Effect of Coiling Temperature on the Evolution of Texture in Ferritic Rolled Ti-IF Steel

The effect of coiling temperatures on the evolution of texture in Ti-IF steel during ferritic hot rolling, cold rolling and annealing was studied. It was found that texture evolution at high temperature coiling is absolutely different from that at low temperature one. The hot band texture includes a strong α-fiber as well as a weak γ-fiber after ferritic hot rolling and low temperature coiling. Both of them intensify after cold rolling and a γ-fiber with peak at {111}〈112〉 is the main texture of annealed samples. However, the main component of the hot band texture after high temperature coiling is v-fiber. After cold rolling, the intensity of γ texture reduces; α fiber （except {111}〈110〉 component） intensifies and a strong and well-proportioned γ-fiber forms in the annealed samples.

Research on flow stress in ferrite deformation of a Ti-IF steel

The experiments of the ferrite warm deformation of ultra-low carbon （ULC） Ti-IF steel were carded out on a hot simulator and the influences of deformation temperature, strain, and strain rate on the flow stress were analyzed. New flow stress models suitable to ferrite warm forming of Ti-IF steel were given on the basis of analyzing the influence of deformation technology parameters on the flow stress.

Annealing Texture Evolution of Ti-IF Steel during Batch Annealing

Texture development and recrystallization behavior of Ti-IF steel during Batch Annealing (BA) were investigared with X-ray diffraction and EBSP in lab, and the results were compared with research work on continu ous annealing (CA) with rapid heating rate. The basic tendencies are similar that eary nucleation takes place in <111>ND fibers, <110>RD fibers are consumed at the end of recrystallization, and <111>ND texture dominates over annealing texture. However, the detailed texture transformation during batch annealing is different and somewhat more compicated than tha in rapid-heating process. Moreover, misorientation plays an important role in texture transformation of BA In addition, the results of EBSP are consistent with that of ODF well.

Steel Surface Defect Recognition in Smart Manufacturing Using Deep Ensemble Transfer Learning-Based Techniques

Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.

Quickly obtaining densely dispersed coherent particles in steel matrix and its related mechanical property

Densely distributed coherent nanoparticles(DCN)in steel matrix can enhance the work-hardening ability and ductility of steel simultaneously.All the routes to this end can be generally classified into the liquid-solid route and the solid-solid route.However,the formation of DCN structures in steel requires long processes and complex steps.So far,obtaining steel with coherent particle enhancement in a short time remains a bottleneck,and some necessary steps remain unavoidable.Here,we show a high-efficiency liquid-phase refining process reinforced by a dynamic magnetic field.Ti-Y-Mn-O particles had an average size of around(3.53±1.21)nm and can be obtained in just around 180 s.These small nanoparticles were coherent with the matrix,implying no accumulated dislocations between the particles and the steel matrix.Our findings have a potential application for improving material machining capacity,creep resistance,and radiation resistance.

Novel Methodologies for Preventing Crack Propagation in Steel Gas Pipelines Considering the Temperature Effect

Using the software ANSYS-19.2/Explicit Dynamics,this study performedfinite-element modeling of the large-diameter steel pipeline cross-section for the Beineu-Bozoy-Shymkent gas pipeline with a non-through straight crack,strengthened by steel wire wrapping.The effects of the thread tensile force of the steel winding in the form of single rings at the crack edges and the wires with different winding diameters and pitches were also studied.The results showed that the strengthening was preferably executed at a minimum value of the thread tensile force,which was 6.4%more effective than that at its maximum value.The analysis of the influence of the winding dia-meters showed that the equivalent stresses increased by 32%from the beginning of the crack growth until the wire broke.The increment in winding diameter decelerated the disclosure of the edge crack and reduced its length by 8.2%.The analysis of the influence of the winding pitch showed that decreasing the distance between the winding turns also led to a 33.6%reduction in the length of the straight crack and a 7.9%reduction in the maximum stres-ses on the strengthened pipeline cross-section.The analysis of the temperature effect on the pipeline material,within a range from-40℃ to+50℃,resulted in a crack length change of up to 5.8%.As the temperature dropped,the crack length decreased.Within such a temperature range,the maximum stresses were observed along the cen-tral area of the crack,which were equal to 413 MPa at+50℃ and 440 MPa at-40℃.The results also showed that the presence of the steel winding in the pipeline significantly reduced the length of crack propagation up to 8.4 times,depending on the temperature effect and design parameters of prestressing.This work integrated the existing methods for crack localization along steel gas pipelines.

