An Fe-0.15C-1.2Si-5Mn-0.09Nb-0.08V-0.07Mo (wt.%) medium manganese steel that was subjected to a novel austenite reverted transformation (ART) annealing process. This paper is based on the conventional ART annealing process, and a deformation and ART annealing process is proposed. The influence of the deformation degree on the microstructure and grain size of the medium manganese steel was determined by scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron backscatter diffraction (EBSD) and X-ray diffraction (XRD). The results show that the deformation had a great effect on the microstructure evolution and grain size. The microstructure of the medium manganese steel after the deformation and ART annealing process was consistent with the theory of austenite reverse transformation, i.e., the martensite reverse transformation into austenite occurred during the deformation and ART annealing process. The final microstructure was a mixture of martensite and austenite. As the deformation degree increases, the martensite gradually refines, and carbides precipitate in the lath martensite. The retained austenite is gradually transformed from very large to small and is distributed between the martensite laths. The results show that when deformation occurs in the austenite region, a small deformation can obtain more retained austenite.

Traditional hot-stamping products have super-high strength, but their plasticity is usually low and their integrated mechanical properties are not excellent. Functionally graded property structures, a relatively novel configuration with a higher material utilization rate, have increasingly captured the attention of researchers. Hot stamping parts with tailored properties display the characteristics of local high strength and high plasticity, which can make up for the limitations of conventional hot stamping and optimize the crash safety performance of vehicles. This new idea provides a means of personalized control in the hot-stamping process. In this paper, a new strategy of local induction heating and press hardening was used for the hot stamping of boron steel parts with tailored properties, of which the microstructure from the hard zone to the soft zone shows a gradient distribution consisting of a martensite phase, multiphase and initial phase, with the hardness ranging from 550 HV to 180 HV. The re-deformation characteristics of hot stamping parts with tailored properties have been studied through the uniaxial tensile test, in cooperation with digital image correlation (DIC) and electron backscattered diffraction (EBSD) techniques. The experiments show that there are easily observable strain distribution characteristics in the re-deformation of hot stamping parts with tailored properties. In the process of tensile deformation, the initial phase zone takes the role of deformation and energy absorption, with the maximum strain before necking reaching 0.32. The local misorientation of this zone was high, and a large number of low angle grain boundaries were formed, while the proportion of small angle grain boundaries increased from 13.5% to 63.3%, and the average grain size decreased from 8.15 μm to 3.43 μm. Meanwhile, the martensite zone takes on the role of anti-collision protection, with a maximum strain of only 0.006, and its local misorientation is mostly unchanged. The re-deformation experimental results show that the hot stamping of boron steel parts with tailored properties meets the functional requirements of a hard zone for anti-collision and a soft zone for energy absorption, suitable for automobile safety parts.

The work rolls in modern thin slab continuous casting and rolling (TSCR) lines operate under both high speeds and heavy loads, making the real-time prediction of uneven roll wear a significant technical challenge. Existing prediction methods often fail to effectively integrate physical wear mechanisms with data-driven approaches. This study presents a novel predictive method that couples production data with mechanistic wear modeling. The proposed model incorporates rolling parameters, temperature effects, and deformation mechanics, and employs particle swarm optimization (PSO) along with segmented solving techniques to introduce adaptive weight factors. Validation results demonstrate the model achieves high accuracy and strong adaptability across production lines, offering a promising framework for wear prediction in TSCR lines and potential applications in other industrial domains.