Fe-Mn-Al-C low density steels exhibit great application potential in aerospace, new energy vehicles, and other related fields due to their low density and excellent strength-ductility combination. For these steels, the characteristics of secondary phases (κ-carbide, B2 ordered phase, and DO₃ ordered phase), including type, morphology, and size, have a significant influence on tensile properties and service performances. In particular, intragranular κ'-carbide at the nanoscale serves as the key strengthening phase, which can significantly enhance strength and wear resistance through either dislocation bypassing or coherent cutting mechanisms. Additionally, the B2 and DO₃ ordered phases can effectively impede dislocation motion, improve work hardening capacity, and consequently enhance both strength and ductility. Therefore, precise control over the precipitation behavior of secondary phases enables the synergistic optimization of strength, ductility, and toughness to meet the requirements of various applications. The composition, structure, and strengthening mechanisms of secondary phases in Fe-Mn-Al-C low density steels is summarized, with an emphasis on their effects on tensile properties as well as service performances such as impact toughness, ductility, and weldability.

Controlling pellet size is regarded as a critical measure to boost blast furnace smelting efficiency. Long detection cycles and prominent measurement errors are frequently encountered in conventional manual particle size inspection, so matched automatic intelligent detection technologies are urgently required. Accordingly, a watershed segmentation algorithm integrated with marker reconstruction strategy is proposed, and influences of three distinct denoising schemes on pellet image segmentation performance are compared based on the above algorithm. Test results are demonstrated as follows: pellet particle size detection is implemented via bilateral filtering preprocessing combined with the marker reconstruction-based watershed algorithm, with segmentation accuracy reaching 89%, while over-segmentation rate and under-segmentation rate are quantified at 11.5% and 7.1%, respectively. Outstanding engineering application values are verified for the developed pellet particle size detection algorithm in scenarios of industrial automatic inspection and product quality control. Innovative ideas and technical supports are supplied by the proposed algorithm for iterative upgrading of online pellet detection technologies.

Currently, iron ore serves as the core basic raw material of the iron and steel industry, and its supply-demand relationship is being gradually transformed. High-grade iron ore resources are being increasingly depleted after long-term large-scale mining, and its price is being continuously raised due to unbalanced supply and demand. Enormous cost pressures are thus imposed on iron and steel enterprises, making the development and utilization of vanadium-titanium magnetite a popular research topic in the current metallurgical industry. As an important mineral resource, vanadium-titanium magnetite contains not only iron elements but also abundant rare metal elements such as vanadium and titanium, and extremely high comprehensive utilization values are possessed by it. Nevertheless, numerous deficiencies are presented when high-proportion vanadium-titanium magnetite is adopted for pellet production. The compressive strength and metallurgical properties of pellets are adversely affected, and its large-scale application in iron and steel production is therefore restricted. Accordingly, in-depth researches on the optimization of process parameters for pelletizing with high-proportion vanadium-titanium ore are carried out, and reasonable blending ratios of vanadium-titanium ore are determined. Great practical significances are embodied in improving the quality of vanadium-titanium magnetite pellets and promoting their efficient application in the iron and steel industry. Experimental results are demonstrated that when the blending ratio of vanadium-titanium ore is set at 70%, the bentonite dosage is controlled at 1.5% and the roasting temperature is maintained at 1 150 ℃, the compressive strength of finished pellets reaches 2 011 N. The mineral compositions of pellet ore are mainly composed of titanium hematite and titanium magnetite, and the generated liquid phases primarily include calcium-iron silicate phase and calcium-iron pyroxene phase. Optimal parameters for vanadium-titanium ore blending ratios are confirmed via optimization experiments, and the quality of vanadium-titanium magnetite pellet ore is effectively improved.