Textures and Properties of Hot Rolled High Strength Ti-IF Steels

The texture evolution in a high strength Ti-IF steel during the processing of hot rolling, cold rolling, and annealing is studied. For comparison, both ferrite rolling and austenite rolling are employed. It is found that the texture type is the. same after ferrite rolling and austenite rolling, but the texture intensity is much higher in the ferrite rolled sample. Furthermore, texture characteristics at the surface are absolutely different from those at the mid sec tion in both ferrite rolled and austenite rolled samples, as well as under the cold rolled and annealed conditions. The shear texture { 110 } 〈001 〉 disappears and orientation rotates along { 110 } 〈001 〉→ { 554 } 〈 225 〉→ { 111 } 〈 112 〉→{111}〈110〉→{223}〈110〉 during cold rolling. Compared to the austenite rolled sample, the properties of the cold rolled and annealed sheet which is subjected to ferrite rolling are higher.

Surface Defects of Cold-Rolled Ti-IF Steel Sheets due to Non-Metallic Inclusions

Surface defects of the cold-rolled sheets of Ti-IF steel were studied and analyzed. After analyzing surface defects of cold-rolled sheets, such as shelling defects, holes and sliver defects by SEM/EDS, a variety of inclusions were found. In addition, the distribution of macro-inclusions in slabs was analyzed by MIDAS method. The results show the macroscopic inclusion bands of head slabs and normal slabs are in 1/8 slab thickness regions of both inner arc side and outer arc side. The formation process of the defects in the cold-rolled sheets was simulated with an experimental cold-rolling machine for comparison. The results show that there were three kinds of inclusions underneath the surface defects: Al2O3, SiO2 and particles from slag entrainment, which were the main reason for defect formation during cold rolling.

Texture Evolution in Ferritic Rolled Ti-IF Steel During Cold Rolling

The texture evolution of ferritic hot rolled Ti-IF steel during cold rolling was investigated in which the reduction was from 15% to 85 %. It was found that α fibre texture was monotonously intensified with the increase in the cold rolling reduction, while y fibre texture changed in a different way. When the cold rolling reduction was in the range of 15 % -- 35 % or 45 % -- 75 %, γ fibre texture was strengthened ; however, when the cold rolling reduction was 35 %- 45 % or 75 %- 85%, the intensity of 7 fibre was reduced. The 7 fibre displayed a maximum intensity for the reduction of 75%, and the highest average plastic strain ratio was simultaneously obtained owing to the favorable recrystallization texture.

Evolution of inclusions in Ti stabilized ultra low carbon steel(Ti-IF) steel

The morphologies evolution of various types of inclusions in Ti-IF steel were observed by a special depth erosion method,and the formation and evolution process were discussed.The results showed that the main inclusions were FeO·xMnO before Al deoxidization and the ratio of integrated oxygen and free oxygen was in ranged of 0.3 to 0.4.In present study,the main effect factors on the morphologies of Al_2O_3 inclusions were [Al]/[O]_(Free)(soluble aluminum divide free oxygen) and initial free oxygen;cluster Al_2O_3 was formed easily with high free oxygen([O]_(Free)) and low[Al]/[O](blew 3 in present study).Otherwise,the dendritic Al_2O_3 was formed;coral-like Al_2O_3 was the mixture of the dendrite Al_2O_3 and spherical Al_2O_3.Some Al_2O_3·TiO_x inclusions appeared because a high[Ti]concentration region existed around 70TiFe(containing 70 percent titanium) particles after 70TiFe addition.The maximum sizes of Al_2O_3 reached 800μm when 3 min aluminum was added;as the time past,the large size Al_2O_3 decreased significantly;the maximum size of Al_2O_3 was blew 100μm and 50μm in calming sample and tundish sample respectively.

Texture development and properties of Ti-IF steels produced by different hot-rolling processes

Texture is a pivotal factor for the deep drawability of interstitial free steel (IF steel). The evolution of microstructure and recrystallization texture of Ti-IF steel through the processing of hot rolling, cold rolling and annealing have been studied. The mechanical properties including strain hardening index n and plastic strain ratio r are also measured. For comparison, ferrite rolling and austenite rolling are both studied. The results show that the intensity of γ-fiber after ferrite rolling is higher than that after austenite rolling. The great balance between the {111}〈110〉 and {111}〈112〉 leads to low △r value after annealing. The size of precipitates in ferrite rolled sample is generally larger than that in austenite rolled sample. Compared to austenite rolled sample, the ferrite rolled and annealed one has better formability and its r value reaches 2.36. Different from laboratory production, the test steels were acquired from industrial trial, and all the result can be used in industrial production directly.

