To advance green ironmaking and expand the utilization of magnetite with complex gangues, the hydrogen reduction behavior and mechanisms of barite-containing magnetite pellets were investigated. The findings revealed that increasing barite led to the increased amount of Ba_xFe_3-xO₄ and Ba-containing silicates in oxidized pellets, which hindered the continuous crystallization of Fe₂O₃. During the reduction process, the reduction of Ba_xFe_3-xO₄ to Ba and Fe by H₂ was challenging, resulting in the formation of BaFeO_2.64. Furthermore, Ba_xFe_3-xO₄ impeded the reaction between Fe₂O₃ and H₂, decreasing the reduction degree and metallization ratio of the pellets. Ba²⁺ diffused into the Fe₂O₃ lattice during oxidation, stabilizing the crystal structure during the initial reduction stage (Fe₂O₃ to Fe₃O₄). In the third reduction stage (Fe_xO to Fe), BaFeO_2.64 inhibited the rapid precipitation of metallic iron, thus preventing the abnormal growth of iron whiskers. Consequently, BaSO₄ reduced the reduction swelling index of barite-containing magnetite pellets in hydrogen. These findings offer a theoretical basis for the future implementation of barite-containing pellets in the hydrogen-based shaft furnace direct reduction process.

Volatile organic compounds (VOCs) present significant risks to both human health and the environment. As a result, there has been increasing research on their formation processes, emission patterns, and emission reduction technologies. Sintered ore, a key raw material in ironmaking, requires substantial amounts of fossil fuels such as coal and coke in its production, leading to significant VOC emissions. However, research on VOC emission patterns during the sintering process remains limited. Thus, the influence of factors such as moisture content in the sintering mixture, fuel type, fuel ratio, and coal-to-coke ratio on VOC emissions was investigated through sintering cup experiments. Additionally, the reaction pathways of hydrocarbons within the sintering bed were analyzed using temperature distribution and thermodynamic calculations. In the sintering process using coke powder, the emissions of total volatile organic compounds and non-methane hydrocarbons per ton of sintered ore are 18.78 and 11.14 g, respectively, whereas emissions from coal are substantially higher at 378.27 and 32.55 g. VOC emissions exhibit a linear correlation with the total volatile matter input during sintering, with most VOCs originating from volatile matter. The improved heat transfer conditions reduce the preheating zone thickness, allowing more VOCs to remain in the high-temperature region.

It is an important way to realize low carbon in China’s iron and steel industry by hydrogen-rich blast furnace smelting process. Sinter is the main blast furnace burden, and its reduction characteristics have a significant influence on ironmaking. The reduction behaviors, including reduction index (RI) and low-temperature reduction disintegration index (RDI), and the reduction mechanism of sinter in hydrogen-rich blast furnace were investigated. The results show that RI increased from 82.85 to 95.53 wt.% with an increase in H₂ content from 0 to 30 vol.%, and the main phase of the reduction product was metallic iron. In the research of RDI, when the H₂ content was increased from 0 to 20 vol.%, RDI_+3.15 increased from 69.61 to 75.38 wt.%, and the main reaction was the reduction of hematite to magnetite. At 600-950 °C, the reduction of sinter in CO and hydrogen-rich atmospheres (H₂:CO = 2) was both controlled by the first-order reaction model, and the apparent activation energy was 33.64 and 44.57 kJ/mol, respectively.

The utilization of metallized pellets in electric arc furnace represents a pivotal strategy for the iron and steel industry to attain a green transformation. However, their low melting rate limits their application, making it essential to understand the melting characteristics of metallized pellet and the factors that influence their melting rate. A model of isolated metallized pellet in slag-iron bath was established and verified by published experimental data. In the molten pool formed by the melting of metallized pellet, the melting process of isolated metallized pellet can be divided into three stages: the frozen shell formation stage, the frozen shell remelting stage, and the metallized pellet parent melting stage. In addition, increasing the preheating temperature of the metallized pellet and the temperature of slag-iron bath, along with reducing size and slag content of metallized pellet, can enhance the melting efficiency. The simulation results indicate that increasing the preheating temperature of the metallized pellet to 1300 K can shorten the melting time by 74.83%; increasing the temperature of slag-iron bath to 1923 K can shorten the melting time by 21.52%; reducing the size of the metallized pellet to 15 mm can shorten the melting time by 41.21%; reducing the slag content of the metallized pellet to 30% can shorten the melting time by 22.79%.

