Steel Processing Research Articles

Study on the Causes and Control Measures of Mg–Al Spinel Inclusions in U75V Heavy Rail Steel

U75V heavy rail steel production uses an aluminum-free deoxidation process; however, large particles of MgO–Al2O3 inclusions form in the steel, which has a great impact on product quality. In this paper, we try to explain how spinel inclusions, which affect the metallurgical quality of heavy rail steel, are produced by thermodynamic and experimental methods, and then determined measures for avoiding such inclusions. The formation mechanism of spinel inclusions in U75V heavy rail steel was determined through the analysis of nozzle clogging in the pouring process and typical inclusions in steel. The results show that there are two types of spinel inclusions in heavy rail steel: one is pure Mg–Al spinel inclusions and the other is Mg–Al spinel inclusions coated with calcium aluminate. The small, pure Mg–Al spinel inclusions were precipitated during the solidification of the molten steel, and the precipitation temperature was related to the composition of the molten steel. The large spinel inclusions were derived from clogging of the submersed nozzle. Mg–Al spinel inclusions coated with calcium aluminate were transformed from CaO–SiO2–Al2O3–MgO complex inclusions in the steel during cooling, and the formation temperature was related to the content of Al2O3 and MgO in the inclusions. The content of Al2O3 and MgO in the inclusions was the key to the formation of the Mg–Al spinel inclusions. Therefore, in order to control the production of spinel inclusions in steel, it is necessary to strictly control the content of impurity elements such as magnesium and aluminum in the alloy auxiliary materials, to reduce the secondary oxidation of liquid steel and to reduce the erosion of refractory materials.

Multi-Physics Modeling of Melting-Solidification Characteristics in Laser Powder Bed Fusion Process of 316L Stainless Steel.

In the laser powder bed fusion process, the melting-solidification characteristics of 316L stainless steel have a great effect on the workpiece quality. In this paper, a multi-physics model was constructed using the finite volume method (FVM) to simulate the melting-solidification process of a 316L powder bed via laser powder bed fusion. In this physical model, the phase change process, the influence of temperature gradient on surface tension of molten pool, and the influence of recoil pressure caused by the metal vapor on molten pool surface were considered. Using this model, the effects of laser scanning speed, hatch space, and laser power on temperature distribution, keyhole depth, and workpiece quality were studied. This study can be used to guide the optimization of process parameters, which is beneficial to the improvement of workpiece quality.

Research on strength and toughness matching of 1.3GPa ultralow temperature resistant bulb flat steel

In this study, an ultralow temperature resistant bulb flat steel with ultra-high strength and good toughness is prepared to meet the application requirements of hull structural steel for polar ships and large ships. The bulb flat steel with a yield strength of 1292 MPa, elongation of 18.5 % and an impact energy of 53 J at − 196 °C is obtained by quenching and tempering process. In order to reveal the tempering response and strengthening mechanism during the quenching and tempering process of bulb flat steel, multi-scale characterization is carried out to explore the microstructural evolution and nano-precipitation behavior of the ultralow temperature bulb flat steel under different tempering processes. The results indicate that the preferential precipitation of (V, Nb)(C, N) causes dislocation multiplication, and the higher dislocation density in the matrix provides more nucleation sites for M2C. The slower diffusion rate of Mo effectively suppresses the rapid coarsening of M2C, which keep a good coherent relationship between the M2C and the α-Fe matrix. Strain concentration is avoided by synergizing the (V, Nb)(C, N) precipitation and the M2C precipitation of the coherent lattice, thus enabling the ultralow temperature resistant bulb flat steel to maintain ultra-high strength with good plasticity and toughness.

Welding Processing of Medium-Manganese Austenitic Steels for Cryogenic Applications

Abstract For several years, the significance of gaseous energy sources (e. g. liquified natural gas and hydrogen) has been increasing worldwide due to environmental and climate policy requirements. Storage and transportation of the liquids occur under cryogenic conditions. This results in specific requirements for the mechanical properties of the materials used at cryogenic temperatures. Nowadays, cold-tough, high-nickel austenites and martensitic steels of type X8Ni9 are used for such purposes. While austenitic materials offer good processing properties, they are not attractive due to their comparatively low strength and high costs. Welding martensitic steel with commonly used nickel-based additives significantly impacts processing quality and process automation due to high magnetic remanence. Additionally, the increased requirements for the storage of liquid hydrogen regarding low-temperature toughness push the conventional low-temperature materials to their limits. A potential solution to the identified challenges can be achieved by using medium- and high-manganese austenitic steels. Within the scope of this work, the medium-manganese steel X2CrMnNiN1775 (1.4371) is investigated as an economical substitute for the conventionally used materials in cryogenic applications. Considering the relevant qualification requirements for welded joints and welding additives, submerged arc welded joints are investigated and their applicability under cryogenic operating temperatures is demonstrated.

