inclusions in steel mainly come from the deoxidization process of molten steel. Based on the different deoxidization processes of GCr15 bearing steel produced by a domestic steel plant, the single deoxidization curves of Al, Si, and Mn and the compound deoxidization curves of Al-Si and Si-Mn under pure molten iron and GCr15 bearing steel production conditions were calculated with the FactSage 8.1 thermodynamic software. The results show that the deoxidization curves under the two conditions are quite different. At the same time, the Al-Ti deoxidation equilibrium curve under the condition of GCr15 bearing steel was calculated. It was found that when [Ti] < 0.0015% in the steel, the equilibrium deoxidation product was Al2O3, and no titanium oxide was generated. Finally, a thermodynamic calculation and analysis of the effect of rare-earth Ce content on the modification of inclusions in GCr15 bearing steel were carried out. The results showed that when T = 1873 K, the rare-earth Ce treatment of aluminum deoxidized the GCr15 bearing steel; when [Al] = 0.015%, Al2O3 inclusions increased with the increase in [Ce] content, and the evolution path was Al11O18Ce→CeAlO3→Ce2O3. When [Si] = 0.28%, [O] > 50 ppm, and CeCrO3 and [O] 0.012% appeared. CeS inclusions appeared in the steel, and the final equilibrium product was CeS + Ce2O3. When [Ce] < 0.012%, CeS was not produced in the steel, and only Ce2O3 existed.

Recyclate-containing magnesia-carbon refractories – Influence on the non-metallic inclusions in steel

The fabrication of MgO–C refractories containing MgO–C recyclates is a current approach for saving resources and energy. However, it must be ensured that the steel quality is not adversely affected, particularly with regard to non-metallic inclusions. The present study investigates MgO–C refractories containing different proportions of MgO–C recyclates (up to 83%) in contact with 16MnCr5 steel melt and a slag. A recyclate-free MgO–C material served as a reference. The tests were performed in a steel-casting simulator. By an automated feature analysis of the solidified steel, manganese sulfide and complex alumina-manganese sulfide inclusions were found to be the predominant ones in all steel samples. No negative influence of the recyclate on the inclusion count and the size distribution of the inclusions, the latter being determined for the sum of alumina and alumina-manganese sulfide as well as for the manganese sulfide inclusions, was observed.

Phase diagrams of oxide system FeO–MnO–MgO–SiO2. Report 1. Binary oxide systems MnO–SiO2 and MgO–SiO2

Manganese and silicon are present in almost any steel, and magnesium can enter the metal from the lining and magnesian slags or be introduced in the form of an alloy, so knowledge of the thermodynamics and phase equilibria of liquid and solid silicate solutions allows to predict the appearance of non-metallic inclusions in steel. The FeO–MnO–MgO–SiO2 system includes six double and four triple phase diagrams of oxide systems. The binary phase diagrams of FeO–MnO, FeO–MgO, FeO–SiO2, and MnO–MgO oxide systems were previously studied. In this work, the phase diagrams of the MnO–SiO2 and MgO–SiO2 systems were considered, the liquidus lines and thermodynamic data on the melting of manganese and magnesium silicates – the enthalpies and entropies of melting of ortho- and metasilicates of manganese (Mn2SiO4 and MnSiO3) and magnesium (Mg2SiO4 and MgSiO3) were calculated. The theory of subregular ionic solutions, which takes into account the dependence of the coordination number on the melt composition, is used to calculate the activities of the components of the oxide melt and accurately describes the diagrams with two immiscible liquids and is consistent with the experimental data presented in the literature. The obtained data on the energy parameters of the theory of subregular ionic solutions for the systems MnO–SiO2 and MgO–SiO2, as well as the thermodynamic data on the formation of manganese and magnesium silicates, make it possible to proceed to the study of ternary oxide systems belonging to the FeO–MgO–MnO–SiO2 system.