Blast furnace (BF) operation state was difficult to characterize, measure, and predict. To solve this problem, an intelligent evaluation and advanced prediction method of BF operation state based on industry big data and machine learning was proposed. Based on the criteria of high productivity, low consumption, high quality, smooth running and long life, five BF parameters were extracted according to production experience and metallurgy process. Using the unsupervised learning, a 4-grade evaluation rule was established to realize the intelligent rating of BF operation state. Based on Kendall and maximal information coefficient, 70 BF parameters with the most characteristic power of BF operation state were determined. The weights of BF parameters were calculated by applying the criteria importance through intercriteria correlation and the grey correlation degree. The weights of raw material, fuel, gas distribution, cooling stave, BF hearth, and iron and slag were 0.241, 0.213, 0.140, 0.098, 0.117 and 0.191, respectively. The weight of data interval was calculated by using the grading algorithm and the monotonicity, and then, the intelligent scoring mechanism based on the multiple weights was formed. It was beneficial to qualitatively and quantitatively characterizing the “black box” BF operation state. Furthermore, combining the algorithm and the evaluation mechanism, a graded prediction model of BF operation state was developed and proposed. It was shown that, compared with the conventional prediction model, the mean absolute error and mean square error of the graded prediction model were reduced by 0.35 and 1.29, respectively, while the explained variation was increased by 14.56%, the hit rate was increased by 5.1% within the error of 3%, and the average hit rate was more than 90.6%. It could be applied to reliably predict the score of BF operation state in the next hour and accurately provide the support for the practical controlling of the running BF.

The operation furnace profile for the high heat load zone was one of the important factors affecting the stable and high-quality production of the blast furnace, but it was difficult to monitor directly. To address this issue, an online calculation model for the operation furnace profile was proposed based on a dual-driven approach combining data and mechanisms, by integrating mechanism experiment, numerical simulation, and machine learning. The experimentally determined slag layer hanging temperature was 1130 °C, and the thermal conductivity ranged from 1.32 to 1.96 m² °C^-1. Based on the 3D slag-hanging numerical simulation model, a database was constructed, containing 2294 sets of mechanism cases for the slag layer. The fusion of data modeling, heat transfer theory, and expert experience enabled the online calculation of key input variables for the operation furnace profile, particularly the quantification of the “black-box” variable of gas temperature. Simulated data were used as inputs, and light gradient boosting machine was applied to construct the online calculation model for the operation furnace profile. This model facilitated the online calculation of the slag layer thickness and other key indices. The coefficient of determination of the model exceeded 0.98, indicating high accuracy. A slag layer state judgment model was constructed, categorizing states as shedding, too thin, normal, and too thick. Real-time data were applied, and the average slag thickness in the high heat load area of the test data ranged from 40 to 80 mm, which was consistent with field experience. The absolute value of the Pearson correlation coefficient between slag layer thickness, thermocouple temperature, and heat load data was above 0.85, indicating that the calculated results closely aligned with the actual trends. A 3D visual online monitoring system for the operation furnace profile was created, and it has been successfully implemented at the blast furnace site.

The instantaneous desulfurization of CaO-Al₂O₃-SiO₂ slag particles in the molten steel was in situ observed using a high-temperature confocal scanning laser microscope. The desulfurization effect of CaO-Al₂O₃-SiO₂ slags with different compositions was discussed. The influence of CaO/Al₂O₃ and CaO/SiO₂ on the desulfurization effect was analyzed. It was shown that in the liquid phase range, the higher CaO/SiO₂ and CaO/Al₂O₃ can significantly improve the desulfurization effect of the slag. A dimensionless desulfurization index S_index was introduced to evaluate the desulfurization ability of CaO-Al₂O₃-SiO₂ slags quantitatively. The S_index values of CaO-Al₂O₃-SiO₂ with different compositions at 1550 °C were calculated. It was suggested to use (65%-75%)CaO-(0-20%)SiO₂-(20%-40%)Al₂O₃ slags to improve the molten steel desulfurization.