Integration of Artificial Intelligence into Metallography: Area-wide Analysis of Microstructural Components of a Jominy Sample

Abstract Analysing the microstructure is an essential part of quality control in many steel manufacturing and processing operations. In this work, a promising method for autonomous analysis of microstructures in low-alloy steels based on artificial intelligence image analysis is presented. This study focuses on the classification of different microstructure components in metallographic images of steel microstructures using a Deep Convolutional Neural Network (DCNN) model. Since the accuracy of the model strongly depends on the size of the data set, a data set consisting of two million optical microscopy images was created to ensure the presence of different microstructure components and their combinations for training the system. The Jominy test was performed to verify the accuracy and capability of the microstructure analysis software. The AI makes it possible to analyse large amounts of image data with high precision and at the same time with less effort than conventional methods of microstructure components analysis.

Sustainable Ironmaking Toward a Future Circular Steel Economy: Exploiting a Critical Oxygen Concentration for Metallurgical Cu Removal from Scrap‐Based Melts

A circular steel economy based on recycling scrap is severely hampered by the increasing accumulation of Cu returning from more and more electrified products, which severely limits processing, application, and safety of steels. As of yet, no viable strategies for its removal have been developed, and the increasing Cu contamination can only be diluted with fresh primary iron. This is not only evoking CO2 emissions from conventional reduction processes, but also merely delays the problem until global demands allow for a circular steel economy. However, the ongoing transformation toward green steel making may offer pathways to overcome this complex metallurgical challenge. It is demonstrated that Cu can be effectively evaporated from Fe–Cu–O melts—representing Fe ore mixed with Cu‐contaminated steel scrap—during hydrogen plasma‐based smelting reduction. This evaporation is found to be strongly influenced by the Cu activity determined by the concentration of oxygen in the liquid, with a critical O concentration of about 22 wt%. Even without the presence of hydrogen, Cu concentrations can thereby be drastically reduced from 1 to less than 0.1 wt%. Potentials and challenges for leveraging these fundamental findings on a laboratory scale for future industrial production of green steel are outlined and discussed.

Prediction of Mechanical Properties of Cold-Rolled Steel Based on Improved Graph Attention Network

The prediction of mechanical properties of cold-rolled steel is very important for the quality control, process optimization, and cost control of cold-rolled steel, but it is still a challenging task to predict accurately. For the existing graph structure of graph attention networks, it is difficult to effectively establish the complex coupling relationship and nonlinear causal relationship between variables. At the same time, it is considered that the process of cold-rolled steel has typical full-flow process characteristics and the graph attention network makes it difficult to extract the path information between the central node and its higher-order neighborhood. The neural Granger causality algorithm is used to extract the latent relationship between variables, and the basic graph structure of mechanical property prediction data is constructed. Secondly, the node embedding layer is added before the graph attention network, which leverages the symmetry nature of Node2vec method by incorporating both breadth-first and depth-first exploration strategies. This ensures a balanced exploration of diverse paths in the graph, capturing not only local structures but also higher-order relationships. The combined graph attention networks are then able to effectively capture the symmetry path information between nodes and dependencies between variables. The accuracy and superiority of this method are verified by experiments in real cold-rolled steel production cases.