Phase diagrams of oxide system FeO–MnO–MgO–SiO2. Report 1. Binary oxide systems MnO–SiO2 and MgO–SiO2

Manganese and silicon are present in almost any steel, and magnesium can enter the metal from the lining and magnesian slags or be introduced in the form of an alloy, so knowledge of the thermodynamics and phase equilibria of liquid and solid silicate solutions allows to predict the appearance of non-metallic inclusions in steel. The FeO–MnO–MgO–SiO2 system includes six double and four triple phase diagrams of oxide systems. The binary phase diagrams of FeO–MnO, FeO–MgO, FeO–SiO2, and MnO–MgO oxide systems were previously studied. In this work, the phase diagrams of the MnO–SiO2 and MgO–SiO2 systems were considered, the liquidus lines and thermodynamic data on the melting of manganese and magnesium silicates – the enthalpies and entropies of melting of ortho- and metasilicates of manganese (Mn2SiO4 and MnSiO3) and magnesium (Mg2SiO4 and MgSiO3) were calculated. The theory of subregular ionic solutions, which takes into account the dependence of the coordination number on the melt composition, is used to calculate the activities of the components of the oxide melt and accurately describes the diagrams with two immiscible liquids and is consistent with the experimental data presented in the literature. The obtained data on the energy parameters of the theory of subregular ionic solutions for the systems MnO–SiO2 and MgO–SiO2, as well as the thermodynamic data on the formation of manganese and magnesium silicates, make it possible to proceed to the study of ternary oxide systems belonging to the FeO–MgO–MnO–SiO2 system.

Effect of Mg-Ce Treatment on Inclusion Characteristics and Pitting Corrosion Behavior in EH420 Marine Steel

Reasonable regulation of nonmetallic inclusions in steel can significantly improve its strength, toughness, and corrosion resistance. In this paper, EH420 marine steel was treated with Mg, Ce, and Mg-Ce to modify the inclusions. The effects of different treatments on the morphology, composition, size distribution, induced intragranular ferrite (IGF) nucleation, and pitting resistance of inclusions were systematically analyzed using various methods. The results show that the Mg-Ce composite treatment can modify irregular MgAl2O4 inclusions into spherical Mg-Ce-O composite inclusions and MgO-dominated inclusions. The density of inclusions is increased from 74.8/mm2 to 186.0/mm2, and the average size of inclusions is decreased from 2.60 μm to 1.07 μm. The Mg-Ce-O composite inclusions are effective inclusions for inducing IGF. Furthermore, the pitting potential is increased from −503 mV to −487 mV, and the corrosion rate is decreased. The order of average electronic work function is ΦMgO < ΦCe2O3 < Φα-Fe < ΦAl2O3. Ce2O3 is hard to induce pitting corrosion due to its similar electronic work function to the steel matrix. Thus, the Mg-Ce composite treatment is better than that for Mg and Ce treatment alone, and has better application prospects.

Different Approaches to Trace the Source of Non-Metallic Inclusions in Steel

Different Approaches to Trace the Source of Non-Metallic Inclusions in Steel

Effect of Cerium on Inclusion Modification in a Secondary-Hardening Steel.

Owing to the continuous increasing of steel strength, mechanical properties including toughness and fatigue performance are becoming increasingly sensitive to inclusions in ultra-high strength steel. Rare-earth treatment is considered as an effective method to reduce the harmful effects of inclusions, but is rarely applied in secondary-hardening steel. In the present study, different amounts of cerium were added in a secondary-hardening steel to investigate the modification effect of Ce on non-metallic inclusions in steel. The characteristics of inclusions were observed experimentally using SEM-EDS and the modification mechanism was analyzed based on thermodynamic calculations. The results indicated that the main inclusions in Ce-free steel are Mg-Al-O + MgS. Thermodynamic calculation indicated that MgAl2O4 is firstly formed in liquid steel and then successively transformed into MgO and MgS during cooling process. When the Ce content is 0.0030%, the typical inclusions in steel were individual Ce2O2S and MgO + Ce2O2S complex inclusions. When the Ce content was increased to 0.0071%, the typical inclusions in steel were individual Ce2O2S- and Mg-containing inclusions. Ce treatment modifies the angular magnesium aluminum spinel inclusions into spherical and ellipsoidal Ce-containing inclusions, thus reducing the harmful effect of inclusion on steel properties.

Properties of typical non-metallic inclusions in steel: First-principles calculations

Properties of typical non-metallic inclusions in steel: First-principles calculations

Research on Intelligent Identification and Grading of Nonmetallic Inclusions in Steels Based on Deep Learning.