In response to the demands for low-carbon transformation and solid waste resource utilization in the iron and steel industry, waste activated carbon (SAC) and anthracite coal (XY) are firstly proposed to be blended and adopted as blast furnace injection fuels. The combustion characteristics of XY, SAC and their mixtures are systematically revealed via non-isothermal thermogravimetric tests and kinetic models. It is indicated that higher ordering degree and graphitization degree are exhibited by XY compared with SAC. Developed pore structures and superior specific surface areas are possessed by SAC, by which the ignition temperature and reaction activation energy of blended fuels can be obviously reduced after blending. The optimal synergistic effect is achieved when the blending proportion of SAC reaches40%(mass fraction). According to kinetic analysis, more accurate fitting results of the combustion process are obtained by the random pore model than the volume model. With the gradual increase of SAC blending ratio, the activation energy of blended fuels is decreased firstly and then increased, and the minimum activation energy is obtained at the blending ratio of 40%(mass fraction), which is determined as the optimal proportion. Complementary advantages between solid waste resource utilization of SAC and high calorific value of XY are realized by blended fuels.

Compositions of converter slags are exerted substantial impacts on endpoint control and steel material consumption, and remarkable fluctuations are induced by multiple factors including hot metal conditions, slag-forming regimes and dephosphorization processes. Aiming at the high dimensionality and strong nonlinearity featured by slag composition characteristic factors, SiO₂, CaO and TFe are selected as research subjects, and a Stacking ensemble learning prediction model based on feature weighting is proposed. Multiple base learners are integrated into the established model, and a feature weighting mechanism is embedded in the meta-learner. Contribution degrees of base models are dynamically adjusted in accordance with the importance of each input feature, and intricate nonlinear correlations are excavated. Model construction, training and cross-validation are completed with historical production data of converters, and critical influential features are identified via SHAP value analysis to supply references for model optimization and process parameter adjustment. Superior performances in terms of mean absolute error, root mean square error and coefficient of determination are demonstrated by the feature-weighted Stacking model when compared with single regression models and various base learner models. Mean absolute errors of SiO₂, CaO, P₂O₅ and TFe are quantified as 0.144, 0.539, 0.037 and 0.403, corresponding root mean square errors are 0.277, 0.787, 0.082 and 0.634, the coefficients of determination reach 92.2%, 84.8%, 79% and 80.1%,respectively.The prominent superiority in prediction precision and stability of the proposed model is verified.

Taken 70 t grade 16Mn steel ingot as the research object, numerical simulation via ProCAST software and physical dissection analysis of steel ingots are adopted. The effects of molten steel superheat and pouring speed on the control of porosity and shrinkage cavity defects as well as segregation behavior of alloy elements in large-sized mould-cast steel ingots are investigated. It is found that effective control over internal porosity defects and macro-segregation of alloy elements inside ingot bodies can be realized under the conditions of molten steel superheat of 50-60 ℃, ingot body pouring speed of 3.0 t/min and riser pouring speed of 1.0 t/min together with favorable riser thermal insulation effect. No shrinkage cavity defects are formed inside ingot bodies after solidification completion. Strip-shaped segregation channels with a width of approximately 400 μm are distributed in the upper-middle regions, where general porosity defects graded as level 1 are detected. The above defects can be eliminated by regulating heating temperature and forging ratio. Segregation inside ingot bodies is mainly induced by fractional crystallization of molten steel, and negative segregation serves as the dominant form. Negative segregation zones of carbon, silicon, manganese, phosphorus and sulfur are formed within the height range of about 50 cm at the bottom of steel ingots. Local negative segregation of carbon elements is observed within the scope of 10 cm away from the surface layer of ingot bodies. Mild negative segregation of phosphorus elements is presented in the whole area of ingot bodies, while large-scale negative segregation zones of sulfur elements are formed near the surface layer at the upper part of ingot bodies.

To improve the automatic identification accuracy and detection efficiency of surface defects on cylindrical roller bearings, a bearing classification model named CGMBNet based on multi-level attention mechanisms is proposed. ResNet18 is adopted as the encoder of the constructed model. An adaptive cross-attention module (ACAM) is utilized to establish cross-layer feature correlations between encoders and decoders, through which critical features are enhanced and noise information is suppressed. A global attention moduleis embedded to complete multi-scale feature fusion and long-range dependency modeling, and local defect details and overall roller structures are characterized collaboratively. A multi-branch optimal adaptive weighting moduleis designed for adaptive optimization of multi-scale prediction outputs to further boost classification performance. Test results are obtained on a bearing classification dataset supplied by a special bearing corporation limited, with a classification accuracy of 90.75% and a mean F1-score of 89.57% achieved by CGMBNet. When compared with conventional classification models, object detection models and state-of-the-art algorithms developed in recent years, classification accuracy is improved by no less than 8.05%, 8.35% and 5.12%, respectively. Superior overall performance is demonstrated, and the effectiveness and practicability of the presented model for surface defect classification of cylindrical rollers are verified.

