The γ-ε martensitic transformation in austenitic steels generally enhances work hardening capacity and fatigue resistance. However, it also induces brittle-like fracture and compromises resistance to hydrogen embrittlement. Understanding the evolution mechanisms of plasticity, stress, and damage during deformation involving γ-ε martensitic transformation is essential to mitigate these adverse effects. To facilitate this understanding, this study reviews the mechanisms of stress concentration and accommodation, identifies cracking and crack arrest sites, and analyzes fracture behaviors associated with γ-ε martensitic transformation under monotonic tensile deformation and cyclic loading conditions. Furthermore, potential strategies for enhancing damage tolerance are introduced.

In order to analyze the structural behavior of Mg²⁺ in the slag and the difference of structural behavior with Ca²⁺, this paper examines the variation rule of microstructure of SiO₂–CaO–Al₂O₃–MgO slag system with the increase of MgO/CaO ratio by molecular dynamics simulation. The results indicate that in the system, Mg²⁺ can exist in three coordination forms when combined with oxygen: 4, 5, and 6. It can form ionic bonds with anions to exist as a network modifier, and it can also participate in network formation by forming [MgO₄] tetrahedra through Mg–O covalent bonds. Under the condition of constant SiO₂ and Al₂O₃ content, as the MgO/CaO ratio increases, the proportion of Mg^IV in the slag first increases and then decreases, with a turning point at 30.25%. In contrast, the Mg^VI ratio shows a decrease followed by an increase, with a minimum value of 17.33%. Ca²⁺ in the system mainly exists as a network modifier, and its modifying effect first decreases and then increases with the increase of MgO/CaO ratio. The stability of the Si–O and Mg–O tetrahedral structures changes little with the increase in Mg²⁺ content, while the stability of the Al–O tetrahedral structure decreases, and Al^IV gradually transforms into higher coordinated Al. The content of bridge oxygen and the high polymerization degree structural units increase with the addition of Mg²⁺, leading to an increase in the melt polymerization degree.

Considering the problems of calcium low yield and instability during the calcium treatment of molten steel, the introduction of ultrasound into the secondary refining process is proposed to enhance the mass transfer coefficient of calcium (MTCC). A computational model including acoustic streaming and thermal effects is established to quantitatively characterize the mass transfer behavior and its influence on mixing time, calcium yield, and vaporization rate, and the effects of acoustic streaming and thermal effects on the MTCC are discussed. The results show that ultrasound significantly improves the local velocity and temperature of the molten steel, effectively promoting calcium transfer from the high-concentration zone to the low-concentration zone. Acoustic streaming plays a dominant role in enhancing MTCC, and the MTCC is two orders of magnitude higher than without ultrasound, corresponding to 7.061×10⁻⁵ and 5.230×10⁻⁷ m·s⁻¹, respectively. The increase in MTCC with ultrasound radiation time is attributed to the thermal effect, increasing from 7.061×10⁻⁵ to 7.816×10⁻⁵ m·s⁻¹. In addition, the MTCC positively correlates with ultrasonic power, while ultrasonic frequency and probe radius have a negative effect on MTCC. Consequently, the mixing time, calcium yield, and vaporization rate are 780 s, 6.32%, and 0.584%·s⁻¹ without ultrasound applied, and those of are 250 s, 26.64%, and 0.295%·s⁻¹ at a power of 96 kW and a frequency of 24 kHz.

To elucidate the removal mechanism for gangue (Al, Si, and P elements) components in low-grade iron ore using an alkaline hydrothermal treatment, model compounds of the gangue components were treated with a 5M NaOH solution using a bath-type reactor. The effective removal of P ions by alkaline treatment at room temperature suggests that these ions are desorbed from α-FeOOH. The Si and Al contained in SiO₂ and kaolinite can also be removed by alkaline hydrothermal treatment, and the extent of removal increases following washing. Na forms compounds with clay minerals and this is inferred to be the cause of the residual Na detected following treatment. The presence of α-FeOOH and Fe₂O₃ is found to have almost no effect on this residual Na content. P in Fe-P compounds can also be removed by alkaline hydrothermal treatment.

Vanadium and titanium magnetite (VTM) possesses considerable value in comprehensive mining and utilization. The varying TiO₂ content in the blast furnace burden during smelting directly influences the formation, location, thickness, permeability, and heat exchange within the cohesive zone of the blast furnace. This paper employs the ‘Qisunny method’ to simulate the reduction droplet of blast furnace burden under the condition of different TiO₂ content in the theoretical composition of blast furnace hearth slag, and systematically researches the change rule of soft-melting and dripping performance of blast furnace burden. It systematically investigates the factors influencing the soft-melting and dripping performance of the blast furnace burden. The results indicate that, as the TiO₂ content in the theoretical composition of blast furnace final slag increases from 7.5 wt% to 25.5 wt%, several changes occur: the softening zone (ΔT_s) widens, while the melting zone (ΔT_m) narrows. Additionally, the temperature range of the cohesive zone (ΔT_c) experiences a slight widening, and the cohesive tends to shift downward. The reduction of iron oxides in the blast furnace burden occurs primarily in the softening zone, whereas the reduction of titanium oxides begins in the melting zone. Furthermore, the simulated operation line gradually deviates from the ideal operation line, necessitating an increase in airflow and coke ratio for optimal performance in the actual operation of the blast furnace.