Non-metallic inclusions are unavoidable defects in steel, and their type, quantity, size, and distribution have a great impact on the quality of steel. At present, non-metallic inclusions are mainly detected manually, which features high work intensity, low efficiency, proneness to misjudgment, and low consistency of results. In this paper, based on deep neural network algorithm, a small number of manually labeled, low-resolution metallographic images collected by optical microscopes are used as the dataset for intelligent boundary extraction, classification, and rating of non-metallic inclusions. The training datasets are cropped into those containing only a single non-metallic inclusion to reduce the interference of background information and improve the accuracy. To deal with the unbalanced distribution of each category of inclusions, the reweighting cross entropy loss and focal loss are respectively used as the category prediction loss and boundary prediction loss of the DeepLabv3+ semantic segmentation model. Finally, the length and width of the minimum enclosing rectangle of the segmented inclusions are measured to calculate the grade of inclusions. The resulting accuracy is 90.34% in segmentation and 90.35% in classification. As is verified, the model-based rating results are consistent with those of manual labeling. For a single sample, the detection time is reduced from 30 min to 15 s, significantly improving the detection efficiency.

Identifying Oxide and CaS Non-Metallic Inclusions in Steel with Raman Spectroscopy

Identification and characterization play an essential role in the study of non-metallic inclusions (NMIs) in monitoring the steel casting process and product quality. This study uses Raman spectroscopy, a versatile, relatively fast, and non-destructive technique for characterizing oxide and sulfide-based inclusion phases in steel samples. The relative Raman peak intensities and positions were used to qualitatively estimate the inclusion phases of Al2O3, CA, C12A7, C3A, MgO·Al2O3, and CaS. For a quantitative estimation, FESEM-EDS (field emission scanning electron microscope with energy dispersive X-ray spectroscopy) was used to evaluate the inclusion phases in the samples. This study demonstrates that Raman spectroscopy can be used to rapidly estimate the content of Al2O3, magnesium aluminate spinel (MgO·Al2O3), and the calcium aluminate phases of CA, C3A, and C12A7 in steel samples to complement established characterization techniques for NMIs analysis such as FESEM-EDS.

THERMODYNAMIC MODELING OF PHASE EQUILIBRIA DURING DEOXIDATION OF THE LOW AND MEDIUM CARBON STEELS BY SILICOMANGANESE

To create digital twins of steelmaking production, it is necessary to develop programs for calculating all stages of smelting and secondary treatment of steel and alloys, including the process of deoxidation. The aim of this work was to construct a phase stability diagram in the form of a surface of solubility of components in a metal (SSCM) of the Fe–Si–Mn–O–C system for carbon concentrations [C] = 0; 0.1; 0.4 wt. % and a temperature of 1600 °C. SSCM will make it possible to establish the stability boundaries of non-metallic inclusions (NI) formed during deoxidation and the degree of deoxidation of low- and medium-carbon steel, depending on the introduced concentrations of silicon and manganese. It is noted that in the system under study, during absence of carbon ([C] = 0 wt. %) an oxide melt of variable composition (FeO, MnO, SiO2) with a minimum concentration of iron oxide prevails as nonmetallic inclusions. With the appearance of carbon in the melt, the region of equilibrium of the liquid metal with the gas phase {CO, CO2} was established, and with an increase in the concentration of carbon, this region increased. For low-carbon steels ([C] = 0.1 wt. %) at industrially significant concentrations of silicon (0.1–0.3 wt. %) and manganese (about 1.0 wt. %), NI in steel also represent oxide melt. In medium-carbon steels ([C] = 0.4 wt. %) at the same silicon and manganese concentrations, the deoxidation product is in the gas phase. In the course of modeling, the isotherms of oxygen solubility in the system under study were also constructed. The simulation results are in good agreement with the literature data. The constructed SSCM can be used to create a digital twin of the deoxidation process of steels.