A new three-dimensional multiphase numerical model was built. The volume of fluid and k-ε turbulence models were used to investigate the hot metal ladle pouring process. During the pouring process, issues such as iron splashing, overflow, and significant heat loss are prevalent. To realize efficient and stable pouring, the effects of ladle tilting velocity, flow rate, and converter tilting angle on the pouring process were examined. The model was verified by comparing the actual pouring time with the numerical results. It is shown that there is a nonlinear relationship between pouring velocity and hot metal flow rate at the ladle mouth. As the mass flow increased and the converter tilting angle decreased, the impact point of the hot metal into the converter pool shifted from the side wall to the bottom, and the impact force increased accordingly. The pouring velocity curve was optimized by the volume difference of the ladle at different angles, and an empirical formula was derived. After the optimization of pouring speed, the flow rate was stabilized between 4000 and 6000 kg/s, and the pouring time was reduced by approximately 30 s. After applying this model in actual production, the hot metal temperature inside the converter increased by approximately 5 °C statistically. This model is potential to enhance the production efficiency, stability, and safety of the pouring process between open containers.

Slag viscosity plays a crucial role in the smelting process. A slag viscosity prediction model was developed by integrating hyperparameter optimization algorithms, machine learning, and SHapley Additive exPlanations (SHAP) analysis. The developed slag viscosity prediction models were evaluated using multiple statistical metrics, leading to the identification of the optimal model—Bayesian optimization-based categorical boosting (BO-CatBoost). And this model was further compared with existing models, including NPL model, FactSage + Roscoe-Einstein (RE) equation, artificial neural network model + RE equation, Riboud model + RE equation, and Zhang model. The results indicate that the slag viscosity prediction model based on BO-CatBoost outperforms all other models, achieving a coefficient of determination of 0.9897, a root mean square error of 1.0619, a mean absolute error of 0.6133, and a hit ratio of 95.1%. The global interpretability analysis of SHAP analysis was used to reveal the importance degree of different features on slag viscosity. The local interpretability analysis of SHAP analysis was used to obtain the quantitative influence of different features on slag viscosity in specific samples. The high-accuracy and interpretable slag viscosity prediction model developed is beneficial to the intelligent design of slag composition.

Continuous casting of high-titanium steels face significant challenges due to steel-flux reactions, which will cause rapid compositional deviations and impair operational stability. A kinetic model to predict real-time mold flux composition evolution by integrating multicomponent mixed-transport-control theory with thermodynamics computing platform was developed in the current study. The model employed a cyclic time-step algorithm to compute thermodynamic equilibrium in reaction layer, mass transfer flux between reaction and bulk layers, and composition updates in reaction and bulk layers. The accuracy of the model was validated by plant trial data. The effect of casting parameters and initial compositions on the evolution of mold flux composition were investigated. The TiO₂ accumulation and SiO₂ consumption in mold flux under varying casting parameters was predicted. It was found that higher casting speeds accelerated compositional equilibrium, while the increase of mold flux consumption rates reduced TiO₂ accumulation. The increase of pool depth resulted in slower consumption and accumulation rates of components like SiO₂ and TiO₂, prolonging the time to reach equilibrium. Additionally, the CaO-Al₂O₃-based flux suppressed the Ti-SiO₂ reaction for the high-titanium steel continuous casting. However, the CaO-Al₂O₃-based flux should limited contents of Na₂O, MnO, and FeO to prevent additional TiO₂ accumulation due to Ti-Na₂O, Ti-MnO, and Ti-FeO reactions. The model provided a reliable tool for understanding and optimizing the continuous casting process of high-titanium steels.

Ductile iron represents an optimal solution for saving material and costs in producing large heavy-section castings in the energy sector. It aimed to investigate the influence of very long solidification time (3, 10 and 20 h) in different casting zones (casting center and transition zone) on the microstructure and mechanical properties of non-standard heavy-section ferritic ductile iron (EN-GJS-400-15) castings. The different solidification conditions significantly influenced the microstructure (graphite and ferrous matrix). The extent of phenomena such as degenerate graphite, solidification defects, hard carbides, and intergranular pearlitic areas and the microstructural coarsening were proportional to the solidification time and attributable to the combined effect of limited undercooling, solid solution diffusion mechanisms, and segregation phenomena. For comparable solidification time, the transition zone was characterized by larger nodules, comparable nodularity, and lower nodule count than the casting center due to more effective diffusion phenomena during cooling. Moreover, the lower segregation phenomena in the transition zone reduced the amount of pearlite and carbides in the intercellular zones. Hardness was only slightly influenced by the different solidification conditions and did not represent a reliable indicator of the microstructural inhomogeneities. These results are essential to refine casting simulations for producing large ferritic ductile iron castings, considering the wide microstructural variability within non-standard heavy-section castings caused by significantly different solidification conditions.