The dynamic deformation and failure behavior of a 2 000 MPa grade ultra-high-strength steel at high strain rates was investigated by quasi-static tensile tests and split Hopkinson pressure bar dynamic compression tests. Stress-strain responses at different temperatures and strain rates were obtained, and the microstructures and fracture morphologies were characterized by optical microscopy, scanning electron microscopy, and transmission electron microscopy. Within the strain rate range of 500-5 100 s^-1, both the dynamic yield strength and plastic flow stress of the tested steel first increased and then decreased with increasing strain rate, indicating that strain rate hardening dominates at relatively low strain rates, while thermal softening caused by adiabatic temperature rise gradually becomes predominant beyond a critical strain rate. A deformed adiabatic shear band formed at 1 966 s^-1, and a transformed adiabatic shear band appeared at 3 533 s^-1. The critical strain rate for failure was approximately 3 700 s^-1, at which the fracture remained predominantly ductile; cleavage features appeared on the fracture surface when the strain rate reached 5 100 s^-1. Based on the quasi-static and dynamic test data, the parameters of the Johnson-Cook model were determined, a quantitative relationship between strain rate and adiabatic temperature rise was established, and the rate-temperature coupling effect was introduced into the model by modifying the thermal softening term.

As the global energy structure transitions toward a low-carbon economy, the demand for hydrogen transportation continues to grow. X65 pipeline steel, a mainstream material for hydrogen transport, is prone to hydrogenembrittlement in high-pressure hydrogen environments, posing a threat to pipeline safety. To investigate the effect of hydrogen blending ratio on the properties of X65 pipeline steel, the steel was subjected to hydrogen blending at ratios ranging from 5%-30%(volume fraction). Slow strain rate tensile tests were conducted, combined with microscopic characterization techniques including scanning electron microscopy, transmission electron microscopy, and energy-dispersive spectroscopy, to systematically characterize the mechanical properties and fracture behavior of the material. The results show that hydrogen addition significantly degrades the mechanical properties. As the hydrogen blending ratio increases, the fracture morphology transitions from ductile microvoid coalescence to brittle fracture, accompanied by pronounced dislocation entanglement and lattice distortion in the microstructure.

The mechanism by which Ni content influences the austenite stability and low-temperature impact toughness of 304L stainless steel was investigated. V-notch impact tests were conducted at room temperature and -196 ℃. The precipitation behavior of quenched martensite and deformation-induced martensite at low temperatures in test steels with varying Ni contents was characterized using multiple techniques, including OM, EBSD, XRD and TEM. The primary factors affecting the low-temperature impact toughness of 304L stainless steel were then clarified. The results show that the precipitation of quenched martensite at low temperature is the main cause of the reduction in material toughness. As the Ni content in the test steel increases from 8.68% to 11.7%, the quenched martensite volume fraction after cold treatment at -196 ℃ gradually decreases from 16% to approximately 0.8%, indicating improved thermal stability of austenite. Consequently, the impact energy increases from 155 to 272 J, and the low-temperature impact toughness is significantly optimized. Furthermore, Ni also enhances the mechanical stability of the austenite phase in 304L stainless steel, effectively suppressing the formation of deformation-induced martensite. For the test steel with 8.68% Ni mass fraction, the deformation-induced martensite volume fraction at the fracture surface of the impact specimen is 78%; when the Ni content increases to 11.7%, this value decreases to about 45%.