Deformation of Non-metallic Inclusions in Steel during Rolling Process: A Review

Deformation of Non-metallic Inclusions in Steel during Rolling Process: A Review

Classification of Nonmetallic Inclusions in Steel by Data‐Driven Machine Learning Methods

Nonmetallic inclusions have strong influence on final steel properties. An important characterization tool to make a comprehensive analysis of nonmetallic inclusions is the scanning electron microscope equipped with energy‐dispersive spectroscopy (SEM‐EDS). A major drawback which prevents its use for online‐steel assessment is the time taken for analysis. Machine learning methods have been previously introduced which circumvents the usage of the EDS for obtaining chemical information of the inclusion by classifying inclusion based on their back scatter electron images. This study introduces a method based on a simpler tabular data input consisting of morphological and mean gray values of inclusions. Naive Bayes and Support Vector Machine classifier models are built using the R statistical programming language. Two steel grades are considered for this study. The prediction results are shown to be satisfactory for both binary (maximum 89%) and 8‐inclusion class (maximum 61%) categorization. The input dataset is further improved by optimizing the image settings to distinguish the different types of nonmetallic inclusions. It is shown that this improvement results in a higher rate of correct predictions for both binary (maximum 98%) and 8‐class categorization (maximum 81%).

Influence of Pulse Current on Inclusion Properties of Alumina in Molten Steel

In the process of metal purification, the study of controlling the morphology and size distribution of non-metallic inclusions in steel based on electric fields has attracted extensive attention. However, the action mechanism of electric fields on inclusions in liquid steel has not been clearly identified. In this paper, the influence of pulse current on the quantity, size, and distribution of alumina inclusions in liquid steel was explored through a high-temperature energization experiment, and the action mechanism of pulse current was expounded based on the angle of interface free energy. The results show that the number of small alumina inclusions in the steel after electric pulse treatment (the pulse current density was 0.1–3 A/cm2, the frequency was 100 Hz, and the pulse waveform was a square wave) was significantly increased compared with the sample without electric pulse treatment, and the particle size was concentrated at 2–5 μm, and the inclusions were concentrated in the upper and lower parts of the sample. However, in the samples without electrification treatment, the large particles of inclusions are the majority, the particle size was concentrated at about 10 μm, and the inclusions were concentrated in the upper and middle parts of the samples. When the pulse current was applied to the molten steel, the interfacial free energy of the internal system of liquid steel alumina inclusion was reduced, the nucleation of alumina inclusion was promoted, the aggregation growth between alumina particles was inhibited, and then the migration and distribution of inclusions were also affected. The experimental results could provide technical support for high-quality steel smelting production to a certain extent.

Y2O3‒SiO2 SYSTEM DIAGRAM

The deoxidizing ability of yttrium has not been sufficiently studied, although it has a high affinity for oxygen. In ferrous metallurgy, yttrium is often used in the form of an alloy with silicon. The effect of each of the components of the silicoyttrium alloy on the process of liquid metal deoxidation can be established by studying the Fe‒Y‒Si‒O system. The composition of the formed non-metallic inclusions in steel in this case is mainly determined by the type of oxide state diagrams of the Y2O3‒SiO2 and FeO‒Y2O3‒SiO2 systems associated with the region of existence of metallic melts. In this work, using the method of thermodynamic modeling of phase equilibrium in oxide system state diagrams and the theory of subregular ionic solutions, we analyze and calculate liquidus lines in the Y2O3‒SiO2 oxide system. A diagram of the state of this system was constructed. According to the thermodynamic analysis, the most stable in the system are three compounds ‒ oxy- Y2O3·SiO2 and yttrium orthosilicate 2Y2O3·3SiO2 (congruent melting, 1979 and 1915 °C, respectively), as well as yttrium pyrosilicate Y2O3·2SiO2 (incongruent melting), which is consistent with the literature data. In the course of the work, the enthalpies and entropies of formation of these yttrium silicates from the components of the oxide melt, as well as the melting constants of these compounds, were obtained. The area of delamination borders on the field of cristobalite at 1668 °C in the concentration range 0.593‒0.980 (ionic fraction of SiO2) or 74.5‒99.0 % (mol.) SiO2. The values of the enthalpy and entropy of yttrium silicates obtained in this work allow us to proceed to the next stage of the study – modeling the state diagram of the ternary oxide system FeO‒Y2O3‒SiO2, which is necessary to consider the interaction of yttrium and silicon with oxygen in liquid iron.