In the context of intelligent manufacturing, the modern hot strip mill process (HSMP) shows characteristics such as diversification of products, multi-specification batch production, and demand-oriented customization. These characteristics pose significant challenges to ensuring process stability and consistency of product performance. Therefore, exploring the potential relationship between product performance and the production process, and developing a comprehensive performance evaluation method adapted to modern HSMP have become an urgent issue. A comprehensive performance evaluation method for HSMP by integrating multi-task learning and stacked performance-related autoencoder is proposed to solve the problems such as incomplete performance indicators (PIs) data, insufficient real-time acquisition requirements, and coupling of multiple PIs. First, according to the existing Chinese standards, a comprehensive performance evaluation grade strategy for strip steel is designed. The random forest model is established to predict and complete the parts of PIs data that could not be obtained in real-time. Second, a stacked performance-related autoencoder (SPAE) model is proposed to extract the deep features closely related to the product performance. Then, considering the correlation between PIs, the multi-task learning framework is introduced to output the subitem ratings and comprehensive product performance rating results of the strip steel online in real-time, where each task represents a subitem of comprehensive performance. Finally, the effectiveness of the method is verified on a real HSMP dataset, and the results show that the accuracy of the proposed method is as high as 94.8%, which is superior to the other comparative methods.

In order to solve the black-box modeling problem and improve the prediction accuracy of model, two distinguished models for tensile strength (Ts) and yield strength (Ys) of hot-rolled strip steel are established based on the industrial hot-rolled data and the algorithm of gene expression programming (GEP). Firstly, the industrial data of hot-rolled strip steel are preprocessed using the Pauta criterion, so as to eliminate outliers. The key input variables that affect Ys and Ts are selected by using the method of the maximal information coefficient (MIC). Secondly, the explicit prediction models of Ys and Ts are established using GEP. Subsequently, the model results based on GEP are compared with those based on the support vector regression (SVR) and the back propagation neural network (BPNN). Finally, the mathematical expression models for Ys and Ts obtained by GEP are used to further analyse the specific relationships between the chemical composition and mechanical property. It is shown that the errors of Ys and Ts based on GEP are less than 4%, and the coefficient of determination (R²) of Ys and Ts based on GEP is above 0.9, which has strong prediction performance. The prediction accuracy of GEP can achieve the same level with SVR and BPNN. It is worth mentioning that the proposed model can not only show the explicit relationship between the chemical composition, production process, and mechanical property of strip steel, but also occupy high prediction accuracy, which can make reliable reference for strip steel product design and optimisation.

20-high mills often face various flatness problems in the production of cold-rolled stainless steel thin strips. The flatness prediction model is essential for flatness control techniques. A novel rapid prediction model for flatness in a 20-high mill is proposed based on a model coupling method capable of forecasting the flatness of cold-rolled stainless steel thin strips under symmetric and asymmetric rolling conditions. The model integrates deformation coordination equations between rolls, force and moment balance equations, strip exit transverse displacement equations, and no-load roll gap equations into a unified set of linear equations. This solution process avoids repeated iterations between the elastic deformation model of the roll system and the plastic deformation model of the strip, which is a limitation of the traditional method and significantly improves the calculation speed and stability. The accuracy of the model was verified via a ZR22B-52 Sendzimir 20-high mill. The measured and calculated flatness values highly coincided, confirming the model’s accuracy. Rolling calculations of 304 stainless steel thin strips demonstrate that the new model results are consistent with those of the traditional method. The calculation time of the new model is only approximately 0.04%-0.35% that of the traditional method. On this basis, the impact of common flatness control methods on the flatness has been analyzed.