Effect of Nitrogen‐Added Shielding Gas on Microstructure Evolution of Welded Joints by Gas Tungsten Arc Welding Process in Duplex Stainless Steel 2205

Duplex stainless steels (DSS), due to their high corrosion resistance, primarily attributed to their austenite‐ferrite duplex microstructure, are widely used in industrial sector such as petrolchimical and gas natural industries. Maintaining this microstructure is crucial during manufacturing processes, including welding which presents the challenge of minimizing ferritization in the heat‐affected zone (HAZ) or weld zone (WZ), as it can significantly reduce their corrosion resistance. This work aims to describe the influence of nitrogen gas in the Ar–N2 shielding atmosphere when depositing beads on 2205 DSS plates, using a gas tungsten arc welding (GTAW) process. It is observed that at low nitrogen load (98%Ar–2%N2), the austenite content reaches 49% in WZ and 29% in HAZ. When the nitrogen percentage is increased (50%Ar–50%N2), the austenite content rises to 67% and 37%, respectively. The joint is evaluated macro‐ and microstructurally to observe the atmosphere impact on weld bead geometry and microstructural development. Additionally, hardness is assessed throughout the weld bead. The work demonstrates that, with a 50%Ar–50%N2 shielding gas mixture, it is possible to limit the formation of ferrite in the WZ and HAZ of DSS during the welding process, without altering the microhardness trend of the welded joint.

Superhydrophobicity on AISI 304 stainless steel through surface sensitization process and etching

In this research, the surface sensitization process of austenitic stainless steel (304) was used to create the superhydrophobicity through a facile etching method with a significantly lower etching time. In order to utilize the surface sensitization process for the purpose of forming island-like chromium carbide on surface, oxyacetylene flame heat-treatment was performed at 650 °C for one min. After HF etching at ambient temperature, treatment in nitric acid, and the reduction of surface energy by stearic acid modification, a water contact angle (WCA) of 167°, water sliding angle (WSA) of 3°, and water contact angle hysteresis (WCAH) of 7° have been achieved. The sensitization process makes the sample susceptible to intergranular corrosion and uniform micro- and nano-sized pores entirely cover the surface after boiling in nitric acid. This phenomenon is the main reason for creating the hierarchical microstructure on the surface of stainless steel in a shorter time.

The Effect of Chemical Composition on the Microstructure and Properties of Multicomponent Nickel-Based Boride Layers Produced on C45 Steel by the Hybrid Method

Layers of iron–nickel Fe-Ni-B-type borides were produced on C45 steel using a new hybrid treatment variant which combines boriding under glow discharge conditions with galvanic nickel precoating. The aim was to investigate whether these layers could constitute an alternative to the previously developed multicomponent Fe-Ni-B-P-type layers produced by a hybrid treatment variant using chemical nickel precoating. The basis for assessing the effects of both alternative treatments was a comparative analysis of the microstructure and performance properties of three model boride layers: iron–nickel boride layers of the Fe-Ni-B and Fe-Ni-B-P types, and reference iron Fe-B-type boride layer. It was demonstrated that the new variant of hybrid treatment produces Fe-Ni-B layers with the highest thickness, slight porosity, the optimal structure of Ni2B boride in the near-surface zone and the best performance properties. These layers show good adhesion, a much higher hardness of 2200 HV0.05 and near-surface compressive stresses of −450 MPa. Fe-Ni-B-P layers show slightly better wear resistance for higher loads, but like Fe-B layers, they are susceptible to spalling. It was demonstrated that Fe-Ni-B layers produced using boriding with nickel galvanic steel precoating could find application in heavy-duty elements of nanobainitic steel processing.

Computational welding simulation of a plasma wire arc additive manufacturing process for high-strength steel

Computational welding simulation of a plasma wire arc additive manufacturing process for high-strength steel

Modeling surface roughness profiles generated by the belt finishing process of a 27MnCr5 carburized steel

Belt finishing is a superfinishing process that enables to reach fine surface roughness. This paper proposes a numerical model, predicting belt finishing roughness profiles of a 27MnCr5 carburized steel. It utilizes belt topographies that are then numerically deformed thanks to a specific morphological filter. A method for determining the morphological filter parameters is proposed and depends on the size of the grains. Then, the deformed topography of the belt virtually scratches the workpiece at a given depth of indentation (computed by a rough contact model). The model predicts surface roughness profiles in the steady state regime. The paper shows a reasonable agreement between experimental and numerical results. The model captures the main trends: the higher the grain size the higher the roughness of the workpiece. Computational time needed for this model is a few minutes, making it a useable solution for the industry.