To address the frequent abrasive wear failure of bucket teeth in small crawler-type handling-excavating vehicles, a high-boron high-speed steel material for bucket teeth was developed, and its microstructure, mechanical properties, and wear resistance were systematically investigated. The results show that the as-cast microstructure mainly consists of α-Fe and M₂B borides (M is Fe, Cr, Mo, etc.), including continuous network-like bright white M₂B_Ⅰ, light blocky M₂B_Ⅱ, and dark rod-like M₂B_Ⅲ. After heat treatment, the sharp corners of the borides become rounded, the network structure is broken, and fine secondary M₂₃(B,C)₆ precipitates are formed. The total volume fraction of the three types of borides is approximately 23%, of which M₂B_Ⅰ and M₂B_Ⅲ together account for 67.41%. Owing to the formation of dense edge dislocations caused by the solid solution of Mo and Cr, these two borides exhibit high hardness and toughness. After heat treatment, the alloy achieves a macrohardness of 64.23HRC and an impact toughness of 8.82 J/cm², and its three-body wear resistance is superior to that of low-carbon alloy steel, V-Mo series high-speed steel, and Cr26 high-chromium cast iron. The heat-treated high-boron high-speed steel combines hardness, toughness, and wear resistance, making it suitable for the harsh working conditions of bucket teeth in small handling-excavating vehicles.

With the large-scale development of wind power generation equipment, higher performance requirements are imposed on fasteners for wind turbines. As a typical bolt steel, 42CrMoVNb contains strong carbide-forming elements such as Nb and V, making its precipitation evolution sensitive to heat treatment, which directly affects the service performance of bolts. To establish the optimal heat treatment process window matching the service requirements of wind power fasteners, Thermo-Calc thermodynamic software was used to simulate the precipitation behavior of 42CrMoVNb bolt steel. The types of precipitated phases in the steel were investigated, with a focus on calculating and analyzing the precipitation behavior of MC phases during tempering. The results show that under equilibrium conditions, 42CrMoVNb bolt steel containsaustenite, ferrite, MC, M₂₃C₆ phases, and cementite. During tempering, (Mo,V)C is the dominant precipitated phase. When the holding time is 120 min, as the tempering temperature increases from 500 to 650 ℃, the size of (Mo,V)C phases coarsens significantly: the size of (Mo,V)C phases precipitated within grains increases from 2.5 to 20.1 nm, that precipitated at dislocations increases from 2.6 to 156.8 nm, and that of phases precipitated at grain boundaries increases from 2.5 to 194.1 nm. Meanwhile, the volume fraction of (Mo,V)C phases increases markedly: the volume fraction of phases precipitated within grains increases from 2.6×10^-5 to 8.9×10^-3, thatprecipitated at dislocations increases from 6.8×10^-12 to 1.8×10^-4, and that precipitated at grain boundaries increases from 2.1×10^-10 to 2.7×10^-3. These differences are primarily attributed to the combined effects of nucleation energy barriers at different crystal defects, atomic diffusion rates, and carbide coarsening rates.

To clarify the effect of Ni mass fraction on the corrosion behavior of Ni-Cr-Mo-V steels, two steels were investigated in artificial seawater using weight-loss tests, electrochemical measurements, microscopic observations, and rust-layer composition analyses. The results indicated that although both steels exhibited bainitic microstructures, steel B predominantly consisted of fine and uniform lath bainite and had a higher Ni mass fraction, resulting in better corrosion resistance. During the initial stage of corrosion, both steels exhibited relatively high corrosion rates, which gradually decreased and subsequently stabilized with increasing immersion time. After 42 d of immersion, the corrosion rate of steel A increased because of local cracking and damage to the corrosion product layer, whereas steel B formed a denser corrosion product layer with fewer cracks, which effectively restricted the penetration of corrosive species toward the substrate.Ni, Cr, and Mo were enriched in the rust layer of steel B, promoting the formation of a compact and protective inner rust layer and thereby further improving its corrosion resistance. With prolonged immersion, the corrosion morphology of both steels gradually transitioned from localized corrosion to relatively uniform corrosion. Owing to its denser and more stable inner rust layer, steel B exhibited superior overall corrosion resistance to steel A.