To address the issue of post-rolling warpage caused by differences in material properties during the composite rolling process of steel/aluminum thin strips, stress analysis was conducted at the entrance, middle, and exit sides of the rolling deformation zone for both steel and aluminum units. The unequal characteristics of the upper and lower contact arc lengths and the micromoment phenomenon, generated by the transfer of force between adjacent units on the aluminum side, were analyzed to explain the mechanical mechanism of warpage in the composite rolling process. A formula for calculating the contact arc length in the upper and lower rolling deformation zones was derived using the relationships among the contact arc length, rolling force, roll parameters, and strip parameters. On the basis of the deformation characteristics of steel and aluminum in the rolling deformation zone, the concepts of the inlet half-rolling zone, relative sliding zone, rolling composite zone, and outlet half-rolling zone were proposed. A quantitative model for characterizing warpage during the composite rolling of steel/aluminum composite thin strips was established. These results indicate that adjusting the diameters of the upper and lower work rolls is an effective method for controlling warpage defects and that warpage plays a dominant role in steel/aluminum thin strip composite rolling. Furthermore, finite element simulations of steel/aluminum composites rolled with different upper and lower roll diameters verified the deformation mechanism and influence of roll diameter differences on warpage defects.

As the main use of TBM (tunnel boring machine) cutter ring (DC53 steel) currently is difficult to meet the requirements of high wear resistance and high toughness synchronously in its service environment, a new HWR0 steel with 4%Cr-3%V for TBM cutter ring has been developed to control carbides in steel. Precipitation behavior of carbides in HWR0 steel was investigated through theoretical calculation by Thermo-Calc and experimental measurement using scanning electron microscope, energy dispersive spectrometer, electron probe X-ray micro-analyzer, and laser particle size analyzer. The results show that three different carbides are precipitated during cooling. And the as-cast electroslag remelting ingot of HWR0 steel primarily consists of many blocky or strip-like MC distributed along grain boundaries, few chrysanthemum-like M₆C concentrated at grain boundary intersections, and a large quantity of fine M₂₃C₆ and M₆C dispersed in the matrix. Compared with DC53 steel, HWR0 steel has more high-hardness carbides MC, which are discontinuously distributed at the grain boundaries, achieving the dual improvement of wear resistance and impact toughness. Cooling rates significantly influence the carbides distribution and grain size. A slower cooling rate exacerbates the segregation of alloying elements, which leads to the localized enrichment of Mo and the subsequent precipitation of M₆C carbides at grain boundary intersections. In contrast, faster cooling rate decreases the element segregation, promotes carbide nucleation and limits the space for carbides growth, which results in finer size and distribution of carbides and grains. Higher cooling rates yield a more homogeneous microstructure with uniform MC compositions and promote the preferential formation of M₆C carbides along grain boundaries, which enhances the mechanical properties.

During the fabrication of large parts by forging, dynamic recrystallization (DRX) is the primary softening mechanism that affects the microstructure and properties of austenitic stainless steel, and an in-depth analysis of this process is necessary. The isothermal hot compression tests were conducted to investigate the hot deformation behavior of Fe-21Cr-15Ni-5Mn-2Mo steel, a novel austenitic stainless steel, at strain rates from 0.01 to 10 s^-1 and temperatures ranging from 950 to 1200 °C. Based on the true stress-strain curves derived from the tests, the constitutive model and hot working map for the steel were constructed, and the microstructure evolution of the steel was systematically analyzed. The critical deformation conditions for the occurrence of DRX were determined using the plotted work hardening rate curve. The findings indicate a significant rise in flow stress as strain rate increases or deformation temperature decreases. Concurrently, the strain needed to attain peak stress progressively grows. The activation energy for deformation of the steel is 595.511 kJ/mol, which results from the competition between dynamic softening and work hardening during its hot deformation process. Low strain rate and low temperature (0.01 s^-1, 950 °C) are the parameters for the instability zone of the steel, and localized flow and deformation bands are the microstructure manifestations of unstable hot processing. The optimal hot working window for the experimental steel is the medium to high strain rate range and medium to high temperature (0.1-10 s^-1, 1100-1200 °C), where the microstructure exhibits randomly oriented, uniformly distributed DRX grains. The bulging of the initial grain boundaries is primarily associated with the nucleation mechanism of DRX. Furthermore, based on the critical strain and peak strain, the kinetics of DRX are predicted by the Avrami equation.