Effect of copper on solidification microstructure and solidification process were investigated for high carbon high speed steel type alloys (Fe-2.0%C-5%Cr-5%Mo-5%V-0~7.5%Cu in mass%). The microstructure of all as-cast specimens with different copper content is dendrite consisting mainly of primary γ, MC-γ eutectic and M₂C-γ eutectic. In the copper-free specimen, the shape of dendrite is granular (or equiaxed), while in the copper added specimens, it is columnar, and the columnarization trend become more pronounced with increasing the amount of copper content. Furthermore, the secondary dendrite arm spacing decreased with increasing amount of copper content. The volume fraction of primary γ dendrite gradually decrease and MC-γ eutectic gradually increase with increasing of the amount of copper content. While the volume fraction of M₂C-γ eutectic is approximately constant regardless of the amount of copper content. The concentrations of chromium, molybdenum and vanadium within microstructure were approximately constant regardless of the amount of copper content in any microstructures. While the concentration of copper within microstructure was higher in order of dendrite, MC-γ eutectic and M₂C-γ eutectic compared with any copper content, and its order corresponds to the solidification prosses. These results suggest that copper tends to remain in the solid phase (that is dendrite) rather than redistribute to the liquid phase during solidification.

The surface defects of continuous casting slab have an extremely negative impact on the quality and productivity of steel. Detection of surface defects poses significant challenges in real-time production line, such as excessive reliance on human intervention, low efficiency, and limited measurement accuracy. To tackle these challenges, this paper proposes a lightweight deep learning model called CCSNet, which can achieve efficient feature fusion while reducing computational costs. The new model solves the balance between accuracy and real-time performance of slab surface defect detection. Firstly, real-time photos of the slab surface were obtained using the visual inspection platform installed on the continuous casting production line. From these photos, a dataset containing five distinct kinds of defects was produced. Next, the model composition is introduced, and the feature information required for detection is generated by training the dataset. Finally, the suggested model’s validity is demonstrated by model testing and comparison with other models. The CCSNet achieves an average accuracy of 90.1% on the self-made dataset, with the model’s weight size being only 6 MB. The detection performance surpasses other traditional models, which can work for real-time defect detection under actual conditions.

The roll-type flatness meter is a critical detection device in the cold rolling process of strip production. It demands extremely high precision while being highly sensitive to strip tension and wrap angle. Research shows that as the strip tension or wrap angle increases, the pressure distribution of the strip changes gradually in the roll circumferential direction, resulting in the output of unimodal and bimodal waveform signals. Owing to the impulsive nature of the unimodal waveform, earlier methods typically extract and use the peak value as the numerical representation of the pressure exerted by strip. However, when both types of waveforms appear in different detection channels simultaneously and the same representation method is applied, significant detection errors can arise. To identify a unified numerical representation method applicable to all output waveforms, this study introduces the concept of the sensor perception zone of roll-type flatness meters. Based on the idea of the influence function, a fast and effective integration-based value method is proposed, which fully considers the mapping relationship between the roll surface pressure in the perception zone and the output waveform signal. This approach eliminates the effects of varying operational conditions and signal outputs, enabling the direct calculation of effective values meeting the same gradient, which serves as the data source for determining the strip flatness distribution in real-time detection. Furthermore, to account for the differences in sensor perception zones while applying the integration method, an integration-based calibration method is proposed. Finally, finite element simulations were conducted to obtain output data under varying wrap angle and varying tension conditions. The correctness of the method is verified according to the calculated Pearson correlation coefficients.

The mechanical properties of work-hardened pure titanium foils pre-treated by short-time continuous electric current were studied. Tensile tests were conducted for the foils pre-treated at different temperatures. A laser speckle-based digital image correlation (DIC) technique was utilized for a full-field strain measurement. The accuracy of the DIC system was confirmed by the correlation with the measurement from a video extensometer. The continuous electric current was kept for around 1 min to pre-treat the foils at different temperatures. The mechanical properties of the foils affected by the continuous electric current were discussed in terms of stress-strain curves and strain field. The results showed that the foils indicated non-uniform temperature distribution along length direction while the strain localized at the area with higher temperatures. The stress indicated the same profiles for the foils without pre-treatment and pre-treated at 160 and 300°C. In the meanwhile, the stress decreased while the fracture strain increased when the pre-treating temperature achieved 450°C, revealing an increase of ductility of the foils pre-treated at a higher temperature. The full-strain field and strain distribution showed that the width of the area with localized strain increased with increasing pre-treating temperature. The Lankford coefficient was calculated based on the obtained strain field, showing an increasing-decreasing tendency with increasing pre-treating temperatures.

As is well known, reconstruction of the initial microstructure before an inter-critical annealing (IA) can optimize the final microstructure as well as improve mechanical performance. In this study, the initial microstructure with “pre-ferrite + martensite” was designed via two-stage cooling in the rolling process. The final microstructure observation showed that bimodal size distributed austenite and double morphologies of austenite. Moreover, lath-shaped austenite existed in the martensitic matrix; blocky austenite occurred at the boundaries of pre-ferrite. According to the numerical simulations of the IA process, blocky austenite tended to be more stable with smaller size and higher Mn concentrations than lath-shaped austenite. The characteristics of austenite were similar at different IA temperatures, but the lower IA temperatures, the smaller size of austenite. An excellent combination of strength (1308 MPa) and ductility (34%) was obtained at 928 K, which was ascribed to a positive TRIP effect induced by the austenite with a large stability gradient.

This report describes an improved application for quantifying trace nickel contents, which are affected by interference from co-existing chromium, cobalt, and molybdenum as matrix constituents. Numerous spectral interferences of atomic and ionic emission lines excited by argon inductively coupled plasma lead to poor application of trace nickel contents in cobalt–chromium–molybdenum alloy and high alloy steel. Reliable and non-skilled application was achieved using nitrogen microwave-induced plasma, which decreased ionic emissions, leading to negligible chemical interference. An excellent lower limit of quantification (0.0005 mass fraction % nickel in 0.20 g sample) was estimated using atomic spectrometric measurements based on the nitrogen microwave-induced plasma and no additional preconcentration.