Corrosion behavior of Q345 steel in a simulated industrial atmosphere

Abstract The corrosion process of Q345 steel in simulated industrial atmospheric surroundings using NaHSO3 as the corrosion medium was performed. Corrosion weight loss, X-ray diffraction (XRD), electron probe microanalysis (EPMA), laser scanning confocal microscopy (LSCM), and scanning electron microscopy (SEM) were utilized to investigate the corrosion behavior of Q345 steel samples at various corrosion times. The results indicate that, in the industrial atmospheric acceleration system, the corrosion law of Q345 steel follows an exponential function model and that as the corrosion period lengthens, the rate of corrosion declines steadily. γ-FeOOH, α-FeOOH, Fe3O4, Fe3O2, and FeO make up the majority of the corrosion products on the surface of rust layer, and the rust layer structure is generally loose in the first stages of corrosion. As the corrosion time lengthens, the corrosion products transform from needle-like to cluster-like and the rust layer will become thicker. In addition, there is a segregation of the elements Cr, S, and O in the rust layer of Q345 steel.

Numerical Simulation of Fluid Flow, Solidification, and Solute Distribution in Billets under Combined Mold and Final Electromagnetic Stirring.

In this study, a three-dimensional segmented coupled model for continuous casting billets under combined mold and final electromagnetic stirring (M-EMS, F-EMS) was developed. The model was verified by comparing carbon segregation in billets with and without EMS through plant experiments. The findings revealed that both M-EMS and F-EMS induce tangential flow in molten steel, impacting solidification and solute distribution processes within the billet. For M-EMS, with operating parameters of 250A-2Hz, the maximum tangential velocity (velocity projected onto the cross-section) was observed at the liquid phase's edge. For F-EMS, with operating parameters of 250A-6Hz, the maximum tangential velocity occurred at fl=0.7. Furthermore, F-EMS accelerated heat transfer in the liquid phase, reducing the central liquid fraction from 0.93 to 0.85. M-EMS intensified the washing effect of molten steel on the solidification front, resulting in the formation of negative segregation within the mold. F-EMS significantly improved the centerline segregation issue, reducing carbon segregation from 1.15 to 1.02. Experimental and simulation results, with and without EMS, were in good agreement, indicating that M+F-EMS leads to a more uniform solute distribution within the billet, with a pronounced improvement in centerline segregation.

An improved toughness process for high-temperature hot-rolled HSLA steel via inclusion-induced acicular ferrite nucleation

A feasible method of enhancing the impact toughness (−20 °C) of high-temperature hot-rolled HSLA steels was proposed. The results show that the toughness of the improved hot-rolled steel containing TiOx-CaO-ZrO2 oxide particles was significantly enhanced by ∼132 J compared with conventional steel containing Al-Ca oxide particles. The fine (Ti, Ca, Zr) Ox particles with high-density of ∼937 counts/mm2 significantly promotes anti-cracking acicular ferrite (AF) nucleation. Moreover, the fractional volume of AF increased from ∼79 % to ∼85 % when the rolling temperature was increased from ∼1055 °C to ∼1085 °C.

Experimental and computational chemistry investigation of the molecular structures of new synthetic quinazolinone derivatives as acid corrosion inhibitors for mild steel

Two new organic compounds, namely, 3-(2-(2-chloroethoxy)ethyl)-2-phenylquinazolin-4(3H)-one (QZ-H) and 3-(2-(2-chloroethoxy)ethyl)-2-(4-chlorophenyl)quinazolin-4(3H)-one (QZ-Cl) were synthesized based on quinazolinone derivatives, and their chemical structures were identified using non-instrumental analytical methods such as proton and carbon nuclear magnetic resonance (1H NMR and 13C NMR). After successfully synthesizing and spectrally characterization, they were tested for mild steel protection in 1.0 M HCl medium. In addition, their anticorrosion characters and molecular structure-activity relationship were evaluated using the classical electrochemical and theoretical methods, respectively. Experimental studies showed that these compounds are good corrosion inhibitors, where their inhibition efficiencies increase with their concentrations to reach a maximum of 87.75 % and 88.76 % at 10−3 M of QZ-H and QZ-Cl, respectively. It is also found that these products react as mixed-type inhibitors. In addition, the estimated activation parameters indicate that the dissolution process of mild steel is endothermic and the products act via physical adsorption on metallic surface according to the Langmuir isotherm. Finally, the theoretical studies using DFT calculations and MD simulations authorize the experimental results and the order of inhibition efficiency.