High-nitrogen martensitic stainless steel (HNMSS) is increasingly recognized for its excellent strength-ductility balance and superior pitting resistance, largely attributed to the solid solution strengthening effect of nitrogen. Despite significant advancements in enhancing its mechanical properties, the precise relationship between alloying elements, particularly nickel (Ni), and microstructural evolution remains insufficiently understood. The role of Ni in HNMSS was investigated by examining the effects of two different Ni contents on microstructure and deformation mechanisms. A combination of mechanical testing and microstructural characterization was employed to assess the materials’ mechanical properties and phase transformations. The key findings reveal that the steel with higher Ni content exhibits improved mechanical performance, primarily due to an increased volume fraction of retained austenite, which activates both transformation-induced plasticity and twinning-induced plasticity effects. The microstructure after deformation forms a similar multilayer core-shell structure, with twinning martensite enveloping the softer austenite, which effectively avoids the risk of cracking caused by direct collision of martensitic variants.

Seamless steel tubes, owing to their excellent integrity, structural properties, and processability, are widely applied in industries such as petroleum transportation, power and chemical industries, and national defense. However, the stability of product quality in seamless steel tube production is often poor, particularly regarding the mechanical properties of intermediate products, which may not meet the required standards. This results in non-conforming products being unable to smoothly proceed to downstream processes. These issues mainly arise from the compactness of the production process, the characteristics of batch production, and the difficulty in managing order insertion. Consequently, optimizing the production process to minimize the impact of non-conforming products on subsequent processes has become a key challenge in seamless steel tube production. An intelligent reorganization production mechanism is proposed based on the full life cycle of seamless steel tubes, aiming at addressing the scheduling problems of tubes with abnormal performance. The mechanism utilizes a performance anomaly prediction model to detect and forecast potential anomalies in steel tubes, and in conjunction with intelligent scheduling strategies, rearranges the production plan for abnormal tubes. Experimental results demonstrate that the proposed mechanism can effectively improve the detection rate of abnormal tubes, significantly reduce time losses and energy consumption during production, and optimize both production cycles and stability. Specifically, the production cycle was shortened by 52 h, and energy consumption was reduced by approximately 12%. Through the intelligent scheduling model, the production plan was successfully optimized, reducing the production cycle and costs while improving production efficiency. The optimized scheduling scheme saved about 12% in production time, while enhancing the stability of the production plan and capacity utilization.

In order to enable efficient and cost-effective rehabilitation of surface-worn hydraulic supports, the synthesis and characterization of a novel Ti(N, B)/AISI431 composite coating formed on the surface of 27MnSi steel are explored via an exothermic in-situ reaction using the ultra-high speed laser cladding (EHLA in German) technique in combination with direct reaction synthesis (DRS). The aim is to mitigate the high residual stress and interfacial stress gradient in the remanufactured AISI431 coating on 27SiMn steel substrate and enhance surface wear resistance. The microstructure, phase composition and interface characteristics are carefully investigated. Much improved wear performance of the composite coating is revealed, mainly attributed to the in-situ formed Ti(N, B) precipitates, refined microstructure, broadened interface zone and reduced residual stress, benefited from the exothermic in-situ Ti(N, B)-reaction. The potential of combining ultra-high speed laser cladding with DRS is demonstrated to create coatings with tailored properties, providing valuable insights for developing advanced wear-resistant materials for industrial applications using EHLA.

A comparative investigation was conducted to evaluate the microstructure, mechanical properties, and corrosion resistance of hot-stamped steels fabricated via the compact strip production (CSP) and conventional cold-rolling methods. CSP steel exhibited an initial microstructure comprising ferrite, pearlite, and minor bainite, which retained a characteristic hot-rolled banded structure with refined ferrite grains (5.7 μm). In contrast, conventionally processed steel displayed coarser equiaxed ferrite (9.8 μm). In terms of mechanical properties, CSP parts demonstrated superior tensile strength (> 1433 MPa) and elongation (> 6.48%) compared to conventional counterparts (average elongation of 5.27%). However, CSP samples showed a 23 HV lower hardness, attributed to a deeper decarburization layer. Enhanced strength in CSP steel was linked to finer prior austenite grains and dislocation density inherited from the initial microstructure, despite potential undissolved cementite at lower austenitizing temperatures. Corrosion testing revealed improved resistance in CSP-processed steel, likely due to finer grains acting as barriers to corrosion propagation. The trade-offs in CSP are highlighted: while achieving higher strength-ductility synergy and corrosion resistance, decarburization effects necessitate optimization to mitigate hardness reduction. The potential of CSP for high-performance automotive applications requiring balanced mechanical and anti-corrosion properties is underscored.

