Microstructural evolution and strengthening mechanism of aligned steel fiber cement-based tail backfills exposed to electromagnetic induction

Cemented tailings backfill(CTB)not only boosts mining safety and cuts surface environmental pollution but also recovers ores previously retained as pillars,thereby improving resource utilization.The use of alternative reinforcing products,such as steel fiber(SF),has continuously strengthened CTB into SFCTB.This approach prevents strength decreases over time and reinforces its long-term durability,especially when mining ore in adjacent underground stopes.In this study,various microstructure and strength tests were performed on SFCTB,considering steel fiber ratio and electromagnetic induction strength effects.Lab findings show that combining steel fibers and their distribution dominantly influences the improvement of the fill’s strength.Fill’s strength rises by fiber insertion and has an evident correlation with fiber insertion and magnetic induction strength.When magnetic induction strength is 3×10^(-4) T,peak uniaxial compressive stress reaches 5.73 MPa for a fiber ratio of 2.0vol%.The cracks’expansion mainly started from the specimen’s upper part,which steadily expanded downward by increasing the load until damage occurred.The doping of steel fiber and its directional distribution delayed crack development.When the doping of steel fiber was 2.0vol%,SFCTBs showed excellent ductility characteristics.The energy required for fills to reach destruction increases when steel-fiber insertion and magnetic induction strength increase.This study provides notional references for steel fibers as underground filling additives to enhance the fill’s durability in the course of mining operations.

Mechanical properties and damage evolution characteristics of waste tire steel fiber-modified cemented paste backfill

During the process of constructional backfill mining,the cemented paste backfill(CPB)typically exhibits a high degree of brittleness and limited resistance to failure.In this study,the mechanical and damage evolution characteristics of waste tire steel fiber(WTSF)-modified CPB were studied through uniaxial compression tests,acoustic emission(AE)tests,and scanning electron microscopy(SEM).The results showed that the uniaxial compressive strength(UCS)decreased when the WTSF content was 0.5%,1%,and 1.5%.When the WTSF content reached 1%,the UCS of the modified CPB exhibited a minimal decrease(0.37 MPa)compared to that without WTSF.When the WTSF content was 0.5%,1%,and 1.5%,peak strain of the WTSF-modified CPB increased by 18%,31.33%,and 81.33%,while the elastic modulus decreased by 21.31%,26.21%,and 45.42%,respectively.The addition of WTSF enhances the activity of AE events in the modified CPB,resulting in a slower progression of the entire failure process.After the failure,the modified CPB retained a certain level of load-bearing capacity.Generally,the failure of the CPB was dominated by tensile cracks.After the addition of WTSF,a gradual increase in the proportion of tensile cracks was observed upon loading the modified CPB sample to the pore compaction stage.The three-dimensional localization of AE events showed that the WTSF-modified CPB underwent progressive damage during the loading,and the samples still showed good integrity after failure.Additionally,the response relationship between energy evolution and damage development of WTSF-modified CPB during uniaxial compression was analyzed,and the damage constitutive model of CPB samples with different WTSF contents was constructed.This study provides a theoretical basis for the enhancement of CPB modified by adding WTSF,serving as a valuable reference for the design of CPB constructional backfill.

Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network

The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.

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.

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.

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.

Analysis of Orange Peel Defect in St14 Steel Sheet by Electron Backscattered Diffraction (EBSD)

In this paper, the orange peel defect in the surface range of the st14 steel sheet has been investigated using the electron backscattered diffraction (EBSD) technique. It has been found that the orange peel defect in the st14 steel sheet was resulted from the local coarse grains which were produced during hot-rolling due to the critical deformation in dual-phase zone. During deep drawing, the coarse grains with {100}<001> microtexture can slip on the {112}<111> slip system to form bulging and yields orange peel defects, while the coarse grains with {112}<110> orientation do not form the defect as the Schmid factor of {112}<111> slip system in it equals zero.

Effect of bending temperatures on the microstructure and springback of a TRIP steel sheet

Transformation-induced plasticity(TRIP)steel possesses high strength and formability,enabling the use of a thinner gauge material and allowing for the fabrication of complex shapes.In this research,we measured the effect of bending temperatures on the microstructure and air-bending springback angle of TRIP steel at temperatures from 25 to 600
C.Real-time in situ X-ray diffraction and scanning electron microscopy were used for pre-and postbending analysis.As the prebending temperature increased from 25
C to 600
C,the retained austenite(RA)volume fraction decreased,and the RA transformed to bainite at temperatures above 400
C.The springback angle was positively correlated with the prebending RA volume fraction,with the smallest springback angle achieved at 400
C.Additionally,the springback angle was positively correlated with the bending angle,because the RA transformation ratio contributed to increased strain hardening.Further microstructure analysis revealed that the RA became elongated in the tension direction as the bending temperatures increased.