Numerical Simulation of Mold Slag Entrapment Behavior in Nonoriented Silicon Steel Production Process

This paper is based on the surface defects of casting billets in the production process of nonoriented silicon steel plates at a steel plant in North China. Taking the parameters of a slab mold in the nonoriented silicon steel production process as a prototype, the flow field characteristics of the mold under the same section, different drawing speed and immersion depth were systematically studied by using a LES (large eddy simulation) and VOF (volume of fluid) coupling algorithm. The results show that under the current conditions, when the critical slag entrapment speed increases from 1.0 m/min to 1.2 m/min, the nozzle insertion depth increases linearly with the critical slag entrapment speed, while when the nozzle insertion depth exceeds 130 mm, the increasing effect of further increasing the nozzle insertion depth on the critical slag entrapment speed begins to decrease. When the drawing speed of continuous casting is kept constant at 1.4 m/min, the abnormal fluctuation height of the steel slag interface is significantly improved when the angle of the water nozzle is increased from 15° to 20°, and the proportion of slag entrapment is also reduced from 0.376% to 0.015%. When the nozzle angle is 25°, the slag entrapment ratio is reduced to 0%, and the steel slag interface also ensures a certain activity. The numerical simulation results were applied to the industrial site, and the slag inclusion rate and crack rate of the billet in the continuous casting process of nonoriented silicon steel were obviously improved after the optimization process.

Evolution of Complex Oxide Inclusions During the Smelting Process of Oxide Metallurgical Steel and Their Effect on Acicular Ferrite Nucleation

Evolution of Complex Oxide Inclusions During the Smelting Process of Oxide Metallurgical Steel and Their Effect on Acicular Ferrite Nucleation

Investigation of Surface Integrity of Conical Hole in Laser Polishing 440C Stainless Steel

In the machining process of 440C stainless steel using ball-end milling, the generation of machined traces is an inevitable outcome. Traditional polishing methods, when applied to the milling surfaces of small conical holes, exhibit significant limitations. However, the application of nanosecond pulsed laser polishing while maintaining dimensional integrity has proven effective in mitigating these issues. The parameters for the laser and scanning strategy were empirically determined through planar experiments. The results showed a reduction in surface roughness to a minimum value, representing a decrease of approximately 41.7%. Further validation experiments were conducted on the ball-milled surface of small conical holes. The rate of roughness reduction in these experiments surpassed that of planar surface polishing with an improvement of approximately 73.6%. The scanning strategy’s applicability was confirmed, and the post-polishing surface morphology was found to be largely in line with the prediction. The remelt layer and heat-affected zone resulting from nanosecond laser polishing were observed to be below 5 μm, significantly preserving the dimensional integrity of the conical hole’s internal surface. Moreover, nanosecond laser polishing was found to substantially enhance the surface hardness of the material, with an increase ranging from 100% to 180%. This study underscores the efficacy of nanosecond laser polishing in enhancing the internal surface of small conical holes, thereby improving the surface properties. Consequently, this method presents a reliable solution for the removal of machined traces from complex internal surfaces.

Novel Reliability Evaluation of Existing Structures Using Digital Image Processing and Random Finite Element Simulation

Reinforced concrete (RC) structures are susceptible to deterioration as a result of concrete cracking and steel corrosion. Evaluations of current RC structures are undertaken to ascertain adherence to building codes, the necessity for enhancements, or to rectify structural inadequacies. Reliability techniques are utilized to quantitatively assess the structural integrity of existing RC structures, taking into account the inherent uncertainties related to the applied loads and the resistance of the structure. The main difficulty in evaluating the safety of current RC structures, namely in determining the reliability index, is to formulate accurate resistance models. This problem is particularly pronounced when observable indications of structural deterioration, such as cracking and steel corrosion, are evident. The primary aim of this research is to implement a new computational framework for evaluating the structural integrity of RC elements. This framework utilizes digital image processing (DIP) techniques in conjunction with random finite element (RFE) simulation. In this approach, actual images of the structure under investigation are employed to construct finite element (FE) models, while random fields are utilized to represent the spatial variability in material properties. The recommended framework is proven to enhance the accuracy of the reliability estimate by mitigating the uncertainty associated with the concrete crack pattern and minimizing the uncertainty related to the corrosion process in steel.