Effect of Phosphorus Grain Boundaries Segregation and Precipitations on Mechanical Properties for Ti-IF Steel after Recrystallization Annealing

The cold-rolled（75% reduction ratio） Ti-IF（interstitial-free） steels of 1 mm thickness were recrystallized by annealing at 810°C for different times.The microstructure,mechanical properties and phosphorus segregation at grain boundary were investigated by means of optical microscopy（OM）,tensile testing and field emission transmission electron microscopy（FE-TEM）.It was observed that recrystallization was completed after annealing at 810°C for 180 s.The yield strength and tensile strength decreased as annealing time increased.The FE-TEM observation showed that after the annealing treatment,the grain boundary was broadened and the dislocations with higher density of phosphorus atoms and phosphide at grain boundaries became evident.The amount of phosphorus segregated at grain boundaries increased with annealing time.

Development and prospects of molten steel deoxidation in steelmaking process

In the long traditional process of steelmaking,excess oxygen is blown into the converter,and alloying elements are used for deoxidation.This inevitably results in excessive deoxidation of products remaining within the steel liquid,affecting the cleanliness of the steel.With the increasing requirements for steel performance,reducing the oxygen content in the steel liquid and ensuring its high cleanliness is necessary.After more than a hundred years of development,the total oxygen content in steel has been reduced from approximately 100×10^(-6)to approximately 10×10^(-6),and it can be controlled below 5×10^(-6)in some steel grades.A relatively stable and mature deoxidation technology has been formed,but further reducing the oxygen content in steel is no longer significant for improving steel quality.Our research team developed a deoxidation technology for bearing steel by optimizing the entire conventional process.The technology combines silicon–manganese predeoxidation,ladle furnace diffusion deoxidation,and vacuum final deoxidation.We successfully conducted industrial experiments and produced interstitial-free steel with natural decarbonization predeoxidation.Non-aluminum deoxidation was found to control the oxygen content in bearing steel to between 4×10^(-6) and 8×10^(-6),altering the type of inclusions,eliminating large particle Ds-type inclusions,improving the flowability of the steel liquid,and deriving a higher fatigue life.The natural decarbonization predeoxidation of interstitial-free steel reduced aluminum consumption and production costs and significantly improved the quality of cast billets.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Effects of iron oxide on crystallization behavior and spatial distribution of spinel in stainless steel slag

Chromium plays a vital role in stainless steel due to its ability to improve the corrosion resistance of the latter.However,the re-lease of chromium from stainless steel slag(SSS)during SSS stockpiling causes detrimental environmental issues.To prevent chromium pollution,the effects of iron oxide on crystallization behavior and spatial distribution of spinel were investigated in this work.The results revealed that FeO was more conducive to the growth of spinels compared with Fe2O3 and Fe3O4.Spinels were found to be mainly distrib-uted at the top and bottom of slag.The amount of spinel phase at the bottom decreased with the increasing FeO content,while that at the top increased.The average particle size of spinel in the slag with 18wt%FeO content was 12.8μm.Meanwhile,no notable structural changes were observed with a further increase in FeO content.In other words,the spatial distribution of spinel changed when the content of iron oxide varied in the range of 8wt%to 18wt%.Finally,less spinel was found at the bottom of slag with a FeO content of 23wt%.

An Integrated Analysis on the Synergistic Reduction of Carbon and Pollution Emissions from China’s Iron and Steel Industry

Decarbonization and decontamination of the iron and steel industry(ISI),which contributes up to 15%to anthropogenic CO_(2) emissions(or carbon emissions)and significant proportions of air and water pollutant emissions in China,are challenged by the huge demand for steel.Carbon and pollutants often share common emission sources,indicating that emission reduction could be achieved synergistically.Here,we explored the inherent potential of measures to adjust feedstock composition and technological structure and to control the size of the ISI to achieve carbon emission reduction(CER)and pollution emission reduction(PER).We investigated five typical pollutants in this study,namely,petroleum hydrocarbon pollutants and chemical oxygen demand in wastewater,particulate matter,SO_(2),and NO_(x) in off gases,and examined synergies between CER and PER by employing cross elasticity for the period between 2022 and 2035.The results suggest that a reduction of 8.7%-11.7%in carbon emissions and 20%-31%in pollution emissions(except for particulate matter emissions)could be achieved by 2025 under a high steel scrap ratio(SSR)scenario.Here,the SSR and electric arc furnace(EAF)ratio serve critical roles in enhancing synergies between CER and PER(which vary with the type of pollutant).However,subject to a limited volume of steel scrap,a focused increase in the EAF ratio with neglection of the available supply of steel scrap to EAF facilities would lead to an increase carbon and pollution emissions.Although CER can be achieved through SSR and EAF ratio optimization,only when the crude steel production growth rate remains below 2.2%can these optimization measures maintain the emissions in 2030 at a similar level to that in 2021.Therefore,the synergistic effects between PER and CER should be considered when formulating a development route for the ISI in the future.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