The corrosion behavior of Q420 steel under constant temperature and freezing-thawing conditions is investigated. The steel exhibits the highest corrosion rate at 25 °C and the lowest corrosion rate at -30 °C, while the steel that undergoes freezing-thawing cycling shows lower corrosion rate than that at 0 °C. The localized corrosion is significantly affected by the temperature variations, with the samples corroded under freezing-thawing conditions showing the highest pit number density and the highest possibility of the pit initiation. The samples immersed at 0 and 25 °C show comparable pit size with higher pit depth, diameter, and volume, attributed to the higher rate of pit propagation along the vertical and horizontal directions. Galvanic corrosion results demonstrate that there are areas of activated metal under the ice, which form microcells with surrounding unaffected areas, attributed to the local ion concentration and the water crystallization.

The influence of cooling rate on the intergranular corrosion (IGC) susceptibility of Nb-Ti dual-stabilized super ferritic stainless steel S44660 following cold rolling and annealing was investigated using an optimized double-loop electrochemical potentiokinetic reactivation (DL-EPR) test, complemented by microstructural characterization. The results revealed that the optimal DL-EPR test conditions consisted of a 2 mol/L H₂SO₄ and 3 mol/L HCl solution, with a scanning rate of 0.1 V min^-1 at 30 °C. Notably, the resistance to IGC in S44660 steel increased progressively with higher cooling rates after annealing. Transmission electron microscopy and electron probe microanalysis showed that the sensitization of S44660 steel was attributed to the formation of Cr-depleted zones along grain boundaries due to M₂₃C₆ precipitates. IGC was no longer observed in S44660 steel when the cooling rate after annealing reached or exceeded 90 °C s^-1. It was confirmed that the nucleation and growth of carbides require a certain amount of time. Increasing the cooling rate after annealing effectively inhibits carbide precipitation and growth, thereby reducing the degree of intergranular Cr depletion and enhancing IGC resistance of S44660 steel.

The corrosion and cavitation erosion (CE) behavior of Co-6Ti-11V-9Cr alloy are investigated in both deionized water and 3.5 wt.% NaCl solution. Utilizing electrochemical methods and CE testing, the research aims to clarify the synergistic effects of CE and corrosion. The results demonstrate that after 8 h of CE exposure, Co-6Ti-11V-9Cr alloy experienced a cumulative mass loss of 1.84 mg in deionized water and 3.42 mg in NaCl solution, leading to mass loss rates of 0.23 and 0.43 mg/h, respectively. In NaCl solution, CE was responsible for 53.8% of the overall damage, with the remaining damage attributed to the combined influences of corrosion and CE. Under CE conditions, both the corrosion potential and corrosion current density of the alloy increased, which accelerated the corrosion process and exacerbated cavitation damage. The material sustained more severe damage in 3.5 wt.% NaCl solution over the same cavitation durations. Ultimately, CE damage mechanism of Co-6Ti-11V-9Cr superalloy was elucidated based on relevant experimental observations.

A systematic study was conducted on the microstructure, mechanical properties, and corrosion resistance of Ti-20Zr-xAl-2.5Sn (x = 5, 7, 9, 11, and 13 wt.%) quaternary alloy. The microstructure of the rolled alloys was characterized by optical microscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The mechanical properties were analyzed through tensile tests, microhardness tests, and friction wear tests. Corrosion performance was evaluated using electrochemical tests, and X-ray photoelectron spectroscopy was employed to analyze the passivation film on the alloy surface. The results show that increasing Al content improves the mechanical properties of the alloy, but excessive Al leads to the creation of Ti₃Al, resulting in a substantial deterioration of the mechanical characteristics of the alloy. The alloy with 7 wt.% Al exhibited the best overall mechanical properties. Electrochemical experiments revealed that higher Al content positively affected the corrosion resistance, with the alloy containing 7 wt.% Al showing the best corrosion resistance, followed by a slight decline. A small amount of Al₂O₃ in the passivation film enhanced the corrosion resistance, but the formation of Al₂O₃ with higher Al content decreased the corrosion performance.

