Planar flow casting technique can be used to prepare Fe⁃3 %Si ribbons. This study investigated the annealing microstructure, texture, and magnetic properties evolution of Fe⁃3 %Si ribbons with a thickness of ~0.05 mm prepared by planar flow casting. The results show that ultra⁃fine microstructures are observed on both the wheel surface and free surface of the planar flow casting ribbons, with the texture primarily characterized by {001}(<001>//ND) and copper ({112}<111>) texture. During annealing, annealing temperature significantly influences the microstructure evolution. That is, when annealed at 950 ℃, grain growth on both surfaces of the Fe⁃3 %Si ribbons is limited; when the annealing temperature increases to 1 050 ℃, some grains exhibit strong growth advantages, while a number of fine grains still remain after being annealed for 3 hours. Overall, the type of main texture components does not change along annealing, and the enhancement of {001} texture implies the growth advantage of {001} grains. In terms of magnetic properties, the magnetic induction B₅₀₀₀ remains~1.68 T before and after annealing. When annealed at 950 ℃, prolonging annealing time does not lead to significant changes in the iron loss P_1.0/400, whereas at 1 050 ℃, the iron loss gradually decreases from >40 W/kg to ~20 W/kg due to the microstructural coarsening with the extended holding time.

Secondary recrystallized Goss orientation ({110}<001>) in high magnetic flux density grain⁃oriented silicon steel originates from shear bands in cold rolled {111}<112> grains. To clarify the deviation correlation between Goss shear band and matrix grain can provide new routines for further enhanced accuracy of secondary recrystallized Goss orientation. In this study, a visco⁃plastic self⁃consistent (VPSC) model together with experimental measurements is used to investigate Goss deviation of shear bands in {111}<112> matrices deviating by up to 10° along φ₁ and φ₂ axes. When {111}<112> matrix deviates solely along φ₁ or φ₂ axis, shear band orientation exhibits similar φ₁ and φ₂ deviation angles from exact Goss orientation. When matrix deviates along both φ₁<90° and φ₂<45°, φ₁ deviation of shear band orientation is smaller than φ₂. Conversely, for matrix deviating along both φ₁<90° and φ₂>45°, φ₁ deviation of shear band orientation is greater than φ₂. Shear band deviation from exact Goss orientation is relatively smaller for {111}<112> matrices deviating along both φ₁<90° and φ₂<45°. Within the matrix deviation range of 84°<φ₁<90° and 35°<φ₂<46°, it is more likely to obtain Goss shear band with deviation angle less than 3°.

Planar flow casting is a rapid solidification forming technology, which has been widely concerned by academia and industry because of its ability to continuously produce metal strips or wires in near net shape. The technical parameters of planar flow casting process are very important to the formability and stability of strip / wire products. This paper summarized the influence of various process parameters on the formability of the strip, including the rotation speed of the copper roller, the pressure difference of the molten liquid, the gap between the nozzle and the copper roller, the width of the nozzle groove, the supercooling degree, the solidification speed, the cooling rate, the heat transfer coefficient and so on. At the same time, the future development of planar flow casting technology was prospected. The neural network algorithm in deep learning will be used to further quantify the influence of various technical parameters, and the application of planar flow casting technology to the industrial production of crystal strips is an important development direction.

With the implementation of new national energy efficiency standards and the promotion of national policies, the transformer manufacturers are required to improve the energy efficiency level of transformers and the grain oriented electrical steel manufacturers are also urged to improve the product level. Baosteel has formed six series⁃more than 70 brands of products, which can meet the needs of different customers. In recent years, Baosteel has developed 0.18 mm new products with excellent magnetic properties. The new products have realized mass production. In this paper, the properties and transformer performance of 0.18 mm products are introduced, expecting favorable for material selection of the transformer manufacturers.

This study focused on a 3.4 %Si non⁃oriented silicon steel. The comprehensive effects of normalization temperature and annealing temperature on the microstructure, texture characteristics, and magnetic properties of the 0.25 mm⁃thick products were studied systematically. The results indicated that normalization treatment at different temperatures could achieve recrystallization and grain growth in the hot⁃rolled sheet, thereby influencing the recrystallized microstructure, texture, and magnetic properties of the final products. With increasing the normalization temperatures, the average grain sizes of the normalized sheets increased obviously, and the microstructure uniformity was improved. In the final annealed sheets, the λ⁃fiber recrystallization texture (<001>∥ND) was notably strengthened, while the γ⁃fiber recrystallization texture (<111>∥ND) was weakened, accompanied by increased grain size. Therefore, with increasing the normalizing temperature, the magnetic induction B₅₀ gradually raised, the low⁃frequency iron loss P_1.5/50 gradually decreased, while the high⁃frequency iron loss P_1.0/400 first decreased and then increased. In addition, annealing treatment at different temperatures had an important influence on the recrystallization microstructure, texture, and magnetic properties. As the annealing temperature increased, the grain sizes of the final annealed sheets significantly increased, and the λ⁃fiber recrystallization texture was weakened gradually, but the γ⁃fiber recrystallization texture was strengthened progressively. Thus, with increasing the annealing temperature, the magnetic induction B₅₀ initially showed a slight increase followed by a gradual decrease, and the low⁃frequency iron loss P_1.5/50 progressively declined, while the high⁃frequency iron loss P_1.0/400 first decreased and then gradually raised. An optimal combination of high magnetic induction and low iron loss was obtained at a normalization temperature of 910 ℃ and annealing temperature of 900 ℃, with the following magnetic properties: magnetic induction B₅₀ of 1.70 T, low⁃frequency iron loss P_1.5/50 of 2.29 W/kg, and high⁃frequency iron loss P_1.0/400 of 13.27 W/kg.