Thermal-mechanical and springback behavior of dual-phase steel at warm temperatures

For non-quenchable dual-phase(DP)steel sheet,the warm forming process can effectively reduce the amount of springback,and the mechanical parameters that influence its elastic and inelastic recovery to decrease exhibit a strong temperature dependence,especially under cyclic loading conditions.In this paper,the monotonic and cyclic loading tests of DP980 steel sheets are conducted at the temperatures ranging from 25℃ to 500℃.The temperature-dependent flow stress,nonlinear elastic recovery,and Bauschinger effect are investigated.The results demonstrate that both the elastic modulus and Bauschinger effect show an exponential law with pre-strain,and decrease with the increase of forming temperature,while there will be an abnormal phenomenon of rebound due to the influence of dynamic strain aging effect.Meanwhile,a linear relationship between the Bauschinger effect and inelastic strain is observed at various temperatures,and the weight of the Bauschinger effect in the total strain reduces with temperature increasing,which indicates that the springback is dominated by linear elastic recovery.Furthermore,the U-draw bending tests are carried out to clarify the influence of Vickers hardness distribution and martensite size effect on the springback behavior.

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%.

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.

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.

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.

Flow characteristics and hot workability of a typical low-alloy high-strength steel during multi-pass deformation

Heavy components of low-alloy high-strength(LAHS) steels are generally formed by multi-pass forging. It is necessary to explore the flow characteristics and hot workability of LAHS steels during the multi-pass forging process, which is beneficial to the formulation of actual processing parameters. In the study, the multi-pass hot compression experiments of a typical LAHS steel are carried out at a wide range of deformation temperatures and strain rates. It is found that the work hardening rate of the experimental material depends on deformation parameters and deformation passes, which is ascribed to the impacts of static and dynamic softening behaviors. A new model is established to describe the flow characteristics at various deformation passes. Compared to the classical Arrhenius model and modified Zerilli and Armstrong model, the newly proposed model shows higher prediction accuracy with a confidence level of 0.98565. Furthermore, the connection between power dissipation efficiency(PDE) and deformation parameters is revealed by analyzing the microstructures. The PDE cannot be utilized to reflect the efficiency of energy dissipation for microstructure evolution during the entire deformation process, but only to assess the efficiency of energy dissipation for microstructure evolution in a specific deformation parameter state.As a result, an integrated processing map is proposed to better study the hot workability of the LAHS steel, which considers the effects of instability factor(IF), PDE, and distribution and size of grains. The optimized processing parameters for the multi-pass deformation process are the deformation parameters of 1223–1318 K and 0.01–0.08 s^(-1). Complete dynamic recrystallization occurs within the optimized processing parameters with an average grain size of 18.36–42.3 μm. This study will guide the optimization of the forging process of heavy components.

A review on the multi-scaled structures and mechanical/thermal properties of tool steels fabricated by laser powder bed fusion additive manufacturing

The laser powder bed fusion(LPBF) process can integrally form geometrically complex and high-performance metallic parts that have attracted much interest,especially in the molds industry.The appearance of the LPBF makes it possible to design and produce complex conformal cooling channel systems in molds.Thus,LPBF-processed tool steels have attracted more and more attention.The complex thermal history in the LPBF process makes the microstructural characteristics and properties different from those of conventional manufactured tool steels.This paper provides an overview of LPBF-processed tool steels by describing the physical phenomena,the microstructural characteristics,and the mechanical/thermal properties,including tensile properties,wear resistance,and thermal properties.The microstructural characteristics are presented through a multiscale perspective,ranging from densification,meso-structure,microstructure,substructure in grains,to nanoprecipitates.Finally,a summary of tool steels and their challenges and outlooks are introduced.

Improving creep strength of the fine-grained heat-affected zone of novel 9Cr martensitic heat-resistant steel via modified thermo-mechanical treatment

The infamous type Ⅳ failure within the fine-grained heat-affected zone (FGHAZ) in G115 steel weldments seriously threatens the safe operation of ultra-supercritical (USC) power plants.In this work,the traditional thermo-mechanical treatment was modified via the replacement of hot-rolling with cold rolling,i.e.,normalizing,cold rolling,and tempering (NCT),which was developed to improve the creep strength of the FGHAZ in G115 steel weldments.The NCT treatment effectively promoted the dissolution of preformed M_(23)C_(6)particles and relieved the boundary segregation of C and Cr during welding thermal cycling,which accelerated the dispersed reprecipitation of M_(23)C_(6) particles within the fresh reaustenitized grains during post-weld heat treatment.In addition,the precipitation of Cu-rich phases and MX particles was promoted evidently due to the deformation-induced dislocations.As a result,the interacting actions between precipitates,dislocations,and boundaries during creep were reinforced considerably.Following this strategy,the creep rupture life of the FGHAZ in G115 steel weldments can be prolonged by 18.6%,which can further push the application of G115 steel in USC power plants.

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.