steel research international最新文献

Traditional bainitic steel for railway switches suffers from strength and toughness reduction due to blocky retained austenite (RA, alongside lengthy, energy-intensive production. Laser-deposited bainitic steel offers high hardness, superior strength-toughness, and short cycles. This study proposes a novel process combining laser deposition using 4340/Ni-coated SiC/FeSi6.5/Al composite powders with short-duration isothermal heat treatment. The Si-Al synergy suppresses carbide precipitation. Microstructure and mechanical properties after isothermal treatment were investigated. Results show transformation completes within 180 min at 300 °C, yielding a composite microstructure of nanoscale lath bainite and high-density thin-film retained austenite. This achieves a maximum strength-ductility product of 30.74 GPa%—1.5 times that of traditionally rolled bainitic steel—demonstrating excellent strength-plasticity synergy. Prolonged isothermal time increases retained austenite content to a peak while its carbon content gradually rises. Blocky retained austenite decreases continuously, film-like increases, and martensite content progressively declines. Tensile strength initially rises then slightly decreases with extended isothermal time, while both elongation and strength-ductility product increase. This approach achieves short-process preparation of high strength-toughness bainitic steel.

Electroslag remelting (ESR) has become an essential technique for producing high-quality alloys due to its capabilities in inclusion removal, compositional homogenization, and microstructure refinement. However, macrosegregation, caused by complex interactions among thermal, solutes, and convective phenomena during solidification, remains a persistent issue affecting ingot quality. This review summarizes the current understanding of macrosegregation formation mechanisms in ESR, including reactions between droplets and slag, thermal and solutal convection, and solute redistribution. The effects of key process parameters such as melting rate, power frequency, filling ratio, cooling intensity, slag thickness, and ingot size on segregation behavior are discussed in detail. Furthermore, advanced control strategies are reviewed, including optimizing electrode design, applying magnetic control, adjusting current pathways, and modifying slag composition. These methods have proven effective in improving solute distribution and reducing macrosegregation. This work aims to provide theoretical guidance and practical references for refining ESR process parameters and developing high-performance remelted alloys.

Electroslag remelting (ESR) has become an essential technique for producing high-quality alloys due to its capabilities in inclusion removal, compositional homogenization, and microstructure refinement. However, macrosegregation, caused by complex interactions among thermal, solutes, and convective phenomena during solidification, remains a persistent issue affecting ingot quality. This review summarizes the current understanding of macrosegregation formation mechanisms in ESR, including reactions between droplets and slag, thermal and solutal convection, and solute redistribution. The effects of key process parameters such as melting rate, power frequency, filling ratio, cooling intensity, slag thickness, and ingot size on segregation behavior are discussed in detail. Furthermore, advanced control strategies are reviewed, including optimizing electrode design, applying magnetic control, adjusting current pathways, and modifying slag composition. These methods have proven effective in improving solute distribution and reducing macrosegregation. This work aims to provide theoretical guidance and practical references for refining ESR process parameters and developing high-performance remelted alloys.

The influence of Al addition on ferritic stainless steel oxidation resistance has been investigated. Experiments are performed using a synthetic automotive exhaust gas at 950 °C and 1050 °C. In the case of 0Al steel, a double-layered oxide scale is observed at both temperatures. At 1050 °C, the oxide scale exhibits cracking due to thermal stress. Oxidizing atmosphere penetration results in Si enrichment at the oxide scale/substrate interface. Ultimately, multilayered Si-rich bands are formed sequentially in the oxide scale. In contrast, 0.7Al steel forms a protective Al₂O₃ layer at the oxide scale/substrate interface, and needle-shaped Al₂O₃ is formed under it due to volume shrinkage. Anion and cation diffusion are inhibited by the upper Al₂O₃ layer, preventing substrate corrosion. The findings of this work demonstrate a significant enhancement of oxidation resistance due to Al addition.

With the growing demand for efficient and sustainable scrap steel recycling, intelligent classification and grading technologies based on image recognition have become essential for enhancing productivity and optimizing resource utilization. This review systematically examines recent advancements in scrap steel image recognition, emphasizing the application of deep learning techniques that outperform traditional machine learning methods in accuracy and efficiency. It highlights how convolutional neural networks and attention mechanisms enhance feature extraction, improve interpretability, and enable robust automatic classification in complex industrial environments. The review also explores the pivotal role of data acquisition strategies in ensuring model performance, underscoring the importance of data quality, diversity, and annotation in developing effective recognition systems. Furthermore, it analyzes the application of attention mechanisms in detail, demonstrating their ability to focus on salient image regions and enhance recognition accuracy. Finally, this review summarizes the key challenges in current research—such as limited domain-specific datasets, poor generalization across diverse scenarios, and constraints on real-time deployment—and outlines future research directions aimed at developing adaptive, interpretable, and scalable intelligent classification systems, providing valuable insights and references for advancing automated scrap steel processing.