With the in⁃depth advancement of the global "dual⁃carbon" strategy, the new energy vehicle (NEV) industry has developed rapidly, and the demand for high⁃grade non⁃oriented silicon steel has also increased accordingly. The design, assembly, and complex operating conditions of NEVs require drive motors to possess characteristics such as high efficiency, high torque, small size, and a wide speed range. Based on the driving characteristics of NEVs, the core requirements of their drive motors for high⁃grade non⁃oriented silicon steel include high magnetic induction, low iron loss at high frequencies, and high strength. Additionally, other general requirements encompass excellent mechanical properties, good punching performance, a smooth and flat surface of the steel sheet with uniform thickness, excellent performance of the insulating film, and a weak magnetic aging phenomenon.

This article focused on experimental materials of high alloy content silicon steel for new energy vehicles. XRD, EBSD and magnetic property measurement techniques were used to compare and analyze the microstructure, inclusions, texture, magnetic properties, and mechanical properties of two thicknesses of 0.20 mm and 0.25 mm silicon steel for new energy vehicles.The results indicated that:when the product thickness reduces from 0.25 mm to 0.20 mm, the average grain size of the sample structure decreases about 7 μm and improves grain uniformity.The textures of products with two thicknesses are mainly {114}<481> and {223}<362>, and the strength of α texture is relatively weak.The size and type of inclusions are similar;the low⁃frequency iron loss increases by about 0.11 W/kg, the high⁃frequency iron loss decreases by about 1.27 W/kg, and the magnetic induction intensity decreases by about 0.32 T, with little difference in mechanical properties.

The effect of hot rolling temperature on hot rolling microstructure of 50W800 non oriented silicon steel was analyzed by means of software analysis and simulation test rolling. The results showed that the end temperature of ferrite austenite transformation was 1 115 ℃, the end temperature of austenite ferrite transformation was 996 ℃, and there was two⁃phase rolling in the finishing rolling process; when the heating temperature is above 1 100 ℃, the precipitation of sulfide and nitride increases greatly with the increase of temperature. Decreasing the heating temperature can reduce the precipitation of sulfide and nitride in non oriented silicon steel, and can reduce the blocking effect of solid solution on grain growth; Increasing the finishing rolling temperature can increase the grain size of hot rolled steel plate, form coarse and uniform ferrite structure, and increase the {001}<110> texture ratio, which is conducive to the improvement of magnetic properties of non oriented silicon steel.

Silicon steel containing 2 %Si is classified as medium⁃grade non⁃oriented electrical steel. During continuous cold rolling, frequent strip fractures occur, predominantly at the weld seams, severely compromising product quality and reducing roll service life. To mitigate strip fracture issues in the fusion zone of silicon steel, the effects of laser welding parameters were investigated utilizing scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), tensile testing, and hardness measurements. Under constant laser parameters (mode, beam quality, and focal position), optimal weld quality was achieved at a power of 5.8 kW and welding speed of 0.03 m/s.

 In April 2021, the issue of black spots was particularly prominent in the production of silicon steel on Shagang's cold rolling and pickling line 1, with a total of 179 tons being rejected. To investigate the causes of these black spot defects, especially the batch occurrence in April, a comprehensive analysis was conducted covering various aspects such as silicon steel grades, defect locations, pickling production time, pickling speed, acid addition time, and acid addition amount. The results indicated that the black spot defects mainly occurred within the first 20 meters of the hot coil's ends, with over 90 % of the defects occurring on the lower surface of the hot coil's head (inner coil). The black spot defects in April were primarily caused by the combined effects of three factors: acid addition amount, regenerated acid concentration, and pickling speed. Through measures such as optimizing the acid addition amount, specifying the regenerated acid concentration, and improving the pickling speed, the black spot defects were effectively controlled.

To investigate the impact of self⁃bonding coating technology on motor performance, the same grade of non⁃oriented silicon steel material was tested using three different methods: self⁃adhesion, welding, and gluing, in the same motor model for comparison. The results showed that motors with the self⁃adhesion process can achieve an efficiency improvement of about 0.5 % and a reduction in iron loss of about 10 % compared to motors using the other two methods. Motors with the self⁃adhesion process exhibit lower iron losses, and their high⁃efficiency range is higher than that of the gluing and welding processes, providing valuable insights into the application of self⁃adhesive coating technology in motors.

This study develops a hybrid modeling approach combining the Hall⁃Petch equation with data mining techniques to predict mechanical properties of non⁃oriented electrical steel for new energy vehicle motors. By integrating materials science principles with industrial big data analytics, a high⁃precision prediction system was established. From over 300 production parameters (including chemical composition, process variables, and magnetic properties), key features were selected via bootstrap random forest, followed by principal component analysis (80.5 % cumulative variance) using JMP software. Comparative analysis of multiple algorithms (random forest, KNN, neural networks, and stepwise regression) revealed random forest as the best standalone model (R²=0.987). The Stacking ensemble model, combining random forest and KNN as base learners with GBDT meta⁃learner, achieved superior performance (R²=0.998, RMSE=1.986). Deployed in manufacturing execution systems (MES), this model enables real⁃time mechanical property prediction using online iron loss data, supporting dynamic process optimization.

This paper reviews the significant achievements in quality improvement and performance enhancement of domestic carbon sleeves over the ten years since the implementation of the industry standard "Carbon Sleeves for Silicon Steel in Continuous Annealing Furnaces". By establishing scientific and reasonable industry standards, the anti⁃buildups, oxidation resistance, wear resistance, and ammonia corrosion resistance of carbon sleeves have been markedly improved, enabling a complete transition from import dependence to full domestic production. Furthermore, the future development trends of carbon sleeves are discussed.