Prospects for green steelmaking technology with low carbon emissions in China

The steel industry is a major source of CO_(2) emissions,and thus,the mitigation of carbon emissions is the most pressing challenge in this sector.In this paper,international environmental governance in the steel industry is reviewed,and the current state of development of low-carbon technologies is discussed.Additionally,low-carbon pathways for the steel industry at the current time are proposed,emphasizing prevention and treatment strategies.Furthermore,the prospects of low-carbon technologies are explored from the perspective of transitioning the energy structure to a“carbon-electricity-hydrogen”relationship.Overall,steel enterprises should adopt hydrogen-rich metallurgical technologies that are compatible with current needs and process flows in the short term,based on the carbon substitution with hydrogen(prevention)and the CCU(CO_(2) capture and utilization)concepts(treatment).Additionally,the capture and utilization of CO_(2) for steelmaking,which can assist in achieving short-term emission reduction targets but is not a long-term solution,is discussed.In conclusion,in the long term,the carbon metallurgical process should be gradually supplanted by a hydrogen-electric synergistic approach,thus transforming the energy structure of existing steelmaking processes and attaining near-zero carbon emission steelmaking technology.

A Hybrid Deep Learning and Machine Learning-Based Approach to Classify Defects in Hot Rolled Steel Strips for Smart Manufacturing

Smart manufacturing is a process that optimizes factory performance and production quality by utilizing various technologies including the Internet of Things(IoT)and artificial intelligence(AI).Quality control is an important part of today’s smart manufacturing process,effectively reducing costs and enhancing operational efficiency.As technology in the industry becomes more advanced,identifying and classifying defects has become an essential element in ensuring the quality of products during the manufacturing process.In this study,we introduce a CNN model for classifying defects on hot-rolled steel strip surfaces using hybrid deep learning techniques,incorporating a global average pooling(GAP)layer and a machine learning-based SVM classifier,with the aim of enhancing accuracy.Initially,features are extracted by the VGG19 convolutional block.Then,after processing through the GAP layer,the extracted features are fed to the SVM classifier for classification.For this purpose,we collected images from publicly available datasets,including the Xsteel surface defect dataset(XSDD)and the NEU surface defect(NEU-CLS)datasets,and we employed offline data augmentation techniques to balance and increase the size of the datasets.The outcome of experiments shows that the proposed methodology achieves the highest metrics score,with 99.79%accuracy,99.80%precision,99.79%recall,and a 99.79%F1-score for the NEU-CLS dataset.Similarly,it achieves 99.64%accuracy,99.65%precision,99.63%recall,and a 99.64%F1-score for the XSDD dataset.A comparison of the proposed methodology to the most recent study showed that it achieved superior results as compared to the other studies.

Interfacial reaction between AZ91D magnesium alloy melt and mild steel under high temperature

The metallurgical quality control of magnesium(Mg)and Mg alloys in melting process is required to ensure a satisfied mechanical and corrosion performance,while the typical used steel crucible introduces impurities and interfacial interaction during melting process.Therefore,a systematic study about impurities diffusion and interfacial interaction between molten Mg and steel is necessary.In the present study,the interfacial reaction between molten AZ91D Mg alloy and mild steel during melting process was investigated with the melting temperatures of 700℃,750℃ and 800℃.The results show that Al(Fe,Mn)intermetallic layer is the intermetallic primarily formed at the interfaces of AZ91D melt and mild steel.Meanwhile,Al_(8)(Mn,Fe)5is indexed between Al(Fe,Mn)and AZ91D.AlFe_(3)C appears between the mild steel and Al(Fe,Mn)at 700℃ and 750℃,but absent at 800℃ due to the increased solubility of carbon in Mg matrix.It is found that the growth of the intermetallic layer is controlled by diffusion mechanism,and Al and Mn are the dominant diffusing species in the whole interfacial reaction process.By measuring the thickness of different layers,the growth constant was calculated.It increases from 1.89(±0.03)×10^(-12)m^(2)·s^(-1)at 700℃ to 3.05(±0.05)×10^(-12)m^(2)·s^(-1)at 750℃,and 5.18(±0.05)×10^(-12)m^(2)·s^(-1)at 800℃.Meanwhile,the content of Fe is linearly increased in AZ91D with the increase of holding time at 700℃ and 750℃,while it shows a significantly increment after holding for 8 h at 800℃,indicating holding temperature is more crucial to determine the Fe content of AZ91D than holding time.