A laser cladding heat source parameter optimization method based on the Newton–Raphson-based optimization-extreme learning machine (NRBO-ELM) and classification and Pareto dominance-based multiobjective evolutionary algorithm (CPS-MOEA) frameworks is proposed to improve the simulation accuracy of the laser cladding temperature field for high-entropy alloy powder. CoCrFeNiMn high-entropy alloy coating is prepared on 316 L stainless steel surfaces by laser cladding technology. An L25(5³) orthogonal test is designed, and the transient temperature field in the laser cladding process is simulated by ANSYS. A mapping model between heat source parameters and molten pool quality characteristics is established based on NRBO-ELM. The heat source parameters are optimized via the CPS-MOEA algorithm to generate the Pareto solution set. A comprehensive evaluation and decision-making method based on weighted rank sum ratio is developed to rank the Pareto solution set and determine the optimal combination of heat source parameters. Results demonstrate the existence of a critical point near shape parameters b and c = 2.5 mm, where the material's low thermal conductivity coefficient, coupled with the nonuniform distribution of heat source energy, results in localized heat accumulation. The NRBO-ELM mapping model accurately predicts the transient temperature field with less than 2% error. The optimized transient temperature field in laser cladding shows consistency with actual experimental measurements.

Quenching & partitioning provides a promising heat treatment approach to obtain high-strength steels with improved formability properties. By exact temperature control, a multiphase microstructure containing tempered martensite and retained austenite is generated. However, this precise temperature control is only limitedly transferable to large industrial scale. Longer heat treatment times allow other microstructural features to occur. This paper deals with the impact of long-term partitioning on the microstructural evolution for different partitioning temperatures. By observation of the X-ray diffraction (XRD) pattern evolution during the treatment, the slower and longer-lasting processes can be investigated. The final microstructure is compared with the estimations by the CCET model, which is an extension of the classical constrained carbon equilibrium (CCE) model, including bainitic phase transformations. Light microscopy and atom probe tomography are applied to certain samples for microstructural characterization. The experimental approach itself using in situ XRD measurement, is successful in monitoring the diffusion-controlled processes and confirmed the microstructural evolution predicted by the CCET model. Low partitioning temperatures result in fine martensite laths and retained austenite films containing high carbon concentrations, whereas higher treatment temperatures favor more pronounced bainite formation and carbide precipitation.

The rapid and complete dissolution of lime in the steelmaking process enhances dephosphorization and desulfurization rates, reduces slag emissions, and improves slag utilization efficiency. Extensive studies have been conducted to investigate lime dissolution mechanisms under static and dynamic conditions. Because steelmaking furnace linings primarily consist of MgO-containing materials, slags typically contain MgO, which influences lime dissolution. However, the mechanistic role of MgO in lime dissolution in steelmaking slag systems remains unclear. In this study, the effect of MgO on the dissolution behavior of lime in CaO–SiO₂–20%FeO–5%P₂O₅–MgO slag with varying MgO content (0–10 wt%) is investigated. The results show four regions around the lime surface: an unmelted CaO region, a CaO–FeO–MgO layer, compound and solid-solution layers, and the original slag region. The formation of solid compounds and solid-solution layers hinders lime dissolution. The average dissolution rate of lime gradually increases, reaches its maximum at 5 wt% MgO, and decreases with further increases in MgO content. The mechanism underlying the MgO effect on lime dissolution is discussed based on the dissolution path, X-ray diffraction analysis, and viscosity calculations.

The evolution behaviors of primary carbides and austenite grains are of great significance to investigate the control mechanisms during isothermal austenitizing of H13 steel. Therefore, this study focuses on the thermal stability and transformation behaviors of V-rich MC and Mo-rich M₂C carbides in H13 steel. They are characterized using scanning electron microscopy, energy dispersive spectroscopy, and high temperature confocal scanning laser microscopy. Meanwhile, the growth behavior of austenite grains is analyzed. The results show that the V-rich MC carbides dissolve gradually during the isothermal austenitizing process of H13 steel, exhibiting good thermal stability. However, the Mo-rich M₂C carbides exhibit thermal instability. In addition, the melting phenomenon of M₂C carbides is more significant than that of MC carbides. Fine-dispersed secondary carbides form around the primary carbides and the grain boundaries. The dissolution rates V₁₀₅₀, V₁₁₀₀, and V₁₁₅₀ of primary carbides are −1.05 ×10⁻⁴, −1.98 × 10⁻⁴, and −9.57 × 10⁻⁵ μm s⁻¹, respectively. With the increase of soaking temperature and time, the austenite grain size increases significantly. Irregular austenite grain boundaries transitioned into a more regular morphology. After soaking at 1050, 1100, and 1150 °C for 720 min, the average equivalent circle diameters of austenite grains are 113.83 ± 51.31, 138.58 ± 54.02, and 181.14 ± 65.55 μm, respectively.

Quenching & partitioning provides a promising heat treatment approach to obtain high-strength steels with improved formability properties. By exact temperature control, a multiphase microstructure containing tempered martensite and retained austenite is generated. However, this precise temperature control is only limitedly transferable to large industrial scale. Longer heat treatment times allow other microstructural features to occur. This paper deals with the impact of long-term partitioning on the microstructural evolution for different partitioning temperatures. By observation of the X-ray diffraction (XRD) pattern evolution during the treatment, the slower and longer-lasting processes can be investigated. The final microstructure is compared with the estimations by the CCET model, which is an extension of the classical constrained carbon equilibrium (CCE) model, including bainitic phase transformations. Light microscopy and atom probe tomography are applied to certain samples for microstructural characterization. The experimental approach itself using in situ XRD measurement, is successful in monitoring the diffusion-controlled processes and confirmed the microstructural evolution predicted by the CCET model. Low partitioning temperatures result in fine martensite laths and retained austenite films containing high carbon concentrations, whereas higher treatment temperatures favor more pronounced bainite formation and carbide precipitation.