The incorporation of KH560-modified steel slag (MSS) as a filler in alkyd coatings significantly impacts their corrosion resistance and mechanical properties. The modification process was characterized using the Fourier transform infrared spectrometer, scanning electron microscope, and X-ray photoelectron spectroscope to understand the chemical and morphological changes induced by KH560 treatment. Three types of coatings were prepared: pure alkyd coating (AC), steel slag/alkyd coating (SS/AC), and KH560-modified steel slag/alkyd coating (MSS/AC). Their corrosion resistance was evaluated by electrochemical impedance spectroscopy and salt spray tests, while mechanical properties such as hardness, adhesion, and flexibility were also assessed. Results show that MSS significantly enhances the hardness, flexibility, and adhesion of the coatings, forming a composite structure (MSS-KH560-alkyd) that significantly improves the performance of MSS/AC. Notably, MSS/AC demonstrated superior hydrophobicity with a water absorption rate of 0.624% and a contact angle of 100.7°. Electrochemical tests revealed an impedance modulus of 3.9 × 10⁷ Ω cm², a corrosion current of 3.39 × 10^-4 mA, and a corrosion potential of - 35 mV for MSS/AC. After a 10-d immersion in a 3.5 wt.% NaCl solution, MSS/AC maintained its protective properties. These findings underscore the potential of MSS as a sustainable and effective filler for alkyd coatings in corrosion protection applications.

Tantalum (Ta) alloys have been widely utilized in rocket, air-breathing engines, and airframe applications. However, traditional Ta alloys suffer from insufficient strength at ultra-high temperature, making it challenging to satisfy the design requirements for next-generation aerospace equipment. We report a novel strategy to design Ta alloys with superior mechanical properties by integrating the multi-principal element concept with compositionally complex carbides. By introducing multiple refractory elements and C, the resultant alloys displayed a thermally stable microstructure consisting of two phases. With the increasing C contents, the microstructure evolved from hypoeutectic to eutectic and then to hypereutectic. These varying microstructural characteristics influenced crack blunting and dislocation accumulation behaviors, leading to different softening resistance at 1600 °C and plasticity at room temperature. Benefiting from the strengthening effects of solid solution and compositionally complex carbides, these alloys exhibited a high strength of ~600 MPa at 1600 °C, significantly superior than that of traditional Ta alloys.

New discovery in whisker growth on Cr-Al-B MAB phase, which was formed during hot-dip aluminizing and subsequent thermal diffusion treatment of Fe-Cr-B cast steel, after cultivation under 400 °C, was found. Specifically, along with the growth of Sn whiskers on the whitened Cr-Al-B MAB phase, Al whiskers also formed, mainly attributed to the thermal activation of whisker cultivation at 400 °C and promotion effect of Sn in the Cr-(Al, Sn)-B MAB phase solid solution. Partial Al whiskers, interspersed with small amounts of Sn, exhibited transparency under the scanning electron microscopy image. These whiskers underwent random growth and sucking back into the matrix simultaneously. The temperature during whisker cultivation had a significant effect on the whisker growth.

Alkaline slag is vital in rare earth steel refining, making it crucial to study the wetting and penetration mechanisms between refractory materials and slag. The effect of Eu₂O₃ doping on the sintering properties of MgO-MgAl₂O₄ refractory materials was investigated while simulating the wetting behavior between the refractory and the CaO-Al₂O₃-SiO₂-MgO quaternary alkaline slag during rare earth steel smelting to improve the material’s resistance to alkaline slag corrosion. The doping of Eu₂O₃ can alter the crystal structure parameters of MgAl₂O₄ and MgO, causing lattice distortion. This lattice activation promotes interionic mass and diffusion, helping reduce porosity and promote densification of the material, further improving sintering properties. At the equilibrium wetting temperature (1723 K), Eu₂O₃ doping increases the interfacial free energy between the slag and refractory material, reducing the spreading coefficient of the molten slag. The contact angle increases from 32.1° to 42.2°, and the residual slag volume increases from 17.9% to 23.5%. The results of thermodynamic analysis show that MgAl₂O₄ and EuAlO₃ formed at the interface block the penetration of molten slag at high temperatures, improving the resistance of MgO-MgAl₂O₄ refractories to alkaline slag corrosion. Based on the capillary theory model, it was calculated that the capillary tension of the slag gradually increases with the addition of Eu₂O₃, while the theoretical penetration depth of the slag gradually decreases. The experimental results showed that the slag erosion depth of the sample decreased from 102.54 to 68.28 μm.